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DC Field | Value | Language |
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dc.contributor.author | Veerasak Punyapornwithaya | en_US |
dc.contributor.author | Chalutwan Sansamur | en_US |
dc.contributor.author | Arisara Charoenpanyanet | en_US |
dc.date.accessioned | 2022-10-16T08:23:53Z | - |
dc.date.available | 2022-10-16T08:23:53Z | - |
dc.date.issued | 2020-01-01 | en_US |
dc.identifier.issn | 19707096 | en_US |
dc.identifier.issn | 18271987 | en_US |
dc.identifier.other | 2-s2.0-85099837135 | en_US |
dc.identifier.other | 10.4081/GH.2020.857 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099837135&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/77717 | - |
dc.description.abstract | Dengue is the worldwide most important mosquito-borne viral disease in humans. A large dengue outbreak occurred in Chiang Mai, Thailand in 2013. The aims of this study were to describe the epidemiology of this outbreak and determine the spatio-temporal pattern in the sub-district with the highest number of dengue cases. Data on patients, including date of illness, were obtained from the Chiang Mai Provincial Public Health Center and analyzed descriptively using R statistical software. The geographic location of patients’ residences was determined from available geographical information databases supplemented with coordinated data collection in the field. A space-time permutation model from SaTScan™ was used to determine disease clusters corresponding to space and time. Results showed that Muang District, the centre of the province, had a higher number of cases than the other 25 districts. The Suthep subdistrict, part of Muang District, had most of the patients: 625 subjects distributed between 213 res idences. The space-time analysis identified a primary cluster and 7 secondary clusters in different time periods. The primary cluster had 128 patients in a period of approximately 3 months. The number of patients in the secondary clusters ranged between 7 and 65. Most of the clusters occurred in densely populated areas during June and July (the rainy season). The finding from this study may support health agencies to plan surveillance campaigns for people at specified local areas with a high incidence of the disease. | en_US |
dc.subject | Medicine | en_US |
dc.subject | Social Sciences | en_US |
dc.title | Epidemiological characteristics and determination of spatio-temporal clusters during the 2013 dengue outbreak in Chiang Mai, Thailand | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Geospatial Health | en_US |
article.volume | 15 | en_US |
article.stream.affiliations | Walailak University | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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