Background: Spatial and temporal identification of malaria-endemic areas is a key component of vector-borne disease control. Strategies to target the most vulnerable populations, the periods of high transmission and the most affected geographical areas, should make vector-borne disease control and prevention programmes more cost-effective. The present study focuses on the spatial and temporal dynamics of malaria cases and the exogenous factors influencing the transmission in an area with pyrethroid-resistant mosquito vector populations.
Methods: A prospective cohort study of 1,806 children under 10 years of age was conducted over 20 months to assess the risk of malaria incidence in the Cove-Zagnanado-Ouinhi (CoZO) health zone located in southern Benin. Childhood malaria data were used to identify malaria hotspots according to months of follow-up using the Kulldoff algorithm. Stability scores were calculated by season to assess incidence heterogeneity. Incidence values by month were aggregated with meteorological data; and demographic data were merged to detect cross-correlation between incidence and meteorological variables. Generalised equation estimators were used to identify the factors explaining the spatio-temporal heterogeneity of malaria incidence in the Cove-Zagnanado-Ouinhi (CoZO) health zone.
Results: We observed spatial heterogeneity in malaria transmission hotspots over the study period, with relative risks ranging from 1.59 (p-value=0.032) to 16.24 (p-value=0.002). Malaria incidence ranged from 1.41 (95% IC: 0.96-2.08) to 13.91 (95% IC: 12.22-15.84) cases per 100 child-months. We also found that there was a significant negative association (correlation coefficient =-0.56) between malaria incidence and temperature; and a slightly positive association (correlation coefficient = 0.58) between malaria incidence and rainfall.
Conclusion: Our results have shown that high-resolution satellite data can be used on a small scale to find the relationship with vector-borne diseases such as malaria.
