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  Subjects -> HEALTH AND SAFETY (Total: 1278 journals)
    - CIVIL DEFENSE (18 journals)
    - DRUG ABUSE AND ALCOHOLISM (87 journals)
    - HEALTH AND SAFETY (509 journals)
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    - OCCUPATIONAL HEALTH AND SAFETY (106 journals)
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HEALTH AND SAFETY (509 journals)                  1 2 3 | Last

Showing 1 - 200 of 203 Journals sorted alphabetically
16 de Abril     Open Access  
A Life in the Day     Hybrid Journal   (Followers: 9)
Acta Informatica Medica     Open Access   (Followers: 1)
Acta Scientiarum. Health Sciences     Open Access  
Adultspan Journal     Hybrid Journal   (Followers: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10)
Advances in Public Health     Open Access   (Followers: 19)
African Health Sciences     Open Access   (Followers: 2)
African Journal for Physical, Health Education, Recreation and Dance     Full-text available via subscription   (Followers: 6)
African Journal of Health Professions Education     Open Access   (Followers: 4)
Afrimedic Journal     Open Access   (Followers: 2)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 2)
AJOB Primary Research     Partially Free   (Followers: 2)
American Journal of Family Therapy     Hybrid Journal   (Followers: 10)
American Journal of Health Economics     Full-text available via subscription   (Followers: 13)
American Journal of Health Education     Hybrid Journal   (Followers: 25)
American Journal of Health Promotion     Hybrid Journal   (Followers: 24)
American Journal of Health Studies     Full-text available via subscription   (Followers: 8)
American Journal of Preventive Medicine     Hybrid Journal   (Followers: 21)
American Journal of Public Health     Full-text available via subscription   (Followers: 176)
American Journal of Public Health Research     Open Access   (Followers: 27)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 2)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2)
Annali dell'Istituto Superiore di Sanità     Open Access  
Annals of Global Health     Open Access   (Followers: 8)
Annals of Health Law     Open Access   (Followers: 3)
Annals of Tropical Medicine and Public Health     Open Access   (Followers: 15)
Applied Biosafety     Hybrid Journal  
Applied Research In Health And Social Sciences : Interface And Interaction     Open Access  
Archives of Medicine and Health Sciences     Open Access   (Followers: 2)
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 8)
Asia Pacific Journal of Health Management     Full-text available via subscription   (Followers: 1)
Asia-Pacific Journal of Public Health     Hybrid Journal   (Followers: 8)
Asian Journal of Gambling Issues and Public Health     Open Access   (Followers: 3)
Association of Schools of Allied Health Professions     Full-text available via subscription   (Followers: 5)
Atención Primaria     Open Access   (Followers: 1)
Australasian Journal of Paramedicine     Open Access   (Followers: 2)
Australian Advanced Aesthetics     Full-text available via subscription   (Followers: 4)
Australian Family Physician     Full-text available via subscription   (Followers: 1)
Australian Indigenous HealthBulletin     Free   (Followers: 6)
Autism & Developmental Language Impairments     Open Access   (Followers: 1)
Behavioral Healthcare     Full-text available via subscription   (Followers: 4)
Best Practices in Mental Health     Full-text available via subscription   (Followers: 6)
Bijzijn     Hybrid Journal   (Followers: 2)
Bijzijn XL     Hybrid Journal   (Followers: 1)
Biomedical Safety & Standards     Full-text available via subscription   (Followers: 9)
BLDE University Journal of Health Sciences     Open Access  
BMC Oral Health     Open Access   (Followers: 5)
BMC Pregnancy and Childbirth     Open Access   (Followers: 19)
BMJ Simulation & Technology Enhanced Learning     Full-text available via subscription   (Followers: 7)
Brazilian Journal of Medicine and Human Health     Open Access  
Buletin Penelitian Kesehatan     Open Access   (Followers: 2)
Buletin Penelitian Sistem Kesehatan     Open Access  
Bulletin of the World Health Organization     Open Access   (Followers: 15)
Cadernos de Educação, Saúde e Fisioterapia     Open Access   (Followers: 1)
Cadernos Saúde Coletiva     Open Access   (Followers: 1)
Canadian Family Physician     Partially Free   (Followers: 11)
Canadian Journal of Community Mental Health     Full-text available via subscription   (Followers: 10)
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 1)
Canadian Journal of Public Health     Full-text available via subscription   (Followers: 18)
Case Reports in Women's Health     Open Access   (Followers: 2)
Case Studies in Fire Safety     Open Access   (Followers: 11)
Central Asian Journal of Global Health     Open Access   (Followers: 2)
Central European Journal of Public Health     Full-text available via subscription   (Followers: 4)
CES Medicina     Open Access  
Child Abuse Research in South Africa     Full-text available via subscription   (Followers: 1)
Child's Nervous System     Hybrid Journal  
Childhood Obesity and Nutrition     Open Access   (Followers: 10)
Children     Open Access   (Followers: 2)
CHRISMED Journal of Health and Research     Open Access  
Christian Journal for Global Health     Open Access  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
Ciencia y Cuidado     Open Access  
Ciencia, Tecnología y Salud     Open Access  
ClinicoEconomics and Outcomes Research     Open Access   (Followers: 1)
CME     Hybrid Journal   (Followers: 1)
CoDAS     Open Access  
Community Health     Open Access   (Followers: 1)
Conflict and Health     Open Access   (Followers: 8)
Curare     Open Access  
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 1)
Day Surgery Australia     Full-text available via subscription   (Followers: 2)
Digital Health     Open Access  
Dramatherapy     Hybrid Journal   (Followers: 2)
Drogues, santé et société     Full-text available via subscription  
Duazary     Open Access   (Followers: 1)
Early Childhood Research Quarterly     Hybrid Journal   (Followers: 13)
East African Journal of Public Health     Full-text available via subscription   (Followers: 2)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 16)
EcoHealth     Hybrid Journal   (Followers: 3)
Education for Health     Open Access   (Followers: 4)
electronic Journal of Health Informatics     Open Access   (Followers: 4)
ElectronicHealthcare     Full-text available via subscription   (Followers: 3)
Elsevier Ergonomics Book Series     Full-text available via subscription   (Followers: 4)
Emergency Services SA     Full-text available via subscription   (Followers: 2)
Ensaios e Ciência: Ciências Biológicas, Agrárias e da Saúde     Open Access  
Environmental Disease     Open Access  
Environmental Sciences Europe     Open Access   (Followers: 2)
Epidemics     Open Access   (Followers: 3)
Epidemiology, Biostatistics and Public Health     Open Access   (Followers: 18)
Ethics, Medicine and Public Health     Full-text available via subscription  
Ethiopian Journal of Health Development     Open Access   (Followers: 8)
Ethiopian Journal of Health Sciences     Open Access   (Followers: 7)
Ethnicity & Health     Hybrid Journal   (Followers: 14)
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 1)
European Medical, Health and Pharmaceutical Journal     Open Access  
Evaluation & the Health Professions     Hybrid Journal   (Followers: 8)
Evidence-based Medicine & Public Health     Open Access   (Followers: 4)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Face à face     Open Access   (Followers: 1)
Families, Systems, & Health     Full-text available via subscription   (Followers: 8)
Family & Community Health     Partially Free   (Followers: 12)
Family Medicine and Community Health     Open Access   (Followers: 3)
Family Relations     Partially Free   (Followers: 11)
Fatigue : Biomedicine, Health & Behavior     Hybrid Journal   (Followers: 1)
Food and Public Health     Open Access   (Followers: 10)
Frontiers in Public Health     Open Access   (Followers: 8)
Gaceta Sanitaria     Open Access   (Followers: 3)
Galen Medical Journal     Open Access  
Geospatial Health     Open Access  
Gesundheitsökonomie & Qualitätsmanagement     Hybrid Journal   (Followers: 11)
Giornale Italiano di Health Technology Assessment     Full-text available via subscription  
Global Health : Science and Practice     Open Access   (Followers: 4)
Global Health Promotion     Hybrid Journal   (Followers: 15)
Global Journal of Health Science     Open Access   (Followers: 5)
Global Journal of Public Health     Open Access   (Followers: 9)
Globalization and Health     Open Access   (Followers: 5)
Hacia la Promoción de la Salud     Open Access  
Hastings Center Report     Hybrid Journal   (Followers: 7)
HEADline     Hybrid Journal  
Health & Place     Hybrid Journal   (Followers: 14)
Health & Justice     Open Access   (Followers: 5)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 7)
Health and Human Rights     Free   (Followers: 8)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 9)
Health and Social Work     Hybrid Journal   (Followers: 46)
Health Behavior and Policy Review     Full-text available via subscription   (Followers: 1)
Health Care Analysis     Hybrid Journal   (Followers: 11)
Health Inform     Full-text available via subscription  
Health Information Management Journal     Hybrid Journal   (Followers: 10)
Health Issues     Full-text available via subscription   (Followers: 1)
Health Policy     Hybrid Journal   (Followers: 32)
Health Policy and Technology     Hybrid Journal  
Health Professional Student Journal     Open Access   (Followers: 1)
Health Promotion International     Hybrid Journal   (Followers: 20)
Health Promotion Journal of Australia : Official Journal of Australian Association of Health Promotion Professionals     Full-text available via subscription   (Followers: 10)
Health Promotion Practice     Hybrid Journal   (Followers: 15)
Health Prospect     Open Access   (Followers: 1)
Health Psychology     Full-text available via subscription   (Followers: 47)
Health Psychology Research     Open Access   (Followers: 18)
Health Psychology Review     Hybrid Journal   (Followers: 39)
Health Renaissance     Open Access  
Health Research Policy and Systems     Open Access   (Followers: 9)
Health SA Gesondheid     Open Access   (Followers: 2)
Health Science Reports     Open Access  
Health Sciences and Disease     Open Access   (Followers: 1)
Health Services Insights     Open Access   (Followers: 1)
Health Systems     Hybrid Journal   (Followers: 2)
Health Voices     Full-text available via subscription  
Health, Culture and Society     Open Access   (Followers: 10)
Health, Risk & Society     Hybrid Journal   (Followers: 9)
Healthcare     Open Access   (Followers: 1)
Healthcare in Low-resource Settings     Open Access   (Followers: 1)
Healthcare Quarterly     Full-text available via subscription   (Followers: 8)
HERD : Health Environments Research & Design Journal     Full-text available via subscription  
Highland Medical Research Journal     Full-text available via subscription  
Hispanic Health Care International     Full-text available via subscription  
HIV & AIDS Review     Full-text available via subscription   (Followers: 9)
Home Health Care Services Quarterly     Hybrid Journal   (Followers: 5)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 2)
Hospitals & Health Networks     Free   (Followers: 2)
IEEE Journal of Translational Engineering in Health and Medicine     Open Access   (Followers: 3)
IMTU Medical Journal     Full-text available via subscription  
Indian Journal of Health Sciences     Open Access   (Followers: 1)
Inmanencia. Revista del Hospital Interzonal General de Agudos (HIGA) Eva Perón     Open Access  
Innovative Journal of Medical and Health Sciences     Open Access  
Institute for Security Studies Papers     Full-text available via subscription   (Followers: 6)
interactive Journal of Medical Research     Open Access  
International Health     Hybrid Journal   (Followers: 4)
International Journal for Equity in Health     Open Access   (Followers: 7)
International Journal for Quality in Health Care     Hybrid Journal   (Followers: 32)
International Journal of Applied Behavioral Sciences     Open Access   (Followers: 2)
International Journal of Behavioural and Healthcare Research     Hybrid Journal   (Followers: 7)
International Journal of Circumpolar Health     Open Access   (Followers: 1)
International Journal of Community Medicine and Public Health     Open Access   (Followers: 5)
International Journal of E-Health and Medical Communications     Full-text available via subscription   (Followers: 2)
International Journal of Environmental Research and Public Health     Open Access   (Followers: 19)
International Journal of Evidence-Based Healthcare     Hybrid Journal   (Followers: 8)
International Journal of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 13)
International Journal of Health & Allied Sciences     Open Access   (Followers: 1)
International Journal of Health Care Quality Assurance     Hybrid Journal   (Followers: 7)
International Journal of Health Geographics     Open Access   (Followers: 6)
International Journal of Health Policy and Management     Open Access   (Followers: 2)
International Journal of Health Professions     Open Access   (Followers: 2)
International Journal of Health Promotion and Education     Hybrid Journal   (Followers: 12)
International Journal of Health Sciences Education     Open Access   (Followers: 2)
International Journal of Health Services     Full-text available via subscription   (Followers: 9)
International Journal of Health Studies     Open Access   (Followers: 3)
International Journal of Health System and Disaster Management     Open Access   (Followers: 2)
International Journal of Healthcare Delivery Reform Initiatives     Full-text available via subscription   (Followers: 1)

        1 2 3 | Last

Journal Cover Epidemics
  [SJR: 1.268]   [H-I: 20]   [3 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1755-4365 - ISSN (Online) 1878-0067
   Published by Elsevier Homepage  [3030 journals]
  • Learning from multi-model comparisons: Collaboration leads to insights,
           but limitations remain

    • Authors: T.D. Hollingsworth; G.F. Medley
      Pages: 1 - 3
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): T.D. Hollingsworth, G.F. Medley


      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.014
      Issue No: Vol. 18 (2017)
       
  • Modelling the elimination of river blindness using long-term
           epidemiological and programmatic data from Mali and Senegal

    • Authors: Martin Walker; Wilma A. Stolk; Matthew A. Dixon; Christian Bottomley; Lamine Diawara; Mamadou O. Traoré; Sake J. de Vlas; María-Gloria Basáñez
      Pages: 4 - 15
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Martin Walker, Wilma A. Stolk, Matthew A. Dixon, Christian Bottomley, Lamine Diawara, Mamadou O. Traoré, Sake J. de Vlas, María-Gloria Basáñez
      The onchocerciasis transmission models EPIONCHO and ONCHOSIM have been independently developed and used to explore the feasibility of eliminating onchocerciasis from Africa with mass (annual or biannual) distribution of ivermectin within the timeframes proposed by the World Health Organization (WHO) and endorsed by the 2012 London Declaration on Neglected Tropical Diseases (i.e. by 2020/2025). Based on the findings of our previous model comparison, we implemented technical refinements and tested the projections of EPIONCHO and ONCHOSIM against long-term epidemiological data from two West African transmission foci in Mali and Senegal where the observed prevalence of infection was brought to zero circa 2007–2009 after 15–17 years of mass ivermectin treatment. We simulated these interventions using programmatic information on the frequency and coverage of mass treatments and trained the model projections using longitudinal parasitological data from 27 communities, evaluating the projected outcome of elimination (local parasite extinction) or resurgence. We found that EPIONCHO and ONCHOSIM captured adequately the epidemiological trends during mass treatment but that resurgence, while never predicted by ONCHOSIM, was predicted by EPIONCHO in some communities with the highest (inferred) vector biting rates and associated pre-intervention endemicities. Resurgence can be extremely protracted such that low (microfilarial) prevalence between 1% and 5% can be maintained for 3–5 years before manifesting more prominently. We highlight that post-treatment and post-elimination surveillance protocols must be implemented for long enough and with high enough sensitivity to detect possible residual latent infections potentially indicative of resurgence. We also discuss uncertainty and differences between EPIONCHO and ONCHOSIM projections, the potential importance of vector control in high-transmission settings as a complementary intervention strategy, and the short remaining timeline for African countries to be ready to stop treatment safely and begin surveillance in order to meet the impending 2020/2025 elimination targets.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.005
      Issue No: Vol. 18 (2017)
       
  • Predicting lymphatic filariasis transmission and elimination dynamics
           using a multi-model ensemble framework

    • Authors: Morgan E. Smith; Brajendra K. Singh; Michael A. Irvine; Wilma A. Stolk; Swaminathan Subramanian; T. Déirdre Hollingsworth; Edwin Michael
      Pages: 16 - 28
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Morgan E. Smith, Brajendra K. Singh, Michael A. Irvine, Wilma A. Stolk, Swaminathan Subramanian, T. Déirdre Hollingsworth, Edwin Michael
      Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG). We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.006
      Issue No: Vol. 18 (2017)
       
  • A comparison of two mathematical models of the impact of mass drug
           administration on the transmission and control of schistosomiasis

    • Authors: J.E. Truscott; D. Gurarie; R. Alsallaq; J. Toor; N. Yoon; S.H. Farrell; H.C. Turner; A.E. Phillips; H.O. Aurelio; J. Ferro; C.H. King; R.M. Anderson
      Pages: 29 - 37
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): J.E. Truscott, D. Gurarie, R. Alsallaq, J. Toor, N. Yoon, S.H. Farrell, H.C. Turner, A.E. Phillips, H.O. Aurelio, J. Ferro, C.H. King, R.M. Anderson
      The predictions of two mathematical models describing the transmission dynamics of schistosome infection and the impact of mass drug administration are compared. The models differ in their description of the dynamics of the parasites within the host population and in their representation of the stages of the parasite lifecycle outside of the host. Key parameters are estimated from data collected in northern Mozambique from 2011 to 2015. This type of data set is valuable for model validation as treatment prior to the study was minimal. Predictions from both models are compared with each other and with epidemiological observations. Both models have difficulty matching both the intensity and prevalence of disease in the datasets and are only partially successful at predicting the impact of treatment. The models also differ from each other in their predictions, both quantitatively and qualitatively, of the long-term impact of 10 years’ school-based mass drug administration. We trace the dynamical differences back to basic assumptions about worm aggregation, force of infection and the dynamics of the parasite in the snail population in the two models and suggest data which could discriminate between them. We also discuss limitations with the datasets used and ways in which data collection could be improved.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.003
      Issue No: Vol. 18 (2017)
       
  • Comparison and validation of two mathematical models for the impact of
           mass drug administration on Ascaris lumbricoides and hookworm infection

    • Authors: Luc E. Coffeng; James E. Truscott; Sam H. Farrell; Hugo C. Turner; Rajiv Sarkar; Gagandeep Kang; Sake J. de Vlas; Roy M. Anderson
      Pages: 38 - 47
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Luc E. Coffeng, James E. Truscott, Sam H. Farrell, Hugo C. Turner, Rajiv Sarkar, Gagandeep Kang, Sake J. de Vlas, Roy M. Anderson
      The predictions of two mathematical models of the transmission dynamics of Ascaris lumbricoides and hookworm infection and the impact of mass drug administration (MDA) are compared, using data from India. One model has an age structured partial differential equation (PDE) deterministic framework for the distribution of parasite numbers per host and sexual mating. The second model is an individual-based stochastic model. Baseline data acquired prior to treatment are used to estimate key transmission parameters, and forward projections are made, given the known MDA population coverage. Predictions are compared with observed post-treatment epidemiological patterns. The two models could equally well predict the short-term impact of deworming on A. lumbricoides and hookworm infection levels, despite being fitted to different subsets and/or summary statistics of the data. As such, the outcomes give confidence in their use as aids to policy formulation for the use of PCT to control A. lumbricoides and hookworm infection. The models further largely agree in a qualitative sense on the added benefit of semi-annual vs. annual deworming and targeting of the entire population vs. only children, as well as the potential for interruption of transmission. Further, this study also illustrates that long-term predictions are sensitive to modelling assumptions about which age groups contribute most to transmission, which depends on human demography and age-patterns in exposure and contribution to the environmental reservoir of infection, the latter being notoriously difficult to empirically quantify.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.001
      Issue No: Vol. 18 (2017)
       
  • Probabilistic forecasts of trachoma transmission at the district level: A
           statistical model comparison

    • Authors: Amy Pinsent; Fengchen Liu; Michael Deiner; Paul Emerson; Ana Bhaktiari; Travis C. Porco; Thomas Lietman; Manoj Gambhir
      Pages: 48 - 55
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Amy Pinsent, Fengchen Liu, Michael Deiner, Paul Emerson, Ana Bhaktiari, Travis C. Porco, Thomas Lietman, Manoj Gambhir
      The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1–6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.01.007
      Issue No: Vol. 18 (2017)
       
  • Measuring and modelling the effects of systematic non-adherence to mass
           drug administration

    • Authors: Louise Dyson; Wilma A. Stolk; Sam H. Farrell; T. Déirdre Hollingsworth
      Pages: 56 - 66
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Louise Dyson, Wilma A. Stolk, Sam H. Farrell, T. Déirdre Hollingsworth
      It is well understood that the success or failure of a mass drug administration campaign critically depends on the level of coverage achieved. To that end coverage levels are often closely scrutinised during campaigns and the response to underperforming campaigns is to attempt to improve coverage. Modelling work has indicated, however, that the quality of the coverage achieved may also have a significant impact on the outcome. If the coverage achieved is likely to miss similar people every round then this can have a serious detrimental effect on the campaign outcome. We begin by reviewing the current modelling descriptions of this effect and introduce a new modelling framework that can be used to simulate a given level of systematic non-adherence. We formalise the likelihood that people may miss several rounds of treatment using the correlation in the attendance of different rounds. Using two very simplified models of the infection of helminths and non-helminths, respectively, we demonstrate that the modelling description used and the correlation included between treatment rounds can have a profound effect on the time to elimination of disease in a population. It is therefore clear that more detailed coverage data is required to accurately predict the time to disease elimination. We review published coverage data in which individuals are asked how many previous rounds they have attended, and show how this information may be used to assess the level of systematic non-adherence. We note that while the coverages in the data found range from 40.5% to 95.5%, still the correlations found lie in a fairly narrow range (between 0.2806 and 0.5351). This indicates that the level of systematic non-adherence may be similar even in data from different years, countries, diseases and administered drugs.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.002
      Issue No: Vol. 18 (2017)
       
  • Elimination of visceral leishmaniasis in the Indian subcontinent: a
           comparison of predictions from three transmission models

    • Authors: Epke A. Le Rutte; Lloyd A.C. Chapman; Luc E. Coffeng; Sarah Jervis; Epco C. Hasker; Shweta Dwivedi; Morchan Karthick; Aritra Das; Tanmay Mahapatra; Indrajit Chaudhuri; Marleen C. Boelaert; Graham F. Medley; Sridhar Srikantiah; T. Deirdre Hollingsworth; Sake J. de Vlas
      Pages: 67 - 80
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Epke A. Le Rutte, Lloyd A.C. Chapman, Luc E. Coffeng, Sarah Jervis, Epco C. Hasker, Shweta Dwivedi, Morchan Karthick, Aritra Das, Tanmay Mahapatra, Indrajit Chaudhuri, Marleen C. Boelaert, Graham F. Medley, Sridhar Srikantiah, T. Deirdre Hollingsworth, Sake J. de Vlas
      We present three transmission models of visceral leishmaniasis (VL) in the Indian subcontinent (ISC) with structural differences regarding the disease stage that provides the main contribution to transmission, including models with a prominent role of asymptomatic infection, and fit them to recent case data from 8 endemic districts in Bihar, India. Following a geographical cross-validation of the models, we compare their predictions for achieving the WHO VL elimination targets with ongoing treatment and vector control strategies. All the transmission models suggest that the WHO elimination target (<1 new VL case per 10,000 capita per year at sub-district level) is likely to be met in Bihar, India, before or close to 2020 in sub-districts with a pre-control incidence of 10 VL cases per 10,000 people per year or less, when current intervention levels (60% coverage of indoor residual spraying (IRS) of insecticide and a delay of 40days from onset of symptoms to treatment (OT)) are maintained, given the accuracy and generalizability of the existing data regarding incidence and IRS coverage. In settings with a pre-control endemicity level of 5/10,000, increasing the effective IRS coverage from 60 to 80% is predicted to lead to elimination of VL 1–3 years earlier (depending on the particular model), and decreasing OT from 40 to 20days to bring elimination forward by approximately 1year. However, in all instances the models suggest that L. donovani transmission will continue after 2020 and thus that surveillance and control measures need to remain in place until the longer-term aim of breaking transmission is achieved.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.01.002
      Issue No: Vol. 18 (2017)
       
  • Comparison and validation of two computational models of Chagas disease: A
           thirty year perspective from Venezuela

    • Authors: Sarah M. Bartsch; Jennifer K. Peterson; Daniel L. Hertenstein; Laura Skrip; Martial Ndeffo-Mbah; Alison P. Galvani; Andrew P. Dobson; Bruce Y. Lee
      Pages: 81 - 91
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): Sarah M. Bartsch, Jennifer K. Peterson, Daniel L. Hertenstein, Laura Skrip, Martial Ndeffo-Mbah, Alison P. Galvani, Andrew P. Dobson, Bruce Y. Lee
      Background Mathematical models can help aid public health responses to Chagas disease. Models are typically developed to fulfill a particular need, and comparing outputs from different models addressing the same question can help identify the strengths and weaknesses of the models in answering particular questions, such as those for achieving the 2020 goals for Chagas disease. Methods Using two separately developed models (PHICOR/CIDMA model and Princeton model), we simulated dynamics for domestic transmission of Trypanosoma cruzi (T. cruzi). We compared how well the models targeted the last 9 years and last 19 years of the 1968–1998 historical seroprevalence data from Venezuela. Results Both models were able to generate the T. cruzi seroprevalence for the next time period within reason to the historical data. The PHICOR/CIDMA model estimates of the total population seroprevalence more closely followed the trends seen in the historic data, while the Princeton model estimates of the age-specific seroprevalence more closely followed historic trends when simulating over 9 years. Additionally, results from both models overestimated T. cruzi seroprevalence among younger age groups, while underestimating the seroprevalence of T. cruzi in older age groups. Conclusion The PHICOR/CIDMA and Princeton models differ in level of detail and included features, yet both were able to generate the historical changes in T. cruzi seroprevalence in Venezuela over 9 and 19-year time periods. Our model comparison has demonstrated that different model structures can be useful in evaluating disease transmission dynamics and intervention strategies.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.02.004
      Issue No: Vol. 18 (2017)
       
  • Forecasting the new case detection rate of leprosy in four states of
           Brazil: A comparison of modelling approaches

    • Authors: David J. Blok; Ronald E. Crump; Ram Sundaresh; Martial Ndeffo-Mbah; Alison P. Galvani; Travis C. Porco; Sake J. de Vlas; Graham F. Medley; Jan Hendrik Richardus
      Pages: 92 - 100
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): David J. Blok, Ronald E. Crump, Ram Sundaresh, Martial Ndeffo-Mbah, Alison P. Galvani, Travis C. Porco, Sake J. de Vlas, Graham F. Medley, Jan Hendrik Richardus
      Background Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins. Methods A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012–2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches. Results Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000. Conclusions All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.01.005
      Issue No: Vol. 18 (2017)
       
  • Data-driven models to predict the elimination of sleeping sickness in
           former Equateur province of DRC

    • Authors: K.S. Rock; A. Pandey; M.L. Ndeffo-Mbah; K.E. Atkins; C. Lumbala; A. Galvani; M.J. Keeling
      Pages: 101 - 112
      Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18
      Author(s): K.S. Rock, A. Pandey, M.L. Ndeffo-Mbah, K.E. Atkins, C. Lumbala, A. Galvani, M.J. Keeling
      Approaching disease elimination, it is crucial to be able to assess progress towards key objectives using quantitative tools. For Gambian human African trypanosomiasis (HAT), the ultimate goal is to stop transmission by 2030, while intermediary targets include elimination as a public health problem − defined as <1 new case per 10,000 inhabitants in 90% of foci, and <2000 reported cases by 2020. Using two independent mathematical models, this study assessed the achievability of these goals in the former Equateur province of the Democratic Republic of Congo, which historically had endemic levels of disease. The two deterministic models used different assumptions on disease progression, risk of infection and non-participation in screening, reflecting biological uncertainty. To validate the models a censor-fit-uncensor procedure was used to fit to health-zone level data from 2000 to 2012; initially the last six years were censored, then three and the final step utilised all data. The different model projections were used to evaluate the expected transmission and reporting for each health zone within each province under six intervention strategies using currently available tools. In 2012 there were 197 reported HAT cases in former Equateur reduced from 6828 in 2000, however this reflects lower active testing for HAT (1.3% of the population compared to 7.2%). Modelling results indicate that there are likely to be <300 reported cases in former Equateur in 2020 if screening continues at the mean level for 2000–2012 (6.2%), and <120 cases if vector control is introduced. Some health zones may fail to achieve <1 new case per 10,000 by 2020 without vector control, although most appear on track for this target using medical interventions alone. The full elimination goal will be harder to reach; between 39 and 54% of health zones analysed may have to improve their current medical-only strategy to stop transmission completely by 2030.

      PubDate: 2017-03-10T03:58:40Z
      DOI: 10.1016/j.epidem.2017.01.006
      Issue No: Vol. 18 (2017)
       
  • Mathematical modeling of ovine footrot in the UK: the effect of
           Dichelobacter nodosus and Fusobacterium necrophorum on the disease
           dynamics

    • Authors: Jolene Atia; Emma Monaghan; Jasmeet Kaler; Kevin Purdy; Laura Green; Matt Keeling
      Abstract: Publication date: Available online 12 April 2017
      Source:Epidemics
      Author(s): Jolene Atia, Emma Monaghan, Jasmeet Kaler, Kevin Purdy, Laura Green, Matt Keeling
      Dichelobacter nodosus is a virulent, invasive, anaerobic bacterium that is believed to be the causative agent of ovine footrot, an infectious bacterial disease of sheep that causes lameness. Another anaerobe, Fusobacterium necrophorum, has been intimately linked with the disease occurrence and severity. Here we examine data from a longitudinal study of footrot on one UK farm, including quantitative PCR (qPCR) estimates of bacterial load of D. nodosus and F.necrophorum. The data is at foot level; all feet were monitored for five weeks assessing disease severity (healthy, interdigital dermatitis (ID), or severe footrot (SFR)) and bacterial load (number of bacteria/swab). We investigate the role of D.nodosus and F.necrophorum in the progress of the disease using a continuous-time Markov model with 12 different states characterising the foot. The transition rates between the adjacent states are the (34) model parameters, these are determined using Metropolis Hasting MCMC. Our aim is to determine the predictive relationship between past and future D. nodosus and F. necrophorum load and disease states. We demonstrate a high level of predictive accuracy at the population level for the D.nodosus model, although the dynamics of individual feet is highly stochastic. However, we note that this predictive accuracy at population level is only high in more diseased states for F.necrophorum model. This supports our hypothesis that D.nodosus load and status of the foot work in combination to give rise to severe footrot and lameness, and that D.nodosus load plays the primary role in the initiation and progression of footrot, while F.necrophorum load rather increases disease severity of SFR.

      PubDate: 2017-04-19T10:58:23Z
      DOI: 10.1016/j.epidem.2017.04.001
       
  • Displacement of Sexual Partnerships in Trials of Sexual Behavior
           Interventions: A Model-Based Assessment of Consequences

    • Authors: Alethea W. McCormick; Nadia N. Abuelezam; Thomas Fussell; George R. Seage; Marc Lipsitch
      Abstract: Publication date: Available online 2 April 2017
      Source:Epidemics
      Author(s): Alethea W. McCormick, Nadia N. Abuelezam, Thomas Fussell, George R. Seage, Marc Lipsitch
      We investigated the impact of the displacement of sexual activity from adherent recipients of an intervention to others within or outside a trial population on the results from hypothetical trials of different sexual behavior interventions. A short-term model of HIV-prevention interventions that lead to female rejection of male partnership requests showed the impact of displacement expected at the start of a trial. An agent-based model, with sexual mixing and other South African specific demographics, evaluated consequences of displacement for sexual behavior interventions targeting young females in South Africa. This model measured the cumulative incidence among adherent, non-adherent, control and non-enrolled females in a hypothetical trial of HIV prevention. When males made more than one attempt to seek a partnership, interventions reduced short-term HIV infection risk among adherent females, but increased it among non-adherent females as well as controls, non-enrolled (females eligible for the trial but not chosen to participate) and ineligible females (females that did not qualify for the trial due to age). The impact of displacement depends on the intervention and the adherence. In both models, the risk to individuals who are not members of the adherent intervention group will increase with displacement leading to a biased calculation for the effect estimates for the trial. Likewise, intent-to-treat effect estimates become nonlinear functions of the proportion adherent.
      Graphical abstract image

      PubDate: 2017-04-05T10:29:35Z
      DOI: 10.1016/j.epidem.2017.03.007
       
  • Assessing the transmission dynamics of measles in Japan, 2016

    • Authors: Hiroshi Nishiura; Kenji Mizumoto; Yusuke Asai
      Abstract: Publication date: Available online 22 March 2017
      Source:Epidemics
      Author(s): Hiroshi Nishiura, Kenji Mizumoto, Yusuke Asai
      Objectives Despite the verification of measles elimination, Japan experienced multiple generations of measles transmission following importation events in 2016. The purpose of the present study was to analyze the transmission dynamics of measles in Japan, 2016, estimating the transmission potential in the partially vaccinated population. Methods All diagnosed measles cases were notified to the government, and the present study analyzed two pieces of datasets independently, i.e., the transmission tree of the largest outbreak in Osaka (n=49) and the final size distribution of all importation events (n=23 events). Branching process model was employed to estimate the effective reproduction number R v, and the analysis of transmission tree in Osaka enabled us to account for the timing of introducing contact tracing and case isolation. Results Employing a negative binomial distribution for the offspring distribution, the model with time-dependent decline in R v due to interventions appeared to best fit to the transmission tree data with R v of 9.20 (95% CI (confidence interval): 2.08, 150.68) and the dispersion parameter 0.32 (95% CI: 0.07, 1.17) before interventions were introduced. The relative transmissibility in the presence of interventions from week 34 was estimated at 0.005. Analyzing the final size distribution, models for subcritical and supercritical processes fitted equally well to the observed data, and the estimated reproduction number from both models did not exclude the possibility that R v >1. Conclusions Our results likely reflect the highly contagious nature of measles, indicating that Japan is at risk of observing multiple generations of measles transmission given imported cases. Considering that importation events may continue in the future, supplementary vaccination of adults needs to be considered.

      PubDate: 2017-03-22T09:51:43Z
      DOI: 10.1016/j.epidem.2017.03.005
       
  • Elucidating transmission dynamics and host-parasite-vector relationships
           for rodent-borne Bartonella spp. in Madagascar

    • Authors: Cara E. Brook; Ying Bai; Emily O. Yu; Hafaliana C. Ranaivoson; Haewon Shin; Andrew P. Dobson; C. Jessica E. Metcalf; Michael Y. Kosoy; Katharina Dittmar
      Abstract: Publication date: Available online 16 March 2017
      Source:Epidemics
      Author(s): Cara E. Brook, Ying Bai, Emily O. Yu, Hafaliana C. Ranaivoson, Haewon Shin, Andrew P. Dobson, C. Jessica E. Metcalf, Michael Y. Kosoy, Katharina Dittmar
      Bartonella spp. are erythrocytic bacteria transmitted via arthropod vectors, which infect a broad range of vertebrate hosts, including humans. We investigated transmission dynamics and host-parasite-vector relationships for potentially zoonotic Bartonella spp. in invasive Rattus rattus hosts and associated arthropod ectoparasites in Madagascar. We identified five distinct species of Bartonella (B. elizabethae 1, B. elizabethae 2, B. phoceensis 1, B. rattimassiliensis 1, and B. tribocorum 1) infecting R. rattus rodents and their ectoparasites. We fit standard epidemiological models to species-specific age-prevalence data for the four Bartonella spp. with sufficient data, thus quantifying age-structured force of infection. Known zoonotic agents, B. elizabethae 1 and 2, were best described by models exhibiting high forces of infection in early age class individuals and allowing for recovery from infection, while B. phoceensis 1 and B. rattimassiliensis 1 were best fit by models of lifelong infection without recovery and substantially lower forces of infection. Nested sequences of B. elizabethae 1 and 2 were recovered from rodent hosts and their Synopsyllus fonquerniei and Xenopsylla cheopsis fleas, with a particularly high prevalence in the outdoor-dwelling, highland-endemic S. fonquerniei. These findings expand on force of infection analyses to elucidate the ecological niche of the zoonotic Bartonella elizabethae complex in Madagascar, hinting at a potential vector role for S. fonquerniei. Our analyses underscore the uniqueness of such ecologies for Bartonella species, which pose a variable range of potential zoonotic threats.

      PubDate: 2017-03-17T09:38:13Z
      DOI: 10.1016/j.epidem.2017.03.004
       
  • The impact of stratified immunity on the transmission dynamics of
           influenza

    • Authors: Hsiang-Yu Yuan; Marc Baguelin; Kin O. Kwok; Nimalan Arinaminpathy; Edwin van Leeuwen; Steven Riley
      Abstract: Publication date: Available online 12 March 2017
      Source:Epidemics
      Author(s): Hsiang-Yu Yuan, Marc Baguelin, Kin O. Kwok, Nimalan Arinaminpathy, Edwin van Leeuwen, Steven Riley
      Although empirical studies show that protection against influenza infection in humans is closely related to antibody titres, influenza epidemics are often described under the assumption that individuals are either susceptible or not. Here we develop a model in which antibody titre classes are enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Fitting only with pre- and post-wave serological data during 2009 pandemic in Hong Kong, we demonstrate that with stratified immunity, the timing and the magnitude of the epidemic dynamics can be reconstructed more accurately than is possible with binary seropositivity data. We also show that increased infectiousness of children relative to adults and age-specific mixing are required to reproduce age-specific seroprevalence observed in Hong Kong, while pre-existing immunity in the elderly is not. Overall, our results suggest that stratified immunity in an aged-structured heterogeneous population plays a significant role in determining the shape of influenza epidemics.

      PubDate: 2017-03-17T09:38:13Z
      DOI: 10.1016/j.epidem.2017.03.003
       
  • On the duration of the period between exposure to HIV and detectable
           infection

    • Authors: Bernhard P. Konrad; Darlene Taylor; Jessica M. Conway; Gina S. Ogilvie; Daniel Coombs
      Abstract: Publication date: Available online 11 March 2017
      Source:Epidemics
      Author(s): Bernhard P. Konrad, Darlene Taylor, Jessica M. Conway, Gina S. Ogilvie, Daniel Coombs
      HIV infection cannot be detected immediately after exposure because plasma viral loads are too small initially. The duration of this phase of infection (the “eclipse period”) is difficult to estimate because precise dates of exposure are rarely known. Therefore, the reliability of clinical HIV testing during the first few weeks of infections is unknown, creating anxiety among HIV-exposed individuals and their physicians. We address this by fitting stochastic models of early HIV infection to detailed viral load records for 78 plasma donors, taken during the period of exposure and infection. We first show that the classic stochastic birth-death model does not satisfactorily describe early infection. We therefore apply a different stochastic model that includes infected cells and virions separately. Since every plasma donor in our data eventually becomes infected, we must condition the model to reflect this bias, before fitting to the data. Applying our best estimates of unknown parameter values, we estimate the mean eclipse period to be 8-10 days. We further estimate the reliability of a negative test t days after potential exposure.

      PubDate: 2017-03-17T09:38:13Z
      DOI: 10.1016/j.epidem.2017.03.002
       
  • Application of the CDC EbolaResponse Modeling Tool to disease predictions

    • Authors: Robert H. Gaffey; Cécile Viboud
      Abstract: Publication date: Available online 10 March 2017
      Source:Epidemics
      Author(s): Robert H. Gaffey, Cécile Viboud
      Model-based predictions were critical in eliciting a vigorous international public health response to the 2014 Ebola Virus Disease outbreak in West Africa. Here, we describe the performances of an extension of the CDC-initiated EbolaResponse Modeling tool to the Ebola Forecasting Challenge. The Challenge offered a controlled environment for epidemiological predictions relying on synthetic Ebola datasets. Transmission risks and proportions of population affected by interventions were fitted to data via least square fitting. Prediction performances were evaluated for 5 prediction time points of 4 synthetic outbreaks. One-to-four week-ahead incidence predictions were well correlated with synthetic observations (rho∼0.8), and overall ranking across various error metrics was 4th of 8 teams participating in the context. EbolaResponse yielded moderately accurate predictions for final size, peak size and timing. The relative success of this easily adaptable mechanistic model, with reassessment of model parameters at fixed intervals, indicates that it can generate relatively accurate short-term forecasts, especially when interventions are staggered. An important downside of the model includes a lack of uncertainty estimates in its current estimation framework. Overall, our results align with the conclusion that simple models with few parameters perform well for short-term prediction of epidemic trajectories.

      PubDate: 2017-03-17T09:38:13Z
      DOI: 10.1016/j.epidem.2017.03.001
       
  • IFC, Ed Board

    • Abstract: Publication date: March 2017
      Source:Epidemics, Volume 18


      PubDate: 2017-03-10T03:58:40Z
       
  • Dynamics Affecting the Risk of Silent Circulation When Oral Polio
           Vaccination Is Stopped

    • Authors: J.S. Koopman; C.J. Henry; J.H. Park; M.C. Eisenberg; E.L. Ionides; J.N. Eisenberg
      Abstract: Publication date: Available online 1 March 2017
      Source:Epidemics
      Author(s): J.S. Koopman, C.J. Henry, J.H. Park, M.C. Eisenberg, E.L. Ionides, J.N. Eisenberg
      Waning immunity could allow transmission of polioviruses without causing poliomyelitis by promoting silent circulation (SC). Undetected SC when oral polio vaccine (OPV) use is stopped could cause difficult to control epidemics. Little is known about waning. To develop theory about what generates SC, we modeled a range of waning patterns. We varied both OPV and wild polio virus (WPV) transmissibility, the time from beginning vaccination to reaching low polio levels, and the infection to paralysis ratio (IPR). There was longer SC when waning continued over time rather than stopping after a few years, when WPV transmissibility was higher or OPV transmissibility was lower, and when the IPR was higher. These interacted in a way that makes recent emergence of prolonged SC a possibility. As the time to reach low infection levels increased, vaccine rates needed to eliminate polio increased and a threshold was passed where prolonged low-level SC emerged. These phenomena were caused by increased contributions to the force of infection from reinfections. The resulting SC occurs at low levels that would be difficult to detect using environmental surveillance. For all waning patterns, modest levels of vaccination of adults shortened SC. Previous modeling studies may have missed these phenomena because 1) they used models with no or very short duration waning and 2) they fit models to paralytic polio case counts. Our analyses show that polio case counts can’t predict SC because nearly identical polio case count patterns can be generated by a range of waning patterns that generate different patterns of SC. We conclude that the possibility of prolonged SC is real but unquantified, that vaccinating modest fractions of adults could reduce SC risk, and that joint analysis of acute flaccid paralysis and environmental surveillance data can help assess SC risks and ensure low risks before stopping OPV.

      PubDate: 2017-03-04T00:45:22Z
      DOI: 10.1016/j.epidem.2017.02.013
       
  • Forecasting Ebola with a Regression Transmission Model

    • Authors: Jason Asher; Leidos
      Abstract: Publication date: Available online 27 February 2017
      Source:Epidemics
      Author(s): Jason Asher, Leidos
      We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes traditional Susceptible-Infected-Recovered (SIR) disease modeling approaches and allows for the flexible consideration of outbreaks with complex trajectories of disease dynamics.

      PubDate: 2017-03-04T00:45:22Z
      DOI: 10.1016/j.epidem.2017.02.009
       
  • Two approaches to forecast Ebola synthetic epidemics

    • Authors: David Champredon; Michael Benjamin Bolker Jonathan Dushoff
      Abstract: Publication date: Available online 24 February 2017
      Source:Epidemics
      Author(s): David Champredon, Michael Li, Benjamin M. Bolker, Jonathan Dushoff
      We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other.

      PubDate: 2017-02-25T00:38:17Z
       
  • A simple approach to measure transmissibility and forecast incidence

    • Authors: Pierre Nouvellet; Anne Cori Tini Garske Isobel Blake Ilaria Dorigatti
      Abstract: Publication date: Available online 24 February 2017
      Source:Epidemics
      Author(s): Pierre Nouvellet, Anne Cori, Tini Garske, Isobel M. Blake, Ilaria Dorigatti, Wes Hinsley, Thibaut Jombart, Harriet L. Mills, Gemma Nedjati-Gilani, Maria D. Van Kerkhove, Christophe Fraser, Christl A. Donnelly, Neil M. Ferguson, Steven Riley
      Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola outbreak in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola outbreak, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes − other than the widespread depletion of susceptible individuals − that produce non-exponential patterns of incidence.

      PubDate: 2017-02-25T00:38:17Z
       
  • Using Data-driven Agent-based models for Forecasting Emerging Infectious
           Diseases

    • Authors: Srinivasan Venkatramanan; Bryan Lewis; Jiangzhuo Chen; Dave Higdon; Anil Vullikanti; Madhav Marathe
      Abstract: Publication date: Available online 22 February 2017
      Source:Epidemics
      Author(s): Srinivasan Venkatramanan, Bryan Lewis, Jiangzhuo Chen, Dave Higdon, Anil Vullikanti, Madhav Marathe
      Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014-15 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.

      PubDate: 2017-02-25T00:38:17Z
      DOI: 10.1016/j.epidem.2017.02.010
       
  • Modelling H5N1 in Bangladesh across spatial scales: model complexity and
           zoonotic transmission risk

    • Authors: Edward M. Hill; Thomas House; Madhur S. Dhingra; Wantanee Kalpravidh; Subhash Morzaria; Muzaffar G. Osmani; Mat Yamage; Xiangming Xiao; Marius Gilbert; Michael J. Tildesley
      Abstract: Publication date: Available online 21 February 2017
      Source:Epidemics
      Author(s): Edward M. Hill, Thomas House, Madhur S. Dhingra, Wantanee Kalpravidh, Subhash Morzaria, Muzaffar G. Osmani, Mat Yamage, Xiangming Xiao, Marius Gilbert, Michael J. Tildesley
      Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human-animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for these discrepancies in transmission behaviour between epidemics, such as changes in surveillance sensitivity and biosecurity practices.

      PubDate: 2017-02-25T00:38:17Z
      DOI: 10.1016/j.epidem.2017.02.007
       
  • Population effect of influenza vaccination under co-circulation of
           

    • Authors: Colin J. Worby; Jacco Wallinga; Marc Lipsitch; Edward Goldstein
      Abstract: Publication date: Available online 21 February 2017
      Source:Epidemics
      Author(s): Colin J. Worby, Jacco Wallinga, Marc Lipsitch, Edward Goldstein
      Some past epidemics of different influenza subtypes (particularly A/H3N2) in the US saw co-circulation of vaccine-type and variant strains. There is evidence that natural infection with one influenza subtype offers short-term protection against infection with another influenza subtype (henceforth, cross-immunity). This suggests that such cross-immunity for strains within a subtype is expected to be strong. Therefore, while vaccination effective against one strain may reduce transmission of that strain, this may also lead to a reduction of the vaccine-type strain's ability to suppress spread of a variant strain. It remains unclear what the joint effect of vaccination and cross-immunity is for co-circulating influenza strains within a subtype, and what is the potential benefit of a bivalent vaccine that protects against both strains. We simulated co-circulation of vaccine-type and variant strains under a variety of scenarios. In each scenario, we considered the case when the vaccine efficacy against the variant strain is lower than the efficacy against the vaccine-type strain (monovalent vaccine), as well the case when vaccine is equally efficacious against both strains (bivalent vaccine). Administration of a bivalent vaccine results in a significant reduction in the overall incidence of infection compared to administration of a monovalent vaccine, even with lower coverage by the bivalent vaccine. Additionally, we found that with greater cross-immunity, increasing coverage levels for the monovalent vaccine becomes less beneficial, while introducing the bivalent vaccine becomes more beneficial. Our work exhibits the limitations of influenza vaccines that have low efficacy against non-vaccine strains, and demonstrates the benefits of vaccines that offer good protection against multiple influenza strains. The results elucidate the need for guarding against the potential co-circulation of non-vaccine strains for an influenza subtype, at least during select seasons, possibly through inclusion of multiple strains within a subtype (particularly A/H3N2) in a vaccine.

      PubDate: 2017-02-25T00:38:17Z
      DOI: 10.1016/j.epidem.2017.02.008
       
  • Gender asymmetry in concurrent partnerships and HIV prevalence

    • Authors: Ka Yin Leung; Kimberly A. Powers; Mirjam Kretzschmar
      Abstract: Publication date: Available online 20 January 2017
      Source:Epidemics
      Author(s): Ka Yin Leung, Kimberly A. Powers, Mirjam Kretzschmar
      The structure of the sexual network of a population plays an essential role in the transmission of HIV. Concurrent partnerships, i.e. partnerships that overlap in time, are important in determining this network structure. Men and women may differ in their concurrent behavior, e.g. in the case of polygyny where women are monogamous while men may have concurrent partnerships. Polygyny has been shown empirically to be negatively associated with HIV prevalence, but the epidemiological impacts of other forms of gender-asymmetric concurrency have not been formally explored. Here we investigate how gender asymmetry in concurrency, including polygyny, can affect the disease dynamics. We use a model for a dynamic network where individuals may have concurrent partners. The maximum possible number of simultaneous partnerships can differ for men and women, e.g. in the case of polygyny. We control for mean partnership duration, mean lifetime number of partners, mean degree, and sexually active lifespan. We assess the effects of gender asymmetry in concurrency on two epidemic phase quantities (R 0 and the contribution of the acute HIV stage to R 0) and on the endemic HIV prevalence. We find that gender asymmetry in concurrent partnerships is associated with lower levels of all three epidemiological quantities, especially in the polygynous case. This effect on disease transmission can be attributed to changes in network structure, where increasing asymmetry leads to decreasing network connectivity.

      PubDate: 2017-01-22T23:02:58Z
      DOI: 10.1016/j.epidem.2017.01.003
       
  • Characterising pandemic severity and transmissibility from data collected
           during first few hundred studies

    • Authors: Andrew J. Black; Nicholas Geard; James M. McCaw; Jodie McVernon; Joshua V. Ross
      Abstract: Publication date: Available online 19 January 2017
      Source:Epidemics
      Author(s): Andrew J. Black, Nicholas Geard, James M. McCaw, Jodie McVernon, Joshua V. Ross
      Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by “First Few Hundred” (FF100) studies, which involve surveillance—possibly in person, or via telephone—of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved. We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.
      Graphical abstract image Highlights

      PubDate: 2017-01-22T23:02:58Z
      DOI: 10.1016/j.epidem.2017.01.004
       
  • A comparative analysis of Chikungunya and Zika transmission

    • Authors: Julien Riou; Chiara Poletto; Pierre-Yves Boëlle
      Abstract: Publication date: Available online 18 January 2017
      Source:Epidemics
      Author(s): Julien Riou, Chiara Poletto, Pierre-Yves Boëlle
      The recent global dissemination of Chikungunya and Zika has fostered public health concern worldwide. To better understand the drivers of transmission of these two arboviral diseases, we propose a joint analysis of Chikungunya and Zika epidemics in the same territories, taking into account the common epidemiological features of the epidemics: transmitted by the same vector, in the same environments, and observed by the same surveillance systems. We analyse eighteen outbreaks in French Polynesia and the French West Indies using a hierarchical time-dependent SIR model accounting for the effect of virus, location and weather on transmission, and based on a disease specific serial interval. We show that Chikungunya and Zika have similar transmission potential in the same territories (transmissibility ratio between Zika and Chikungunya of 1.04 [95% credible interval: 0.97; 1.13]), but that detection and reporting rates were different (around 19% for Zika and 40% for Chikungunya). Temperature variations between 22°C and 29°C did not alter transmission, but increased precipitation showed a dual effect, first reducing transmission after a two-week delay, then increasing it around five weeks later. The present study provides valuable information for risk assessment and introduces a modelling framework for the comparative analysis of arboviral infections that can be extended to other viruses and territories.

      PubDate: 2017-01-22T23:02:58Z
      DOI: 10.1016/j.epidem.2017.01.001
       
  • Clustering of contacts relevant to the spread of infectious disease

    • Authors: Xiong Xiao; Albert Jan van Hoek; Michael G. Kenward; Alessia Melegaro; Mark Jit
      Pages: 1 - 9
      Abstract: Publication date: Available online 26 August 2016
      Source:Epidemics
      Author(s): Xiong Xiao, Albert Jan van Hoek, Michael G. Kenward, Alessia Melegaro, Mark Jit
      Objective Infectious disease spread depends on contact rates between infectious and susceptible individuals. Transmission models are commonly informed using empirically collected contact data, but the relevance of different contact types to transmission is still not well understood. Some studies select contacts based on a single characteristic such as proximity (physical/non-physical), location, duration or frequency. This study aimed to explore whether clusters of contacts similar to each other across multiple characteristics could better explain disease transmission. Methods Individual contact data from the POLYMOD survey in Poland, Great Britain, Belgium, Finland and Italy were grouped into clusters by the k medoids clustering algorithm with a Manhattan distance metric to stratify contacts using all four characteristics. Contact clusters were then used to fit a transmission model to sero-epidemiological data for varicella-zoster virus (VZV) in each country. Results and discussion Across the five countries, 9–15 clusters were found to optimise both quality of clustering (measured using average silhouette width) and quality of fit (measured using several information criteria). Of these, 2–3 clusters were most relevant to VZV transmission, characterised by (i) 1–2 clusters of age-assortative contacts in schools, (ii) a cluster of less age-assortative contacts in non-school settings. Quality of fit was similar to using contacts stratified by a single characteristic, providing validation that single stratifications are appropriate. However, using clustering to stratify contacts using multiple characteristics provided insight into the structures underlying infection transmission, particularly the role of age-assortative contacts, involving school age children, for VZV transmission between households.

      PubDate: 2016-08-28T16:49:29Z
      DOI: 10.1016/j.epidem.2016.08.001
      Issue No: Vol. 17 (2016)
       
  • Estimating infectious disease transmission distances using the overall
           distribution of cases

    • Authors: Henrik Salje; Derek A.T. Cummings; Justin Lessler
      Pages: 10 - 18
      Abstract: Publication date: Available online 7 October 2016
      Source:Epidemics
      Author(s): Henrik Salje, Derek A.T. Cummings, Justin Lessler
      The average spatial distance between transmission-linked cases is a fundamental property of infectious disease dispersal. However, the distance between a case and their infector is rarely measurable. Contact-tracing investigations are resource intensive or even impossible, particularly when only a subset of cases are detected. Here, we developed an approach that uses onset dates, the generation time distribution and location information to estimate the mean transmission distance. We tested our method using outbreak simulations. We then applied it to the 2001 foot-and-mouth outbreak in Cumbria, UK, and compared our results to contact-tracing activities. In simulations with a true mean distance of 106m, the average mean distance estimated was 109m when cases were fully observed (95% range of 71–142). Estimates remained consistent with the true mean distance when only five percent of cases were observed, (average estimate of 128m, 95% range 87–165). Estimates were robust to spatial heterogeneity in the underlying population. We estimated that both the mean and the standard deviation of the transmission distance during the 2001 foot-and-mouth outbreak was 8.9km (95% CI: 8.4km-9.7km). Contact-tracing activities found similar values of 6.3km (5.2km-7.4km) and 11.2km (9.5km-12.8km), respectively. We were also able to capture the drop in mean transmission distance over the course of the outbreak. Our approach is applicable across diseases, robust to under-reporting and can inform interventions and surveillance.

      PubDate: 2016-10-10T11:54:55Z
      DOI: 10.1016/j.epidem.2016.10.001
      Issue No: Vol. 17 (2016)
       
  • Modelling the impact of co-circulating low pathogenic avian influenza
           viruses on epidemics of highly pathogenic avian influenza in poultry

    • Authors: Sema Nickbakhsh; Matthew D. Hall; Ilaria Dorigatti; Samantha J. Lycett; Paolo Mulatti; Isabella Monne; Alice Fusaro; Mark E.J. Woolhouse; Andrew Rambaut; Rowland R. Kao
      Pages: 27 - 34
      Abstract: Publication date: December 2016
      Source:Epidemics, Volume 17
      Author(s): Sema Nickbakhsh, Matthew D. Hall, Ilaria Dorigatti, Samantha J. Lycett, Paolo Mulatti, Isabella Monne, Alice Fusaro, Mark E.J. Woolhouse, Andrew Rambaut, Rowland R. Kao
      It is well known that highly pathogenic avian influenza (HPAI) viruses emerge through mutation of precursor low pathogenic avian influenza (LPAI) viruses in domestic poultry populations. The potential for immunological cross-protection between these pathogenic variants is recognised but the epidemiological impact during co-circulation is not well understood. Here we use mathematical models to investigate whether altered flock infection parameters consequent to primary LPAI infections can impact on the spread of HPAI at the population level. First we used mechanistic models reflecting the co-circulatory dynamics of LPAI and HPAI within a single commercial poultry flock. We found that primary infections with LPAI led to HPAI prevalence being maximised under a scenario of high but partial cross-protection. We then tested the population impact in spatially-explicit simulations motivated by a major avian influenza A(H7N1) epidemic that afflicted the Italian poultry industry in 1999–2001. We found that partial cross-protection can lead to a prolongation of HPAI epidemic duration. Our findings have implications for the control of HPAI in poultry particularly for settings in which LPAI and HPAI frequently co-circulate.

      PubDate: 2016-10-30T13:58:21Z
      DOI: 10.1016/j.epidem.2016.10.005
      Issue No: Vol. 17 (2016)
       
  • Impact of waning acquired immunity and asymptomatic infections on
           case-control studies for enteric pathogens

    • Authors: Arie H. Havelaar; Arno Swart
      Pages: 56 - 63
      Abstract: Publication date: December 2016
      Source:Epidemics, Volume 17
      Author(s): Arie H. Havelaar, Arno Swart
      Case-control studies of outbreaks and of sporadic cases of infectious diseases may provide a biased estimate of the infection rate ratio, due to selecting controls that are not at risk of disease. We use a dynamic mathematical model to explore biases introduced in results drawn from case-control studies of enteric pathogens by waning and boosting of immunity, and by asymptomatic infections, using Campylobacter jejuni as an example. Individuals in the population are either susceptible (at risk of infection and disease), fully protected (not at risk of either) or partially protected (at risk of infection but not of disease). The force of infection is a function of the exposure frequency and the exposure dose. We show that the observed disease odds ratios are indeed strongly biased towards the null, i.e. much lower than the infection rate ratio, and furthermore even not proportional to it. The bias could theoretically be controlled by sampling controls only from the reservoir of susceptible individuals. The population at risk is in a dynamic equilibrium, and cannot be identified as those who are not and have never experienced disease. Individual-level samples to measure protective immunity would be required, complicating the design, cost and execution of case-control studies.

      PubDate: 2016-12-05T08:43:55Z
      DOI: 10.1016/j.epidem.2016.11.004
      Issue No: Vol. 17 (2016)
       
  • Using age-stratified incidence data to examine the transmission
           consequences of pertussis vaccination

    • Authors: J.C. Blackwood; D.A.T. Cummings; S. Iamsirithaworn; P. Rohani
      Pages: 1 - 7
      Abstract: Publication date: Available online 19 March 2016
      Source:Epidemics
      Author(s): J.C. Blackwood, D.A.T. Cummings, S. Iamsirithaworn, P. Rohani
      Pertussis is a highly infectious respiratory disease that has been on the rise in many countries worldwide over the past several years. The drivers of this increase in pertussis incidence remain hotly debated, with a central and long-standing hypothesis that questions the ability of vaccines to eliminate pertussis transmission rather than simply modulate the severity of disease. In this paper, we present age-structured case notification data from all provinces of Thailand between 1981 and 2014, a period during which vaccine uptake rose substantially, permitting an evaluation of the transmission impacts of vaccination. Our analyses demonstrate decreases in incidence across all ages with increased vaccine uptake — an observation that is at odds with pertussis case notification data in a number of other countries. To explore whether these observations are consistent with a rise in herd immunity and a reduction in bacterial transmission, we analyze an age-structured model that incorporates contrasting hypotheses concerning the immunological and transmission consequences of vaccines. Our results lead us to conclude that the most parsimonious explanation for the combined reduction in incidence and the shift to older age groups in the Thailand data is vaccine-induced herd immunity.

      PubDate: 2016-03-21T10:23:07Z
      DOI: 10.1016/j.epidem.2016.02.001
      Issue No: Vol. 16 (2016)
       
  • A Model For Sea Lice (Lepeophtheirus salmonis) Dynamics In A Seasonally
           Changing Environment

    • Authors: Matthew A. Rittenhouse; Crawford W. Revie; Amy Hurford
      Pages: 8 - 16
      Abstract: Publication date: Available online 26 March 2016
      Source:Epidemics
      Author(s): Matthew A. Rittenhouse, Crawford W. Revie, Amy Hurford
      Sea lice (Lepeophtheirus salmonis) are a significant source of monetary losses on salmon farms. Sea lice exhibit temperature-dependent development rates and salinity-dependent mortality, but to date no deterministic models have incorporated these seasonally varying factors. To understand how environmental variation and life history characteristics affect sea lice abundance, we derive a delay differential equation model and parameterize the model with environmental data from British Columbia and southern Newfoundland. We calculate the lifetime reproductive output for female sea lice maturing to adulthood at different times of the year and find differences in the timing of peak reproduction between the two regions. Using a sensitivity analysis, we find that sea lice abundance is more sensitive to variation in mean annual water temperature and mean annual salinity than to variation in life history parameters. Our results suggest that effective sea lice management requires consideration of site-specific temperature and salinity patterns and, in particular, that the optimal timing of production cycles and sea lice treatments might vary between regions.

      PubDate: 2016-03-31T10:36:27Z
      DOI: 10.1016/j.epidem.2016.03.003
      Issue No: Vol. 16 (2016)
       
  • Information Content of Household-Stratified Epidemics

    • Authors: T.M. Kinyanjui; L. Pellis; T. House
      Pages: 17 - 26
      Abstract: Publication date: Available online 26 March 2016
      Source:Epidemics
      Author(s): T.M. Kinyanjui, L. Pellis, T. House
      Household structure is a key driver of many infectious diseases, as well as a natural target for interventions such as vaccination programs. Many theoretical and conceptual advances on household-stratified epidemic models are relatively recent, but have successfully managed to increase the applicability of such models to practical problems. To be of maximum realism and hence benefit, they require parameterisation from epidemiological data, and while household-stratified final size data has been the traditional source, increasingly time-series infection data from households are becoming available. This paper is concerned with the design of studies aimed at collecting time-series epidemic data in order to maximize the amount of information available to calibrate household models. A design decision involves a trade-off between the number of households to enrol and the sampling frequency. Two commonly used epidemiological study designs are considered: cross-sectional, where different households are sampled at every time point, and cohort, where the same households are followed over the course of the study period. The search for an optimal design uses Bayesian computationally intensive methods to explore the joint parameter-design space combined with the Shannon entropy of the posteriors to estimate the amount of information in each design. For the cross-sectional design, the amount of information increases with the sampling intensity i.e. the designs with the highest number of time points have the most information. On the other hand, the cohort design often exhibits a trade-off between the number of households sampled and the intensity of follow-up. Our results broadly support the choices made in existing epidemiological data collection studies. Prospective problem-specific use of our computational methods can bring significant benefits in guiding future study designs.

      PubDate: 2016-03-31T10:36:27Z
      DOI: 10.1016/j.epidem.2016.03.002
      Issue No: Vol. 16 (2016)
       
  • Estimates of the risk of large or long-lasting outbreaks of Middle East
           respiratory syndrome after importations outside the Arabian Peninsula

    • Authors: Damon J.A. Toth; Windy D. Tanner; Karim Khader; Adi V. Gundlapalli
      Pages: 27 - 32
      Abstract: Publication date: Available online 7 May 2016
      Source:Epidemics
      Author(s): Damon J.A. Toth, Windy D. Tanner, Karim Khader, Adi V. Gundlapalli
      We quantify outbreak risk after importations of Middle East respiratory syndrome outside the Arabian Peninsula. Data from 31 importation events show strong statistical support for lower transmissibility after early transmission generations. Our model projects the risk of ≥10, 100, and 500 transmissions as 11%, 2%, and 0.02%, and ≥1, 2, 3, and 4 generations as 23%, 14%, 0.9%, and 0.05%, respectively. Our results suggest tempered risk of large, long-lasting outbreaks with appropriate control measures.

      PubDate: 2016-05-11T06:05:25Z
      DOI: 10.1016/j.epidem.2016.04.002
      Issue No: Vol. 16 (2016)
       
  • Co-feeding transmission facilitates strain coexistence in Borrelia
           burgdorferi, the Lyme disease agent

    • Authors: S.L. States; C.I. Huang; S. Davis; D.M. Tufts; M.A. Diuk-Wasser
      Abstract: Publication date: Available online 26 December 2016
      Source:Epidemics
      Author(s): S.L. States, C.I. Huang, S. Davis, D.M. Tufts, M.A. Diuk-Wasser
      Coexistence of multiple tick-borne pathogens or strains is common in natural hosts and can be facilitated by resource partitioning of the host species, within-host localization, or by different transmission pathways. Most vector-borne pathogens are transmitted horizontally via systemic host infection, but transmission may occur in the absence of systemic infection between two vectors feeding in close proximity, enabling pathogens to minimize competition and escape the host immune response. In a laboratory study, we demonstrate this co-feeding transmission can occur for a rapidly-cleared strain of Borrelia burgdorferi, the Lyme disease agent, between two stages of the tick vector Ixodes scapularis while feeding on their dominant host, Peromyscus leucopus. In contrast, infections rapidly become systemic for the persistently infecting strain. In a field study, we assessed opportunities for co-feeding transmission by measuring co-occurrence of two tick stages on ears of small mammals over two years at multiple sites. Finally, in a modeling study, we assessed the importance of co-feeding on R 0 , the basic reproductive number. The model indicated that co-feeding increases the fitness of rapidly-cleared strains in regions with synchronous immature tick feeding. Our results are consistent with increased diversity of B. burgdorferi in areas of higher synchrony in immature feeding – such as the midwestern United States. A higher relative proportion of rapidly-cleared strains, which are less human pathogenic, would also explain lower Lyme disease incidence in this region. Finally, if co-feeding transmission also occurs on refractory hosts, it may facilitate the emergence and persistence of new pathogens with a more limited host range.

      PubDate: 2016-12-27T16:16:23Z
      DOI: 10.1016/j.epidem.2016.12.002
       
  • Real-time forecasting of infectious disease dynamics with a stochastic
           semi-mechanistic model

    • Authors: Sebastian Funk; Anton Camacho; Adam J. Kucharski; Rosalind M. Eggo; W. John Edmunds
      Abstract: Publication date: Available online 16 December 2016
      Source:Epidemics
      Author(s): Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rosalind M. Eggo, W. John Edmunds
      Real-time forecasts of infectious diseases can help public health planning, especially during outbreaks. If forecasts are generated from mechanistic models, they can be further used to target resources or to compare the impact of possible interventions. However, paremeterising such models is often difficult in real time, when information on behavioural changes, interventions and routes of transmission are not readily available. Here, we present a semi-mechanistic model of infectious disease dynamics that was used in real time during the 2013–2016 West African Ebola epidemic, and show fits to a Ebola Forecasting Challenge conducted in late 2015 with simulated data mimicking the true epidemic. We assess the performance of the model in different situations and identify strengths and shortcomings of our approach. Models such as the one presented here which combine the power of mechanistic models with the flexibility to include uncertainty about the precise outbreak dynamics may be an important tool in combating future outbreaks.

      PubDate: 2016-12-19T15:47:27Z
      DOI: 10.1016/j.epidem.2016.11.003
       
  • Defining epidemics in computer simulation models: How do definitions
           influence conclusions?

    • Authors: Carolyn Orbann; Lisa Sattenspiel; Erin Miller; Jessica Dimka
      Abstract: Publication date: Available online 12 December 2016
      Source:Epidemics
      Author(s): Carolyn Orbann, Lisa Sattenspiel, Erin Miller, Jessica Dimka
      Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.

      PubDate: 2016-12-19T15:47:27Z
      DOI: 10.1016/j.epidem.2016.12.001
       
  • Modelling and Bayesian analysis of the Abakaliki Smallpox Data

    • Authors: Jessica E. Stockdale; Theodore Kypraios; Philip D. O’Neill
      Abstract: Publication date: Available online 9 December 2016
      Source:Epidemics
      Author(s): Jessica E. Stockdale, Theodore Kypraios, Philip D. O’Neill
      The celebrated Abakaliki smallpox data have appeared numerous times in the epidemic modelling literature, but in almost all cases only a specific subset of the data is considered. The only previous analysis of the full data set relied on approximation methods to derive a likelihood and did not assess model adequacy. The data themselves continue to be of interest due to concerns about the possible re-emergence of smallpox as a bioterrorism weapon. We present the first full Bayesian statistical analysis using data-augmentation Markov chain Monte Carlo methods which avoid the need for likelihood approximations and which yield a wider range of results than previous analyses. We also carry out model assessment using simulation-based methods. Our findings suggest that the outbreak was largely driven by the interaction structure of the population, and that the introduction of control measures was not the sole reason for the end of the epidemic. We also obtain quantitative estimates of key quantities including reproduction numbers.

      PubDate: 2016-12-12T16:37:20Z
      DOI: 10.1016/j.epidem.2016.11.005
       
  • IFC, Ed Board

    • Abstract: Publication date: December 2016
      Source:Epidemics, Volume 17


      PubDate: 2016-12-12T16:37:20Z
       
  • Estimation of age-specific rates of reactivation and immune boosting of
           the varicella zoster virus

    • Authors: Isabella Marinelli; Alies van Lier; Hester de Melker; Andrea Pugliese; Michiel van Boven
      Abstract: Publication date: Available online 22 November 2016
      Source:Epidemics
      Author(s): Isabella Marinelli, Alies van Lier, Hester de Melker, Andrea Pugliese, Michiel van Boven
      Studies into the impact of vaccination against the varicella zoster virus (VZV) have increasingly focused on herpes zoster (HZ), which is believed to be increasing temporally in vaccinated populations with decreasing infection pressure. This idea can be traced back to Hope-Simpson's hypothesis, in which a person's immune status determines the likelihood that he/she will develop HZ. Immunity decreases over time, and can be boosted by contact with a person experiencing varicella (exogenous boosting) or by a reactivation attempt of the virus (endogenous boosting). Here we use transmission models to estimate age-specific rates of reactivation and immune boosting, exogenous as well as endogenous, using zoster incidence data from the Netherlands (2002-2011, n =7, 026). The boosting and reactivation rates are estimated with splines, enabling these quantities to be optimally informed by the data. The analyses show that models with high levels of exogenous boosting and estimated or zero endogenous boosting, constant rate of loss of immunity, and reactivation rate increasing with age (to more than 5% per year in the elderly) give the best fit to the data. Estimates of the rates of immune boosting and reactivation are strongly correlated. This has important implications as these parameters determine the fraction of the population with waned immunity. We conclude that independent evidence on rates of immune boosting and reactivation in persons with waned immunity are needed to robustly predict the impact of varicella vaccination on the incidence of HZ.

      PubDate: 2016-11-27T16:26:14Z
      DOI: 10.1016/j.epidem.2016.11.001
       
  • Using phenomenological models for forecasting the 2015 Ebola Challenge

    • Authors: Bruce Pell; Yang Kuang; Cecile Viboud; Gerardo Chowell
      Abstract: Publication date: Available online 19 November 2016
      Source:Epidemics
      Author(s): Bruce Pell, Yang Kuang, Cecile Viboud, Gerardo Chowell
      Background The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. Materials and Methods We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. Results During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points. Conclusions Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak.

      PubDate: 2016-11-20T15:57:48Z
      DOI: 10.1016/j.epidem.2016.11.002
       
  • Targeting pediatric versus elderly populations for norovirus vaccines: a
           model-based analysis of mass vaccination options

    • Authors: Molly K. Steele; Justin V. Remais; Manoj Gambhir; John W. Glasser; Andreas Handel; Umesh D. Parashar; Benjamin A. Lopman
      Abstract: Publication date: Available online 24 October 2016
      Source:Epidemics
      Author(s): Molly K. Steele, Justin V. Remais, Manoj Gambhir, John W. Glasser, Andreas Handel, Umesh D. Parashar, Benjamin A. Lopman
      Background Noroviruses are the leading cause of acute gastroenteritis and foodborne diarrheal disease in the United States. Norovirus vaccine development has progressed in recent years, but critical questions remain regarding which age groups should be vaccinated to maximize population impact. Methods We developed a deterministic, age-structured compartmental model of norovirus transmission and immunity in the U.S. population. The model was fit to age-specific monthly U.S. hospitalizations between 1996 and 2007. We simulated mass immunization of both pediatric and elderly populations assuming realistic coverages of 90% and 65%, respectively. We considered two mechanism of vaccine action, resulting in lower vaccine efficacy (lVE) between 22% and 43% and higher VE (hVE) of 50%. Results Pediatric vaccination was predicted to avert 33% (95% CI: 27%, 40%) and 60% (95% CI: 49%, 71%) of norovirus episodes among children under five years for lVE and hVE, respectively. Vaccinating the elderly averted 17% (95% CI: 12%, 20%) and 38% (95% CI: 34%, 42%) of cases in 65+ year olds for lVE and hVE, respectively. At a population level, pediatric vaccination was predicted to avert 18-21 times more cases and twice as many deaths per vaccinee compared to elderly vaccination. Conclusions The potential benefits are likely greater for a pediatric program, both via direct protection of vaccinated children and indirect protection of unvaccinated individuals, including adults and the elderly. These findings argue for a clinical development plan that will deliver a vaccine with a safety and efficacy profile suitable for use in children.

      PubDate: 2016-10-30T13:58:21Z
      DOI: 10.1016/j.epidem.2016.10.006
       
  • Estimate of the reproduction number of the 2015 Zika virus outbreak in
           Barranquilla, Colombia, and estimation of the relative role of sexual
           transmission

    • Authors: Sherry Towers; Fred Brauer; Carlos Castillo-Chavez; Andrew K.I. Falconar; Anuj Mubayi; Claudia M.E. Romero-Vivas
      Abstract: Publication date: Available online 17 October 2016
      Source:Epidemics
      Author(s): Sherry Towers, Fred Brauer, Carlos Castillo-Chavez, Andrew K.I. Falconar, Anuj Mubayi, Claudia M.E. Romero-Vivas
      Background In 2015, the Zika arbovirus (ZIKV) began circulating in the Americas, rapidly expanding its global geographic range in explosive outbreaks. Unusual among mosquito-borne diseases, ZIKV has been shown to also be sexually transmitted, although sustained autochthonous transmission due to sexual transmission alone has not been observed, indicating the reproduction number (R0) for sexual transmission alone is less than 1. Critical to the assessment of outbreak risk, estimation of the potential attack rates, and assessment of control measures, are estimates of the basic reproduction number, R0. Methods We estimated the R0 of the 2015 ZIKV outbreak in Barranquilla, Colombia, through an analysis of the exponential rise in clinically identified ZIKV cases (n=359 to the end of November, 2015). Findings The rate of exponential rise in cases was ρ=0.076 days−1, with 95% CI [0.066,0.087] days−1. We used a vector-borne disease model with additional direct transmission to estimate the R0; assuming the R0 of sexual transmission alone is less than 1, we estimated the total R0=3.8 [2.4,5.6], and that the fraction of cases due to sexual transmission was 0.23 [0.01,0.47] with 95% confidence. Interpretation This is among the first estimates of R0 for a ZIKV outbreak in the Americas, and also among the first quantifications of the relative impact of sexual transmission.

      PubDate: 2016-10-30T13:58:21Z
      DOI: 10.1016/j.epidem.2016.10.003
       
  • PRESERVING PRIVACY WHILST MAINTAINING ROBUST EPIDEMIOLOGICAL PREDICTIONS

    • Authors: Marleen Werkman; Michael J. Tildesley; Ellen Brooks-Pollock; Matt J. Keeling
      Abstract: Publication date: Available online 13 October 2016
      Source:Epidemics
      Author(s): Marleen Werkman, Michael J. Tildesley, Ellen Brooks-Pollock, Matt J. Keeling
      Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use.

      PubDate: 2016-10-16T12:05:26Z
      DOI: 10.1016/j.epidem.2016.10.004
       
  • Data-driven outbreak forecasting with a simple nonlinear growth model

    • Authors: Joceline Lega; Heidi E. Brown
      Abstract: Publication date: Available online 11 October 2016
      Source:Epidemics
      Author(s): Joceline Lega, Heidi E. Brown
      Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders.

      PubDate: 2016-10-16T12:05:26Z
      DOI: 10.1016/j.epidem.2016.10.002
       
  • The IDEA model: A single equation approach to the ebola forecasting
           challenge

    • Authors: Ashleigh R. Tuite; David N. Fisman
      Abstract: Publication date: Available online 28 September 2016
      Source:Epidemics
      Author(s): Ashleigh R. Tuite, David N. Fisman
      Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in the face of emerging infectious diseases, such as the 2014–2015 West African Ebola epidemic, but little is known about the relative performance of alternate forecasting approaches. The RAPIDD Ebola Forecasting Challenge (REFC) tested the ability of eight mathematical models to generate useful forecasts in the face of simulated Ebola outbreaks. We used a simple, phenomenological single-equation model (the “IDEA” model), which relies only on case counts, in the REFC. Model fits were performed using a maximum likelihood approach. We found that the model performed reasonably well relative to other more complex approaches, with performance metrics ranked on average 4th or 5th among participating models. IDEA appeared better suited to long- than short-term forecasts, and could be fit using nothing but reported case counts. Several limitations were identified, including difficulty in identifying epidemic peak (even retrospectively), unrealistically precise confidence intervals, and difficulty interpolating daily case counts when using a model scaled to epidemic generation time. More realistic confidence intervals were generated when case counts were assumed to follow a negative binomial, rather than Poisson, distribution. Nonetheless, IDEA represents a simple phenomenological model, easily implemented in widely available software packages that could be used by frontline public health personnel to generate forecasts with accuracy that approximates that which is achieved using more complex methodologies.

      PubDate: 2016-10-02T11:40:32Z
      DOI: 10.1016/j.epidem.2016.09.001
       
  • IFC, Ed Board

    • Abstract: Publication date: September 2016
      Source:Epidemics, Volume 16


      PubDate: 2016-09-21T11:15:26Z
       
 
 
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