Objective: To understand the epidemiologic characters of influenza in China from 2001 to 2003.
Methods: Data of outpatient visits for influenza-like illness (ILI) each week and outbreaks of influenza were collected through National Influenza Surveillance Network, which includes 11 northern and 12 southern provinces of China. Samples were collected in the outpatients of ILI from 2001 to 2003 and influenza viruses were isolated and identified.
Results: Epidemiological and laboratory surveillance data showed that the annual seasonality of influenza epidemic was clear. The peak of epidemic of influenza in northern areas was in winter season, during December to January. However, there were three peaks distributed to Spring (Apr.-May.), Summer (Jun.-Aug.) and Winter (Dec.-Jan.) seasons in the southern areas. In the peak months, the number of ILI visits per day and per surveillance hospital had increased two-fold in northern and by 37% in southern China. The baseline of percentages for ILI visits, which calculated with 75th percentiles (P75), was 13.68% in the north and 13.08% in southern China. The age distribution of ILI was related to seasonal types of influenza. When the predominated strain of the season was influenza B virus, the ratio of the ILI visits younger than 15 year-old, increased obviously. When the predominated stains became influenza A virus, the ratio of patient visits for ILI aged over 25 year-old increased. Of 63 outbreaks of influenza, 92% of them occurred at primary and middle schools and usually occurred in May (32%). The type of strains usually changed around June.
Conclusion: The quality of national influenza surveillance system is reliable since it was matched between percentages of ILI visits and rates of influenza virus isolation. The different epidemiologic characteristics in north and south of China was noticed. Peak in spring was shown in southern area and which called for more analysis. The change of the types of strains in the outbreaks during April to June in the southern China could provide data for better understanding on the trend of epidemics in the next season.