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. 2014 Dec;11(12):974-80.
doi: 10.1089/fpd.2014.1802.

Effects of climate change on Salmonella infections

Luma Akil  1 , H Anwar Ahmad, Remata S Reddy
Affiliations

Effects of climate change on Salmonella infections

Luma Akil et al. Foodborne Pathog Dis. 2014 Dec.

Abstract

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 (R(2)=0.554; R(2)=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.

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Figures

<b>FIG. 1.</b>
FIG. 1.
Total monthly Salmonella cases in Mississippi from 2002 to 2011. Highest rates of Salmonella were observed during the summer.
<b>FIG. 2.</b>
FIG. 2.
Time series analysis for Salmonella outbreaks from January 2002 to December 2011.
<b>FIG. 3.</b>
FIG. 3.
Mississippi regression analysis between temperature and Salmonella. y=3.5252x – 137.65; R2=0.554.
<b>FIG. 4.</b>
FIG. 4.
Regression analysis between temperature and Salmonella in Mississippi, Tennessee, and Alabama. y=2.2769x – 62.171; R2=0.4154.
<b>FIG. 5.</b>
FIG. 5.
General Regression NN and Polynomial Net (Group Method of Data Handling, Polynomial Net) Models to forecast the Salmonella outbreak from January 2002 through December 2011.
See this image and copyright information in PMC

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