Research
Variations in entomological indices in relation to weather patterns and malaria incidence in East African highlands: implications for epidemic prevention and control
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* Corresponding author: Mojca Kristan kristan.mojca@gmail.com
1 London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
2 National Malaria Control Programme, Ministry of Health, P O Box 7272, Kampala, Uganda
3 Division of Malaria Control, Ministry of Health, P O Box 20750, Nairobi, Kenya
Malaria Journal 2008, 7:231 doi:10.1186/1475-2875-7-231
Published: 4 November 2008Abstract
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.