New study in PNAS on the dynamics and drivers of the 2024-2025 chikungunya virus epidemic on Réunion Island

Published on September 16, 2026, by Simon Dellicour

Our new study on the dynamics and drivers of the 2024-2025 chikungunya virus epidemic on Réunion Island has been published in PNAS. Réunion Island experienced a massive chikungunya virus epidemic in 2024-2025, with >54,000 confirmed cases. This is the second major chikungunya epidemic on the island, following the first one that peaked 20 years ago. It has been asserted that this new outbreak finds its origin in a single introduction event into the island, offering an opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. We sequenced >3,000 viral genomes collected during the epidemic. Harnessing this genomic dataset, we used several phylogeographic and phylodynamic approaches to unravel the paths taken by the transmission chain and the external factors that might have impacted its dispersal and epidemiological dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic with viral transition events being more frequent from and toward more populated areas. Our analyses also reveal that the transmission chain was overall spatially intermixed, with frequent exchanges among residential areas. In addition, we show that the temporal dynamic and intensity of the epidemic were associated with climatic variables, namely temperature and precipitation. Our results also show that, in theory, the population immunity – resulting from this epidemic and the previous one (2005-2006) – could be sufficient to explain on its own the decrease in the transmission rate that led to the end of the epidemic. While a short-term resurgence cannot be excluded, the risk of a large-scale circulation of the virus in the human population appears therefore relatively limited in the upcoming seasons. Read the whole study here.

Figure 1. Sampling map and phylodynamic analyses of the 2024-2025 chikungunya virus (CHIKV) epidemic on Réunion Island. A: topographic map of the island displaying the spatio-temporal distribution of the CHIKV genomic samples collected and sequenced during the epidemic (dots coloured according to sampling date). On this map, red lines and grey areas correspond to the main roads and residential areas, respectively. B: distribution of the number of sampled CHIKV genomes on a weekly basis (histogram coloured according to time), as well as the evolution of monthly average of the daily mean temperature (red curve) and monthly cumulative precipitation (blue curve) during the course of the epidemic. C: dynamics through time of the overall effective viral population size (Ne) as estimated with a phylodynamic analysis based on sampling-aware skygrid analysis; the grey curve and surrounding ribbon coloured according to time correspond to the posterior median estimate and associated 95% highest posterior density (HPD) intervals, respectively. D: dynamics through time of the effective reproduction number (R_t) as estimated with a phylodynamic analysis based on an episodic birth-death-sampling model; with vertical boxes coloured according to time corresponding to weekly 95% HPD intervals, and the grey thick lines to weekly posterior median estimates (see the text for further detail on both the sampling-aware skygrid and episodic birth-death-sampling analyses). See also below the GitHub repository associated with our study for an animation of the dispersal history of viral lineages during the epidemic.

Figure 2. Analyses of the associations between climatic covariates and the epidemiological dynamics of the 2024-2025 chikungunya virus (CHIKV) epidemic on Réunion Island. Specifically, we tested the associations between two climatic variables – monthly average of daily mean temperatures or monthly cumulative precipitation – and both monthly viral effective population size (Ne) and the effective reproduction number (R_t). To this end, we used generalised linear model (GLM) extensions of the sampling-aware skygrid coalescent model (referred to as the “skygrid-GLM” analyses) and of the episodic birth-death-sampling model (referred to as the “EBDS-GLM” analyses), respectively. We display the relationship between monthly climatic values and monthly Ne (obtained from the joint analysis of genomic sequence and climatic covariate data; A-D) or monthly R_t (E-H); Ne and R_t posterior median estimates being displayed along with their 95% highest posterior density (HPD) interval (which is not always visible when too small). In addition, we also report the posterior median and 95% HPD interval of the GLM coefficient (β) associated with each climatic covariate and analysis. While the first row of graphs reports the results of the analyses conducted without considering a time lag, the second row of graphs reports the analyses based on a one-month lag period between the monthly climatic covariates and Ne or R_t (each monthly Ne and R_t being then associated with the climatic value of the previous month). A statistically supported association between the covariate and Ne or R_t is inferred when the 95% HPD interval of the GLM coefficient excludes zero.

Reference: Frumence E, Klitting R, Serres K, Shao Y, Monti F, Vincent M, Gill MS, Suchard MA, Lemey P, de Lamballerie X, Jaffar-Bandjee MC, Dellicour S (2026). Unraveling the epidemiological and dispersal dynamics of the 2024-2025 chikungunya virus epidemic on Réunion Island. Proceedings of the National Academy of Sciences of the USA 123: e2621019123