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Effects of Climate Change on Salmonella Infections

机译:气候变化对沙门氏菌感染的影响

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摘要

>Background: Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used.>Methods: Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN.>Results: A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R2=0.554; R2=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed.>Conclusion: There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections.
机译:>背景:据报道,气候变化和全球变暖加剧了食源性病原体的传播。为了了解这些对沙门氏菌感染的影响,使用了诸如回归分析和神经网络(NN)等建模方法。>方法:密西西比州(MS),田纳西州(TN)和阿拉巴马州沙门氏菌暴发的每月数据使用方差分析和时间序列分析对2002年至2011年(AL)进行了分析。收集气象数据,并使用回归分析和NN检验与沙门氏菌的相关性。>结果:观察到沙门氏菌感染呈季节性趋势(p <0.001)。在MS和组合状态(MS,TN,AL)模型(R 2 = 0.554; R 2 = 0.415)的MS中,高温和沙门氏菌感染之间存在强正相关。 , 分别)。 NN模型显示出温度升高对沙门氏菌爆发的强烈影响。在这项研究中,升高1°F会导致MS中沙门氏菌增加4例。但是,没有观察到月平均降水率与沙门氏菌感染之间的相关性。>结论:一致的证据表明,细菌性病原体的胃肠道感染与环境温度呈正相关,因为温度升高可使复制更快。美国,特别是南部各州的变暖趋势可能会增加沙门氏菌感染率。

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