title: Effects of climate change on Salmonella infections.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 1 Department of Biology/Environmental Science, Jackson State University , Jackson, Mississippi.
authors.forename: Luma
authors.initials: L
authors.lastname: Akil
authors.forename: H Anwar
authors.initials: HA
authors.lastname: Ahmad
authors.forename: Remata S
authors.initials: RS
authors.lastname: Reddy
mesh_terms: D000407:Alabama / Q000453:epidemiology; D057231:Climate Change; D004196:Disease Outbreaks; D006801:Humans; D008922:Mississippi / Q000453:epidemiology; D016571:Neural Networks, Computer; D011159:Population Surveillance; D012044:Regression Analysis; D012480:Salmonella Infections / Q000453:epidemiology*; D012621:Seasons; D013714:Tennessee / Q000453:epidemiology