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.
| Brooks, Logan C; Farrow, David C; Hyun, Sangwon et al. (2018) Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. PLoS Comput Biol 14:e1006134 |
| Biggerstaff, Matthew; Johansson, Michael; Alper, David et al. (2018) Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics 24:26-33 |
| España, Guido; Grefenstette, John; Perkins, Alex et al. (2018) Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014-2016 outbreak in Colombia. Sci Rep 8:12201 |
| Paternina-Caicedo, Angel; Driessen, Julia; Roberts, Mark et al. (2018) Heterogeneity Between States in the Health and Economic Impact of Measles Immunization in the United States. Open Forum Infect Dis 5:ofy137 |
| Althouse, Benjamin M; Guerbois, Mathilde; Cummings, Derek A T et al. (2018) Role of monkeys in the sylvatic cycle of chikungunya virus in Senegal. Nat Commun 9:1046 |
| Buchanich, Jeanine M; Doerfler, Shannon M; Lann, Michael F et al. (2018) Improvement in racial disparities in years of life lost in the USA since 1990. PLoS One 13:e0194308 |
| Lauer, Stephen A; Sakrejda, Krzysztof; Ray, Evan L et al. (2018) Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014. Proc Natl Acad Sci U S A 115:E2175-E2182 |
| Grantz, Kyra H; Rane, Madhura S; Salje, Henrik et al. (2017) Reply to Shanks and Brundage: Many plausible mechanisms of pandemic mortality disparities. Proc Natl Acad Sci U S A 114:E3588-E3589 |
| Farrow, David C; Brooks, Logan C; Hyun, Sangwon et al. (2017) A human judgment approach to epidemiological forecasting. PLoS Comput Biol 13:e1005248 |
| Brownwright, Tenley K; Dodson, Zan M; van Panhuis, Willem G (2017) Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa. BMC Public Health 17:957 |
Showing the most recent 10 out of 221 publications
