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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Sempa, Joseph B; Dushoff, Jonathan; Daniels, Michael J et al. (2016) Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort. Am J Epidemiol 184:67-77
Parpia, Alyssa S; Ndeffo-Mbah, Martial L; Wenzel, Natasha S et al. (2016) Effects of Response to 2014-2015 Ebola Outbreak on Deaths from Malaria, HIV/AIDS, and Tuberculosis, West Africa. Emerg Infect Dis 22:433-41
Durham, David P; Ndeffo-Mbah, Martial L; Skrip, Laura A et al. (2016) National- and state-level impact and cost-effectiveness of nonavalent HPV vaccination in the United States. Proc Natl Acad Sci U S A 113:5107-12
Eggo, Rosalind M; Scott, James G; Galvani, Alison P et al. (2016) Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model. Proc Natl Acad Sci U S A 113:2194-9
Atkins, Katherine E; Pandey, Abhishek; Wenzel, Natasha S et al. (2016) Retrospective Analysis of the 2014-2015 Ebola Epidemic in Liberia. Am J Trop Med Hyg 94:833-9
Gilbert, Jennifer A; Medlock, Jan; Townsend, Jeffrey P et al. (2016) Determinants of Human African Trypanosomiasis Elimination via Paratransgenesis. PLoS Negl Trop Dis 10:e0004465
Biggerstaff, Matthew; Alper, David; Dredze, Mark et al. (2016) Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge. BMC Infect Dis 16:357
Ndeffo-Mbah, Martial L; Durham, David P; Skrip, Laura A et al. (2016) Evaluating the effectiveness of localized control strategies to curtail chikungunya. Sci Rep 6:23997
Fallah, Mosoka P; Skrip, Laura A; Gertler, Shai et al. (2015) Quantifying Poverty as a Driver of Ebola Transmission. PLoS Negl Trop Dis 9:e0004260
Greenhalgh, Scott; Galvani, Alison P; Medlock, Jan (2015) Disease elimination and re-emergence in differential-equation models. J Theor Biol 387:174-80

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