Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
3475120 | Journal of Acute Disease | 2015 | 5 Pages |
Abstract
ObjectiveTo explore socio-demographic data of the population as proxies for risk factors in disease transmission modeling at different geographic scales.MethodsPatient records of confirmed H1N1 influenza were analyzed at three geographic aggregation levels together with population census statistics.ResultsThe study confirmed that four population factors were related in different degrees to disease incidence, but the results varied according to spatial resolution. The degree of association actually decreased when data of a higher spatial resolution were used.ConclusionsWe concluded that variables at suitable spatial resolution may be useful in improving the predictive powers of models for disease outbreaks.
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Authors
Poh-Chin Lai, Chun Bong Chow, Ho Ting Wong, Kim Hung Kwong, Shao Haei Liu, Wah Kun Tong, Wai Keung Cheung, Wing Leung Wong, Yat Wah Kwan,