Space time analysis of dengue fever diagnosed through a network of laboratories in India from 2014-2017

Background & objectives: The Department of Health Research and the Indian Council of Medical Research, Government of India, have established Virus Research and Diagnostic Laboratory Network (VRDLN) to strengthen the laboratory capacity in the country for providing timely diagnosis of disease out...

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Veröffentlicht in:Journal of vector borne diseases 2020-07, Vol.57 (3), p.221-225
Hauptverfasser: Joshua, Vasna, Kanagasabai, K, Sabarinathan, R, Ravi, M, Kirubakaran, B, Ramachandran, V, Shete, Vishal, Gowri, A, Murhekar, Manoj
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Sprache:eng
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Zusammenfassung:Background & objectives: The Department of Health Research and the Indian Council of Medical Research, Government of India, have established Virus Research and Diagnostic Laboratory Network (VRDLN) to strengthen the laboratory capacity in the country for providing timely diagnosis of disease outbreaks. Fifty-one VRDLs were functional as on December 2017 and had reported about dengue fever across Indian states. The objectives of the study were to detect space time clusters and purely temporal clusters of dengue using Kulldorff's SaTScan statistics using patient level information; and to identify regions at greater risk of developing the disease using Kriging technique aggregating at district level. Methods: A total of 211,432 patients from 51 VRDLs were investigated for IgM antibodies or NS1 antigen against dengue virus during the period from 1 January 2014 to 31 December 2017 and among them 60,096 (28.4%) were found to be positive. Kulldorff's space time analysis was used to identify significant clusters over space and time. Kriging technique was used to interpolate dengue data for areas not physically sampled using the relationship in the spatial arrangement of the data set. Maps obtained using both the methods were overlaid to identify the regions at greater risk of developing the disease. Results: Kulldorff Space time Scan Statistics using the Bernoulli model with monthly precision revealed eight statistically significant clusters (P
ISSN:0972-9062
DOI:10.4103/0972-9062.311774