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Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

Journal Article · · Journal of Infectious Diseases
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  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Tulane Univ., New Orleans, LA (United States)
+ Show Author Affiliations
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. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
National Institutes of Health; USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1342861
Report Number(s):
LA-UR-16-23668
Journal Information:
Journal of Infectious Diseases, Journal Name: Journal of Infectious Diseases Journal Issue: suppl 4 Vol. 214; ISSN 0022-1899
Publisher:
Infectious Diseases Society of AmericaCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (19)

On the predictability of infectious disease outbreaks journal February 2019
Disease modeling for public health: added value, challenges, and institutional constraints journal November 2019
Summary results of the 2014-2015 DARPA Chikungunya challenge journal May 2018
Google Health Trends performance reflecting dengue incidence for the Brazilian states journal March 2020
Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples journal December 2019
Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment journal May 2019
Epidemiological data challenges: planning for a more robust future through data standards text January 2018
Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture: L. HELD, S. MEYER AND J. BRACHER journal June 2017
The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities journal November 2019
Big Data for Infectious Disease Surveillance and Modeling journal November 2016
Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area Region of Sierra Leone, 2014–15 journal November 2018
Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts journal November 2018
Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15 journal February 2019
Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus journal January 2019
Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology journal January 2018
Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards journal November 2018
Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts journal January 2019
On the predictability of infectious disease outbreaks preprint January 2017
Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture text January 2017

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