Evaluation of the Performance of Rapid Diagnostic Tests for Malaria Diagnosis and Mapping of Different Plasmodium Species in Mali.

BACKGROUND: The first-line diagnosis of malaria in Mali is based on the use of rapid diagnostic tests (RDT) that detect the Histidin Rich Protein 2 (HRP2) antigen specific to Plasmodium falciparum. Our study, based on a real-time polymerase chain reaction (qPCR) gold standard, aimed to describe the distribution of the Plasmodium species in each administrative region of Mali and to assess the performance of RDTs. METHODS: We randomly selected 150 malaria-negative and up to 30 malaria-positive RDTs in 41 sites distributed in 9 regions of Mali. DNA extracted from the RDT nitrocellulose strip was assayed with a pan-Plasmodium qPCR. Positive samples were then analyzed with P. falciparum-, P. malariae-, P. vivax-, or P. ovale-specific qPCRs. RESULTS: Of the 1496 RDTs, 258 (18.6%) were positive for Plasmodium spp., of which 96.9% were P. falciparum. The P. vivax prevalence reached 21.1% in the north. RDT displayed acceptable diagnostic indices; the lower CI95% bounds of Youden indices were all ≥0.50, except in the north (Youden index 0.66 (95% CI [0.44-0.82]) and 0.63 (95% CI [0.33-0.83]. CONCLUSIONS: Overall, RDT diagnostic indices are adequate for the biological diagnosis of malaria in Mali. We recommend the use of RDTs detecting P. vivax-specific antigens in the north.

Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015-2019.

INTRODUCTION: Despite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali. METHODS: Daily malaria cases were collected from the consultation records of the 35 health areas of Kati's health district, for the period 2015-2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use-land cover were extracted from open-access remote sensing sources, while data on the Niger River's height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis. RESULTS: The peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person-weeks in 2015, to 27 cases per 1000 person-weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July-corresponding to a persisting transmission during the dry season-and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period. DISCUSSION: Our study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.

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.