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, 10 (1), 5512

Epidemic Dynamics of Respiratory Syncytial Virus in Current and Future Climates


Epidemic Dynamics of Respiratory Syncytial Virus in Current and Future Climates

Rachel E Baker et al. Nat Commun.


A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.

Conflict of interest statement

The authors declare no competing interests.


Fig. 1
Fig. 1
Broad-scale patterns of RSV are correlated with local climate. a Incidence time series for each county (USA) and state (Mexico) in the dataset. b Timing of onset (color) and dynamic pattern (shape) of each location in the dataset. c Example incidence time series for four location exhibiting distinct dynamic patterns. d Correlation between timing of epidemic onset and mean climate conditions (averages over temporal range of data). e Spatial correlation between RSV time series.
Fig. 2
Fig. 2
Specific humidity and precipitation drive RSV transmission. a Model results for the predicted effect of specific humidity and precipitation on transmission. b Normalized monthly RSV cases before peak incidence (gray) and after peak incidence (yellow) against mean monthly humidity for four locations in the dataset, month-of-year shown in point. c Mean incidence averaged biennially for the same four locations demonstrating distinct dynamic patterns. d Seasonal trajectory in terms of humidity and precipitation for same locations in 2010 and 2100, month-of-year shown in point. e Boxplot showing seasonal change in transmission grouped by dynamic pattern.
Fig. 3
Fig. 3
Projections of RSV dynamics under different climate scenarios. a Simulated effect of log mean transmission and log seasonal transmission change (annual max–min transmission values) on dynamic pattern, holding births and initial population constant, with trajectories of current and future climate shown with arrows on the right plot. b Simulated epidemic dynamics for three states in Mexico using precipitation projections from all climate models (gray lines). Ninetieth percentile (red), 50th percentile (orange), and 10th percentile (blue) in terms of the amplitude of seasonal transmission are highlighted. c Map of the uncertainty in the projected 2100–2010 difference to log seasonal transmission change where outer circles is the upper 90th percentile and inset blue squares are the 10th percentile projections.
Fig. 4
Fig. 4
Comparison with influenza results. Removing precipitation from our regression model and including a quadratic humidity term reveals a very similar response (a) to earlier work on influenza (b adapted from Tamerius et al., Fig 3a) suggesting potential similar mechanisms underlie the climate effect on the two diseases. Predicted minimum transmission for RSV occurs at 11.16 g/kg, with influenza found to be similarly 11–12 g/kg. Tamerius et al. suggest precipitation may drive the right hand side of the humidity-influenza curve, as we find for RSV.

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    1. Metcalf C. Jessica E., Walter Katharine S., Wesolowski Amy, Buckee Caroline O., Shevliakova Elena, Tatem Andrew J., Boos William R., Weinberger Daniel M., Pitzer Virginia E. Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead. Proceedings of the Royal Society B: Biological Sciences. 2017;284(1860):20170901. doi: 10.1098/rspb.2017.0901. - DOI - PMC - PubMed
    1. Mordecai EA, et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 2013;16:22–30. doi: 10.1111/ele.12015. - DOI - PubMed
    1. Pascual M, Rodó X, Ellner SP, Colwell R, Bouma MJ. Cholera dynamics and El Nino-southern oscillation. Science. 2000;289:1766–1769. doi: 10.1126/science.289.5485.1766. - DOI - PubMed
    1. Koelle K, Rodó X, Pascual M, Yunus M, Mostafa G. Refractory periods and climate forcing in cholera dynamics. Nature. 2005;436:696. doi: 10.1038/nature03820. - DOI - PubMed
    1. Caminade Cyril, Kovats Sari, Rocklov Joacim, Tompkins Adrian M., Morse Andrew P., Colón-González Felipe J., Stenlund Hans, Martens Pim, Lloyd Simon J. Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences. 2014;111(9):3286–3291. doi: 10.1073/pnas.1302089111. - DOI - PMC - PubMed

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