Summary results of the 2014-2015 DARPA Chikungunya challenge

dc.contributor.authorDel Valle, Sara Y.en
dc.contributor.authorMcMahon, Benjamin H.en
dc.contributor.authorAsher, Jasonen
dc.contributor.authorHatchett, Richarden
dc.contributor.authorLega, Joceline C.en
dc.contributor.authorBrown, Heidi E.en
dc.contributor.authorLeany, Mark E.en
dc.contributor.authorPantazis, Yannisen
dc.contributor.authorRoberts, David J.en
dc.contributor.authorMoore, Seanen
dc.contributor.authorPeterson, A. Townsenden
dc.contributor.authorEscobar, Luis E.en
dc.contributor.authorQiao, Huijieen
dc.contributor.authorHengartner, Nicholas W.en
dc.contributor.authorMukundan, Harshinien
dc.contributor.departmentFish and Wildlife Conservationen
dc.date.accessioned2018-06-07T13:10:38Zen
dc.date.available2018-06-07T13:10:38Zen
dc.date.issued2018-05-30en
dc.date.updated2018-06-03T04:04:49Zen
dc.description.abstractBackground: Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting. Methods: To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners. Results: Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy. Conclusion: We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBMC Infectious Diseases. 2018 May 30;18(1):245en
dc.identifier.doihttps://doi.org/10.1186/s12879-018-3124-7en
dc.identifier.urihttp://hdl.handle.net/10919/83479en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderThe Author(s)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleSummary results of the 2014-2015 DARPA Chikungunya challengeen
dc.title.serialBMC Infectious Diseasesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
12879_2018_Article_3124.pdf
Size:
1.78 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description: