Limits...
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.


Related in: MedlinePlus

Prevalence of RDT+ infections in the Lake Kariba region a decade after target elimination date of 2020 under various post-2015 intervention scenarios.Clusters are divided into high-burden (n = 64) and low-burden (n = 51) groups as indicated in the inset map. Cluster prevalence in each scenario is the mean of 100 samples from the joint posterior distribution of 10 best-fit habitat availability pairs for each cluster. Post-2015 MDA: MDA is distributed in 2014 and 2015 to all HFCAs in all scenarios. Scenarios 1–4: MDAs discontinued after 2015. Scenarios 5–10, 12–13: MDAs continue annually between 2016 and 2020, a total of 5 additional distributions. Scenario 11: MDA is distributed only in 2017 and 2018. Scenarios 10–11, indicated with asterisk: post-2015 MDAs have 70% coverage. All other MDA distributions have coverage as indicated in S5 Fig. MDA region: MDAs are distributed to clusters in all HFCAs except in scenarios 8, 9, and 11, where only clusters in high-burden HFCAs receive MDAs after 2015. Case management: In scenario 1, case management is maintained at rates observed during 2012–13 surveillance. In all other scenarios, case management rates increase and plateau as shown in S4 Fig. ITN usage: In scenarios 1, 2, and 5 (“current”), ITNs are not distributed after 2015. In scenarios 3, 6, 8, and 10 (“ramp”), ITN usage after 2015 ramps up following historical rates. In all other scenarios (“aggr”), ITNs are distributed at an aggressive 80% coverage biannually between 2018–22. Importation: Infections imported from outside the 12-HFCA study area.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5120780&req=5

pcbi.1005192.g006: Prevalence of RDT+ infections in the Lake Kariba region a decade after target elimination date of 2020 under various post-2015 intervention scenarios.Clusters are divided into high-burden (n = 64) and low-burden (n = 51) groups as indicated in the inset map. Cluster prevalence in each scenario is the mean of 100 samples from the joint posterior distribution of 10 best-fit habitat availability pairs for each cluster. Post-2015 MDA: MDA is distributed in 2014 and 2015 to all HFCAs in all scenarios. Scenarios 1–4: MDAs discontinued after 2015. Scenarios 5–10, 12–13: MDAs continue annually between 2016 and 2020, a total of 5 additional distributions. Scenario 11: MDA is distributed only in 2017 and 2018. Scenarios 10–11, indicated with asterisk: post-2015 MDAs have 70% coverage. All other MDA distributions have coverage as indicated in S5 Fig. MDA region: MDAs are distributed to clusters in all HFCAs except in scenarios 8, 9, and 11, where only clusters in high-burden HFCAs receive MDAs after 2015. Case management: In scenario 1, case management is maintained at rates observed during 2012–13 surveillance. In all other scenarios, case management rates increase and plateau as shown in S4 Fig. ITN usage: In scenarios 1, 2, and 5 (“current”), ITNs are not distributed after 2015. In scenarios 3, 6, 8, and 10 (“ramp”), ITN usage after 2015 ramps up following historical rates. In all other scenarios (“aggr”), ITNs are distributed at an aggressive 80% coverage biannually between 2018–22. Importation: Infections imported from outside the 12-HFCA study area.

Mentions: To compare how case management, ITN usage, and MDA coverage contribute to reducing malaria burden separately and together, a variety of post-2015 intervention scenarios were simulated (Figs 6–10). For each simulation, the fraction of total study area population living in clusters where no local transmission has occurred over a month-long period was measured for each month between January 2012 and January 2030. Elimination was counted to have been achieved if no infection events occurred anywhere in the simulated study area over a continuous three-year period.


Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model
Prevalence of RDT+ infections in the Lake Kariba region a decade after target elimination date of 2020 under various post-2015 intervention scenarios.Clusters are divided into high-burden (n = 64) and low-burden (n = 51) groups as indicated in the inset map. Cluster prevalence in each scenario is the mean of 100 samples from the joint posterior distribution of 10 best-fit habitat availability pairs for each cluster. Post-2015 MDA: MDA is distributed in 2014 and 2015 to all HFCAs in all scenarios. Scenarios 1–4: MDAs discontinued after 2015. Scenarios 5–10, 12–13: MDAs continue annually between 2016 and 2020, a total of 5 additional distributions. Scenario 11: MDA is distributed only in 2017 and 2018. Scenarios 10–11, indicated with asterisk: post-2015 MDAs have 70% coverage. All other MDA distributions have coverage as indicated in S5 Fig. MDA region: MDAs are distributed to clusters in all HFCAs except in scenarios 8, 9, and 11, where only clusters in high-burden HFCAs receive MDAs after 2015. Case management: In scenario 1, case management is maintained at rates observed during 2012–13 surveillance. In all other scenarios, case management rates increase and plateau as shown in S4 Fig. ITN usage: In scenarios 1, 2, and 5 (“current”), ITNs are not distributed after 2015. In scenarios 3, 6, 8, and 10 (“ramp”), ITN usage after 2015 ramps up following historical rates. In all other scenarios (“aggr”), ITNs are distributed at an aggressive 80% coverage biannually between 2018–22. Importation: Infections imported from outside the 12-HFCA study area.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120780&req=5

pcbi.1005192.g006: Prevalence of RDT+ infections in the Lake Kariba region a decade after target elimination date of 2020 under various post-2015 intervention scenarios.Clusters are divided into high-burden (n = 64) and low-burden (n = 51) groups as indicated in the inset map. Cluster prevalence in each scenario is the mean of 100 samples from the joint posterior distribution of 10 best-fit habitat availability pairs for each cluster. Post-2015 MDA: MDA is distributed in 2014 and 2015 to all HFCAs in all scenarios. Scenarios 1–4: MDAs discontinued after 2015. Scenarios 5–10, 12–13: MDAs continue annually between 2016 and 2020, a total of 5 additional distributions. Scenario 11: MDA is distributed only in 2017 and 2018. Scenarios 10–11, indicated with asterisk: post-2015 MDAs have 70% coverage. All other MDA distributions have coverage as indicated in S5 Fig. MDA region: MDAs are distributed to clusters in all HFCAs except in scenarios 8, 9, and 11, where only clusters in high-burden HFCAs receive MDAs after 2015. Case management: In scenario 1, case management is maintained at rates observed during 2012–13 surveillance. In all other scenarios, case management rates increase and plateau as shown in S4 Fig. ITN usage: In scenarios 1, 2, and 5 (“current”), ITNs are not distributed after 2015. In scenarios 3, 6, 8, and 10 (“ramp”), ITN usage after 2015 ramps up following historical rates. In all other scenarios (“aggr”), ITNs are distributed at an aggressive 80% coverage biannually between 2018–22. Importation: Infections imported from outside the 12-HFCA study area.
Mentions: To compare how case management, ITN usage, and MDA coverage contribute to reducing malaria burden separately and together, a variety of post-2015 intervention scenarios were simulated (Figs 6–10). For each simulation, the fraction of total study area population living in clusters where no local transmission has occurred over a month-long period was measured for each month between January 2012 and January 2030. Elimination was counted to have been achieved if no infection events occurred anywhere in the simulated study area over a continuous three-year period.

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.


Related in: MedlinePlus