title: Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model.
abstract: As more regions approach malaria elimination, understanding how different interventions interact to reduce transmission becomes critical. The Lake Kariba area of Southern Province, Zambia, is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission intensities. In 2012-13, six rounds of mass test-and-treat drug campaigns were carried out in the Lake Kariba region. A spatial dynamical model of malaria transmission in the Lake Kariba area, with transmission and climate modeled at the village scale, was calibrated to the 2012-13 prevalence survey data, with case management rates, insecticide-treated net usage, and drug campaign coverage informed by surveillance. The model captured the spatio-temporal trends of decline and rebound in malaria prevalence in 2012-13 at the village scale. Various interventions implemented between 2016-22 were simulated to compare their effects on reducing regional transmission and achieving and maintaining elimination through 2030. Simulations predict that elimination requires sustaining high coverage with vector control over several years. When vector control measures are well-implemented, targeted mass drug campaigns in high-burden areas further increase the likelihood of elimination, although drug campaigns cannot compensate for insufficient vector control. If infections are regularly imported from outside the region into highly receptive areas, vector control must be maintained within the region until importations cease. Elimination in the Lake Kariba region is possible, although human movement both within and from outside the region risk damaging the success of elimination programs.
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Milen
authors.initials: M
authors.lastname: Nikolov
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Caitlin A
authors.initials: CA
authors.lastname: Bever
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Alexander
authors.initials: A
authors.lastname: Upfill-Brown
authors.affiliation: National Malaria Control Centre, Lusaka, Zambia.
authors.forename: Busiku
authors.initials: B
authors.lastname: Hamainza
authors.affiliation: PATH Malaria Control and Elimination Program in Africa (MACEPA), Lusaka, Zambia.
authors.forename: John M
authors.initials: JM
authors.lastname: Miller
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Philip A
authors.initials: PA
authors.lastname: Eckhoff
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Edward A
authors.initials: EA
authors.lastname: Wenger
authors.affiliation: Institute for Disease Modeling, Bellevue, WA, United States.
authors.forename: Jaline
authors.initials: J
authors.lastname: Gerardin
mesh_terms: D000962:Antimalarials / Q000627:therapeutic use*; D003198:Computer Simulation; D060740:Disease Eradication / Q000379:methods / Q000706:statistics & numerical data*; D005260:Female; D006293:Health Promotion / Q000706:statistics & numerical data*; D006801:Humans; D008288:Malaria / Q000453:epidemiology* / Q000517:prevention & control*; D008297:Male; D015233:Models, Statistical; D009032:Mosquito Control / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods; D011159:Population Surveillance / Q000379:methods; D015995:Prevalence; D012307:Risk Factors; D062211:Spatio-Temporal Analysis; D015024:Zambia / Q000453:epidemiology
chemical_list: D000962:Antimalarials