Journal of Advances in Mathematics and Computer Science

Aims/ objectives: Cyclic recurrence of measles epidemics in developing countries induced high mortality, especially among malnourished children. In Niger, as the disease exhibits clearly seasonal outbreaks, we observe increasing incidence during the dry season, from October to June. In this article, we perform an inference on reported cases during 2017-2018 measles outbreak to yield effective reproductive number,\(\mathit{R}\)_{\(\mathit{p}\)}, for each of the eight administrative regions in Niger. Our method is based on the stochastic model SIR with vaccination of measles, relying on the Metropolis-Hastings algorithm as an analysis tool. The choice of this model takes into account the random uctuations inherent to the epidemiological characteristics of rural populations of Niger, notably a high prevalence of measles in children under 5 years, coupled with very low immunization coverage. It follows from this analysis that some regions of Niger remained potentially vulnerable to measles outbreaks due to a very high \(\mathit{R}\)_{\(\mathit{p}\)} value in these regions, As evidenced by our simulation of epidemic trends in these regions, this is the case of the regions of Tahoua and Zinder. However, the low birth rate had sheltered certain regions from measles outbreaks, such as the Diffa and Dosso regions. We have indeed noted two dominant factors that explain the high values of \(\mathit{R}\)_{\(\mathit{p}\)} in these eight regions, the low vaccination coverage and the high birth rate. Mathematical models allow a better understanding of the dynamics of disease spread in a population. However, difficulty in data collection processes and estimation of statistics parameters limit their range in statistical analysis of epidemic spread. Other hand the numerical resolution takes a long time computationally.