Accounting for behavioral responses during a flu epidemic using home television viewing. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Springborn, Michael; Chowell, Gerardo; MacLachlan, Matthew; Fenichel, Eli
• Is Part Of: BMC infectious diseases. Volume 15:Issue 1(2015)
• Page Start: 1
• Page End: 14
• Publication Date: 2015-12
• Subjects: Epidemic model -- Social distancing -- A/H1N1 -- Influenza -- SIR; Communicable diseases -- Periodicals; Sexually Transmitted Diseases -- Periodicals; 616.905
• DOI: 10.1186/s12879-014-0691-0
• Languages: English
• ISSNs: 1471-2334

Abstract

Abstract Background Theory suggests that individual behavioral responses impact the spread of flu-like illnesses, but this has been difficult to empirically characterize. Social distancing is an important component of behavioral response, though analyses have been limited by a lack of behavioral data. Our objective is to use media data to characterize social distancing behavior in order to empirically inform explanatory and predictive epidemiological models. Methods We use data on variation in home television viewing as a proxy for variation in time spent in the home and, by extension, contact. This behavioral proxy is imperfect but appealing since information on a rich and representative sample is collected using consistent techniques across time and most major cities. We study the April-May 2009 outbreak of A/H1N1 in Central Mexico and examine the dynamic behavioral response in aggregate and contrast the observed patterns of various demographic subgroups. We develop and calibrate a dynamic behavioral model of disease transmission informed by the proxy data on daily variation in contact rates and compare it to a standard (non-adaptive) model and a fixed effects model that crudely captures behavior. Results We find that after a demonstrable initial behavioral response (consistent with social distancing) at the onset of the outbreak, there was attenuation in the response before the conclusion of the public health intervention. We find substantial differences in the behavioral response across age subgroups and socioeconomic levels. We also find that the dynamic behavioral and fixed effects transmission models better account for variation in new confirmed cases, generate more stable estimates of the baseline rate of transmission over time and predict the number of new cases over a short horizon with substantially less error. Conclusions Results suggest that A/H1N1 had an innate transmission potential greater than previously thought but this was masked by behavioral responses. Observed differences in behavioral response across demographic groups indicate a potential benefit from targeting social distancing outreach efforts.