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. 2015 Jan 8;11(1):e1004591.
doi: 10.1371/journal.ppat.1004591. eCollection 2015 Jan.

Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States

Virginia E Pitzer  1 , Cécile Viboud  2 , Wladimir J Alonso  2 , Tanya Wilcox  2 , C Jessica Metcalf  3 , Claudia A Steiner  4 , Amber K Haynes  5 , Bryan T Grenfell  6
Affiliations

Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States

Virginia E Pitzer et al. PLoS Pathog. .

Abstract

Epidemics of respiratory syncytial virus (RSV) are known to occur in wintertime in temperate countries including the United States, but there is a limited understanding of the importance of climatic drivers in determining the seasonality of RSV. In the United States, RSV activity is highly spatially structured, with seasonal peaks beginning in Florida in November through December and ending in the upper Midwest in February-March, and prolonged disease activity in the southeastern US. Using data on both age-specific hospitalizations and laboratory reports of RSV in the US, and employing a combination of statistical and mechanistic epidemic modeling, we examined the association between environmental variables and state-specific measures of RSV seasonality. Temperature, vapor pressure, precipitation, and potential evapotranspiration (PET) were significantly associated with the timing of RSV activity across states in univariate exploratory analyses. The amplitude and timing of seasonality in the transmission rate was significantly correlated with seasonal fluctuations in PET, and negatively correlated with mean vapor pressure, minimum temperature, and precipitation. States with low mean vapor pressure and the largest seasonal variation in PET tended to experience biennial patterns of RSV activity, with alternating years of "early-big" and "late-small" epidemics. Our model for the transmission dynamics of RSV was able to replicate these biennial transitions at higher amplitudes of seasonality in the transmission rate. This successfully connects environmental drivers to the epidemic dynamics of RSV; however, it does not fully explain why RSV activity begins in Florida, one of the warmest states, when RSV is a winter-seasonal pathogen. Understanding and predicting the seasonality of RSV is essential in determining the optimal timing of immunoprophylaxis.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Patterns of RSV activity across the United States for hospitalization and laboratory testing data.
(A) Time series of weekly RSV hospitalizations in select states. Raw hospitalization data is shown in blue, while the rescaled data accounting for the addition of an RSV-specific ICD-9 code in September 1996 is shown in green. (B) Age distribution of RSV hospitalizations across ten states. (C) Center of gravity of RSV activity in states with at least ten consecutive years of laboratory reports. (D) Strength of biennial cycle in RSV activity, as indicated by the ratio of the biennial to annual Fourier amplitude for laboratory report data.
Figure 2
Figure 2. Transmission dynamic model for RSV and fit to age-specific hospitalization data.
(A) Compartmental diagram illustrating the structure of the model. White boxes represent infection states in the model, while grey boxes represent diseased/observed states (severe lower respiratory disease, D, and observed cases, H). (B) Model fit to weekly RSV hospitalization data for California and Florida. The ICD9-CM coded hospitalization data is shown in blue, the rescaled data is shown in green, and the fitted models are shown in red. (C) Age distribution of RSV hospitalizations in California and Florida for hospitalization data and fitted models.
Figure 3
Figure 3. Relationship between estimated seasonality parameters for model fit to laboratory report data and select climatic factors.
The estimated amplitude of seasonal forcing in RSV transmission (top) and the estimated seasonal offset parameter (bottom: φ = 0 represents January 1 and φ = −0.2 represents October 19) is plotted against (A) annual mean vapor pressure (hecta-Pascals), (B) annual mean minimum temperature (°C), (C) annual mean precipitation (mm/month), and (D) amplitude (relative to the annual mean) and timing of trough in potential evapotranspiration (PET; 0 = January 1, 0.1 = February 6). The colorbar on the right indicates the ratio of the biennial to annual Fourier amplitude for the observed data (outer circle) and fitted model (inner diamond). Select states are labeled: Arizona (AZ), Florida (FL), Georgia (GA), Hawaii (HI), Louisiana (LA), Montana (MT), New York (NY), South Dakota (SD), Texas (TX), Wyoming (WY).
Figure 4
Figure 4. Monthly patterns of RSV activity and potential evapotranspiration.
(A) The mean number of RSV hospitalizations per 100,000 total population per month for select states, beginning in July. (B) The monthly mean potential evapotranspiration (mm/day) is plotted for each state.
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