We propose to improve our understanding of the important determinants of dengue and polio epidemiology and immunology by splitting each problem into manageable units. By conducting a comprehensive comparison of multiple alternative hypotheses on long-term, spatially replicated, serotype-specific and age-specific data from different countries, we will pin down the nature and duration of immunity, the epidemiological impact of repeat infections, the role played by sub-clinical immune boosting, and the respective contributions of population demography, seasonality, and the structure of the human contact network. A key additional phase for polio research will aim to address the vaccine-related aspects of the problem by focusing on time periods with known vaccine usage (oral or inactivated polio vaccine) and immunization coverage. Ultimately, our findings will generate transmission models that are empirically validated for answering urgent policy needs for both dengue and polio. This work will rely heavily on the use of mathematical models of transmission and statistical methods for extracting information from high-dimensional data, encompassing space, age, serotype or climatic drivers. A major ingredient in this project, therefore, is the development, use, and dissemination of novel methodological tools that will be implemented in open-source public software. Finally, we will bring together the intellectual fruits of this effort to develop optimal, cost-effective immunization policy
Effective immunization strategies are critical for public health. Disentangling the complex interaction of factors that affect the epidemiology, immunology, evolution, and control is an urgent priority. Using extensive databases of dengue and polio incidence, we will test competing hypotheses using cutting-edge statistical methods and use the result to develop optimal, cost-effective immunization policies.
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| Liu, Quan-Hui; Ajelli, Marco; Aleta, Alberto et al. (2018) Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci U S A 115:12680-12685 |
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| Dinh, Vu; Tung Ho, Lam Si; Suchard, Marc A et al. (2018) Consistency and convergence rate of phylogenetic inference via regularization. Ann Stat 46:1481-1512 |
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