Infectious disease preparedness and control are critically dependent on knowledge of disease transmission patterns across space and time. The study of these patterns is greatly dependent on high-resolution public health data and adaptable tools for statistical inference. This Project aims to improve knowledge on spatiotemporal transmission patterns for infectious diseases in the United States and abroad leading to improved control strategies and better preparedness. This Project will contribute the first components of an integrated decision support pathway created by the MIDAS Center. It will create new insights in disease transmission patterns, develop innovative statistical and parameter estimation tools and will create a comprehensive high-resolution data resource for the MIDAS Network, policy makers, and the community at large.
Specific aims are: 1) acquisition, curation, and integration of new large scale epidemiologic, vital statistics, and demographic data to improve the case-law of infectious disease dynamics, resulting in an essential data resource for modeling;2) creation of innovative tools for the analysis of spatiotemporal disease transmission patterns, using spatiotemporal statistics and flexible non-linear parameter estimation methods;and 3) inferring individual level transmission dynamics from newly integrated disease data, contact patterns, and genetic data using advanced computational methods. This will result in new knowledge and scientific insight in the impact of pathogen natural history, vaccination, and other factors on spatiotemporal dynamics of distinct childhood infections and also in individual level transmission networks. This Project will shift the paradigm for the study of infectious disease transmission dynamics from ad hoc analyses of specific situations constrained by limited data availability to the systematic exploration of transmission dynamics for a wide range of pathogens and geographies. This Project will build on previous successes of the MIDAS Center to integrate and make available large scale public health datasets and to create innovative non-linear parameter estimation methods. This Project will advance the field by accelerating resources and tools for infectious disease modeling, resulting in better preparedness and control of existing and emerging threats
This Project will generate a new data resource and new statistical inference tools for modeling of infectious disease dynamics and in-depth knowledge on determinants of disease spread for specific pathogens and across pathogens. This will address the critical need for high-resolution data and adaptable inference tools among modelers and policy makers to advance public heath preparedness for 21st century challenges.
|Lessler, Justin; Cummings, Derek A T (2016) Mechanistic Models of Infectious Disease and Their Impact on Public Health. Am J Epidemiol 183:415-22|
|Salje, Henrik; Cauchemez, Simon; Alera, Maria Theresa et al. (2016) Reconstruction of 60 Years of Chikungunya Epidemiology in the Philippines Demonstrates Episodic and Focal Transmission. J Infect Dis 213:604-10|
|Clapham, Hannah E; Rodriguez-Barraquer, Isabel; Azman, Andrew S et al. (2016) Dengue Virus (DENV) Neutralizing Antibody Kinetics in Children After Symptomatic Primary and Postprimary DENV Infection. J Infect Dis 213:1428-35|
|Chadsuthi, Sudarat; Iamsirithaworn, Sopon; Triampo, Wannapong et al. (2016) The impact of rainfall and temperature on the spatial progression of cases during the chikungunya re-emergence in Thailand in 2008-2009. Trans R Soc Trop Med Hyg 110:125-33|
|Guclu, Hasan; Read, Jonathan; Vukotich Jr, Charles J et al. (2016) Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA. PLoS One 11:e0151139|
|Grantz, Kyra H; Rane, Madhura S; Salje, Henrik et al. (2016) Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918. Proc Natl Acad Sci U S A 113:13839-13844|
|Poon, Leo L M; Song, Timothy; Rosenfeld, Roni et al. (2016) Quantifying influenza virus diversity and transmission in humans. Nat Genet 48:195-200|
|Dalziel, Benjamin D; BjÃ¸rnstad, Ottar N; van Panhuis, Willem G et al. (2016) Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns. PLoS Comput Biol 12:e1004655|
|Nisalak, Ananda; Clapham, Hannah E; Kalayanarooj, Siripen et al. (2016) Forty Years of Dengue Surveillance at a Tertiary Pediatric Hospital in Bangkok, Thailand, 1973-2012. Am J Trop Med Hyg 94:1342-7|
|Lessler, Justin; Salje, Henrik; Van Kerkhove, Maria D et al. (2016) Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia. Am J Epidemiol 183:657-63|
Showing the most recent 10 out of 199 publications