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

• Published in: BMC infectious diseases Vol. 20; no. 1; pp. 1 - 424
• Main Authors: Dieng, Sokhna, Ba, El Hadj, Cissé, Badara, Sallah, Kankoe, Guindo, Abdoulaye, Ouedraogo, Boukary, Piarroux, Martine, Rebaudet, Stanislas, Piarroux, Renaud, Landier, Jordi, Sokhna, Cheikh, Gaudart, Jean
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 17-06-2020; BioMed Central; BMC
• Subjects: Analysis; Annual rainfall; Disease hot spots; Disease transmission; Geostatistical analyses; Human health and pathology; Infectious diseases; Life Sciences; Malaria; Malaria hotspots; Microbiology and Parasitology; Non-linear associations; Parasitology; Population; Public health; Rain; Rainfall; Remote sensing; Santé publique et épidémiologie; Spatial clusters; Spatio-temporal dynamic; Surveillance; Temporal variations; Vector-borne diseases; Vegetation; Vegetation type; Villages; Weather; Senegal; Burkina Faso; United States > US; Spatial clusters; Geostatistical analyses; Non-linear associations; Malaria hotspots; Spatio-temporal dynamic
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-020-05145-w

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. 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. 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. 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.