Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades

Abstract

Lassa fever is a severe viral hemorrhagic fever caused by a zoonotic virus that repeatedly spills over to humans from its rodent reservoirs. It is currently not known how climate and land use changes could affect the endemic area of this virus, currently limited to parts of West Africa. By exploring the environmental data associated with virus occurrence using ecological niche modelling, we show how temperature, precipitation and the presence of pastures determine ecological suitability for virus circulation. Based on projections of climate, land use, and population changes, we find that regions in Central and East Africa will likely become suitable for Lassa virus over the next decades and estimate that the total population living in ecological conditions that are suitable for Lassa virus circulation may drastically increase by 2070. By analysing geotagged viral genomes using spatially-explicit phylogeography and simulating virus dispersal, we find that in the event of Lassa virus being introduced into a new suitable region, its spread might remain spatially limited over the first decades.

Fig. 1. Environmental factors included in the ecological niche modelling (ENM) analyses of Mastomys natalensis and Lassa virus, and their corresponding ENM response curves.

Response curves and relative importance (RI) obtained for the ENM analyses of M. natalensis and Lassa virus are coloured in red and green, respectively. The ten response curves reported for each ENM analysis correspond to ten independent repetitions of the boosted regression trees (BRT) analysis. These response curves indicate the relationship between the environmental values and the response (i.e., the ecological suitability of M. natalensis or Lassa virus). In addition to the seven environmental factors displayed in this figure, two additional factors were also included in the ENM analyses, the non-forested primary land, and non-forested secondary land.

Fig. 2. Projected ecological niche suitability of Mastomys natalensis and Lassa virus, as well as human population at risk of exposure to Lassa virus.

a Projected ecological niche suitability of Mastomys natalensis (M. natalensis) and Lassa virus for the current period, 2030, 2050, and 2070. Each future projection (i.e., for 2030, 2050, and 2070) was performed according to four different bias-adjusted global climate models and three different representative concentration pathways (RCPs), i.e., greenhouse gas concentration scenarios considered by the Intergovernmental Panel on Climate Change (IPCC): RCP 2.6, RCP 6.0, and RCP 8.5. Here, we only report the projections obtained under RCP 6.0 (see Fig. S1 for the other scenarios as well as for the standard deviations associated with all projections, and see Fig. S2 for explicit differences between current and future projections). For a specific time period, we report ecological niche suitability averaged over the projections obtained with the four different climatic models (see the text for further detail). b Projections of the human population at risk of exposure to Lassa virus for the current period, 2030, 2050, and 2070. For those estimations, we also re-estimate these projections while fixing the human population, i.e., not using the future projections of human population to estimate the number of people at risk (see also Fig. S3 for spatially-explicit estimation of future human exposure to Lassa virus and Fig. S6 for the estimations of the human population at risk of exposure to Lassa virus under all RCP scenarios). Source data are provided as a Source Data file.

Fig. 3. Spatiotemporal diffusion of Lassa virus lineages in the western Africa region and Nigeria. Maximum clade credibility (MCC) tree obtained by continuous phylogeographic inference based on 1000 posterior trees.

A separate phylogeographic analysis was performed for segments L and S as well as, in the case of the Nigerian data set, on clades II, III, and VI. These MCC trees are superimposed on 80% highest posterior density (HPD) intervals reflecting phylogeographic uncertainty. Nodes of the trees, as well as HPD regions, are coloured according to their time of occurrence, and oldest nodes (and corresponding HPD regions) are here plotted on top of youngest nodes. The trees are superimposed on maps displaying the main rivers present in the study area and classified according to their Strahler number S, which measures the importance of a river by looking at the number of upstream rivers connected to it. International borders are represented by grey dashed lines. See also Figs. S9 and S10 for visualisations clade by clade. The “MRU clade” groups the subclades circulating in the Mano River Union (MRU) and Mali (also called lineages IV and V); “NGA clade II”, “III” and “VI” correspond to the main clades circulating in Nigeria (also called lineages II, III and VI, respectively).

Fig. 4. Phylogeographic simulations of viral spread following a successful introduction into a new ecologically suitable area.

Phylogeographic simulations are based on the tree topologies inferred for Lassa virus clade II (segment S). Virus dispersal was constrained by ecological suitability using ecological niche projections for Lassa virus in 2050 according to scenarios RCP 6.0 (a) and RCP 8.5 (b). 95% highest posterior density (HPD) polygons are coloured according to time and based on 1000 simulations starting from the same ancestral location. For each set of simulations, a zoom on the outcome is shown. For the illustration, five distinct phylogeographic simulations per scenario are also displayed in Fig. S14.