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Respiratory syncytial virus infection trend is associated with meteorological factors

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AbstractRespiratory syncytial virus (RSV) infects young children and causes influenza-like illness. RSV circulation and prevalence differ among countries and climates. To better understand whether climate factors influence the seasonality of RSV in Thailand, we examined RSV data from children ≤ 5 years-old who presented with respiratory symptoms from January 2012–December 2018. From a total of 8,209 nasopharyngeal samples, 13.2% (1,082/8,209) was RSV-positive, of which 37.5% (406/1,082) were RSV-A and 36.4% (394/1,082) were RSV-B. The annual unimodal RSV activity from July–November overlaps with the rainy season. Association between meteorological data including monthly average temperature, relative humidity, rainfall, and wind speed for central Thailand and the incidence of RSV over 7-years was analyzed using Spearman’s rank and partial correlation. Multivariate time-series analysis with an autoregressive integrated moving average (ARIMA) model showed that RSV activity correlated positively with rainfall (r = 0.41) and relative humidity (r = 0.25), but negatively with mean temperature (r = − 0.27). The best-fitting ARIMA (1,0,0)(2,1,0)12 model suggests that peak RSV activity lags the hottest month of the year by 4 months. Our results enable possible prediction of RSV activity based on the climate and could help to anticipate the yearly upsurge of RSV in this region.
Title: Respiratory syncytial virus infection trend is associated with meteorological factors
Description:
AbstractRespiratory syncytial virus (RSV) infects young children and causes influenza-like illness.
RSV circulation and prevalence differ among countries and climates.
To better understand whether climate factors influence the seasonality of RSV in Thailand, we examined RSV data from children ≤ 5 years-old who presented with respiratory symptoms from January 2012–December 2018.
From a total of 8,209 nasopharyngeal samples, 13.
2% (1,082/8,209) was RSV-positive, of which 37.
5% (406/1,082) were RSV-A and 36.
4% (394/1,082) were RSV-B.
The annual unimodal RSV activity from July–November overlaps with the rainy season.
Association between meteorological data including monthly average temperature, relative humidity, rainfall, and wind speed for central Thailand and the incidence of RSV over 7-years was analyzed using Spearman’s rank and partial correlation.
Multivariate time-series analysis with an autoregressive integrated moving average (ARIMA) model showed that RSV activity correlated positively with rainfall (r = 0.
41) and relative humidity (r = 0.
25), but negatively with mean temperature (r = − 0.
27).
The best-fitting ARIMA (1,0,0)(2,1,0)12 model suggests that peak RSV activity lags the hottest month of the year by 4 months.
Our results enable possible prediction of RSV activity based on the climate and could help to anticipate the yearly upsurge of RSV in this region.

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