Variations in entomological indices in relation to weather patterns and malaria incidence in East African highlands: implications for epidemic prevention and control

• Published in: Malaria Journal
• Main Authors: Rapuoda Beth, Okia Michael, Beard James, Abeku Tarekegn A, Kristan Mojca, Sang James, Cox Jonathan
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2008
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/1475-2875-7-231; https://doaj.org/article/b2afc98cf65f48a893be37d52523850b

Abstract Background Malaria epidemics remain a significant public health issue in the East African highlands. The aim of this study was to monitor temporal variations in vector densities in relation to changes in meteorological factors and malaria incidence at four highland sites in Kenya and Uganda and to evaluate the implications of these relationships for epidemic prediction and control. Methods Mosquitoes were collected weekly over a period of 47 months while meteorological variables and morbidity data were monitored concurrently. Mixed-effects Poisson regression was used to study the temporal associations of meteorological variables to vector densities and of the latter to incidence rates of Plasmodium falciparum . Results Anopheles gambiae s.s. was the predominant vector followed by Anopheles arabiensis . Anopheles funestus was also found in low densities. Vector densities remained low even during periods of malaria outbreaks. Average temperature in previous month and rainfall in previous two months had a quadratic and linear relationship with An. gambiae s.s. density, respectively. A significant statistical interaction was also observed between average temperature and rainfall in the previous month. Increases in densities of this vector in previous two months showed a linear relationship with increased malaria incidence. Conclusion Although epidemics in highlands often appear to follow abnormal weather patterns, interactions between meteorological, entomological and morbidity variables are complex and need to be modelled mathematically to better elucidate the system. This study showed that routine entomological surveillance is not feasible for epidemic monitoring or prediction in areas with low endemicity. However, information on unusual increases in temperature and rainfall should be used to initiate rapid vector surveys to assess transmission risk.