Increasing intensity of enterovirus outbreaks projected with climate change

Abstract

Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.

Fig. 1. Spatial correlations of enterovirus outbreaks and climate.

A Maps showing the mean timing of cases in terms of week-of-year of polio outbreaks in the USA, EVA71 outbreaks in mainland China, CVA16 outbreaks in mainland China. B Scatter plot of mean annual temperature for US state or Chinese province against mean timing of cases for polio, EVA71, and CVA16. Local polynomial regression line is shown in black with 95% confidence intervals (CVA16, EVA71, n = 31; polio, n = 49). C Normalized average weekly cases of polio in the USA and EVA71 in mainland China and Japan. Locations are ordered by latitude. Supplementary Fig. 1 shows the result for CVA16. D Scatter plot of average temperature range (mean max.–mean. min) and epidemic intensity (see “Methods”) for polio, EVA71, and CVA16. Again, each point indicates a US state or Chinese province.

Fig. 2. The climate drivers of enterovirus transmission.

A Estimated effects of temperature on transmission for polio, EVA71, and CVA16 (n = 30823, 8161, 8810). X-axis depicts the coefficient on temperature and the bar shows the 95% confidence interval on this estimate. For each pathogen, fixed effects (dummy variables) for location, year, and either schooling or week are included additively (y-axis). B Simulation results over varied seasonal temperature ranges and mean transmission rate using the EVA71 estimated temperature and schooling coefficients. The time series of observed mean weekly cases for five provinces is shown on the left-hand side. Points on surface indicate the predicted dynamics within these locations. Similar plots for CVA16 and polio are shown in (C) and (D), respectively. The ratio of the schooling peak to temperature peak for locations with two annual peaks for EVA71 is shown in (E). White region indicates there is only one peak per year.

Fig. 3. Climate change projections.

Percentage change in mean epidemic peak size and maximum epidemic peak size for EVA71 (left) and CVA16 (right) under climate change scenario SSP585 in 2080–2100. Climate models are shown on the x-axis and the Chinese province is shown on the y-axis, ordered by latitude.

Fig. 4. Characterizing uncertainty.

Donut plot shows the relative contribution of climate model, temperature coefficient, and inter-annual temperature variability in determining the size of simulated outbreaks of EVA71 or CVA16 under climate scenario SSP585. Uncertainty is analysed using model projections for Beijing.