Association between meteorological factors, spatiotemporal effects, and prevalence of influenza A subtype H7 in environmental samples in Zhejiang province, China

BACKGROUND

Human infection with the H7N9 virus has been reported recurrently since spring 2013. Given low pathogenicity of the virus in poultry, the outbreak cannot be noticed easily until a case of human infection is reported. Studies showed that the prevalence of influenza A subtype H7 in environmental samples is associated with the number of human H7N9 infection, with the latter associated with meteorological factors. Understanding the association between meteorological factors and the prevalence of H7 subtype in the environmental samples can shed light on how the virus propagates in the environment for disease control.

METHOD

Environmental samples and meteorological data (precipitation, temperature, relative humidity, sunshine duration, and wind speed) collected in Zhejiang province, China, during 2013-2017 were used. A Bayesian hierarchical binomial logistic spatiotemporal model which captures spatiotemporal effects was adopted to model the prevalence of H7 subtype with the meteorological factors.

RESULTS

The monthly overall prevalence of H7 subtype in the environmental samples was usually <30%. Compared with the odds at median, moderately low precipitation (49.19-115.60 mm), moderately long sunshine duration (4.22-9.25 h) and low temperature (<9.33 °C) were statistically significantly associated with a higher adjusted odds of detecting an H7-positive sample, whereas moderately high precipitation (119.51-146.85 mm), short and moderately short sunshine duration (<1.77 h; 4.00-4.17 h), and high temperature (>23.09 °C) were statistically significantly associated with a lower adjusted odds. The adjusted odds increased multiplicatively by 1.11 per 1% increase in relative humidity.

CONCLUSION

Since the prevalence of H7 subtype in environmental samples was associated with meteorological conditions and the number of human H7N9 infection, an environmental surveillance program which incorporates meteorological conditions in planning allows for early detection of the spread of the virus in the environment and better preparation for the outbreak in the human population.

Identification of meteorological factors associated with human infection with avian influenza A H7N9 virus in Zhejiang Province, China

BACKGROUND

Since the first reported human infection with an avian-origin influenza A (H7N9) virus in China in early 2013, there have been recurrent outbreaks of the virus in the country. Previous studies have shown that meteorological factors are associated with the risk of human infection with the virus; however, their possible nonlinear and lagged effects were not commonly taken into account.

METHOD

To quantify the effect of meteorological factors on the risk of human H7N9 infection, daily laboratory-confirmed cases of human H7N9 infection and meteorological factors including total rainfall, average wind speed, average temperature, average relative humidity, and sunshine duration of the 11 sub-provincial/prefecture cities in Zhejiang during the first four outbreaks (13 March 2013-30 June 2016) were analyzed. Separate models were built for the 6 sub-provincial/prefecture cities with the greatest number of reported cases using a combination of logistic generalized additive model and distributed lag nonlinear models, which were then pooled by a multivariate meta-regression model to determine their overall effects.

RESULTS

According to the meta-regression model, for rainfall, the log adjusted overall cumulative odds ratio was statistically significant when log of rainfall was >4.0, peaked at 5.3 with a value of 12.42 (95% confidence intervals (CI): [3.23, 21.62]). On the other hand, when wind speed was 2.1-3.0 m/s or 6.3-7.1 m/s, the log adjusted overall cumulative odds ratio was statistically significant, peaked at 7.1 m/s with a value of 6.75 (95% CI: [0.03, 13.47]). There were signs of nonlinearity and lag effects in their associations with the risk of infection.

CONCLUSION

As rainfall and wind speed were found to be associated with the risk of human H7N9 infection, weather conditions should be taken into account when it comes to disease surveillance, allowing prompt actions when an outbreak takes place.