The primary goal of our proposal is to enhance the nation's public health response capability by quantifying agent-based model uncertainty and the impact of behavioral modification on the spread of infectious diseases. Our goal is to improve the understanding of the impact of emergent behavior on the accuracy and applicability of predictive models of disease spread. We will evaluate the implications of uncertainty in human and population behavioral response to a pandemic in mathematical model formulations. This foundational understanding will help improve all existing epidemiological models thereby potentiating the ability of public health practitioners and policy-makers to effectively manage a burgeoning epidemic regardless of the tool being used. We will leverage existing epidemiologic and behavioral simulation infrastructure to develop new mathematical approaches to incorporate different types of diseases and behavioral changes alone and in combination with other intervention strategies. We will validate the models and quantify sensitivity of computational models to parameters, and known disease spread patterns. These models will be constructed for use in estimating prevalence and incidence and will allow us to compare systematically the relative effects of preventive measures, such as behavioral changes, isolation, contact tracing, quarantine, and vaccination. First, we will develop novel approaches to characterize emergent behavior and extend the mathematical foundation and software infrastructure for modeling behavior changes in response to an epidemic. Secondly, we will quantify the epidemic progression uncertainty caused by the distribution of behavioral responses. These behavioral models will be implemented and validated in an existing high-fidelity agent-based activity simulator model. Finally, we will disseminate these advances so they can be useful, and used, in other epidemiological simulations.

Public Health Relevance

The data for an ongoing epidemic is sparse, inexact, and often just unavailable. Therefore, quantifying parameter and computational uncertainties is crucial for forecasting the impact of disease spread. We cannot assume impact of the uncertain parameters is negligible;especially when decisions based on the model will impact the lives of countless people. One of the fundamental limitations of the current models is in how well they capture changes in human behavior in response to an ongoing endemic.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZGM1-CBCB-3 (MI))
Program Officer
Sheeley, Douglas
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Los Alamos National Lab
Los Alamos
United States
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Priedhorsky, Reid; Osthus, Dave; Daughton, Ashlynn R et al. (2017) Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda. CSCW Conf Comput Support Coop Work 2017:1812-1834
Azizi, Asma; Dewar, Jeremy; Wu, Tong et al. (2017) Generating Bipartite Networks with a Prescribed Joint Degree Distribution. J Complex Netw 5:839-857
Osthus, Dave; Hickmann, Kyle S; Caragea, Petru?a C et al. (2017) Forecasting seasonal influenza with a state-space SIR model. Ann Appl Stat 11:202-224
Shutt, Deborah P; Manore, Carrie A; Pankavich, Stephen et al. (2017) Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America. Epidemics 21:63-79
Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas et al. (2016) Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast. J Infect Dis 214:S404-S408
Bergsman, Louis D; Hyman, James M; Manore, Carrie A (2016) A mathematical model for the spread of west nile virus in migratory and resident birds. Math Biosci Eng 13:401-24
Fairchild, Geoffrey; Del Valle, Sara Y; De Silva, Lalindra et al. (2015) Eliciting Disease Data from Wikipedia Articles. Proc Int AAAI Conf Weblogs Soc Media 2015:26-33
Chowell, Gerardo; Viboud, C├ęcile; Hyman, James M et al. (2015) The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates. PLoS Curr 7:
Pawelek, Kasia A; Salmeron, Cristian; Del Valle, Sara (2015) Connecting within and between-hosts dynamics in the influenza infection-staged epidemiological models with behavior change. J Coupled Syst Multiscale Dyn 3:233-243
Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid et al. (2015) Forecasting the 2013-2014 influenza season using Wikipedia. PLoS Comput Biol 11:e1004239

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