Abstract
Influenza seasonality study is critical for policy-makers to choose an optimal time for influenza vaccination campaign, especially for subtropical regions where influenza seasonality and periodicity are unclear. In this study, we explored the seasonality and periodicity of influenza in Hefei, China during 2010 to 2015 using five proxies originated from three data sources of clinical surveillance of influenza-like illness (ILI), laboratory surveillance of influenza and death registration of pneumonia and influenza. We combined both wavelets analysis and de-linear-trend regression with Fourier harmonic terms to estimate seasonal characteristics of epidemic phase, peak time, amplitude, ratio of dominant seasonality. We found both annual cycle of influenza epidemics peaking in December-February and semi-annual cycle peaking in December-February and June-July in subtropical city Hefei, China. Compared to proxies developed by ILI and death registration data separately, influenza proxies incorporated laboratory surveillance data performed better seasonality and periodicity, especially in semi-annual periodicity in Hefei. Proxy of ILI consultation rate showed more timeliness peak than other proxies, and could be useful in developing the early warning model for influenza epidemics. Our study suggests to integrate clinical and laboratory surveillance of influenza for future influenza seasonality studies in subtropical regions.
Highlights
In response to the growing recognition that more needs to be done in preventing, monitoring and controlling influenza worldwide, influenza Surveillance has been identified to be extremely important by the World Health Organization (WHO) Global Agenda on Influenza Surveillance and Control[5]
The heat-map and wavelet analysis indicated proxies incorporating laboratory surveillance data performed better to study influenza seasonality than other proxies based on clinical surveillance only or death registry only
The inferiority of clinical surveillance could be explained by the non-specific symptoms of influenza
Summary
The wavelets plots showed that virus positive rate indicators (flu%, annflu% and ILI × flu%) depicted semi-annual periodicity more significant than the other proxies (Fig. 3). The virus positive rate indicators (flu%, annflu% and ILI × flu%) showed a statistically significant semi-annual periodicity pattern (P < 0.05) at the years around 2013 to 2015, while not statistically significant for other years (P ≥ 0.05). ILI rate may perform better than other influenza proxies in developing an early warning system for influenza epidemics
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