ArticleScientific reports2021
Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03074435 (Insecticide Resistance Management in Burkina Faso and Côte D'Ivoire), which is not on this map. Cited by 14 papers.
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Insecticide Resistance Management in Burkina Faso and Côte D'Ivoire: Research on Vector Control Strategies
Who cites it
14 citing papers in PubMed.
- Determinants of malaria infection across different districts of Khyber Pakhtunkhwa, Pakistan: a cross-sectional study.Malaria journal · 2026Article
- Spatio-temporal dynamics of malaria incidence in children under five years of age and correlation with meteorological variability in the North and Far North regions of Cameroon between 2015-2022.Parasite epidemiology and control · 2025Article
- Malaria epidemiology in the Korhogo area, Northern Côte d'Ivoire: baseline assessment prior to a randomized controlled trial.Malaria journal · 2025Review
- Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018.Scientific reports · 2025Article
- Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso.International journal of environmental research and public health · 2024Article
- Spatio-temporal characterization of malaria prevalence in a peri-urban resource limited setting of Western Kenya Highlands.African health sciences · 2024Article
- Prevalence of asymptomatic malaria at the communal level in Burkina Faso: an application of the small area estimation approach.Population health metrics · 2024Article
- Exploring malaria prediction models in Togo: a time series forecasting by health district and target group.BMJ open · 2024Article
- Notification of malaria cases in the Brazilian Amazon Basin from 2010 to 2020: an analysis of the reporting times.Malaria journal · 2023Article
- Article
- Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015-2019.International journal of environmental research and public health · 2022Article
- Climate factors and dengue fever in Burkina Faso from 2017 to 2019.Journal of public health in Africa · 2022Article
- Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine.Sensors (Basel, Switzerland) · 2022Article
- A meta-epidemiological assessment of transparency indicators of infectious disease models.PloS one · 2022Article
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14 authors.
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Abstract
Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.
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