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dc.contributor.authorCastro, Marcia C.en_US
dc.contributor.authorMaheu-Giroux, Mathieuen_US
dc.contributor.authorChiyaka, Christinahen_US
dc.contributor.authorSinger, Burton H.en_US
dc.date.accessioned2016-10-11T20:27:46Z
dc.date.issued2016en_US
dc.identifier.citationCastro, Marcia C., Mathieu Maheu-Giroux, Christinah Chiyaka, and Burton H. Singer. 2016. “Malaria Incidence Rates from Time Series of 2-Wave Panel Surveys.” PLoS Computational Biology 12 (8): e1005065. doi:10.1371/journal.pcbi.1005065. http://dx.doi.org/10.1371/journal.pcbi.1005065.en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29002645
dc.description.abstractMethodology to estimate malaria incidence rates from a commonly occurring form of interval-censored longitudinal parasitological data—specifically, 2-wave panel data—was first proposed 40 years ago based on the theory of continuous-time homogeneous Markov Chains. Assumptions of the methodology were suitable for settings with high malaria transmission in the absence of control measures, but are violated in areas experiencing fast decline or that have achieved very low transmission. No further developments that can accommodate such violations have been put forth since then. We extend previous work and propose a new methodology to estimate malaria incidence rates from 2-wave panel data, utilizing the class of 2-component mixtures of continuous-time Markov chains, representing two sub-populations with distinct behavior/attitude towards malaria prevention and treatment. Model identification, or even partial identification, requires context-specific a priori constraints on parameters. The method can be applied to scenarios of any transmission intensity. We provide an application utilizing data from Dar es Salaam, an area that experienced steady decline in malaria over almost five years after a larviciding intervention. We conducted sensitivity analysis to account for possible sampling variation in input data and model assumptions/parameters, and we considered differences in estimates due to submicroscopic infections. Results showed that, assuming defensible a priori constraints on model parameters, most of the uncertainty in the estimated incidence rates was due to sampling variation, not to partial identifiability of the mixture model for the case at hand. Differences between microscopy- and PCR-based rates depend on the transmission intensity. Leveraging on a method to estimate incidence rates from 2-wave panel data under any transmission intensity, and from the increasing availability of such data, there is an opportunity to foster further methodological developments, particularly focused on partial identifiability and the diversity of a priori parameter constraints associated with different human-ecosystem interfaces. As a consequence there can be more nuanced planning and evaluation of malaria control programs than heretofore.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1005065en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4980052/pdf/en
dash.licenseLAAen_US
dc.subjectMedicine and Health Sciencesen
dc.subjectParasitic Diseasesen
dc.subjectMalariaen
dc.subjectTropical Diseasesen
dc.subjectPhysical Sciencesen
dc.subjectMathematicsen
dc.subjectProbability Theoryen
dc.subjectMarkov Modelsen
dc.subjectInfectious Diseasesen
dc.subjectInfectious Disease Controlen
dc.subjectEarth Sciencesen
dc.subjectAtmospheric Scienceen
dc.subjectMeteorologyen
dc.subjectRainen
dc.subjectBiology and Life Sciencesen
dc.subjectOrganismsen
dc.subjectProtozoansen
dc.subjectParasitic Protozoansen
dc.subjectMalarial Parasitesen
dc.subjectPharmacologyen
dc.subjectDrugsen
dc.subjectAntimalarialsen
dc.subjectGeographyen
dc.subjectHuman Geographyen
dc.subjectHousingen
dc.subjectSocial Sciencesen
dc.subjectEpidemiologyen
dc.subjectDisease Vectorsen
dc.subjectInsect Vectorsen
dc.subjectMosquitoesen
dc.subjectAnimalsen
dc.subjectInvertebratesen
dc.subjectArthropodaen
dc.subjectInsectsen
dc.titleMalaria Incidence Rates from Time Series of 2-Wave Panel Surveysen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorCastro, Marcia C.en_US
dc.date.available2016-10-11T20:27:46Z
dc.identifier.doi10.1371/journal.pcbi.1005065*
dash.contributor.affiliatedCastro, Marcia


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