Dynamics of tsetse natural infection rates in the Mouhoun river, Burkina Faso, in relation with environmental factors

In Burkina Faso, the cyclical vectors of African animal trypanosomoses (AAT) are riverine tsetse species, namely Glossina palpalis gambiensis Vanderplank (G.p.g.) and Glossina tachinoides Westwood (G.t.) (Diptera: Glossinidae). Experimental work demonstrated that environmental stress can increase the sensitivity of tsetse to trypanosome infection. Seasonal variations of the tsetse infection rates were monitored monthly over 17 months (May 2006–September 2007) in two sites (Douroula and Kadomba). In total, 1423 flies were dissected and the infection of the proboscis, middle intestine and salivary glands was noted. All the positive organs were analyzed using monospecific polymerase chain reaction (PCR) primers. To investigate the role of different environmental factors, fly infection rates were analyzed using generalized linear mixed binomial models using the species, sex, and monthly averages of the maximum, minimum and mean daily temperatures, rainfalls, Land Surface Temperature day (LSTd) and night (LSTn) as fixed effects and the trap position as a random effect. The overall infection rate was 10% from which the predominant species was T. congolense (7.6% of the flies), followed by T. vivax (2.2% of the flies). The best model (lowest AICc) for the global infection rates was the one with the maximum daily temperature only as fixed effect (p < 0.001). For T. congolense, the best model was the one with the tsetse species, sex, maximum daily temperature and rainfalls as fixed effect, where the maximum daily temperature was the main effect (p < 0.001). The number of T. vivax infections was too low to allow the models to converge. The maturation rate of T. congolense was very high (94%), and G. t. harbored a higher maturation rate (p = 0.03). The results are discussed in view of former laboratory studies showing that temperature stress can increase the susceptibility of tsetse to trypanosomes, as well as the possibility to improve AAT risk mapping using satellite images.