Spatio-temporal variation in malaria transmission intensity in five agro-ecosystems in Mvomero district, Tanzania

Abstract

Abstract. In Africa, malaria is predominantly a rural disease where agriculture forms the backbone of the economy. Various agro-ecosystems and crop production systems have an impact on mosquito productivity, and hence malaria transmission intensity. This study was carried out to determine spatial and temporal variations in anopheline mosquito population and malaria transmission intensity in five villages, representing different agro-ecosystems in Mvomero district, Tanzania, so as to provide baseline information for malaria interventions. The agro-ecosystems consisted of irrigated sugarcane, flooding rice irrigation, non-flooding rice irrigation, wet savannah and dry savannah. In each setting, adult mosquitoes were sampled monthly using Centers for Disease Control and Prevention (CDC) light traps from August 2004 to July 2005. A total of 35,702 female mosquitoes were collected. Anopheles gambiae sensu lato was the most abundant (58.9%) mosquito species. An. funestus accounted for 12.0% of the mosquitoes collected. There was a substantial village to village variation and seasonality in the density of Anopheles mosquito population, with peaks in May towards the end of the warm and rainy season. Significantly larger numbers of anophelines were collected from traditional flooding rice irrigation ecosystem (70.7%) than in non-flooding rice irrigation (8.6%), sugarcane (7.0%), wet savannah (7.3%) and dry savannah (6.4%). The overall sporozoite rates for An. gambiae and An. funestus were 3.4% and 2.3%, respectively. The combined overall sporozoite rate (An. gambiae+An. funestus) was 3.2%. The mean annual entomological inoculation rate (EIR) for An. gambiae s.l. was 728 infective bites per person per year and this was significantly higher in traditional flooding rice irrigation (1351) than in other agro-ecosystems. The highest EIRs for An. gambiae s.l. and An. funestus were observed during May 2005 (long rainy season) and December 2004 (short rainy season), respectively. The findings support the evidence that malaria transmission risk varies even between neighbouring villages and is influenced by agro-ecosystems. This study therefore, demonstrates the need to generate spatial and temporal data on transmission intensity on smaller scales taking into consideration agro-ecosystems that will identify area-specific transmission intensity to guide targeted control of malaria operations.

Population Diversity of Mosquito Fauna in and around Tandojam- Pakistan

“…In another study on Anopheles prevalence was conducted in Tanzania which reveals that there was significantly larger number of Anopheles were collected from traditional flooding rice irrigation ecosystem (70.7%) 11 . In another study of district Peshawar (Pakistan) the mosquitoes collected from polluted water ditches contain two genera Culex and Anopheles, more than 99% collected mosquitoes belong to genus Culex and remaining belongs to genus Anopheles 12 .…”

Malaria entomological profile in Tanzania from 1950 to 2010: a review of mosquito distribution, vectorial capacity and insecticide resistance

“…north, north-east and east. From early 1960s to 1985 all research activities on malaria vectors were almost concentrated in Tanga (Gillies and Wilkes, 1965 ;Balirwa, 1975;Lines et al, 1987;Magesa et al, 1991;Wilkes et al, 1995;Mboera et al, , 2010Temu et al, 1998;Malima, 1999;Braimah et al, 2005;Kulkarni et al, 2006a;Kigadye et al, 2010). Majority of the articles indicated the occurrence of An.…”

A review of spatial technologies with applications for malaria transmission modelling and control in Africa

“…It is widely acknowledged that the malaria transmission dynamics are closely related to socioeconomic, climatic and environmental factors; the latter including altitude, precipitation, temperature and land use and land cover (Craig et al, 2004;Mboera et al, 2010). Malaria has, therefore, been defined as an environmental disease (Hay et al, 2000;Craig et al, 2004;Moshe and Andrew, 2004;Raso et al, 2012).…”

“…This facilitated detection of malaria risk areas and prediction of malaria transmissions both spatially and temporally, which in turn, provided guidance to malaria control managers in decision-making and policy planning for cost-effective resources allocation (Mills et al, 2008). In many studies in Africa, particular emphases have been given in mapping incidence and/or prevalence (Craig et al, 1999;Yeshiwondim et al, 2009), identification of mosquito habitats (Hayes et al, 1985;Hay et al, 2000), and spatio-temporal prediction/forecasting transmission risk in relation to environment (Mboera et al, 2010;Riedel et al, 2010;Stensgaard et al, 2011). The advent of new spatial data sources with spatial statistics and geo-statistics offered great potential to increase our understanding of the link between malaria disease and environmental variables.…”

“…Although efforts of combating malaria have yielded dramatic decreases in the number of cases and deaths in most endemic regions, devastating statistics reported in the WHO (2012) report that 655,000 died from this disease, with 86% of the victims being children under 5 years of age. Furthermore, 80% of the cases reported and 91% of the total deaths of the 216 million cases worldwide occurred in Africa.It is widely acknowledged that the malaria transmission dynamics are closely related to socioeconomic, climatic and environmental factors; the latter including altitude, precipitation, temperature and land use and land cover (Craig et al, 2004;Mboera et al, 2010). Malaria has, therefore, been defined as an environmental disease (Hay et al, 2000;Craig et al, 2004;Moshe and Andrew, 2004;Raso et al, 2012).…”