Based on daily data of influenza-like cases from four sentinel hospitals as well as meteorological data in Xuzhou from October 2005 to May 2011, number of patients with respiratory diseases in Xuzhou was analyzed, and...Based on daily data of influenza-like cases from four sentinel hospitals as well as meteorological data in Xuzhou from October 2005 to May 2011, number of patients with respiratory diseases in Xuzhou was analyzed, and then relationship between meteorological factors and number of patients with respiratory diseases was discussed. The prediction model of number of patients with respiratory diseases in each month was established to forecast number of patients with respiratory diseases using meteorological data. The results show that people suffered from respiratory diseases more frequently in January and from June to September in Xuzhou. Meteorological factors correlated highly with number of patients with re- spiratory diseases are different due to the difference in climatic characteristics among various seasons. The prediction model could obtain good effect.展开更多
基金Supported by the Social Development Project of Xuzhou Science and Technology Bureau(XZZD1160)
文摘Based on daily data of influenza-like cases from four sentinel hospitals as well as meteorological data in Xuzhou from October 2005 to May 2011, number of patients with respiratory diseases in Xuzhou was analyzed, and then relationship between meteorological factors and number of patients with respiratory diseases was discussed. The prediction model of number of patients with respiratory diseases in each month was established to forecast number of patients with respiratory diseases using meteorological data. The results show that people suffered from respiratory diseases more frequently in January and from June to September in Xuzhou. Meteorological factors correlated highly with number of patients with re- spiratory diseases are different due to the difference in climatic characteristics among various seasons. The prediction model could obtain good effect.
