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Modelling Insecticide Resistance of Malaria Vector Populations in Tanzania

Donal BisanzioRTI International, Washington, District of Columbia;
School of Medicine, Nottingham University, Nottingham, United Kingdom;

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Mohamed AllyNational Malaria Control Program, Dar es Salaam, Tanzania;

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Abdullah S. AliZanzibar Malaria Elimination Program, Stonetown, Zanzibar;

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Chonge KitojoUS President’s Malaria Initiative, US Agency for International Development, Dar es Salaam, Tanzania;

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Naomi SerbantezUS President’s Malaria Initiative, US Agency for International Development, Dar es Salaam, Tanzania;

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William N. KisinzaNational Institute for Medical Research, Amani Research Center, Tanzania

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Stephen MagesaNational Institute for Medical Research, Amani Research Center, Tanzania

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Richard ReithingerRTI International, Washington, District of Columbia;

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ABSTRACT.

Anopheline mosquito insecticide resistance is a major threat to malaria control efforts and ultimately countries’ ability to eliminate malaria. Using publicly available and published data we conducted spatial analyses to document and model the geo-spatial distribution of Anopheles gambiae s.l. insecticide resistance in Tanzania at national, regional, district and sub-district levels for the 2011 – 2017 period. We document anopheline mosquito resistance to all four major insecticide classes, with overall mosquito mortality declining from 2011 to 2016, and mean reductions of 1.6%, 0.5%, 0.4%, and 9.9% observed for organophosphates, carbamates, organochlorines and pyrethroids, respectively. An insecticide resistance map modeled for 2017 predicted that anopheline vector mortality was still above the 90% susceptibility threshold for all insecticide classes, except for pyrethroids. Using the model’s output we calculated that resistance to organophosphates, carbamates, organochlorines, and pyrethroids is expected to exist in 11.6%, 15.6%, 8.1%, and 19.5% of Tanzania’s territory, respectively, with areas in the Lake Zone and eastern Tanzania particularly affected. The methodology to predictively model available insecticide resistance data can readily be updated annually, allowing policy makers and malaria program management staff to continuously adjust their vector control approaches and plans, and determine where specific insecticides from various classes should be used to maximize intervention effectiveness.

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Author Notes

Address correspondence to Donal Bisanzio, RTI International, Washington, DC. E-mail: dbisanzio@rti.org

Financial support: Analyses were performed with internal RTI funds. Insecticide resistance data as reported through the IRMapper platform was collected by the Tanzania National Malaria Control Program, the Zanzibar Malaria Elimination Program, and academic research institutions through the support of various multilateral, bilateral and academic research funding sources. Partial funding for manuscript development was provided by the US President's Malaria Initiative via the U.S. Agency for International Development Okoa Maisha Dhibiti Malaria project (Cooperative Agreement 72062118CA-00002).

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