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Activity Number: 81 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #313624
Title: Attributing Spatial Repellent Efficacy to Entomological Effects Modulated by Human Behavior
Author(s): Annaliese Wieler* and Nicole L Achee and John P Grieco and Neil Lobo and Sean Moore and Alex Perkins
Companies: University of Notre Dame and Eck Institute for Global Health, University of Notre Dame and Eck Institute for Global Health, University of Notre Dame and Eck Institute for Global Health, University of Notre Dame and Eck Institute for Global Health, University of Notre Dame and Eck Institute for Global Health, University of Notre Dame
Keywords: malaria; mosquito; clinical trial; spatial repellent; vector-borne disease; Bayesian
Abstract:

Mosquito-borne diseases remain significant causes of human health burden. Despite progress to combat malaria using mosquito control strategies such as bed nets, progress towards elimination has stalled due to residual transmission. Thus, new classes of vector control interventions are needed to further reduce human infections. One new class of intervention currently under WHO assessment in clinical trials are spatial repellents (SR). In studies, SRs have demonstrated the ability to modify key mosquito behaviors essential for survival and propagation including host-seeking and blood-feeding, but the influence of these effects on epidemiological impact is not fully understood. Here we use data generated from a clinical trial and applied a model of malaria transmission to address this question. Specifically, data sources were used to formulate likelihoods within a Bayesian framework, using MCMC to determine the effects of the SR used as intervention in the clinical trial on two model parameters of interest: 1) mosquito longevity and 2) mosquito biting rate. Results from this study are anticipated to inform projections of SR impact and optimal use patterns on a larger scale.


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