To explore the effect of climatic factors on local transmission within counties, we performed multilevel Poisson regression. Climatic data (temperature, relative humidity, and precipitation) during May-December 2009 were obtained from the National Meteorological Bureau of China (18) . Owing to probable time lags, the climatic variables were processed by calculating the average value for the current day and a lag of 1-3 days, which is the observed incubation period of pandemic influenza (19) . Poisson regression deals with the daily number of laboratoryconfirmed cases per county. The inclusion of the population size for each county as an offset makes it an analysis of incidence. To account for possible confounding, we included school summer vacation and public holidays, the proportion of the school-age population (ages 6-19 years), population density, and the density of medical facilities as correction factors in the analysis. The percentage change in incidence in response to the change of the variable by a given amount (10°C for temperature, 10% for relative humidity, 1 mm for precipitation, 10% for school-age population, 1,000 persons per km 2 for population density, and number of facilities per 10,000 persons) was used to reflect the impact of each variable. The 95% confidence intervals and corresponding P values were estimated after correcting for overdispersion because of the nature of infectious diseases with spatial clustering patterns (20, 21) . For temperature, we also included a quadratic term in the analysis.