Assessing the role of human mobility on malaria transmission

South Sudan accounts for a large proportion of all annual malaria cases in Africa. In recent years, the country has witnessed an unprecedented number of people on the move, refugees, internally displaced people, people who have returned to their counties or areas of origin, stateless people and other populations of concern, posing challenges to malaria control. Thus, one can claim that human mobility is one of the contributing factors to the resurgence of malaria. The aim of this paper is to assess the impact of human mobility on the burden of malaria disease in South Sudan. For this, we formulate an SIR-type model that describes the transmission dynamics of malaria disease between multiple patches. The proposed model is a system of stochastic differential equations consisting of ordinary differential equations perturbed by a stochastic Wiener process. For the deterministic part of the model, we calculate the basic reproduction number. Concerning the whole stochastic model, we use the maximum likelihood approach to fit the model to weekly malaria data of 2011 from Central Equatoria State, Western Bahr El Ghazal State and Warrap State. Using the parameters estimated on the fitted model, we simulate the future observation of the disease pattern. The disease was found to persist in the low transmission patches when there is human inflow in these patches and although the intervention coverage reaches 75%.