Environmental biosurveillance for epidemic prediction: Experience with rift valley fever

Jean Paul Chretien, Assaf Anyamba, Jennifer Small, Compton J. Tucker, Seth C. Britch, Kenneth J. Linthicum

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Despite established links between climate and infectious disease activity, few biosurveillance systems use climatic data to forecast epidemics. The El Niño/Southern Oscillation (ENSO) affects weather worldwide and in East Africa is associated with flooding and Rift Valley fever, a mosquito-borne viral disease of economically important livestock and humans. Following a regional ENSO-associated outbreak in 1997-1998, several agencies created a system to forecast RVF using satellite-based monitoring of ENSO and other climatic phenomena. The system generated 5 alerts since 2005. Following 3, in South Africa (2008), Sudan (2007), and East Africa (2006), RVF occurred in high-risk areas (no other RVF outbreaks were reported in monitored areas). Alerts for the Arabian Peninsula (2005) and Sudan (2005) were not followed by RVF reports, though the latter preceded a large Yellow Fever epidemic. Future directions for the system include decision analysis to guide public health interventions and extension to other climate-associated risks.

Original languageEnglish
Title of host publicationBiosurveillance and Biosecurity - International Workshop, BioSecure 2008, Proceedings
Pages169-174
Number of pages6
DOIs
StatePublished - 2008
Externally publishedYes
EventInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008 - Raleigh, NC, United States
Duration: Dec 2 2008Dec 2 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5354 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008
Country/TerritoryUnited States
CityRaleigh, NC
Period12/2/0812/2/08

Keywords

  • Biosurveillance
  • Forecasting
  • Modeling
  • Remote sensing

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