수두 유행을 평가하기 위한 신규 지표 개발
Developing a new index to assess varicella outbreak
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Background: Varicella is the most common infectious disease reported despite the high vaccination rate. Interventions that target humans are particularly effective for varicella because humans are its only natural host. On the other hand, the existing national varicella surveillance systems lack the information to identify an outbreak. Therefore, a new index to assess varicella outbreaks was developed. Methods: The residential addresses of 2,718 varicella cases reported in Daegu in 2016 were converted to geographic coordinates and the distances between new varicella case and previous cases within 21 days were calculated from the date analyzed. Two cases were considered to be adjacent if the distance between them was less than 1 km. Finally, a proximity index was introduced by dividing the number of adjacent cases by the number of new cases on the date analyzed. Results: First, time-series charts and scatter plots were used to verify that the proximity index reflected the spatial closeness of the different varicella cases. The proximity index is helpful in identifying outbreaks from a list of single varicella cases. In addition, in this study, a new epidemic characteristic of varicella based on the proximity index was shown. Conclusion: The proximity index introduced in this study can be used to determine the likelihood of an outbreak from a single case of varicella, and it can be embedded in a web-based national varicella surveillance system that is currently in operation
Abstract
Background: Varicella is the most common infectious disease reported despite the high vaccination rate. Interventions that target humans are particularly effective for varicella because humans are its only natural host. On the other hand, the existing national varicella surveillance systems lack the information to identify an outbreak. Therefore, a new index to assess varicella outbreaks was developed. Methods: The residential addresses of 2,718 varicella cases reported in Daegu in 2016 were converted to geographic coordinates and the distances between new varicella case and previous cases within 21 days were calculated from the date analyzed. Two cases were considered to be adjacent if the distance between them was less than 1 km. Finally, a proximity index was introduced by dividing the number of adjacent cases by the number of new cases on the date analyzed. Results: First, time-series charts and scatter plots were used to verify that the proximity index reflected the spatial closeness of the different varicella cases. The proximity index is helpful in identifying outbreaks from a list of single varicella cases. In addition, in this study, a new epidemic characteristic of varicella based on the proximity index was shown. Conclusion: The proximity index introduced in this study can be used to determine the likelihood of an outbreak from a single case of varicella, and it can be embedded in a web-based national varicella surveillance system that is currently in operation
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