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dc.contributor.authorCaminade, Cyril
dc.contributor.authorHeath, Andrew E.
dc.contributor.authorJones, Anne E.
dc.contributor.authorMacLeod, David A.
dc.contributor.authorGouda, Krushna C.
dc.contributor.authorMurty, Upadhyayula Suryanarayana
dc.contributor.authorGoswami, Prashant
dc.contributor.authorMutheneni, Srinivasa R.
dc.contributor.authorMorse, Andrew P.
dc.contributor.authorMurty, Upadhyayula Suryanarayana
dc.contributor.authorLauderdale, Jonathan
dc.date.accessioned2014-08-21T18:35:32Z
dc.date.available2014-08-21T18:35:32Z
dc.date.issued2014-08
dc.date.submitted2014-03
dc.identifier.issn1475-2875
dc.identifier.urihttp://hdl.handle.net/1721.1/88964
dc.description.abstractBackground: Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. Methods: The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. Results and discussion: The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.en_US
dc.publisherBioMed Central Ltden_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1475-2875-13-310en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_US
dc.sourceBioMed Central Ltden_US
dc.titleTowards seasonal forecasting of malaria in Indiaen_US
dc.typeArticleen_US
dc.identifier.citationLauderdale, Jonathan M., et al. "Towards seasonal forecasting of malaria in India." Malaria Journal 2014, 13:310.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciencesen_US
dc.contributor.mitauthorLauderdale, Jonathanen_US
dc.relation.journalMalaria Journalen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2014-08-16T03:06:31Z
dc.language.rfc3066en
dc.rights.holderJonathan M Lauderdale et al.; licensee BioMed Central Ltd.
dspace.orderedauthorsLauderdale, Jonathan M; Caminade, Cyril; Heath, Andrew E; Jones, Anne E; MacLeod, David A; Gouda, Krushna C; Murty, Upadhyayula; Goswami, Prashant; Mutheneni, Srinivasa R; Morse, Andrew Pen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-2993-7484
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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