We aim to improve infectious disease surveillance and control through mathematical modeling, optimization, and translational collaborations with public health decision makers. Methodologically, we will advance the application of mathematical modeling to inform public heath policy decisions by (i) integrating large-scale optimization, economic analyses, and uncertainty quantification into mathematical models of disease transmission in complex and dynamic populations, and by (ii) developing goal-oriented optimization methods for integrating diverse data sources to improve infectious disease surveillance systems. We will apply these approaches using data on influenza, respiratory syncytial virus (RSV), pertussis, West Nile virus (WNV), and dengue from around the world to elucidate the complex drivers of outbreaks and control and to identify highly effective, economical, and feasible control policies. We will disseminate our models and results to public health authorities and develop user-friendly modeling tools to facilitate preparedness and real-time decision- making regarding the optimal distribution of limited disease control resources. Thus, our interdisciplinary research will expand the methodological toolkit for modeling infectious disease dynamics, provide better strategies for tracking and mitigating epidemics, and make science, data, and models more broadly accessible to public health agencies engaged in the global fight against infectious diseases.

Public Health Relevance

By applying optimization, economic, and uncertainty quantification methods to mathematical models of both disease dynamics and surveillance systems, we will answer fundamental questions about the spread of influenza, respiratory syncytial virus (RSV), pertussis, dengue, and West Nile virus (WNV), identify innovative strategies for improving the detection and control of these diseases, and produce translational public health decision-support tools.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZGM1-BBCB-5 (MI))
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Sheeley, Douglas
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Yale University
Public Health & Prev Medicine
Schools of Medicine
New Haven
United States
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Pandey, Abhishek; Atkins, Katherine E; Medlock, Jan et al. (2014) Strategies for containing Ebola in West Africa. Science 346:991-5
Johnson, Matthew P; Gutfraind, Alexander; Ahmadizadeh, Kiyan (2014) Evader Interdiction: Algorithms, Complexity and Collateral Damage. Ann Oper Res 222:341-359
Lewnard, Joseph A; Ndeffo Mbah, Martial L; Alfaro-Murillo, Jorge A et al. (2014) Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modelling analysis. Lancet Infect Dis 14:1189-95
Ndeffo Mbah, Martial L; Durham, David P; Medlock, Jan et al. (2014) Country- and age-specific optimal allocation of dengue vaccines. J Theor Biol 342:15-22
Fitzpatrick, Meagan C; Hampson, Katie; Cleaveland, Sarah et al. (2014) Cost-effectiveness of canine vaccination to prevent human rabies in rural Tanzania. Ann Intern Med 160:91-100
von Terzi, Britta; Turnbull, Peter C B; Bellan, Steve E et al. (2014) Failure of Sterne- and Pasteur-like strains of Bacillus anthracis to replicate and survive in the urban bluebottle blow fly Calliphora vicina under laboratory conditions. PLoS One 9:e83860
Lipsitch, Marc; Galvani, Alison P (2014) Ethical alternatives to experiments with novel potential pandemic pathogens. PLoS Med 11:e1001646
Bellan, Steve E; Turnbull, Peter C B; Beyer, Wolfgang et al. (2013) Effects of experimental exclusion of scavengers from carcasses of anthrax-infected herbivores on Bacillus anthracis sporulation, survival, and distribution. Appl Environ Microbiol 79:3756-61
Ndeffo Mbah, Martial L; Poolman, Eric M; Atkins, Katherine E et al. (2013) Potential cost-effectiveness of schistosomiasis treatment for reducing HIV transmission in Africa--the case of Zimbabwean women. PLoS Negl Trop Dis 7:e2346
Ndeffo Mbah, Martial L; Medlock, Jan; Meyers, Lauren Ancel et al. (2013) Optimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty. Vaccine 31:3079-89

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