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Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.
Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y.
Afiliación
  • Moran KR; Analytics, Intelligence, and Technology Division.
  • Fairchild G; Analytics, Intelligence, and Technology Division.
  • Generous N; Analytics, Intelligence, and Technology Division.
  • Hickmann K; Theoretical Division.
  • Osthus D; Computer, Computational & Statistical Sciences Division.
  • Priedhorsky R; High Performance Computing Division, Los Alamos National Laboratory, New Mexico.
  • Hyman J; Theoretical Division.
  • Del Valle SY; Department of Mathematics, Tulane University, New Orleans, Louisiana.
J Infect Dis ; 214(suppl_4): S404-S408, 2016 12 01.
Article en En | MEDLINE | ID: mdl-28830111
Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conducta / Enfermedades Transmisibles / Epidemias / Predicción Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Infect Dis Año: 2016 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conducta / Enfermedades Transmisibles / Epidemias / Predicción Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Infect Dis Año: 2016 Tipo del documento: Article