Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

Bâ²,

et al. 2020

Preprint

Background: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation.Methods: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model.Results : The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown.Conclusion : In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed.Trial registrationThe data used in this work were obtained from a clinical trial registered at www.clinicaltrials.gov under # NCT 00712374.

Section: Discussionmentioning

confidence: 54%

Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

Bâ²,

et al. 2020

Preprint

“…Rainfall was positively associated with the risk of being a hotspot, and this non-linear association depended on vegetation type. While the relationship between rainfall and malaria occurrence has been widely discussed [15,34,35,46,47], our study indicates that the impact of rainfall on malaria occurrence depends on both amount rainfall and vegetation type, and this interaction in turn modifies hotspot distribution. Thus, for villages dominated by open shrublands, the risk of being a hotspot increases from the first rains and then reaches a plateau from 22mm/week, likely because heavy rains destroy breeding sites [48,49].…”

Section: Discussionmentioning

confidence: 61%

Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

Bâ

et al. 2019

Preprint

Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly cumulative malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected for each period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The cumulative malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). According to the spatial interpolation, 2 zones located in the south of the study area had the highest risk of being a hotspot (ORmin=1.90, 95%CI: 1.02-3.56; ORmax=60.65, 95%CI: 26.86-136.95). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Our analysis shows also the importance of adapting control strategies to the local context and dynamic patterns. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.

“…Our results showed that rainfall was positively associated with the risk of being a hotspot, and this non-linear association depended on vegetation type. While the relationship between rainfall and malaria occurrence has been widely discussed [15,33,34,46,47], our study indicates that the impact of rainfall on malaria occurrence depends on both amount of rainfall and vegetation type, and this interaction in turn modifies hotspot distribution. Thus, for villages dominated by open shrublands, the risk of being a hotspot increases from the first rains and then reaches a plateau from 22mm/week, likely because heavy rains destroy breeding sites [48,49].…”

Section: Discussionmentioning

confidence: 61%

Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

Bâ²,

et al. 2020

Preprint

Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. The association between village location in the study area and the hotspot status was also showed. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Knowing the similar environmental and meteorological particularities of hotspots, surveillance on these factors could lead targeted public health interventions in local context. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.