The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China
Objectives: 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. Methods: We applied a zero truncated Poisson regress...
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doaj-9de4b2c23ce741d2b2736658104c6c262020-11-24T22:43:34ZengElsevierInternational Journal of Infectious Diseases1201-97121878-35112015-01-0130C12212410.1016/j.ijid.2014.11.010The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from ChinaYi Zhang0Cindy Feng1Chunna Ma2Peng Yang3Song Tang4Abby Lau5Wenjie Sun6Quanyi Wang7Beijing Centre for Disease Prevention and Control (CDC), No. 16 He Pingli Middle St, Dongcheng District, Beijing 100013, ChinaSchool of Public Health and Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, CanadaBeijing Centre for Disease Prevention and Control (CDC), No. 16 He Pingli Middle St, Dongcheng District, Beijing 100013, ChinaBeijing Centre for Disease Prevention and Control (CDC), No. 16 He Pingli Middle St, Dongcheng District, Beijing 100013, ChinaSchool of Environment and Sustainability, University of Saskatchewan, Saskatoon, CanadaTulane Infectious Disease Department, Tulane University, New Orleans, Louisiana, USASchool of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA 70112, USABeijing Centre for Disease Prevention and Control (CDC), No. 16 He Pingli Middle St, Dongcheng District, Beijing 100013, ChinaObjectives: 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. Methods: 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. Results: 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. Conclusions: 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.http://www.sciencedirect.com/science/article/pii/S1201971214016981Influenza A H7N9TemperatureHumidityZero truncationPenalized spline function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Zhang Cindy Feng Chunna Ma Peng Yang Song Tang Abby Lau Wenjie Sun Quanyi Wang |
spellingShingle |
Yi Zhang Cindy Feng Chunna Ma Peng Yang Song Tang Abby Lau Wenjie Sun Quanyi Wang The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China International Journal of Infectious Diseases Influenza A H7N9 Temperature Humidity Zero truncation Penalized spline function |
author_facet |
Yi Zhang Cindy Feng Chunna Ma Peng Yang Song Tang Abby Lau Wenjie Sun Quanyi Wang |
author_sort |
Yi Zhang |
title |
The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China |
title_short |
The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China |
title_full |
The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China |
title_fullStr |
The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China |
title_full_unstemmed |
The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China |
title_sort |
impact of temperature and humidity measures on influenza a (h7n9) outbreaks—evidence from china |
publisher |
Elsevier |
series |
International Journal of Infectious Diseases |
issn |
1201-9712 1878-3511 |
publishDate |
2015-01-01 |
description |
Objectives: 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.
Methods: 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.
Results: 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.
Conclusions: 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. |
topic |
Influenza A H7N9 Temperature Humidity Zero truncation Penalized spline function |
url |
http://www.sciencedirect.com/science/article/pii/S1201971214016981 |
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