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Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

Abstract 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.
PMID
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Authors

Mayor MeshTerms

Behavior

Epidemics

Keywords

Internet data

disease

forecasting

modeling

weather

Journal Title the journal of infectious diseases
Publication Year Start




PMID- 28830111
OWN - NLM
STAT- MEDLINE
DA  - 20170823
DCOM- 20170825
LR  - 20170827
IS  - 1537-6613 (Electronic)
IS  - 0022-1899 (Linking)
VI  - 214
IP  - suppl_4
DP  - 2016 Dec 01
TI  - Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human
      Behavior and Internet Data Streams in Epidemic Forecast.
PG  - S404-S408
LID - 10.1093/infdis/jiw375 [doi]
AB  - 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.
CI  - Published by Oxford University Press for the Infectious Diseases Society of
      America 2016. This work is written by (a) US Government employee(s) and is in the
      public domain in the US.
FAU - Moran, Kelly R
AU  - Moran KR
AD  - Analytics, Intelligence, and Technology Division.
FAU - Fairchild, Geoffrey
AU  - Fairchild G
AD  - Analytics, Intelligence, and Technology Division.
FAU - Generous, Nicholas
AU  - Generous N
AD  - Analytics, Intelligence, and Technology Division.
FAU - Hickmann, Kyle
AU  - Hickmann K
AD  - Theoretical Division.
FAU - Osthus, Dave
AU  - Osthus D
AD  - Computer, Computational & Statistical Sciences Division.
FAU - Priedhorsky, Reid
AU  - Priedhorsky R
AD  - High Performance Computing Division, Los Alamos National Laboratory, New Mexico.
FAU - Hyman, James
AU  - Hyman J
AD  - Theoretical Division.
AD  - Department of Mathematics, Tulane University, New Orleans, Louisiana.
FAU - Del Valle, Sara Y
AU  - Del Valle SY
AD  - Analytics, Intelligence, and Technology Division.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - United States
TA  - J Infect Dis
JT  - The Journal of infectious diseases
JID - 0413675
SB  - AIM
SB  - IM
MH  - *Behavior
MH  - Communicable Diseases/*epidemiology
MH  - Computer Simulation
MH  - *Epidemics
MH  - Forecasting/*methods
MH  - Humans
MH  - Information Storage and Retrieval
MH  - Internet
MH  - Models, Theoretical
PMC - PMC5181546
OTO - NOTNLM
OT  - Internet data
OT  - disease
OT  - forecasting
OT  - modeling
OT  - weather
EDAT- 2017/08/24 06:00
MHDA- 2017/08/26 06:00
CRDT- 2017/08/24 06:00
PMCR- 2017/12/01
AID - 2527912 [pii]
AID - 10.1093/infdis/jiw375 [doi]
PST - ppublish
SO  - J Infect Dis. 2016 Dec 1;214(suppl_4):S404-S408. doi: 10.1093/infdis/jiw375.