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.
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.
|Manore, Carrie A; Hickmann, Kyle S; Hyman, James M et al. (2015) A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease. J Biol Dyn 9:52-72|
|Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid et al. (2015) Forecasting the 2013-2014 influenza season using Wikipedia. PLoS Comput Biol 11:e1004239|
|Chowell, Gerardo; Viboud, CÃ©cile; 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:|
|Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina et al. (2014) Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol 10:e1003892|
|Mniszewski, S M; Manore, C A; Bryan, C et al. (2014) Towards a Hybrid Agent-based Model for Mosquito Borne Disease. Summer Comput Simul Conf (2014) 2014:|
|Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M et al. (2014) Optimizing human activity patterns using global sensitivity analysis. Comput Math Organ Theory 20:394-416|
|Ngonghala, Calistus N; Del Valle, Sara Y; Zhao, Ruijun et al. (2014) Quantifying the impact of decay in bed-net efficacy on malaria transmission. J Theor Biol 363:247-61|
|Priedhorsky, Reid; Culotta, Aron; Del Valle, Sara Y (2014) Inferring the Origin Locations of Tweets with Quantitative Confidence. CSCW :1523-1536|
|Manore, Carrie A; Hickmann, Kyle S; Xu, Sen et al. (2014) Comparing dengue and chikungunya emergence and endemic transmission in A. aegypti and A. albopictus. J Theor Biol 356:174-91|
|Del Valle, Sara Y; Hyman, J M; Chitnis, Nakul (2013) Mathematical models of contact patterns between age groups for predicting the spread of infectious diseases. Math Biosci Eng 10:1475-97|
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