Objective To reveal the epidemiological characteristics and incidence laws of varicella in Qinzhou City and explore the correlations between meteorological factors and varicella incidence risk and the lag effects. Methods Number of varicella cases in Qinzhou City from January 1st, 2018, to December 31st, 2023, were collected from the Infectious Disease Reporting Information Management System in the China Disease Control and Prevention Information System; daily meteorological data (temperature, air pressure, average wind speed, precipitation, relative humidity, sunshine duration) during the same period were obtained from the National Meteorological Science Data Center; based on the aforementioned data, a time-series database was established. Spearman rank correlation analysis was used to test the correlations between number of varicella cases and meteorological factors, as well as among meteorological factors, followed by principal component factor analysis to identify latent climatic patterns influencing disease dynamics. Time-series analysis and a Poisson regression-fit generalized additive model were applied to explore the correlations and lag effects between meteorological factors and varicella incidence risk. Results From 2018 to 2023, varicella in Qinzhou City exhibited a bimodal epidemic pattern, with the highest peak from November to January of the following year and a secondary peak from April to June. Correlation analysis between number of daily varicella cases and meteorological factors showed that number of daily varicella cases was negatively correlated with average temperature, average precipitation, and average humidity, and positively correlated with average air pressure and average wind speed (all P<0.01), but no statistical correlation was found between number of daily varicella cases and average sunshine duration (P>0.05). Correlation analysis among the meteorological factors showed that there was no statistically significant correlation between average precipitation and average sunshine duration/average wind speed, or between average sunshine duration and average wind speed (all P>0.05), while correlations between the rest meteorological factors were statistically significant (all P<0.05). Principal component analysis and factor analysis yielded two principal components, with a cumulative contribution rate of 70.16%. Based on the comprehensive results of principal component calculation formula and multiple linear regression equation, the influence of the 6 meteorological factors on number of daily varicella cases ranked from largest to smallest as daily average air pressure, daily average temperature, daily average humidity, daily average precipitation, daily average wind speed, and daily average sunshine duration. All meteorological factors showed certain lag effects on number of daily varicella cases. The maximum impacts occurred after lagging 15 days for daily average air pressure (RR=1.77), 5 days for daily average sunshine duration (RR=1.73), 11 days for both daily average precipitation (RR=0.73) and daily average humidity (RR=0.83), 21 days for daily average temperature (RR=1.27), and 17 days for daily average wind speed (RR=2.69). Exposure-response analysis indicated: (1) The daily average temperature showed an inverted S-shape correlation with the daily varicella incidence risk, more specifically, positive correlation (RR>1) when the daily average temperature was at 20-26 ℃, and negative correlation (RR<1) when it was at 14-18 ℃ or >27 ℃. (2) The daily average air pressure combined an inverted L- and V- shape correlation with daily varicella incidence risk: positive correlation was observed when the daily average air pressure was within 990-1,007 hPa or exceeded 1,039 hPa (RR>1); modest impact when it was within 990-1,007 hPa and increasing daily varicella incidence risk with higher air pressure when it was at >1,039 hPa; negative correlation was observed when it was within 1,016-1,032 hPa (RR<1). (3) Daily average precipitation showed positive correlation with daily varicella incidence risk when it was at <1 mm or within 19-23 mm (RR>1), and negative correlation when it was at 2-7 mm (RR<1). Interaction analysis results showed that higher temperatures and air pressures increased number of varicella cases, particularly when the temperature was at 20-26 ℃; the most varicella cases occurred when the temperature was at 20-26 ℃ and there was 0 mm precipitation; at higher air pressures, number of daily varicella cases increased with decreasing precipitation. Conclusion Monitoring temperature and air pressure changes enable early warning about varicella outbreaks, helping to implement preventive measures in advance, reduce epidemic spread, and mitigate the peak of varicella epidemics.  

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