The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China

Abstract

To examine the non-linear effects of meteorological factors on the incidence of influenza A H7N9 and to determine what meteorological measure, and on which day preceding symptom onset, has the most significant effect on H7N9 infection. We applied a zero truncated Poisson regression model incorporating smoothed spline functions to assess the non-linear effect of temperature (maximum, minimum, and daily difference) and relative humidity on H7N9 human case numbers occurring in China from February 19, 2013 to February 18, 2014, adjusting for the effects of age and gender. Both daily minimum and daily maximum temperature contributed significantly to human infection with the influenza A H7N9 virus. Models incorporating the non-linear effect of minimum or maximum temperature on day 13 prior to disease onset were found to have the best predictive ability. For minimum temperature, high risk was found to range from approximately 5 to 9°C and moderate risk from -10 to 0°C; temperatures of >9°C represented a low risk. For maximum temperature, high risk was found to range from approximately 13 to 18°C and moderate risk from 0 to 4°C; temperatures of >18°C represented a low risk. Relative humidity was not significantly associated with the incidence of infection. The incidence of H7N9 was higher for males compared to females (p<0.01) and it peaked at around 60-70 years of age. We provide direct evidence to support the role of climate conditions in the spread of H7N9 and thereby address a critical question fundamental to our understanding of the epidemiology and evolution of H7N9. These findings could be used to inform targeted surveillance and control efforts aimed at reducing the future spread of H7N9.