Background: In their behavior, Africans generally pour dirty water around their homes. This dirty water becomes stagnant at a given moment, which hence constitutes aquatic habitats (AH). These AH are sought after by mosquitoes for egg-laying and larval development. Recent studies have shown the effectiveness of destroying AH around host habitats (humans and animals) in reducing the incidence of malaria. In this paper, an agent-based model (ABM) is proposed for controlling the incidence of malaria through population sensitizing campaigns on the harmful effects of aquatic habitats around houses.
Methods: The environment is constituted of houses, AH, mosquitoes, humans, and a hospital that will allow humans to heal themselves when they have malaria. The dynamics of malaria’s spread is linked to the dynamics of individuals (humans and mosquitoes) populations. The dynamic of the mosquito is represented by two phases: egg-laying and a phase of seeking blood. The dynamic of human is animated by the presence in the health center and houses. Their dynamic also results in hitting the mosquito when a human is bitten by it. Initially, the same number of houses and AH have been considered. Thereafter, houses are fixed and the AH are destroyed each time by 10% of the number of starting Aquatics habitats. The number of infected humans varied also from 0 to 90 which led to a total of 1001 simulations.
Results: The results show that when the number of houses and AH is equal, we find approximately the same results as the field data. At each reduction of AH, the incidence and prevalence tend more and more towards 0. On the other hand, when there is no AH and infected humans in the environment, the prevalence and incidence are at 0.
Conclusions: The study shows that every time we destroy the AH, it increasingly inhibits the growth of mosquitoes and malaria. But when there is no AH site, even if there are infected people in the environment, the disease disappears completely. Therefore the global destruction of the AH in an environment is to be recommended. Using many parameters in the same model is also recommended.