Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015

Abstract

Background: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors.

Methods: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection.

Results: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods.

Conclusion: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.

Keywords: Hotspots; Malaria; Spatial clusters; Spatio-temporal dynamic.

Fig. 1

Health area limits and locations of health facilities. Black lines correspond to the limits of the HAs (Thiessen polygons). Each green triangle represents the location of each health facility. The top green rectangle is a zoom of the central urban area (Ouagadougou)

Fig. 2

Weekly meteorological factors and malaria incidence from 2011 to 2015. Upper left y-axis represents malaria incidence (1000 person-weeks, red curve); lower left y-axis represents rainfall (mm, blue bar chart), maximum and minimum humidity (%, respectively continuous and dashed green curves); upper right y-axis represents maximum and minimum temperature (°C, respectively continuous and dashed black curves). The white/grey background (upper panel) represents the different transmission periods (white for intermediate, light grey for low, and dark grey for high)

Fig. 3

Relationship between malaria incidence and the first meteorological factor (rainfall, rain events, humidity), the second meteorological component (maximum and minimum temperatures), and time. The continuous black curves represent adaptive smooth relationships of malaria incidence according to the first meteorological component (a), the second meteorological component (b), and time (c), with a CI of 95% (dashed black curves)

Fig. 4

Spatial pattern of incidence per health area and spatial hotspots for low transmission periods. The choropleth map presents the incidence rate (/1000 person-weeks) for the combined LTPs over the 5 years. The red circles represent the high-risk clusters. The attached Table presents the RRs for each hotspot along with the number of HAs

Fig. 5

Spatial pattern of incidence per health area and spatial hotspots for high transmission periods. The choropleth map presents the incidence rate (/1000 person-weeks) for the combined HTPs over the 5 years. The red circles represent the high-risk clusters. The attached Table presents the RRs for each hotspot along with the number of HAs

Fig. 6

Spatial pattern of incidence per health area and spatial hotspots for intermediate transmission periods. The choropleth map presents the incidence rate (/1000 person-weeks) for the combined ITPs over the 5 years. The red circles represent the high-risk clusters. The attached Table presents the RRs for each hotspot along with the number of HAs