Spatial-temporal pattern of malaria in Burkina Faso from 2013 to 2020

Sangaré, Ibrahim; Ouattara, Cheick Ahmed; Soma, Dieudonné Diloma; Soma, Daouda; Assogba, Benoît Sessinou; Namountougou, Moussa; Tougri, Gautier; Savadogo, Léon G. Blaise

doi:10.1016/j.parepi.2022.e00261

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…The choropleth maps provided a visualization of this variability, showing the geographical distribution of malaria incidence over time. This finding is consistent with other studies that have reported an heterogeneity in malaria incidence in Burkina Faso in recent years [ 3 , 20 ].…”

Section: Discussionsupporting

confidence: 93%

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

Bationo,

Cissoko,

Katilé

et al. 2023

PLoS ONE

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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.

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Section: Discussionsupporting

confidence: 93%

“…In Burkina Faso, few studies of this kind have been carried out at the national level, defining different zones in terms of incidence, meteorological, environmental and socioeconomic factors, which would help in the decision to implement control and prevention policies in the areas most at risk [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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

Bationo,

Cissoko,

Katilé

et al. 2023

PLoS ONE

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“…This is attributed to a high bias and obscurity rate, particularly in SSA, due to out-ofsample generalization over space and time, erroneous diagnosis, and under-counting due to varying healthseeking behaviour and policy (e.g., Yamba et al (2022)). Our major limitation in Nouna was the inability to distinguish between true missing values and "no data reported" as the case from July-October 2019, when personnel from the health sector were on strike (Sangaré et al, 2022), see Figure 11. Uncertainty in malaria case estimates in SSA ranges from approximately 30%-50% of the total numbers reported in the high-prevalence SSA countries (Sullivan, 2010).…”

Section: Uncertainty Around Reported Malaria Casesmentioning

confidence: 99%

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

Dieng,

Tompkins,

Arnault

et al. 2024

Water Resources Research

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With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.

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“…In Burkina Faso, malaria transmission is seasonal with a peak of incidence observed during and just after the rainy season [ 8 ]. This seasonal peak varies across the three major geographic zones linked with the duration of the rainy season; for instance, up to 3 months in the North, 6 months in the Centre, and 9 months in the South of the country [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Relative effects of climate factors and malaria control interventions on changes of parasitaemia risk in Burkina Faso from 2014 to 2017/2018

Traoré,

Singhal,

Millogo

et al. 2024

BMC Infect Dis

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Background In Burkina Faso, the prevalence of malaria has decreased over the past two decades, following the scale-up of control interventions. The successful development of malaria parasites depends on several climatic factors. Intervention gains may be reversed by changes in climatic factors. In this study, we investigated the role of malaria control interventions and climatic factors in influencing changes in the risk of malaria parasitaemia. Methods Bayesian logistic geostatistical models were fitted on Malaria Indicator Survey data from Burkina Faso obtained in 2014 and 2017/2018 to estimate the effects of malaria control interventions and climatic factors on the temporal changes of malaria parasite prevalence. Additionally, intervention effects were assessed at regional level, using a spatially varying coefficients model. Results Temperature showed a statistically important negative association with the geographic distribution of parasitaemia prevalence in both surveys; however, the effects of insecticide-treated nets (ITNs) use was negative and statistically important only in 2017/2018. Overall, the estimated number of infected children under the age of 5 years decreased from 704,202 in 2014 to 290,189 in 2017/2018. The use of ITNs was related to the decline at national and regional level, but coverage with artemisinin-based combination therapy only at regional level. Conclusion Interventions contributed more than climatic factors to the observed change of parasitaemia risk in Burkina Faso during the period of 2014 to 2017/2018. Intervention effects varied in space. Longer time series analyses are warranted to determine the differential effect of a changing climate on malaria parasitaemia risk.

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Spatial-temporal pattern of malaria in Burkina Faso from 2013 to 2020

Cited by 6 publications

References 15 publications

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

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

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

Relative effects of climate factors and malaria control interventions on changes of parasitaemia risk in Burkina Faso from 2014 to 2017/2018

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