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Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Representative calibration of larval habitat availabilities in a single cluster located in Munyumbwe HFCA.(A) Sampling over larval habitat availability scale factors for arabiensis and funestus identifies pairs of scale factors with good fits to: cluster-level prevalence by rapid diagnostic test (RDT) from surveillance data (blue, best fit to prevalence), HFCA-level seasonality of weekly reported clinical cases counts (yellow, best fit to clinical cases), and both prevalence and seasonality of clinical cases (red, best combined fit). Surface shows the residual error in the combined fit to both prevalence and clinical cases. (B) RDT prevalence between December 2011 and April 2014 observed during surveillance (black) and in simulation. Blue: Simulation of single pair of arabiensis and funestus habitat availability scales that result in the best fit to the surveilled prevalence without accounting for fit to seasonality of clinical case counts. Yellow: Simulation of single pair of habitat scales that result in the best fit to seasonality of clinical case counts without accounting for fit to prevalence. Red: Simulation of pairs of habitat scales that result in best (bold line) and top 10 (thin lines) fits when optimizing fit to both surveilled prevalence and seasonality of reported clinical case counts. (C) Normalized seasonality of clinical case counts observed in Munyumbwe health clinic reported data (black) and simulation of a single Munyumbwe cluster (blue, yellow, red, defined as in panel B).
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pcbi.1005192.g003: Representative calibration of larval habitat availabilities in a single cluster located in Munyumbwe HFCA.(A) Sampling over larval habitat availability scale factors for arabiensis and funestus identifies pairs of scale factors with good fits to: cluster-level prevalence by rapid diagnostic test (RDT) from surveillance data (blue, best fit to prevalence), HFCA-level seasonality of weekly reported clinical cases counts (yellow, best fit to clinical cases), and both prevalence and seasonality of clinical cases (red, best combined fit). Surface shows the residual error in the combined fit to both prevalence and clinical cases. (B) RDT prevalence between December 2011 and April 2014 observed during surveillance (black) and in simulation. Blue: Simulation of single pair of arabiensis and funestus habitat availability scales that result in the best fit to the surveilled prevalence without accounting for fit to seasonality of clinical case counts. Yellow: Simulation of single pair of habitat scales that result in the best fit to seasonality of clinical case counts without accounting for fit to prevalence. Red: Simulation of pairs of habitat scales that result in best (bold line) and top 10 (thin lines) fits when optimizing fit to both surveilled prevalence and seasonality of reported clinical case counts. (C) Normalized seasonality of clinical case counts observed in Munyumbwe health clinic reported data (black) and simulation of a single Munyumbwe cluster (blue, yellow, red, defined as in panel B).

Mentions: Preliminary entomological data indicated that both Anopheles arabiensis and Anopheles funestus are present in the study area (personal communication with Javan Chanda), with arabiensis biting rates highest between January and April during the warm rainy season while funestus peaks in September at the beginning of the hot dry season (Fig 2A). Relative abundances of arabiensis and funestus govern the seasonality of malaria transmission, while absolute abundances determine the intensity. For each village-scale cluster, combinations of larval habitat availabilities for arabiensis and funestus were simulated with appropriate patterns of ITN usage, case management rates, and MTAT coverage. The resulting simulated prevalence and clinical case counts were compared with surveillance data to select the combination of habitats yielding the best fit to field observations (Fig 3 and S8 Fig, Methods).


Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model
Representative calibration of larval habitat availabilities in a single cluster located in Munyumbwe HFCA.(A) Sampling over larval habitat availability scale factors for arabiensis and funestus identifies pairs of scale factors with good fits to: cluster-level prevalence by rapid diagnostic test (RDT) from surveillance data (blue, best fit to prevalence), HFCA-level seasonality of weekly reported clinical cases counts (yellow, best fit to clinical cases), and both prevalence and seasonality of clinical cases (red, best combined fit). Surface shows the residual error in the combined fit to both prevalence and clinical cases. (B) RDT prevalence between December 2011 and April 2014 observed during surveillance (black) and in simulation. Blue: Simulation of single pair of arabiensis and funestus habitat availability scales that result in the best fit to the surveilled prevalence without accounting for fit to seasonality of clinical case counts. Yellow: Simulation of single pair of habitat scales that result in the best fit to seasonality of clinical case counts without accounting for fit to prevalence. Red: Simulation of pairs of habitat scales that result in best (bold line) and top 10 (thin lines) fits when optimizing fit to both surveilled prevalence and seasonality of reported clinical case counts. (C) Normalized seasonality of clinical case counts observed in Munyumbwe health clinic reported data (black) and simulation of a single Munyumbwe cluster (blue, yellow, red, defined as in panel B).
© Copyright Policy
Related In: Results  -  Collection

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pcbi.1005192.g003: Representative calibration of larval habitat availabilities in a single cluster located in Munyumbwe HFCA.(A) Sampling over larval habitat availability scale factors for arabiensis and funestus identifies pairs of scale factors with good fits to: cluster-level prevalence by rapid diagnostic test (RDT) from surveillance data (blue, best fit to prevalence), HFCA-level seasonality of weekly reported clinical cases counts (yellow, best fit to clinical cases), and both prevalence and seasonality of clinical cases (red, best combined fit). Surface shows the residual error in the combined fit to both prevalence and clinical cases. (B) RDT prevalence between December 2011 and April 2014 observed during surveillance (black) and in simulation. Blue: Simulation of single pair of arabiensis and funestus habitat availability scales that result in the best fit to the surveilled prevalence without accounting for fit to seasonality of clinical case counts. Yellow: Simulation of single pair of habitat scales that result in the best fit to seasonality of clinical case counts without accounting for fit to prevalence. Red: Simulation of pairs of habitat scales that result in best (bold line) and top 10 (thin lines) fits when optimizing fit to both surveilled prevalence and seasonality of reported clinical case counts. (C) Normalized seasonality of clinical case counts observed in Munyumbwe health clinic reported data (black) and simulation of a single Munyumbwe cluster (blue, yellow, red, defined as in panel B).
Mentions: Preliminary entomological data indicated that both Anopheles arabiensis and Anopheles funestus are present in the study area (personal communication with Javan Chanda), with arabiensis biting rates highest between January and April during the warm rainy season while funestus peaks in September at the beginning of the hot dry season (Fig 2A). Relative abundances of arabiensis and funestus govern the seasonality of malaria transmission, while absolute abundances determine the intensity. For each village-scale cluster, combinations of larval habitat availabilities for arabiensis and funestus were simulated with appropriate patterns of ITN usage, case management rates, and MTAT coverage. The resulting simulated prevalence and clinical case counts were compared with surveillance data to select the combination of habitats yielding the best fit to field observations (Fig 3 and S8 Fig, Methods).

View Article: PubMed Central - PubMed

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.

No MeSH data available.