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A Summary of the Journal Titled Accounting for behavioral responses during a flu epidemic using home television viewing

Written By Fajar Rahmana on Monday, March 30, 2015 | Monday, March 30, 2015

Review Journal

Name of Jornal
BMC Infection Diseases
Title of Journal
Accounting for behavioral responses during a flu epidemic using home television viewing
Date of Journal
2015
Methods
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. Study in  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. 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.
The Main Discussion
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.
Summary
Results from both behavioral models (FE and DB) suggested  that  social distancing  was a key factor  in constraining the  initial  wave of A/H1N1  in Central  Mexico. In the  absence  of a behavioral  response, the  estimated  counterfactual path  of new cases escalated rapidly in initial weeks rather  than  stabilizing and eventu- ally falling as was observed.  The  assumption of fixed be- havior in the standard (SD) model led to shortcomings in estimation and  prediction.  Estimates  of the  baseline  rate of transmission systematically shifted over time. If the baseline  rate  of transmission is interpreted as a measure of biological infectivity in the standard model, this is likely to  lead to an  underestimate of this  parameter, as in our setting, given confounding effects of behavioral responses. This suggests that A/H1N1 had an innate transmission potential  much  greater  than  previously  thought but  this was  masked  by behavioral  responses.  This  has  implications  for management advice including  the  allocation  of resources between pharmaceutical and nonpharmaceutical interventions. Furthermore, the error  in near term predictions  of  new  cases  through   time  was  also  substantially greater under  the standard model compared to the behav- ioral models. This error was also systematic. The standard model  consistently  led to over-prediction in the  number of new cases.
Conclusion
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.
Suggestion
The Reader suggest that use many media data to characterize social distancing behavior in order to empirically inform explanatory and predictive epidemiological models.
                               
               
Nama : Fajar Rahmana
Kelas  : 4EB17
NPM   : 22211643
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