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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Main Authors: Yi Zhang, Cindy Feng, Chunna Ma, Peng Yang, Song Tang, Abby Lau, Wenjie Sun, Quanyi Wang
Format: Article
Language:English
Published: Elsevier 2015-01-01
Series:International Journal of Infectious Diseases
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1201971214016981
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spelling 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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