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.