%0 Book Section %1 Valle:2013:ch %A del Valle, Sara Y. %A Mniszewski, Susan M. %A Hyman, James M. %B Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases %C New York, NY %D 2013 %E Manfredi, Piero %E D'Onofrio, Alberto %I Springer %K epidemes %P 59--77 %R 10.1007/978-1-4614-5474-8_4 %T Modeling the Impact of Behavior Changes on the Spread of Pandemic Influenza %X We use mathematical models to assess the impact of behavioral changes in response to an emerging epidemic. Evaluating the quantitative and qualitative impact of public health interventions on the spread of infectious diseases is a crucial public health objective. The recent avian influenza (H5N1) outbreaks and the 2009 H1N1 pandemic have raised significant global concerns about the emergence of a deadly influenza virus causing a pandemic of catastrophic proportions. Mitigation strategies based on behavior changes are some of the only options available in the early stages of an emerging epidemic when vaccines are unlikely to be available and there are only limited stockpiles of antiviral medications. Mathematical models that capture these behavior changes can quantify the relative impact of different mitigation strategies, such as closing schools, in slowing the spread of an infectious disease. Including behavior changes in mathematical models increases complexity and is often left out of the analysis. We present a simple differential equation model which allows for people changing their behavior to decrease their probability of infection. We also describe a large-scale agent-based model that can be used to analyze the impact of isolation scenarios such as school closures and fear-based home isolation during a pandemic. The agent-based model captures realistic individual-level mixing patterns and coordinated reactive changes in human behavior in order to better predict the transmission dynamics of an epidemic. Both models confirm that changes in behavior can be effective in reducing the spread of disease. For example, our model predicts that if school closures are implemented for the duration of the pandemic, the clinical attack rate could be reduced by more than 50\%. We also verify that when interventions are stopped too soon, a second wave of infection can occur. %@ 978-1-4614-5474-8

@inbook{Valle:2013:ch, abstract = {We use mathematical models to assess the impact of behavioral changes in response to an emerging epidemic. Evaluating the quantitative and qualitative impact of public health interventions on the spread of infectious diseases is a crucial public health objective. The recent avian influenza (H5N1) outbreaks and the 2009 H1N1 pandemic have raised significant global concerns about the emergence of a deadly influenza virus causing a pandemic of catastrophic proportions. Mitigation strategies based on behavior changes are some of the only options available in the early stages of an emerging epidemic when vaccines are unlikely to be available and there are only limited stockpiles of antiviral medications. Mathematical models that capture these behavior changes can quantify the relative impact of different mitigation strategies, such as closing schools, in slowing the spread of an infectious disease. Including behavior changes in mathematical models increases complexity and is often left out of the analysis. We present a simple differential equation model which allows for people changing their behavior to decrease their probability of infection. We also describe a large-scale agent-based model that can be used to analyze the impact of isolation scenarios such as school closures and fear-based home isolation during a pandemic. The agent-based model captures realistic individual-level mixing patterns and coordinated reactive changes in human behavior in order to better predict the transmission dynamics of an epidemic. Both models confirm that changes in behavior can be effective in reducing the spread of disease. For example, our model predicts that if school closures are implemented for the duration of the pandemic, the clinical attack rate could be reduced by more than 50{\%}. We also verify that when interventions are stopped too soon, a second wave of infection can occur.}, added-at = {2017-09-28T13:33:12.000+0200}, address = {New York, NY}, author = {del Valle, Sara Y. and Mniszewski, Susan M. and Hyman, James M.}, biburl = {https://www.bibsonomy.org/bibtex/2239c8c397823b78245c047df1c0ef420/krevelen}, booktitle = {Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases}, doi = {10.1007/978-1-4614-5474-8_4}, editor = {Manfredi, Piero and D'Onofrio, Alberto}, interhash = {ea1566d93be8c9dd8d2ccd23301785fc}, intrahash = {239c8c397823b78245c047df1c0ef420}, isbn = {978-1-4614-5474-8}, keywords = {epidemes}, pages = {59--77}, publisher = {Springer}, timestamp = {2017-09-28T13:33:36.000+0200}, title = {Modeling the Impact of Behavior Changes on the Spread of Pandemic Influenza}, year = 2013 }