Premium
El análisis espacio‐temporal de pacientes hospitalizados con dengue en áreas rurales de Tailandia revela importantes intervalos temporales en el patrón de transmisión del virus del dengue
Author(s) -
Aldstadt Jared,
Yoon InKyu,
Tannitisupawong Darunee,
Jarman Richard G.,
Thomas Stephen J.,
Gibbons Robert V.,
Uppapong Angkana,
Iamsirithaworn Sopon,
Rothman Alan L.,
Scott Thomas W.,
Endy Timothy
Publication year - 2012
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/j.1365-3156.2012.03040.x
Subject(s) - dengue fever , dengue virus , transmission (telecommunications) , outbreak , medicine , cluster analysis , confidence interval , virus , virology , statistics , mathematics , computer science , telecommunications
Objective To determine the temporal intervals at which spatial clustering of dengue hospitalisations occurs. Methods Space‐time analysis of 262 people hospitalised and serologically confirmed with dengue virus infections in Kamphaeng Phet, Thailand was performed. The cases were observed between 1 January 2009 and 6 May 2011. Spatial coordinates of each patient’s home were captured using the Global Positioning System. A novel method based on the Knox test was used to determine the temporal intervals between cases at which spatial clustering occurred. These intervals are indicative of the length of time between successive illnesses in the chain of dengue virus transmission. Results The strongest spatial clustering occurred at the 15–17‐day interval. There was also significant spatial clustering over short intervals (2–5 days). The highest excess risk was observed within 200 m of a previous hospitalised case and significantly elevated risk persisted within this distance for 32–34 days. Conclusions Fifteen to seventeen days are the most likely serial interval between successive dengue illnesses. This novel method relies only on passively detected, hospitalised case data with household locations and provides a useful tool for understanding region‐specific and outbreak‐specific dengue virus transmission dynamics.