Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China

A cartogram map was constructed; spatial autocorrelation analysis and spatial, temporal, and space-time cluster analysis were conducted in Liaoning Province, China over the period 1988-2001.When the number of permutation test was set to 999, Moran's I was 0.3854, and was significant at significance level of 0.001. Spatial cluster analysis identified one most likely cluster and four secondary likely clusters. Temporal cluster analysis identified 1998-2001 as the most likely cluster. Space-time cluster analysis identified one most likely cluster and two secondary likely clusters.Spatial, temporal, and space-time scan statistics may be useful in supervising the occurrence of HFRS in Liaoning Province, China. The result of this study can not only assist health departments to develop a better prevention strategy but also potentially increase the public health intervention's effectiveness.Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantavirus, with characteristics of fever, hemorrhage, kidney damage, and hypotension [1-3]. HFRS is recognized as a notifiable public health problem in China [4]. Currently, HFRS is endemic in 28 of 31 provinces in mainland China [5]. HFRS is severely endemic in mainland China and accounts for 90% of the total cases reported worldwide [6], and remains a significant public health problem with 20 000-50 000 human cases diagnosed annually [7]. Liaoning Province is one of the most seriously affected areas with the most cases in China.The occurrence of HFRS is regular in space, time and space-time. It is important to study the cluster patterns of HFRS to establish the risk factors behind the spread of HFRS, and to prevent and control HFRS better. Obviously, It is necessary to conduct scan statistics, which are an elegant way to solve problems of multiple testing when there are closely overlapping spatial areas and/or time intervals being evaluated. Scan statistics are now commonly used for disease cluster detect