Disease mapping aims to determine the underlying disease risk scattered from health data. This methodology enables to represent this disease risk by a gradation of colours on a map. Our aim is to apply disease mapping to infectious diseases, when a primary case can result in secondary cases, by direct or vector transmission. Contagion can lead to overdispersion and strengthen spatial and temporal structures. This study highlighted the relevance of using the negative binomial distribution to model such data. It also showed the need to take into account both spatial and temporal dimensions in this type of epidemiological study.