A Simulation Optimization Approach to Epidemic Forecasting

Simple item page
dc.contributor.authorNsoesie, Elaine O.en
dc.contributor.authorBeckman, Richard J.en
dc.contributor.authorShashaani, Saraen
dc.contributor.authorNagaraj, Kalyani S.en
dc.contributor.authorMarathe, Madhav V.en
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.date.accessioned2017-11-14T17:49:23Zen
dc.date.available2017-11-14T17:49:23Zen
dc.date.issued2013-06-27en
dc.description.abstractReliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individualbased model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.en
dc.description.sponsorshipThis work has been partially supported by NSF PetaApps Grant OCI-0904844, NSF NetSE Grant CNS-1011769, NSF SDCI Grant OCI-1032677, DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0001 NIH MIDAS Grant 2U01GM070694-09 and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00337; the U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0067164en
dc.identifier.issue6en
dc.identifier.urihttp://hdl.handle.net/10919/80384en
dc.identifier.volume8en
dc.language.isoen_USen
dc.publisherPLOSen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleA Simulation Optimization Approach to Epidemic Forecastingen
dc.title.serialPLOS Oneen
dc.typeArticle - Refereeden

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
MaratheEpidemicForecasting2013.PDF
Size:
612.74 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Destination Area: Global Systems Science (GSS)
Scholarly Works, Computer Science
Scholarly Works, Fralin Life Sciences Institute