Efforts to control dengue virus (DENV) transmission are constrained by the inadequacy of current tools to combat the vectors and virus, and by our limited understanding of the biological, social, and human behavioral dimensions of virus transmission, their heterogeneities and the interactions between them. Heterogeneity is a driver of both biological and epidemiological complexity, and needs to be accurately quantified in order to improve surveillance, generate disease predictions and design science-based interventions. Our underlying hypothesis is that transmission of DENV in urban areas is driven by heterogeneities in movement patterns and infectiousness of humans with a range of disease manifestations, and in opportunities for encounters between humans and mosquito vectors. To test this hypothesis novel theoretical and quantitative analyses will be applied to a unique database derived from diverse empirical field data from this and previous projects through the following three aims. (1) Develop novel conceptual and empirical models that accommodate heterogeneities in human movement in response to disease, mosquito population dynamics and virus transmission. Generated mathematical models will integrate the spectrum of DENV severity (including inapparent infections), individual variations in human mobility and infectiousness, and variations in vector productivity, abundance, and flight dispersal. (2) Quantify epidemiologic patterns of DENV transmission in space and time and derive key parameters for transmission models. Novel statistical techniques will be applied to retrospectively and prospectively collected field data to inform the primary structure of models as well as define their parameterization. (3) Assess the impacts of surveillance and control programs, including vaccination and vector control, on the dynamics of DENV transmission and propagation. Various scenarios to assess the impacts of alternative intervention strategies targeting potential "super-spreaders" and "key locations" of dengue transmission will be simulated through model testing and uncertainty analyses. Of particular interest is the hidden impact of inapparent and unreported mild cases, a potentially key, understudied element in DENV dynamics and control.

Public Health Relevance

Dengue is the most important global human mosquito-borne viral infection, with 3.97 billion people in 128 countries at risk and an estimated 390 million new infections each year. Control efforts are constrained by the inadequacy of current tools and limited understanding of the biological, social, and behavioral determinants of virus transmission. Novel modeling approaches will consider the full range of these determinants, including the role of inapparent infections and movement patterns are needed.

National Institute of Health (NIH)
Research Program Projects (P01)
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Special Emphasis Panel (ZAI1)
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University of California Davis
United States
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