tetano
Editor, Senior Moderator
Biomed Res Int. 2015;2015:751738. doi: 10.1155/2015/751738. Epub 2015 Sep 6.
[h=1]A Bayesian Outbreak Detection Method for Influenza-Like Illness.[/h] Garc?a YE[SUP]1[/SUP], Christen JA[SUP]1[/SUP], Capistr?n MA[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Epidemic outbreak detection is an important problem in public health and the development of reliable methods for outbreak detection remains an active research area. In this paper we introduce a Bayesian method to detect outbreaks of influenza-like illness from surveillance data. The rationale is that, during the early phase of the outbreak, surveillance data changes from autoregressive dynamics to a regime of exponential growth. Our method uses Bayesian model selection and Bayesian regression to identify the breakpoint. No free parameters need to be tuned. However, historical information regarding influenza-like illnesses needs to be incorporated into the model. In order to show and discuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.
PMID: 26425552 [PubMed - in process]
[h=1]A Bayesian Outbreak Detection Method for Influenza-Like Illness.[/h] Garc?a YE[SUP]1[/SUP], Christen JA[SUP]1[/SUP], Capistr?n MA[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Epidemic outbreak detection is an important problem in public health and the development of reliable methods for outbreak detection remains an active research area. In this paper we introduce a Bayesian method to detect outbreaks of influenza-like illness from surveillance data. The rationale is that, during the early phase of the outbreak, surveillance data changes from autoregressive dynamics to a regime of exponential growth. Our method uses Bayesian model selection and Bayesian regression to identify the breakpoint. No free parameters need to be tuned. However, historical information regarding influenza-like illnesses needs to be incorporated into the model. In order to show and discuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.
PMID: 26425552 [PubMed - in process]