BMC infectious diseases · 2019
Time series analysis of mumps and meteorological factors in Beijing, China.
СтатьяНазвание и аннотация: Перевод в очереди
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Hao Yu, Wang Ran-Ran, Han Ling, Wang Hong, Zhang Xuan, Tang Qiao-Ling, Yan Long, He Juan
Аннотация
BACKGROUND: Over the past decades there have been outbreaks of mumps in many countries, even in populations that were vaccinated. Some studies suggest that the incidence of mumps is related to meteorological changes, but the results of these studies vary in different regions. To date there is no reported study on correlations between mumps incidence and meteorological parameters in Beijing, China.
METHODS: A time series analysis incorporating selected weather factors and the number of mumps cases from 1990 to 2012 in Beijing was performed. First, correlations between meteorological variables and the number of mumps cases were assessed. A seasonal autoregressive integrated moving average model with explanatory variables (SARIMAX) was then constructed to predict mumps cases.
RESULTS: Mean temperature, rainfall, relative humidity, vapor pressure, and wind speed were significantly associated with mumps incidence. After constructing the SARIMAX model, mean temperature at lag 0 (β = 0.016, p < 0.05, 95% confidence interval 0.001 to 0.032) was positively associated with mumps incidence, while vapor pressure at lag 2 (β = -0.018, p < 0.05, 95% confidence interval -0.038 to -0.002) was negatively associated. SARIMAX (1, 1, 1) (0, 1, 1)12 with temperature at lag 0 was the best predictive construct.
CONCLUSIONS: The incidence of mumps in Beijing from 1990 to 2012 was significantly correlated with meteorological variables. Combining meteorological variables, a predictive SARIMAX model that could be used to preemptively estimate the incidence of mumps in Beijing was established.
METHODS: A time series analysis incorporating selected weather factors and the number of mumps cases from 1990 to 2012 in Beijing was performed. First, correlations between meteorological variables and the number of mumps cases were assessed. A seasonal autoregressive integrated moving average model with explanatory variables (SARIMAX) was then constructed to predict mumps cases.
RESULTS: Mean temperature, rainfall, relative humidity, vapor pressure, and wind speed were significantly associated with mumps incidence. After constructing the SARIMAX model, mean temperature at lag 0 (β = 0.016, p < 0.05, 95% confidence interval 0.001 to 0.032) was positively associated with mumps incidence, while vapor pressure at lag 2 (β = -0.018, p < 0.05, 95% confidence interval -0.038 to -0.002) was negatively associated. SARIMAX (1, 1, 1) (0, 1, 1)12 with temperature at lag 0 was the best predictive construct.
CONCLUSIONS: The incidence of mumps in Beijing from 1990 to 2012 was significantly correlated with meteorological variables. Combining meteorological variables, a predictive SARIMAX model that could be used to preemptively estimate the incidence of mumps in Beijing was established.