Risk Maps of Lassa Fever in West Africa

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Abstract
Lassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965–2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region. Three different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absence∶presence clusters were used in each of Models 2 and 3, the 2 absence∶1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0.91 (Model 2) or 0.86 (Model 3), making them ‘Excellent’. The Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d'Ivoire, Togo and Benin, and 10% of Ghana. Previous studies on the eco-epidemiology of Lassa fever in Guinea, West Africa, have shown that the reservoir is two to three times more infected by Lassa virus in the rainy season than in the dry season. None of the intrinsic variables of the murine population, such as abundance or reproduction, was able to explain this seasonal variation in prevalence. We therefore here investigate the importance of extrinsic environmental variables, partly influenced by the idea that in the case of nephropathia epidemica in Europe contamination of the environment, and therefore survival of the pathogen outside the host, appears to be an important factor in this disease's epidemiology. We therefore made an extensive review of the literature, gathering information about the geographical location of sites where Lassa fever has been certainly identified. Environmental data for these sites (rainfall, temperature, vegetation and altitude) were gathered from a variety of sources, both satellites and ground-based meteorological stations. Several statistical treatments were applied to produce Lassa ‘risk maps’. These maps all indicate a strong influence of rainfall, and a lesser influence of temperature in defining high risk areas. The area of greatest risk is located between Guinea and Cameroon.