Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali

Climate change and health in the Sahel: a systematic review

The Sahel region is projected to be highly impacted by the more frequent hazards associated with climate change, including increased temperature, drought and flooding. This systematic review examined the evidence for climate change-related health consequences in the Sahel. The databases used were Medline (PubMed), Embase (Ovid), Web of Science (Clarivate) and CABI Global Health. Hand searches were also conducted, which included directly engaging Sahelian researchers and hand-searching in the African Journals Online database. Of the 4153 studies found, 893 were identified as duplicates and the remaining 3260 studies were screened (title and abstract only) and then assessed for eligibility. A total of 81 studies were included in the systematic review. Most studies focused on vector-borne diseases, food security, nutrition and heat-related stress. Findings suggest that mosquito distribution will shift under different climate scenarios, but this relationship will not be linear with temperature, as there are other variables to consider. Food insecurity, stunting (chronic malnutrition) and heat-related mortality are likely to increase if no action is taken owing to the projected impact of climate change on environmental factors and agriculture. Seventy-one per cent of manuscripts (n = 58) had first authors from institutions in North America or Europe, of which 39.7% (n = 23) included co-authors from African institutions.

Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022

Objectives

Following the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali.

Methods

Cohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria.

Results

A total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season.

Conclusion

Determining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.

Exploring malaria prediction models in Togo: a time series forecasting by health district and target group

OBJECTIVES

Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. This study aimed to explore the possibility of using routine malaria data and satellite-derived climate data to forecast malaria cases in Togo.

METHODS

Generalised additive (mixed) models were developed to forecast the monthly number of malaria cases in 40 health districts and three target groups. Routinely collected malaria data from 2013 to 2016 and meteorological and vegetation data with a time lag of 1 or 2 months were used for model training, while the year 2017 was used for model testing. Two methods for selecting lagged meteorological and environmental variables were compared: a first method based on statistical approach ('SA') and a second method based on biological reasoning ('BR'). Both methods were applied to obtain a model per target group and health district and a mixed model per target group and health region with the health district as a random effect. The predictive skills of the four models were compared for each health district and target group.

RESULTS

The most selected predictors in the models per district for the 'SA' method were the normalised difference vegetation index, minimum temperature and mean temperature. The 'SA' method provided the most accurate models for the training period, except for some health districts in children ≥5 years old and adults and in pregnant women. The most accurate models for the testing period varied by health district and target group, provided either by the 'SA' method or the 'BR' method. Despite the development of models with four different approaches, the number of malaria cases was inaccurately forecasted.

CONCLUSIONS

These models cannot be used as such in malaria control activities in Togo. The use of finer spatial and temporal scales and non-environmental data could improve malaria prediction.

Stratification and Adaptation of Malaria Control Interventions in Chad

Malaria remains the leading cause of morbidity and mortality in Chad. The World Health Organization (WHO) has recommended that endemic countries stratify malaria to guide interventions. Thus, the Republic of Chad has initiated a stratification process based on malaria incidence with the aim of defining transmission risk and proposing interventions. We collected routine malaria data from health facilities from 2017-2021, the national survey on malaria indicators, the entomological data of NMCP operational research, the demographic and health surveys, and remote sensing of environmental data. Stratification was based on the adjusted incidence of malaria to guide interventions. The adjusted incidence of malaria was, on average, 374 cases per 1000 people in the country. However, it varied according to health districts. Health districts were stratified into very low malaria incidence (n = 25), low malaria incidence (n = 20), moderate malaria incidence (n = 46) and high malaria incidence (n = 38). Micro-stratification in health districts with very low incidence was carried out to identify districts with incidence <10 cases per 1000 person with a view to a malaria pre-elimination programme. Appropriate malaria control interventions were proposed based on the strata identified. Stratification enables the country to target interventions to accelerate the reduction of the burden caused by malaria with a pre-elimination goal.

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

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.

The West Africa ICEMR Partnerships for Guiding Policy to Improve the Malaria Prevention and Control

The Mali National Malaria Control Program (NMCP) recently established a phased set of goals for eliminating malaria in Mali by 2030. Over the past decade, the scale-up of NMCP-led malaria control interventions has led to considerable progress, as evidenced by multiple malariometric indicators. The West Africa International Center of Excellence in Malaria Research (WA-ICEMR) is a multidisciplinary research program that works closely with the NMCP and its partners to address critical research needs for malaria control. This coordinated effort includes assessing the effectiveness of control interventions based on key malaria research topics, including immune status, parasite genetic diversity, insecticide and drug resistance, diagnostic accuracy, malaria vector populations and biting behaviors, and vectorial capacity. Several signature accomplishments of the WA-ICEMR include identifying changing malaria age demographic profiles, testing innovative approaches to improve control strategies, and providing regular reporting on drug and insecticide resistance status. The NMCP and WA-ICEMR partnership between the WA-ICEMR and the NMCP offers a comprehensive research platform that informs the design and implementation of malaria prevention and control research programs. These efforts build local expertise and capacity for the next generation of malaria researchers and guide local policy, which is crucial in sustaining efforts toward eliminating malaria in West Africa.

A Decade of Progress Accelerating Malaria Control in Mali: Evidence from the West Africa International Center of Excellence for Malaria Research

This article highlights over a decade of signature achievements by the West Africa International Centers for Excellence in Malaria Research (WA-ICEMR) and its partners toward guiding malaria prevention and control strategies. Since 2010, the WA-ICEMR has performed longitudinal studies to monitor and assess malaria control interventions with respect to space-time patterns, vector transmission indicators, and drug resistance markers. These activities were facilitated and supported by the Mali National Malaria Control Program. Research activities included large-scale active and passive surveillance and expanded coverage of universal long-lasting insecticide-treated bed nets and seasonal malaria chemoprevention (SMC). The findings revealed substantial declines in malaria occurrence after the scale-up of control interventions in WA-ICEMR study sites. WA-ICEMR studies showed that SMC using sulfadoxine-pyrimethamine plus amodiaquine was highly effective in preventing malaria among children under 5 years of age. An alternative SMC regimen (dihydroartemisinin plus piperaquine) was shown to be potentially more effective and provided advantages for acceptability and compliance over the standard SMC regimen. Other findings discussed in this article include higher observed multiplicity of infection rates for malaria in historically high-endemic areas, continued antimalarial drug sensitivity to Plasmodium falciparum, high outdoor malaria transmission rates, and increased insecticide resistance over the past decade. The progress achieved by the WA-ICEMR and its partners highlights the critical need for maintaining current malaria control interventions while developing novel strategies to disrupt malaria transmission. Enhanced evaluation of these strategies through research partnerships is particularly needed in the wake of reported artemisinin resistance in Southeast Asia and East Africa.

Sub-national tailoring of seasonal malaria chemoprevention in Mali based on malaria surveillance and rainfall data

Background

In malaria endemic countries, seasonal malaria chemoprevention (SMC) interventions are performed during the high malaria transmission in accordance with epidemiological surveillance data. In this study we propose a predictive approach for tailoring the timing and number of cycles of SMC in all health districts of Mali based on sub-national epidemiological surveillance and rainfall data. Our primary objective was to select the best of two approaches for predicting the onset of the high transmission season at the operational scale. Our secondary objective was to evaluate the number of malaria cases, hospitalisations and deaths in children under 5 years of age that would be prevented annually and the additional cost that would be incurred using the best approach.

Methods

For each of the 75 health districts of Mali over the study period (2014–2019), we determined (1) the onset of the rainy season period based on weekly rainfall data; (ii) the onset and duration of the high transmission season using change point analysis of weekly incidence data; and (iii) the lag between the onset of the rainy season and the onset of the high transmission. Two approaches for predicting the onset of the high transmission season in 2019 were evaluated.

Results

In the study period (2014–2019), the onset of the rainy season ranged from week (W) 17 (W17; April) to W34 (August). The onset of the high transmission season ranged from W25 (June) to W40 (September). The lag between these two events ranged from 5 to 12 weeks. The duration of the high transmission season ranged from 3 to 6 months. The best of the two approaches predicted the onset of the high transmission season in 2019 to be in June in two districts, in July in 46 districts, in August in 21 districts and in September in six districts. Using our proposed approach would prevent 43,819 cases, 1943 hospitalisations and 70 deaths in children under 5 years of age annually for a minimal additional cost. Our analysis shows that the number of cycles of SMC should be changed in 36 health districts.

Conclusion

Adapting the timing of SMC interventions using our proposed approach could improve the prevention of malaria cases and decrease hospitalisations and deaths. Future studies should be conducted to validate this approach.

Graphical Abstract

Stratification at the health district level for targeting malaria control interventions in Mali

Malaria is the leading cause of morbidity and mortality in Mali. Between 2017 and 2020, the number of cases increased in the country, with 2,884,827 confirmed cases and 1454 reported deaths in 2020. We performed a malaria risk stratification at the health district level in Mali with a view to proposing targeted control interventions. Data on confirmed malaria cases were obtained from the District Health Information Software 2, data on malaria prevalence and mortality in children aged 6-59 months from the 2018 Demographic and Health Survey, entomological data from Malian research institutions working on malaria in the sentinel sites of the National Malaria Control Program (NMCP), and environmental data from the National Aeronautics and Space Administration. A stratification of malaria risk was performed. Targeted malaria control interventions were selected based on spatial heterogeneity of malaria incidence, malaria prevalence in children, vector resistance distribution, health facility usage, child mortality, and seasonality of transmission. These interventions were discussed with the NMCP and the different funding partners. In 2017-2019, median incidence across the 75 health districts was 129.34 cases per 1000 person-years (standard deviation = 86.48). Risk stratification identified 12 health districts in very low transmission areas, 19 in low transmission areas, 20 in moderate transmission areas, and 24 in high transmission areas. Low health facility usage and increased vector resistance were observed in high transmission areas. Eight intervention combinations were selected for implementation. Our work provides an updated risk stratification using advanced statistical methods to inform the targeting of malaria control interventions in Mali. This stratification can serve as a template for continuous malaria risk stratifications in Mali and other countries.

Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012-2017) from 1400 persons who sought treatment at Dangassa's community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.