Abstract
Spatial distribution of bacillary dysentery incidence was mapped at the district level in Wuhan, China. And a generalized additive time series model was used to examine the effect of daily weather factors on bacillary dysentery in the high-risk areas, after controlling for potential confounding factors. Central districts were found to be the high-risk areas. The time series analysis found an acute effect of meteorological factors on bacillary dysentery occurrence. A positive association was found for mean temperature (excess risk (ER) for 1°C increase being 0.94% (95% confidence interval (CI): 0.46% to 1.43% on the lag day 2), while a negative effect was observed for relative humidity and rainfall, the ER for 1% increase in relative humidity was −0.21% (95% CI: −0.34% to −0.08%), and the ER for 1 mm increase in rainfall was −0.23% (95% CI: −0.37% to −0.09%). This study suggests that bacillary dysentery prevention and control strategy should consider local weather variations.
Highlights
Spatial distribution of bacillary dysentery incidence was mapped at the district level in Wuhan, China
A positive association was found for mean temperature (excess risk (ER) for 16C increase being 0.94% (95% confidence interval (CI): 0.46% to 1.43% on the lag day 2), while a negative effect was observed for relative humidity and rainfall, the ER for 1% increase in relative humidity was 20.21%, and the ER for 1 mm increase in rainfall was 20.23%
We did the time series analysis in the low-risk areas, and found a similar result as that in high-risk areas (Figure s1), briefly, we found a positive effect of temperature on the current day (ER 5 0.68%, 95% confidence intervals (95% CI): 0.09% to 1.27%), and a negative effect of relative humidity on lag[0] (ER 5 20.15%, 95% CI: 20.28% to 20.02%)
Summary
Spatial distribution of bacillary dysentery incidence was mapped at the district level in Wuhan, China. A large scale study observed a strong association between extreme precipitation www.nature.com/scientificreports and water-borne infectious disease outbreaks with 2-month lag in the United States[11]. These studies mainly used monthly data to detect the association between temperatures and dysentery transmission, more finer data, such as daily or weekly data, could make the estimation more accurate. The present study, using existing surveillance data, described the spatial distribution of bacillary dysentery incidence at the district level in Wuhan, and quantified the association between daily meteorological factors (including daily mean temperature, relative humidity, rainfall, and wind speed) and bacillary dysentery occurrence in high-risk areas of Wuhan
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
