Malaria in Burkina Faso: A comprehensive analysis of spatiotemporal distribution of incidence and environmental drivers, and implications for control strategies

Abstract

Background: The number of malaria cases worldwide has increased, with over 241 million cases and 69,000 more deaths in 2020 compared to 2019. Burkina Faso recorded over 11 million malaria cases in 2020, resulting in nearly 4,000 deaths. The overall incidence of malaria in Burkina Faso has been steadily increasing since 2016. This study investigates the spatiotemporal pattern and environmental and meteorological determinants of malaria incidence in Burkina Faso.

Methods: We described the temporal dynamics of malaria cases by detecting the transmission periods and the evolution trend from 2013 to 2018. We detected hotspots using spatial scan statistics. We assessed different environmental zones through a hierarchical clustering and analyzed the environmental and climatic data to identify their association with malaria incidence at the national and at the district's levels through generalized additive models. We also assessed the time lag between malaria peaks onset and the rainfall at the district level. The environmental and climatic data were synthetized into indicators.

Results: The study found that malaria incidence had a seasonal pattern, with high transmission occurring during the rainy seasons. We also found an increasing trend in the incidence. The highest-risk districts for malaria incidence were identified, with a significant expansion of high-risk areas from less than half of the districts in 2013-2014 to nearly 90% of the districts in 2017-2018. We identified three classes of health districts based on environmental and climatic data, with the northern, south-western, and western districts forming separate clusters. Additionally, we found that the time lag between malaria peaks onset and the rainfall at the district level varied from 7 weeks to 17 weeks with a median at 10 weeks. Environmental and climatic factors have been found to be associated with the number of cases both at global and districts levels.

Conclusion: The study provides important insights into the environmental and spatiotemporal patterns of malaria in Burkina Faso by assessing the spatio temporal dynamics of Malaria cases but also linking those dynamics to the environmental and climatic factors. The findings highlight the importance of targeted control strategies to reduce the burden of malaria in high-risk areas as we found that Malaria epidemiology is complex and linked to many factors that make some regions more at risk than others.

Fig 1. Malaria incidence rate distribution from epidemic year from 2013–2014 to 2017–2018.

The X axis represents the incidence rate per 100,000-person week. The Y axis with the different shade of grey color represents the epidemics years from 2013–2014 to 2017–2018. The figure displays ridgelines to visualize the distribution of malaria incidence rates for each epidemic year. Each ridge (the shaded area) represents the distribution of incidence rates for a specific epidemic year.

Fig 2. Time series of malaria incidence in Burkina Faso and its seasonal and trend components.

Additive decomposition was used, and the decomposed curves were smoothed using the locally estimated scatterplot smoothing method. Raw time series (Data panel). Time series noise (Remainder panel). Time series seasons (Seasonal panel). Time series trend (Trend panel).

Fig 3. Transmission periods from 2013 to 2018, defined by the change point analysis.

Low transmission period (Light grey area); High transmission period (Dark grey area); Intermediate transmission period (Tan area).

Fig 4. Incidence of malaria by health district, from 2013–2014 to 2017–2018; epidemic year incidence per 100,000 person/year in Burkina Faso.

Fig 5. Overall trends in malaria incidence by epidemic year, from 2013–2014 to 2017–2018; weekly epidemic year incidence per 100,000 person/year with generalized additive model smoothing: Weekly incidence time series (black line) and smoothed time series (orange lines).

Fig 6. High risk health districts hotspots for each epidemic year and transmission period.

A district is defined as a hotspot if it belongs to one of the significant clusters detected by the Kulldorf scan statistics.

Fig 7. Mean rainfall (mm) maps by health district, from epidemic year 2013–2014 to 2017–2018.

Fig 8. Temporal trends (time series) of mean rainfall (mm) from 2013 to 2018 (Blue bars) and malaria incidence time series from 2013 to 2018 (red line).

Fig 9. Maximum temperatures (°C) maps by health district, from epidemic year 2013–2014 to 2017–2018.

Fig 10. Temporal trends (time series) in maximum temperatures (°C) from 2013to 2018 (red line) and malaria incidence time series from 2013 to 2018 (black line).

Fig 11. Mean NDVI maps by health district, from epidemic year 2013–2014 to 2017–2018.

Fig 12. Temporal trends (time series) in NDVI from 2013to 2018 (black line) and malaria incidence time series from 2013 to 2018 (red line).

Fig 13. Dendrogram showing districts classification into environmental profiles.

Profile 1 (Purple): High maximum temperatures, high mean temperatures, average minimum temperatures, and low rainfalls, profile 2 (Orange): High minimum temperatures, high NDVIs, high atmospheric pressures, and high medium rainfalls. and profile 3 (Green): high rainfalls, high NDVIs, and medium mean temperatures.

Fig 14. Mapping of health districts into environmental classes.

Profile 1 (Purple): High maximum temperatures, high mean temperatures, average minimum temperatures, and low rainfalls, profile 2 (Orange): High minimum temperatures, high NDVIs, high atmospheric pressures, and high medium rainfalls. and profile 3 (Green): high rainfalls, high NDVIs, and medium mean temperatures.

Fig 15. Relation between malaria cases and SENIs.

Panel A: Relation between malaria global cases and SENI1 (mostly rainfall and mean temperature);. Panel B: Relation between malaria global cases and SENI2 (mostly temperature); Panel C, D,E and F: Diagnostic plot of multivariate generalized additive model.

Fig 16. Time lags (in weeks) between cases and rainfalls at districts level.

The lags had Minimum of 7 weeks, a maximum of 17 weeks and a median of 10 weeks.

Fig 17

Relation between malaria cases and SEDIs at district level; Panel A: Relation between malaria global cases and SEDI1 (mostly maximum temperature); Panel B: Relation between malaria global cases and SEDI2 (mostly min temperature vegetation and latitude). Panel C: Relation between malaria global cases and SEDI3 (rainfall and longitude). Panel D,E,F and G:Diagnostic plot of multivariate generalized additive model.