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  Subjects -> HEALTH AND SAFETY (Total: 1404 journals)
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HEALTH AND SAFETY (625 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 203 Journals sorted alphabetically
16 de Abril     Open Access  
Acta Informatica Medica     Open Access   (Followers: 1)
Acta Scientiarum. Health Sciences     Open Access   (Followers: 1)
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: 25)
African Health Sciences     Open Access   (Followers: 3)
African Journal for Physical, Health Education, Recreation and Dance     Full-text available via subscription   (Followers: 7)
African Journal of Health Professions Education     Open Access   (Followers: 6)
Afrimedic Journal     Open Access   (Followers: 2)
Ageing & Society     Hybrid Journal   (Followers: 43)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4)
AJOB Primary Research     Partially Free   (Followers: 3)
American Journal of Family Therapy     Hybrid Journal   (Followers: 11)
American Journal of Health Economics     Full-text available via subscription   (Followers: 16)
American Journal of Health Education     Hybrid Journal   (Followers: 32)
American Journal of Health Promotion     Hybrid Journal   (Followers: 30)
American Journal of Health Sciences     Open Access   (Followers: 9)
American Journal of Health Studies     Full-text available via subscription   (Followers: 12)
American Journal of Preventive Medicine     Hybrid Journal   (Followers: 28)
American Journal of Public Health     Full-text available via subscription   (Followers: 240)
American Journal of Public Health Research     Open Access   (Followers: 28)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 5)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5)
Annales des Sciences de la Santé     Open Access  
Annals of Global Health     Open Access   (Followers: 10)
Annals of Health Law     Open Access   (Followers: 3)
Annals of Tropical Medicine and Public Health     Open Access   (Followers: 13)
Applied Biosafety     Hybrid Journal  
Applied Research In Health And Social Sciences: Interface And Interaction     Open Access   (Followers: 3)
Apuntes Universitarios     Open Access   (Followers: 1)
Archive of Community Health     Open Access   (Followers: 1)
Archives of Medicine and Health Sciences     Open Access   (Followers: 3)
Arquivos de Ciências da Saúde     Open Access  
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 10)
Asia Pacific Journal of Health Management     Full-text available via subscription   (Followers: 4)
Asia-Pacific Journal of Public Health     Hybrid Journal   (Followers: 9)
Asian Journal of Gambling Issues and Public Health     Open Access   (Followers: 4)
Atención Primaria     Open Access   (Followers: 1)
Australasian Journal of Paramedicine     Open Access   (Followers: 3)
Australian Advanced Aesthetics     Full-text available via subscription   (Followers: 4)
Australian Family Physician     Full-text available via subscription   (Followers: 3)
Australian Indigenous HealthBulletin     Free   (Followers: 7)
Autism & Developmental Language Impairments     Open Access   (Followers: 9)
Behavioral Healthcare     Full-text available via subscription   (Followers: 7)
Bijzijn     Hybrid Journal   (Followers: 1)
Bijzijn XL     Hybrid Journal  
Biomedical Safety & Standards     Full-text available via subscription   (Followers: 8)
Birat Journal of Health Sciences     Open Access  
BLDE University Journal of Health Sciences     Open Access  
BMC Oral Health     Open Access   (Followers: 7)
BMC Pregnancy and Childbirth     Open Access   (Followers: 22)
BMJ Simulation & Technology Enhanced Learning     Hybrid Journal   (Followers: 9)
Boletin Médico de Postgrado     Open Access  
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: 18)
Cadernos de Educação, Saúde e Fisioterapia     Open Access   (Followers: 1)
Cadernos Saúde Coletiva     Open Access   (Followers: 1)
Cambridge Quarterly of Healthcare Ethics     Hybrid Journal   (Followers: 11)
Canadian Family Physician     Partially Free   (Followers: 13)
Canadian Journal of Community Mental Health     Full-text available via subscription   (Followers: 11)
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 2)
Canadian Journal of Public Health     Hybrid Journal   (Followers: 23)
Cannabis and Cannabinoid Research     Hybrid Journal   (Followers: 1)
Carta Comunitaria     Open Access  
Case Reports in Women's Health     Open Access   (Followers: 4)
Case Studies in Fire Safety     Open Access   (Followers: 23)
Central Asian Journal of Global Health     Open Access   (Followers: 2)
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: 11)
Children     Open Access   (Followers: 2)
CHRISMED Journal of Health and Research     Open Access   (Followers: 2)
Christian Journal for Global Health     Open Access  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
Ciencia e Innovación en Salud     Open Access  
Ciencia y Cuidado     Open Access   (Followers: 1)
Ciencia y Salud Virtual     Open Access  
Ciencia, Tecnología y Salud     Open Access   (Followers: 2)
Clinical and Experimental Health Sciences     Open Access   (Followers: 1)
ClinicoEconomics and Outcomes Research     Open Access   (Followers: 2)
Clocks & Sleep     Open Access   (Followers: 1)
CME     Hybrid Journal   (Followers: 2)
CoDAS     Open Access  
Community Health     Open Access   (Followers: 3)
Conflict and Health     Open Access   (Followers: 7)
Contraception and Reproductive Medicine     Open Access   (Followers: 1)
Cuadernos de la Escuela de Salud Pública     Open Access  
Curare     Open Access  
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 9)
Day Surgery Australia     Full-text available via subscription   (Followers: 2)
Digital Health     Open Access   (Followers: 4)
Disaster Medicine and Public Health Preparedness     Hybrid Journal   (Followers: 13)
Diversity of Research in Health Journal     Open Access  
Dramatherapy     Hybrid Journal   (Followers: 2)
Drogues, santé et société     Open Access   (Followers: 1)
Duazary     Open Access   (Followers: 1)
Düzce Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi / Journal of Duzce University Health Sciences Institute     Open Access  
Early Childhood Research Quarterly     Hybrid Journal   (Followers: 21)
East African Journal of Public Health     Full-text available via subscription   (Followers: 4)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 22)
EcoHealth     Hybrid Journal   (Followers: 4)
Education for Health     Open Access   (Followers: 6)
electronic Journal of Health Informatics     Open Access   (Followers: 6)
ElectronicHealthcare     Full-text available via subscription   (Followers: 3)
Elsevier Ergonomics Book Series     Full-text available via subscription   (Followers: 5)
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   (Followers: 3)
Environmental Sciences Europe     Open Access   (Followers: 1)
Epidemics     Open Access   (Followers: 5)
Epidemiologic Perspectives & Innovations     Open Access   (Followers: 5)
Epidemiology, Biostatistics and Public Health     Open Access   (Followers: 19)
Ethics, Medicine and Public Health     Full-text available via subscription   (Followers: 6)
Ethiopian Journal of Health Development     Open Access   (Followers: 7)
Ethiopian Journal of Health Sciences     Open Access   (Followers: 8)
Ethnicity & Health     Hybrid Journal   (Followers: 13)
Eurasian Journal of Health Technology Assessment     Open Access  
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 4)
European Medical, Health and Pharmaceutical Journal     Open Access   (Followers: 1)
Evaluation & the Health Professions     Hybrid Journal   (Followers: 10)
Evidence-based Medicine & Public Health     Open Access   (Followers: 8)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Expressa Extensão     Open Access  
Face à face     Open Access   (Followers: 1)
Families, Systems, & Health     Full-text available via subscription   (Followers: 9)
Family & Community Health     Hybrid Journal   (Followers: 13)
Family Medicine and Community Health     Open Access   (Followers: 9)
Family Relations     Partially Free   (Followers: 13)
Fatigue : Biomedicine, Health & Behavior     Hybrid Journal   (Followers: 2)
Finnish Journal of eHealth and eWelfare : Finjehew     Open Access  
Food and Public Health     Open Access   (Followers: 16)
Food Quality and Safety     Open Access   (Followers: 1)
Frontiers in Public Health     Open Access   (Followers: 7)
Gaceta Sanitaria     Open Access   (Followers: 3)
Galen Medical Journal     Open Access   (Followers: 1)
Ganesha Journal     Open Access  
Gazi Sağlık Bilimleri Dergisi     Open Access  
Geospatial Health     Open Access  
Gesundheitsökonomie & Qualitätsmanagement     Hybrid Journal   (Followers: 9)
Giornale Italiano di Health Technology Assessment     Full-text available via subscription  
Global Challenges     Open Access  
Global Health : Science and Practice     Open Access   (Followers: 7)
Global Health Promotion     Hybrid Journal   (Followers: 15)
Global Journal of Health Science     Open Access   (Followers: 10)
Global Journal of Public Health     Open Access   (Followers: 13)
Global Medical & Health Communication     Open Access   (Followers: 2)
Global Mental Health     Open Access   (Followers: 8)
Global Reproductive Health     Open Access  
Global Security : Health, Science and Policy     Open Access   (Followers: 1)
Globalization and Health     Open Access   (Followers: 5)
Hacia la Promoción de la Salud     Open Access  
Hastane Öncesi Dergisi     Open Access  
Hastings Center Report     Hybrid Journal   (Followers: 4)
HCU Journal     Open Access  
HEADline     Hybrid Journal  
Health & Place     Hybrid Journal   (Followers: 15)
Health & Justice     Open Access   (Followers: 5)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 10)
Health and Human Rights     Free   (Followers: 10)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 9)
Health and Social Work     Hybrid Journal   (Followers: 57)
Health Behavior and Policy Review     Full-text available via subscription   (Followers: 3)
Health Care Analysis     Hybrid Journal   (Followers: 15)
Health Equity     Open Access  
Health Inform     Full-text available via subscription  
Health Information Management Journal     Hybrid Journal   (Followers: 23)
Health Issues     Full-text available via subscription   (Followers: 2)
Health Notions     Open Access  
Health Policy     Hybrid Journal   (Followers: 43)
Health Policy and Technology     Hybrid Journal   (Followers: 4)
Health Professional Student Journal     Open Access   (Followers: 4)
Health Promotion International     Hybrid Journal   (Followers: 22)
Health Promotion Journal of Australia : Official Journal of Australian Association of Health Promotion Professionals     Full-text available via subscription   (Followers: 8)
Health Promotion Practice     Hybrid Journal   (Followers: 16)
Health Prospect     Open Access   (Followers: 1)
Health Psychology     Full-text available via subscription   (Followers: 53)
Health Psychology Bulletin     Open Access   (Followers: 1)
Health Psychology Research     Open Access   (Followers: 20)
Health Psychology Review     Hybrid Journal   (Followers: 42)
Health Renaissance     Open Access  
Health Research Policy and Systems     Open Access   (Followers: 14)
Health SA Gesondheid     Open Access   (Followers: 2)
Health Science Reports     Open Access  
Health Sciences and Disease     Open Access   (Followers: 2)
Health Security     Hybrid Journal  
Health Services Insights     Open Access   (Followers: 1)
Health Systems     Hybrid Journal   (Followers: 4)
Health Voices     Full-text available via subscription  
Health, Culture and Society     Open Access   (Followers: 12)
Health, Risk & Society     Hybrid Journal   (Followers: 14)
Healthcare     Open Access   (Followers: 3)
Healthcare in Low-resource Settings     Open Access   (Followers: 1)
Healthcare Quarterly     Full-text available via subscription   (Followers: 8)
Healthcare Technology Letters     Open Access  
Healthy Aging Research     Open Access  
HERD : Health Environments Research & Design Journal     Full-text available via subscription  

        1 2 3 4 | Last

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Journal Prestige (SJR): 1.812
Citation Impact (citeScore): 3
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1755-4365 - ISSN (Online) 1878-0067
Published by Elsevier Homepage  [3183 journals]
  • Estimating age-mixing patterns relevant for the transmission of airborne

    • Abstract: Publication date: Available online 20 March 2019Source: EpidemicsAuthor(s): Nicky McCreesh, Carl Morrow, Keren Middelkoop, Robin Wood, Richard G White IntroductionAge-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody ‘sharing air’ indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not ‘close’) is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data.MethodsWe estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group.ResultsPatterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses.ConclusionsPatterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts.
  • Confronting data sparsity to identify potential sources of Zika virus
           spillover infection among primates

    • Abstract: Publication date: Available online 19 March 2019Source: EpidemicsAuthor(s): Barbara A. Han, Subhabrata Majumdar, Flavio P. Calmon, Benjamin S. Glicksberg, Raya Horesh, Abhishek Kumar, Adam Perer, Elisa B. von Marschall, Dennis Wei, Aleksandra Mojsilović, Kush R. Varshney The recent Zika virus (ZIKV) epidemic in the Americas ranks among the largest outbreaks in modern times. Like other mosquito-borne flaviviruses, ZIKV circulates in sylvatic cycles among primates that can serve as reservoirs of spillover infection to humans. Identifying sylvatic reservoirs is critical to mitigating spillover risk, but relevant surveillance and biological data remain limited for this and most other zoonoses. We confronted this data sparsity by combining a machine learning method, Bayesian multi-label learning, with a multiple imputation method on primate traits. The resulting models distinguished flavivirus-positive primates with 82% accuracy and suggest that species posing the greatest spillover risk are also among the best adapted to human habitations. Given pervasive data sparsity describing animal hosts, and the virtual guarantee of data sparsity in scenarios involving novel or emerging zoonoses, we show that computational methods can be useful in extracting actionable inference from available data to support improved epidemiological response and prevention.
  • Impact of sexual trajectories of men who have sex with men on the
           reduction in HIV transmission by pre-exposure prophylaxis

    • Abstract: Publication date: Available online 14 March 2019Source: EpidemicsAuthor(s): Ganna Rozhnova, Janneke Heijne, Maartje Basten, Chantal den Daas, Amy Matser, Mirjam Kretzschmar Changes in sexual risk behavior over the life course in men who have sex with men (MSM) can influence population-level intervention efficacy. Our objective was to investigate the impact of incorporating sexual trajectories describing long-term changes in risk levels on the reduction in HIV prevalence by pre-exposure prophylaxis (PrEP) among MSM. Based on the Amsterdam Cohort Study data, we developed two models of HIV transmission in a population stratified by sexual behavior. In the first model, individuals were stratified into low, medium and high risk levels and did not change their risk levels. The second model had the same stratification but incorporated additionally three types of sexual behavior trajectories. The models assumed universal antiretroviral treatment of HIV+ MSM, and PrEP use by high risk HIV− MSM. We computed the relative reduction in HIV prevalence in both models for annual PrEP uptakes of 10% to 80% at different time points after PrEP introduction. We then investigated the impact of sexual trajectories on the effectiveness of PrEP intervention. The impact of sexual trajectories on the overall prevalence and prevalence in individuals at low, medium and high risk levels varied with PrEP uptake and time after PrEP introduction. Compared to the model without sexual trajectories, the model with trajectories predicted a higher impact of PrEP on the overall prevalence, and on the prevalence among the medium and high risk individuals. In low risk individuals, there was more reduction in prevalence during the first 15 years of PrEP intervention if sexual trajectories were not incorporated in the model. After that point, at low risk level there was more reduction in the model with trajectories. In conclusion, our study predicts that sexual trajectories increase the estimated impact of PrEP on reducing HIV prevalence when compared to a population where risk levels do not change.
  • Efficient vaccination strategies for epidemic control using network

    • Abstract: Publication date: Available online 6 March 2019Source: EpidemicsAuthor(s): Yingrui Yang, Ashley McKhann, Sixing Chen, Guy Harling, Jukka-Pekka Onnela BackgroundNetwork-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness.MethodsWe simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks.ResultsIn regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals’ degrees at three was as effective as using complete networks.Conclusions:Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required.
  • Control of Ebola virus disease outbreaks: comparison of health care
           worker-targeted and community vaccination strategies

    • Abstract: Publication date: Available online 2 March 2019Source: EpidemicsAuthor(s): Alexis Robert, Anton Camacho, W. John Edmunds, Marc Baguelin, Jean-Jacques Muyembe Tamfum, Alicia Rosello, Sakoba Kéïta, Rosalind M. Eggo BackgroundHealth care workers (HCW) are at risk of infection during Ebola virus disease outbreaks and therefore may be targeted for vaccination before or during outbreaks. The effect of these strategies depends on the role of HCW in transmission which is understudied.MethodsTo evaluate the effect of HCW-targeted or community vaccination strategies, we used a transmission model to explore the relative contribution of HCW and the community to transmission. We calibrated the model to data from multiple Ebola outbreaks. We quantified the impact of ahead-of-time HCW-targeted strategies, and reactive HCW and community vaccination.ResultsWe found that for some outbreaks (we call “type 1”) HCW amplified transmission both to other HCW and the community, and in these outbreaks prophylactic vaccination of HCW decreased outbreak size. Reactive vaccination strategies had little effect because type 1 outbreaks ended quickly. However, in outbreaks with longer time courses (“type 2 outbreaks”), reactive community vaccination decreased the number of cases, with or without prophylactic HCW-targeted vaccination. For both outbreak types, we found that ahead-of-time HCW-targeted strategies had an impact at coverage of 30%.ConclusionsThe vaccine strategies tested had a different impact depending on the transmission dynamics and previous control measures. Although we will not know the characteristics of a new outbreak, ahead-of-time HCW-targeted vaccination can decrease the total outbreak size, even at low vaccine coverage.
  • Systematic biases in disease forecasting - the role of behavior change

    • Abstract: Publication date: Available online 28 February 2019Source: EpidemicsAuthor(s): Ceyhun Eksin, Keith Paarporn, Joshua S. Weitz In a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with those from a modified SIR model in which individuals reduce contacts as a function of the current or cumulative number of cases. Dynamics with behavior change exhibit significantly reduced final case counts even though the initial speed of disease spread is nearly identical for both of the models. We show that this difference in final size projections depends critically in the behavior change of individuals. These results also provide a rationale for integrating behavior change into iterative forecast models. Hence, we propose to use a Kalman filter to update models with and without behavior change as part of iterative forecasts. When the ground truth outbreak includes behavior change, sequential predictions using a simple SIR model perform poorly despite repeated observations while predictions using the modified SIR model are able to correct for initial forecast errors. These findings highlight the value of incorporating behavior change into baseline epidemic and dynamic forecast models.
  • Assessing reporting delays and the effective reproduction number: The
           Ebola epidemic in DRC, May 2018–January 2019

    • Abstract: Publication date: Available online 3 February 2019Source: EpidemicsAuthor(s): A. Tariq, K. Roosa, K. Mizumoto, G. Chowell On August 1, 2018, the Democratic Republic of Congo declared its 10th and largest outbreak of Ebola inflicting North Khivu and Ituri provinces. The spread of Ebola to Congolese urban centers along with deliberate attacks on the health care workers has hindered epidemiological surveillance activities, leading to substantial reporting delays. Reporting delays distort the epidemic incidence pattern misrepresenting estimates of epidemic potential and the outbreak trajectory. To assess the impact of reporting delays, we conducted a real-time analysis of the dynamics of the ongoing Ebola outbreak in the DRC using epidemiological data retrieved from the World Health Organization Situation Reports and Disease Outbreak News. We analyzed temporal trends in reporting delays, epidemic curves of crude and reporting-delay adjusted incidences and changes in the effective reproduction number, Rt. As of January 15, 2019, 663 Ebola cases have been reported in the Democratic Republic of Congo. The average reporting delay exhibited 81.1% decline from a mean of 17.4 weeks (95% CI 13–24.1) in May, 2018 to 3.3 weeks (95% CI 2.7–4.2) in September, 2018 (F-test statistic = 44.9, p = 0.0067). The Ebola epidemic has shown a two-wave pattern with the first surge in cases occurring between July 30 and August 13, 2018 and the second on September 24, 2018. During the last 4 generation intervals, the trend in the mean Rt has exhibited a slight decline (rho = −0.37, p 
  • Managing Marek’s disease in the egg industry

    • Abstract: Publication date: Available online 2 February 2019Source: EpidemicsAuthor(s): Carly Rozins, Troy Day, Scott Greenhalgh The industrialization of farming has had an enormous impact. To most, this impact is viewed solely in the context of productivity, but the denser living conditions and shorter rearing periods of industrial livestock farms provide pathogens with an ideal opportunity to spread and evolve. For example, the industrialization of poultry farms drove the Marek’s disease virus (MDV) to evolve from a mild paralytic syndrome to a highly contagious, globally prevalent, deadly disease. Fortunately, the economic catastrophe that would occur from MDV evolution is prevented through the widespread use of live imperfect vaccines that limit disease symptoms, but fail to prevent transmission. Unfortunately, the continued rollout of such imperfect vaccines is steering MDV evolution towards even greater virulence, and the ability to evade vaccine protection. Thus, there is a need to investigate alternative economically viable control measures for their ability to inhibit MDV spread and evolution. In what follows we examine the economic viability of standard husbandry practices for their ability to inhibit the spread of both virulent MDV and very virulent MDV throughout an industrialized egg farm. To do this, we parameterize a MDV transmission model and calculate the loss in egg production due to MDV. We find that MDV strain and the cohort duration have the greatest influence on both disease burden and egg production. Additionally, our findings show that for long cohort durations, conventional cages result in the least per capita loss in egg production due to MDV infection, while Aviary systems perform best over shorter cohort durations. Finally, we find that the least per capita loss in egg production for flocks infected with the more virulent MDV strains occurs when cohort durations are sufficiently short. These results highlight the important decisions that managers will face when implementing new hen husbandry practices.
  • A dynamic network model to disentangle the roles of steady and casual
           partners for HIV transmission among MSM

    • Abstract: Publication date: Available online 2 February 2019Source: EpidemicsAuthor(s): D. Hansson, K.Y. Leung, T. Britton, S. Strömdahl HIV is a sexually transmitted infection (STI) whose transmission process is highly dependent on the sexual network structure of the population under consideration. Most sexual behaviour data is egocentric in nature. We develop a stochastic dynamic sexual network model that utilises this type of egocentric network data. The model incorporates both steady and casual sex partners, and can be seen as a stochastic form of a generalised pair-formation model. We model the spread of an infection where individuals are susceptible, infectious, or successfully treated (and unable to transmit) and derive analytical expressions for several epidemiological quantities. We use sexual behaviour and HIV prevalence data that was gathered among 403 MSM at an STI clinic in Stockholm. To accurately capture transmission dynamics for this population, we need to explicitly model both casual sex partners and steady partnerships. Our model yields an estimate for the mean time until diagnosis followed by successful treatment that is in line with literature. This study indicates that small reductions in the time to diagnosis, and thereby, beginning of treatment, may substantially reduce HIV prevalence. Moreover, we find that moderate increases in condom use with casual sex partners have greater impact on reducing prevalence than the same increases in condom use with steady sex partners. This result demonstrates the relative importance of casual contacts on the HIV transmission dynamics among MSM in Sweden. Our results highlight the importance of HIV testing and condom-use interventions, and the role that casual and steady partners play in this, in order to turn the epidemiological trend in Sweden towards decreased HIV incidence.
  • Near-term forecasts of influenza-like illness: An evaluation of
           autoregressive time series approaches

    • Abstract: Publication date: Available online 17 January 2019Source: EpidemicsAuthor(s): Sasikiran Kandula, Jeffrey Shaman Seasonal influenza in the United States is estimated to cause 9–35 million illnesses annually, with resultant economic burden amounting to $47-$150 billion. Reliable real-time forecasts of influenza can help public health agencies better manage these outbreaks. Here, we investigate the feasibility of three autoregressive methods for near-term forecasts: an Autoregressive Integrated Moving Average (ARIMA) model with time-varying order; an ARIMA model fit to seasonally adjusted incidence rates (ARIMA-STL); and a feed-forward autoregressive artificial neural network with a single hidden layer (AR-NN). We generated retrospective forecasts for influenza incidence one to four weeks in the future at US National and 10 regions in the US during 5 influenza seasons. We compared the relative accuracy of the point and probabilistic forecasts of the three models with respect to each other and in relation to two large external validation sets that each comprise at least 20 other models.Both the probabilistic and point forecasts of AR-NN were found to be more accurate than those of the other two models overall. An additional sub-analysis found that the three models benefitted considerably from the use of search trends based 'nowcast' as a proxy for surveillance data, and these three models with use of nowcasts were found to be the highest ranked models in both validation datasets. When the nowcasts were withheld, the three models remained competitive relative to models in the validation sets. The difference in accuracy among the three models, and relative to models of the validation sets, was found to be largely statistically significant.Our results suggest that autoregressive models even when not equipped to capture transmission dynamics can provide reasonably accurate near-term forecasts for influenza. Existing support in open-source libraries make them suitable non-naïve baselines for model comparison studies and for operational forecasts in resource constrained settings where more sophisticated methods may not be feasible.
  • A practical generation-interval-based approach to inferring the strength
           of epidemics from their speed

    • Abstract: Publication date: Available online 10 January 2019Source: EpidemicsAuthor(s): Sang Woo Park, David Champredon, Joshua S. Weitz, Jonathan Dushoff Infectious disease outbreaks are often characterized by the reproduction number R and exponential rate of growth r. R provides information about outbreak control and predicted final size, but estimating R is difficult, while r can often be estimated directly from incidence data. These quantities are linked by the generation interval – the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to obtain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of R. We show that estimating generation interval mean and variance provides insight into the relationship between R and r. We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r–R relationship and provide robust estimates of R.
  • Assessing the role of dens in the spread, establishment and persistence of
           sarcoptic mange in an endangered canid

    • Abstract: Publication date: Available online 8 January 2019Source: EpidemicsAuthor(s): Diego Montecino-Latorre, Brian L. Cypher, Jaime L. Rudd, Deana L. Clifford, Jonna A.K. Mazet, Janet E. Foley Sarcoptic mange is a skin disease caused by the mite Sarcoptes scabiei that can devastate populations of wild species. S. scabiei can survive off-host and remain infective for specific periods. In den-dwelling species, dislodged mites could be protected from the environmental conditions that impair their survival thus supporting pathogen transmission. To assess the potential role of dens in the spread, establishment, and persistence of sarcoptic mange in a population of hosts, we constructed an agent-based model of the endangered San Joaquin kit fox (SJKF; Vulpes macrotis mutica) population in Bakersfield, California, that explicitly considered the denning ecology and behavior of this species. We focused on this SJKF urban population because of their vulnerability and because a sarcoptic mange epizootic is currently ongoing. Further, SJKF is a social species that lives in family groups year-round and contact between individuals from different family groups is rare, but they will occupy the same dens intermittently. If mites remain infective in dens, they could support intra-family disease transmission via direct (den sharing) and indirect (contaminated den) contact, but also inter-family transmission if susceptible individuals from different families occupy contaminated dens. Simulations showed that den-associated transmission significantly increases the chances for the mite to spread, to establish and to persist. These findings hold for different within-den S. scabiei off-host survival periods assessed. Managers dealing with S. scabiei in this species as well as in other den-dwelling species should consider den-associated transmission as they could be targeted as part of the control strategies against this mite.
  • Model-based estimates of transmission of respiratory syncytial virus
           within households

    • Abstract: Publication date: Available online 15 December 2018Source: EpidemicsAuthor(s): Ivy K. Kombe, Patrick K. Munywoki, Marc Baguelin, D. James Nokes, Graham F. Medley IntroductionRespiratory syncytial virus (RSV) causes a significant respiratory disease burden in the under 5 population. The transmission pathway to young children is not fully quantified in low-income settings, and this information is required to design interventions.MethodsWe used an individual level transmission model to infer transmission parameters using data collected from 493 individuals distributed across 47 households over a period of 6 months spanning the 2009/2010 RSV season. A total of 208 episodes of RSV were observed from 179 individuals. We model competing transmission risk from within household exposure and community exposure while making a distinction between RSV groups A and B.ResultsWe find that 32–53% of all RSV transmissions are between members of the same household; the rate of pair-wise transmission is 58% (95% CrI: 30–74%) lower in larger households (≥8 occupants) than smaller households; symptomatic individuals are 2–7 times more infectious than asymptomatic individuals i.e. 2.48 (95% CrI: 1.22–5.57) among symptomatic individuals with low viral load and 6.7(95% CrI: 2.56–16) among symptomatic individuals with high viral load; previous infection reduces susceptibility to re-infection within the same epidemic by 47% (95% CrI: 17%–68%) for homologous RSV group and 39% (95%CrI: -8%-69%) for heterologous group; RSV B is more frequently introduced into the household, and RSV A is more rapidly transmitted once in the household.DiscussionOur analysis presents the first transmission modelling of cohort data for RSV and we find that it is important to consider the household social structuring and household size when modelling transmission. The increased infectiousness of symptomatic individuals implies that a vaccine against RSV related disease would also have an impact on infection transmission. Together, the weak cross immunity between RSV groups and the possibility of different transmission niches could form part of the explanation for the group co-existence.Graphical abstractGraphical abstract for this article
  • The role of age-mixing patterns in HIV transmission dynamics: Novel
           hypotheses from a field study in Cape Town, South Africa

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Roxanne Beauclair, Niel Hens, Wim Delva BackgroundAge-disparate relationships are thought to put young women at increased risk of HIV, though current evidence is inconclusive. Studying population-level age-mixing patterns as well as individual-level measures of age difference variation may provide insight into the persistence and magnitude of the epidemic in South Africa.MethodsWe used data from a survey in Cape Town (n = 506) to describe age-mixing dynamics in the four population strata of HIV negative and HIV positive male and female participants. Mixed-effects models were used to calculate the average increase in partner age for each year increase in age of participant, the average partner age for 15 year olds, and the between-subject and the within-subject standard deviation of partner ages. We conducted 2000 bootstrap replications of the models. Using negative binomial models, we also explored whether HIV status was associated with participants having a larger range in partner ages.ResultsHIV positive women had large variability in partner ages at the population level, and at the individual level had nearly three times the expected range of partner ages compared to HIV negative women. This pattern may increase the potential for HIV transmission across birth cohorts and may partially explain the persistence of the epidemic in South Africa. Young men, who have been previously absent from the age-disparity discourse, also choose older partners who may be putting them at increased risk of HIV infection due to the high HIV prevalence among older age categories of women.
  • Tuberculosis outbreak investigation using phylodynamic analysis

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Denise Kühnert, Mireia Coscolla, Daniela Brites, David Stucki, John Metcalfe, Lukas Fenner, Sebastien Gagneux, Tanja Stadler The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis. In this study, we investigate and compare the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. Specifically, we (i) test if the outbreak data sets contain enough genetic variation to estimate short-term evolutionary rates and (ii) reconstruct epidemiological parameters such as the effective reproduction number.The first outbreak occurred in the Swiss city of Bern (1987–2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.There is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. Thanks to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, there is much uncertainty in the epidemiological estimates concerning the sparsely sampled (9%) WTK outbreak. Our results suggest that both outbreaks peaked around 1990, although they were only recognized as outbreaks in 1993 (Bern) and 2004 (WTK). Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4–5 years).Our work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.
  • Concurrency of partnerships, consistency with data, and control of
           sexually transmitted infections

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Trystan Leng, Matt J. Keeling Sexually transmitted infections (STIs) are a globally increasing public health problem. Mathematical models, carefully matched to available epidemiological and behavioural data, have an important role to play in predicting the action of control measures. Here, we explore the effect of concurrent sexual partnerships on the control of a generic STI with susceptible-infected-susceptible dynamics. Concurrency refers to being in more than one sexual partnership at the same time, and is difficult to measure accurately. We assess the impact of concurrency through the development of three nested pair-formation models: one where infection can only be transmitted via stable sexual partnerships, one where infection can also be transmitted via casual partnerships between single individuals, and one where those individuals in stable partnerships can also acquire infection from casual partnerships. For each model, we include the action of vaccination before sexual debut to inform about the ability to control. As expected, for a fixed transmission rate, concurrency increases both the endemic prevalence of infection and critical level of vaccination required to eliminate the disease significantly. However, when the transmission rate is scaled to maintain a fixed endemic prevalence across models, concurrency has a far smaller impact upon the critical level of vaccination required. Further, when we also constrain the models to have a fixed number of new partnerships over time (both long-term and casual), then increasing concurrency can slightly decrease the critical level of vaccination. These results highlight that accurate measures and models of concurrency may not always be needed for reliable forecasts when models are closely matched to prevalence data. We find that, while increases in concurrency within a population are likely to generate public-health problems, the inclusion of concurrency may be unnecessary when constructing models to determine the efficacy of the control of STIs by vaccination.
  • A model for leptospire dynamics and control in the Norway rat (Rattus
           norvegicus) the reservoir host in urban slum environments

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Amanda Minter, Peter J. Diggle, Federico Costa, James Childs, Albert I. Ko, Mike Begon Leptospirosis is a zoonosis that humans can contract via contact with animal reservoirs directly or with water contaminated with their urine. The primary reservoir of pathogenic leptospires within urban slum environments is the Norway rat (Rattus norvegicus). Motivated by the annual outbreaks of human leptospirosis in slum urban settings, the within population infection dynamics of the Norway rat were investigated in Pau da Lima, an community in Salvador, Brazil. A mechanistic model of the dynamics of leptospire infection was informed by extensive field and laboratory data was developed and explored analytically. To identify the intraspecific transmission route of most importance, a global sensitivity analysis of the basic reproduction number to its components was performed. In addition, different methods of rodent control were investigated by calculating target reproduction numbers. Our results suggest environmental transmission plays an important role in the maintenance of infection in the rodent population. To control numbers of wild Norway rats, combinations of controls are recommended but environmental control should also be investigated to reduce prevalence of infection in rats.
  • Modeling epidemics: A primer and Numerus Model Builder implementation

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Wayne M. Getz, Richard Salter, Oliver Muellerklein, Hyun S. Yoon, Krti Tallam Epidemiological models are dominated by compartmental models, of which SIR formulations are the most commonly used. These formulations can be continuous or discrete (in either the state-variable values or time), deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SIR dynamical systems models, and we outline how they can be easily and rapidly constructed using Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using NMB network and mapping tools.
  • Modelling the global spread of diseases: A review of current practice and

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Caroline E. Walters, Margaux M.I. Meslé, Ian M. Hall Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data.We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult.We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible.
  • Practical unidentifiability of a simple vector-borne disease model:
           Implications for parameter estimation and intervention assessment

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Yu-Han Kao, Marisa C. Eisenberg Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset. Identifiability analysis allows us to examine whether model parameters can be estimated uniquely—a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquito-borne disease, using the 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions. However, in spite of the unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field. While we only consider the simplest case for the model, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions and inferences, and particularly highlights the importance of combining laboratory, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.
  • Estimating the effective reproduction number of dengue considering
           temperature-dependent generation intervals

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Claudia T. Codeço, Daniel A.M. Villela, Flavio C. Coelho The effective reproduction number, Rt, is a measure of transmission that can be calculated from standard incidence data to timely detect the beginning of epidemics. It has being increasingly used for surveillance of directly transmitted diseases. However, current methods for Rt estimation do not apply for vector borne diseases, whose transmission cycle depends on temperature. Here we propose a method that provides dengue's Rt estimates in the presence of temperature-mediated seasonality and apply this method to simulated and real data from two cities in Brazil where dengue is endemic. The method shows good precision in the simulated data. When applied to the real data, it shows differences in the transmission profile of the two cities and identifies periods of higher transmission.
  • Kernel-density estimation and approximate Bayesian computation for
           flexible epidemiological model fitting in Python

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Michael A. Irvine, T. Déirdre Hollingsworth Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.
  • Identifying human encounters that shape the transmission of Streptococcus
           pneumoniae and other acute respiratory infections

    • Abstract: Publication date: December 2018Source: Epidemics, Volume 25Author(s): Olivier le Polain de Waroux, Stefan Flasche, Adam J Kucharski, Celine Langendorf, Donny Ndazima, Juliet Mwanga-Amumpaire, Rebecca F Grais, Sandra Cohuet, W John Edmunds Although patterns of social contacts are believed to be an important determinant of infectious disease transmission, it remains unclear how the frequency and nature of human interactions shape an individual’s risk of infection. We analysed data on daily social encounters individually matched to data on S. pneumoniae carriage and acute respiratory symptoms (ARS), from 566 individuals who took part in a survey in South-West Uganda. We found that the frequency of physical (i.e. skin-to-skin), long (≥1 h) and household contacts – which capture some measure of close (i.e. relatively intimate) contact – was higher among pneumococcal carriers than non-carriers, and among people with ARS compared to those without, irrespective of their age. With each additional physical encounter the age-adjusted risk of carriage and ARS increased by 6% (95%CI 2–9%) and 7% (2–13%) respectively. In contrast, the number of casual contacts (
  • The role of intra and inter-hospital patient transfer in the dissemination
           of heathcare-associated multidrug-resistant pathogens

    • Abstract: Publication date: Available online 30 November 2018Source: EpidemicsAuthor(s): T.N. Vilches, M.F. Bonesso, H.M. Guerra, C.M.C.B. Fortaleza, A.W. Park, C.P. Ferreira Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiella pneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibiting drug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for such infections. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase (KPC, an enzyme that inactivates several antimicrobials), we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R0 that measures the average number of new infections caused by a single infectious individual, was calculated in different scenarios defined by both the links between hospital environments (regular wards and intensive care units) and between different hospitals (patient transfer). Numerical simulation was used to illustrate the infection dynamics in this set of scenarios. The sensitivity of R0 to model input parameters, such as hospital connectivity and patient-hospital staff contact rates was also established, highlighting the differential importance of factors amenable to change on pathogen transmission and control.
  • Fogarty International Center collaborative networks in infectious disease
           modeling: Lessons learnt in research and capacity building

    • Abstract: Publication date: Available online 23 October 2018Source: EpidemicsAuthor(s): Martha I. Nelson, James O. Lloyd-Smith, Lone Simonsen, Andrew Rambaut, Edward C. Holmes, Gerardo Chowell, Mark A. Miller, David J. Spiro, Bryan Grenfell, Cécile Viboud Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the ‘big data’ revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
  • The impact of influenza vaccination on infection, hospitalisation and
           mortality in the Netherlands between 2003 and 2015

    • Abstract: Publication date: Available online 11 October 2018Source: EpidemicsAuthor(s): J.A. Backer, J. Wallinga, A. Meijer, G.A. Donker, W. van der Hoek, M. van Boven Influenza epidemics annually cause substantial morbidity and mortality. For this reason, vaccination is offered yearly to persons with an elevated risk for complications. Assessments of the impact of vaccination are, however, hampered by year-to-year variation in epidemic size and vaccine effectiveness.We estimate the impact of the current vaccination programme comparing simulations with vaccination to counterfactual simulations without vaccination. The simulations rely on an age- and risk-structured transmission model that tracks the build-up and loss of immunity over successive seasons, and that allows the vaccine match to vary between seasons. The model parameters are estimated with a particle Monte Carlo method and approximate Bayesian computation, using epidemiological data on vaccine effectiveness and epidemic size in the Netherlands over a period of 11 years.The number of infections, hospitalisations and deaths vary greatly between years because waning of immunity and vaccine match may differ every season, which is in line with observed variation in influenza epidemic sizes. At an overall coverage of 21%, vaccination has averted on average 13% (7.2–19%, 95% range) of infections, 24% (16–36%) of hospitalisations, and 35% (16–50%) of deaths. This suggests that vaccination is mainly effective in protecting vaccinees from infection rather than reducing transmission. As the Dutch population continues to grow and age, the vaccination programme is projected (up to 2025) to gain in impact, despite a decreasing infection attack rate.
  • Geographic transmission hubs of the 2009 influenza pandemic in the United

    • Abstract: Publication date: Available online 10 October 2018Source: EpidemicsAuthor(s): Stephen M. Kissler, Julia R. Gog, Cécile Viboud, Vivek Charu, Ottar N. Bjørnstad, Lone Simonsen, Bryan T. Grenfell A key issue in infectious disease epidemiology is to identify and predict geographic sites of epidemic establishment that contribute to onward spread, especially in the context of invasion waves of emerging pathogens. Conventional wisdom suggests that these sites are likely to be in densely-populated, well-connected areas. For pandemic influenza, however, epidemiological data have not been available at a fine enough geographic resolution to test this assumption. Here, we make use of fine-scale influenza-like illness incidence data derived from electronic medical claims records gathered from 834 3-digit ZIP (postal) codes across the US to identify the key geographic establishment sites, or “hubs”, of the autumn wave of the 2009 A/H1N1pdm influenza pandemic in the United States. A mechanistic spatial transmission model is fit to epidemic onset times inferred from the data. Hubs are identified by tracing the most probable transmission routes back to a likely first establishment site. Four hubs are identified: two in the southeastern US, one in the central valley of California, and one in the midwestern US. According to the model, 75% of the 834 observed ZIP-level outbreaks in the US were seeded by these four hubs or their epidemiological descendants. Counter-intuitively, the pandemic hubs do not coincide with large and well-connected cities, indicating that factors beyond population density and travel volume are necessary to explain the establishment sites of the major autumn wave of the pandemic. Geographic regions are identified where infection can be statistically traced back to a hub, providing a testable prediction of the outbreak's phylogeography. Our method therefore provides an important way forward to reconcile spatial diffusion patterns inferred from epidemiological surveillance data and pathogen sequence data.
  • Vaccinating children against influenza increases variability in epidemic

    • Abstract: Publication date: Available online 10 October 2018Source: EpidemicsAuthor(s): J.A. Backer, M. van Boven, W. van der Hoek, J. Wallinga Seasonal influenza causes a high disease burden. Many influenza vaccination programmes target the elderly and persons at high risk of complications. Some countries have recommended or even implemented a paediatric vaccination programme. Such a programme is expected to reduce influenza transmission in the population, offering direct protection to the vaccinated children and indirect protection to the elderly.We study the impact of a child vaccination programme with an age- and risk-structured transmission model, calibrated to data of 11 influenza seasons in the Netherlands. The model tracks the build-up of immunes and susceptibles in each age cohort over time, and it allows for seasonal variation in vaccine match and antigenic drift. Different vaccination strategies are evaluated for three target age groups (2–3, 2–12 and 2–16 year olds) over the full range of vaccination coverages (0–100%).The results show that the paediatric vaccination programme has only a limited impact on the elderly age groups, which account for most influenza morbidity and mortality. This is due to two notable changes in infection dynamics. First, an age shift is observed: influenza infections are reduced in vaccinated children, but are increased in young adults with limited natural immunity after years of vaccination. These young adults assume the role of driving the epidemic. Second, a year with low influenza activity can be followed by a large epidemic due to build-up of susceptibles. This variation of the infection attack rate increases with increasing vaccination coverage.The increased variability in the infection attack rate implies that health care facilities should be prepared for rare but larger peaks in influenza patients. Moreover, vaccinating the group with the highest transmission potential, results in a larger dependency on a secure vaccine supply. These arguments should be taken into account in the decision to introduce mass vaccination of school-aged children against influenza.
  • Contagion! The BBC Four Pandemic – The model behind the documentary

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Petra Klepac, Stephen Kissler, Julia Gog To mark the centenary of the 1918 influenza pandemic, the broadcasting network BBC have put together a 75-min documentary called ‘Contagion! The BBC Four Pandemic’. Central to the documentary is a nationwide citizen science experiment, during which volunteers in the United Kingdom could download and use a custom mobile phone app called BBC Pandemic, and contribute their movement and contact data for a day.As the ‘maths team’, we were asked to use the data from the app to build and run a model of how a pandemic would spread in the UK. The headline results are presented in the TV programme. Here, we document in detail how the model works, and how we shaped it according the incredibly rich data coming from the BBC Pandemic app.We have barely scratched the depth of the volunteer data available from the app. The work presented in this article had the sole purpose of generating a single detailed simulation of a pandemic influenza-like outbreak in the UK. When the BBC Pandemic app has completed its collection period, the vast dataset will be made available to the scientific community (expected early 2019). It will take much more time and input from a broad range of researchers to fully exploit all that this dataset has to offer. But here at least we were able to harness some of the power of the BBC Pandemic data to contribute something which we hope will capture the interest and engagement of a broad audience.
  • Age difference between heterosexual partners in Britain: Implications for
           the spread of Chlamydia trachomatis

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Joost H. Smid, Victor Garcia, Nicola Low, Catherine H. Mercer, Christian L. Althaus Heterosexual partners often differ in age. Integrating realistic patterns of sexual mixing by age into dynamic transmission models has been challenging. The effects of these patterns on the transmission of sexually transmitted infections (STI) including Chlamydia trachomatis (chlamydia), the most common bacterial STI are not well understood. We describe age mixing between new heterosexual partners using age- and sex-specific data about sexual behavior reported by people aged 16–63 years in the 2000 and 2010 British National Surveys of Sexual Attitudes and Lifestyles. We incorporate mixing patterns into a compartmental transmission model fitted to age- and sex-specific, chlamydia positivity from the same surveys, to investigate C. trachomatis transmission. We show that distributions of ages of new sex partners reported by women and by men in Britain are not consistent with each other. After balancing these distributions, new heterosexual partnerships tend to involve men who are older than women (median age difference 2, IQR −1, 5 years). We identified the most likely age combinations of heterosexual partners where incident C. trachomatis infections are generated. The model results show that in>50% of chlamydia transmitting partnerships, at least one partner is ≥25 years old. This study illustrates how sexual behavior data can be used to reconstruct detailed sexual mixing patterns by age, and how these patterns can be integrated into dynamic transmission models. The proposed framework can be extended to study the effects of age-dependent transmission on incidence in any STI.
  • Role of animal movement and indirect contact among farms in transmission
           of porcine epidemic diarrhea virus

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Kimberly VanderWaal, Andres Perez, Montse Torremorrell, Robert M. Morrison, Meggan Craft Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement.
  • Using state-space models to predict the abundance of juvenile and adult
           sea lice on Atlantic salmon

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Adel Elghafghuf, Raphael Vanderstichel, Sophie St-Hilaire, Henrik Stryhn Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses.In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out.
  • Epidemics on dynamic networks

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Jessica Enright, Rowland Raymond Kao In many populations, the patterns of potentially infectious contacts are transients that can be described as a network with dynamic links. The relative timescales of link and contagion dynamics and the characteristics that drive their tempos can lead to important differences to the static case. Here, we propose some essential nomenclature for their analysis, and then review the relevant literature. We describe recent advances in they apply to infection processes, considering all of the methods used to record, measure and analyse them, and their implications for disease transmission. Finally, we outline some key challenges and opportunities in the field.
  • Robust qualitative estimation of time-varying contact rates in uncertain

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Marco Tulio Angulo, Jorge X. Velasco-Hernandez We will inevitably face new epidemics where the lack of long time-series data and the uncertainty about the outbreak dynamics make difficult to obtain quantitative predictions. Here we present an algorithm to qualitatively infer time-varying contact rates from short time-series data, letting us predict the start, relative magnitude and decline of epidemic outbreaks. Using real time-series data of measles, dengue, and the current zika outbreak, we demonstrate our algorithm can outperform existing algorithms based on estimating reproductive numbers.
  • The distribution of district-level leprosy incidence in India is
           geometric-stable, consistent with subcriticality

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Thomas M. Lietman, Lee Worden, Fengchen Liu, Travis C. Porco Mathematical models predict that the community-level incidence of a controlled infectious disease across a region approaches a geometric distribution. This could hold over larger regions, if new cases remain proportional to existing cases. Leprosy has been disappearing for centuries, making an excellent candidate for testing this hypothesis. Here, we show the annual new case detection rate of leprosy in Indian districts to be consistent with a geometric distribution. For 2008–2013, goodness-of-fit testing was unable to exclude the geometric, and the shape parameter of the best fit negative binomial distribution was close to unity (0.95, 95% CI 0.87–1.03). Ramifications include that a district-level cross-sectional survey may reveal whether an infectious disease is headed towards elimination, that apparent outliers are expected and not necessarily representative of program failure, and that proportion 1/e of a small geographical unit may not meet a control threshold even when a larger area has.
  • Acute illness from Campylobacter jejuni may require high doses while
           infection occurs at low doses

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): Peter F.M. Teunis, Axel Bonačić Marinović, David R. Tribble, Chad K. Porter, Arno Swart Data from a set of different studies on the infectivity and pathogenicity of Campylobacter jejuni were analyzed with a multilevel model, allowing for effects of host species (nonhuman primates and humans) and different strains of the pathogen.All challenge studies involved high doses of the pathogen, resulting in all exposed subjects to become infected. In only one study a dose response effect (increasing trend with dose) for infection was observed. High susceptibility to infection with C. jejuni was found in a joint analysis of outbreaks and challenge studies. For that reason four outbreaks, associated with raw milk consumption, were also included in the present study.The high doses used for inoculation did not cause all infected subjects to develop acute enteric symptoms. The observed outcomes are consistent with a dose response effect for acute symptoms among infected subjects: a conditional illness dose response relation. Nonhuman primates and human volunteers did not appear to have different susceptibilities for developing enteric symptoms, but exposure in outbreaks (raw milk) did lead to a higher probability of symptomatic campylobacteriosis.
  • Identifying genotype specific elevated-risk areas and associated herd risk
           factors for bovine tuberculosis spread in British cattle

    • Abstract: Publication date: September 2018Source: Epidemics, Volume 24Author(s): R.J. Orton, M. Deason, P.R. Bessell, D.M. Green, R.R. Kao, L.C.M. Salvador Bovine tuberculosis (bTB) is a chronic zoonosis with major health and economic impact on the cattle industry. Despite extensive control measures in cattle and culling trials in wildlife, the reasons behind the expansion of areas with high incidence of bTB breakdowns in Great Britain remain unexplained. By balancing the importance of cattle movements and local transmission on the observed pattern of cattle outbreaks, we identify areas at elevated risk of infection from specific Mycobacterium bovis genotypes. We show that elevated-risk areas (ERAs) were historically more extensive than previously understood, and that cattle movements alone are insufficient for ERA spread, suggesting the involvement of other factors. For all genotypes, we find that, while the absolute risk of infection is higher in ERAs compared to areas with intermittent risk, the statistically significant risk factors are remarkably similar in both, suggesting that these risk factors can be used to identify incipient ERAs before this is indicated by elevated incidence alone. Our findings identify research priorities for understanding bTB dynamics, improving surveillance and guiding management to prevent further ERA expansion.Graphical abstractGraphical abstract for this article
  • Sexual role and HIV-1 set point viral load among men who have sex with men

    • Abstract: Publication date: Available online 30 August 2018Source: EpidemicsAuthor(s): Sarah E. Stansfield, John E. Mittler, Geoffrey S. Gottlieb, James T. Murphy, Deven T. Hamilton, Roger Detels, Steven M. Wolinsky, Lisa P. Jacobson, Joseph B. Margolick, Charles R. Rinaldo, Joshua T. Herbeck, Steven M. Goodreau BackgroundHIV-1 set point viral load (SPVL) is a highly variable trait that influences disease progression and transmission risk. Men who are exclusively insertive (EI) during anal intercourse require more sexual contacts to become infected than exclusively receptive (ER) men. Thus, we hypothesize that EIs are more likely to acquire their viruses from highly infectious partners (i.e., with high SPVLs) and to have higher SPVLs than infected ERs.MethodsWe used a one-generation Bernoulli model, a dynamic network model, and data from the Multicenter AIDS Cohort Study (MACS) to examine whether and under what circumstances MSM differ in SPVL by sexual role.ResultsBoth models predicted higher SPVLs in EIs than role versatile (RV) or ER men, but only in scenarios where longer-term relationships predominated. ER and RV men displayed similar SPVLs. EI men remained far less likely than ER men to become infected, however. When the MACS data were limited by some estimates of lower sex partner counts (a proxy for longer relationships), EI men had higher SPVLs; these differences were clinically relevant (>0.3 log10 copies/mL) and statistically significant (p 
  • Correlations between stochastic epidemics in two interacting populations

    • Abstract: Publication date: Available online 30 August 2018Source: EpidemicsAuthor(s): Sophie R. Meakin, Matt J. Keeling It is increasingly apparent that heterogeneity in the interaction between individuals plays an important role in the dynamics, persistence, evolution and control of infectious diseases. In epidemic modelling two main forms of heterogeneity are commonly considered: spatial heterogeneity due to the segregation of populations and heterogeneity in risk at the same location. The transition from random-mixing to heterogeneous-mixing models is made by incorporating the interaction, or coupling, within and between subpopulations. However, such couplings are difficult to measure explicitly; instead, their action through the correlations between subpopulations is often all that can be observed. Here, using moment-closure methodology supported by stochastic simulation, we investigate how the coupling and resulting correlation are related. We focus on the simplest case of interactions, two identical coupled populations, and show that for a wide range of parameters the correlation between the prevalence of infection takes a relatively simple form. In particular, the correlation can be approximated by a logistic function of the between population coupling, with the free parameter determined analytically from the epidemiological parameters. These results suggest that detailed case-reporting data alone may be sufficient to infer the strength of between population interaction and hence lead to more accurate mathematical descriptions of infectious disease behaviour.Graphical abstractGraphical abstract for this article
  • Dengue modeling in rural Cambodia: Statistical performance versus
           epidemiological relevance

    • Abstract: Publication date: Available online 29 August 2018Source: EpidemicsAuthor(s): Clara Champagne, Richard Paul, Sowath Ly, Veasna Duong, Rithea Leang, Bernard Cazelles Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, and the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible classes) is assessed in each model. Our analyses on transmission dynamics in a rural endemic setting highlight that two-strain models with interacting effects better reproduce the long term data, but they are difficult to parameterize when relying on incidence cases only. On the other hand, considering the available data, incorporating vector and asymptomatic components seems of limited added-value when seasonality and underreporting are already accounted for.
  • Transmission on empirical dynamic contact networks is influenced by data
           processing decisions

    • Abstract: Publication date: Available online 29 August 2018Source: EpidemicsAuthor(s): Daniel E. Dawson, Trevor S. Farthing, Michael W. Sanderson, Cristina Lanzas Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R0, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.
  • Superensemble forecast of respiratory syncytial virus outbreaks at
           national, regional, and state levels in the United States

    • Abstract: Publication date: Available online 9 July 2018Source: EpidemicsAuthor(s): Julia Reis, Teresa Yamana, Sasikiran Kandula, Jeffrey Shaman Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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