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.
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.
| Grantz, Kyra H; Chabaari, Winnie; Samuel, Ramolotja Kagiso et al. (2018) Spatial distribution of leprosy in India: an ecological study. Infect Dis Poverty 7:20 |
| Fitzpatrick, Meagan C; Gray, Glenda E; Galvani, Alison P (2018) The Challenge of Vanquishing HIV for the Next Generation-Facing the Future. JAMA Pediatr 172:609-610 |
| Ndeffo-Mbah, Martial L; Vigliotti, Vivian S; Skrip, Laura A et al. (2018) Dynamic Models of Infectious Disease Transmission in Prisons and the General Population. Epidemiol Rev 40:40-57 |
| Du, Zhanwei; Yang, Yongjian; Gao, Chao et al. (2018) The temporal network of mobile phone users in Changchun Municipality, Northeast China. Sci Data 5:180228 |
| Ertem, Zeynep; Raymond, Dorrie; Meyers, Lauren Ancel (2018) Optimal multi-source forecasting of seasonal influenza. PLoS Comput Biol 14:e1006236 |
| Perofsky, Amanda C; Lewis, Rebecca J; Meyers, Lauren Ancel (2018) Terrestriality and bacterial transfer: a comparative study of gut microbiomes in sympatric Malagasy mammals. ISME J : |
| Sah, Pratha; Medlock, Jan; Fitzpatrick, Meagan C et al. (2018) Optimizing the impact of low-efficacy influenza vaccines. Proc Natl Acad Sci U S A 115:5151-5156 |
| Durham, David P; Fitzpatrick, Meagan C; Ndeffo-Mbah, Martial L et al. (2018) Evaluating Vaccination Strategies for Zika Virus in the Americas. Ann Intern Med 168:621-630 |
| Bellan, Steven E; Champredon, David; Dushoff, Jonathan et al. (2018) Couple serostatus patterns in sub-Saharan Africa illuminate the relative roles of transmission rates and sexual network characteristics in HIV epidemiology. Sci Rep 8:6675 |
| Fallah, Mosoka P; Skrip, Laura A; Raftery, Philomena et al. (2017) Bolstering Community Cooperation in Ebola Resurgence Protocols: Combining Field Blood Draw and Point-of-Care Diagnosis. PLoS Med 14:e1002227 |
Showing the most recent 10 out of 88 publications
