Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study

Introduction: In India, during the last ten years, the dengue fever increased in incidence and in territorial extent. Several studies already described the dengue fever incidence in terms of time, place and person But in spatio-temporal analysis, space and time will be analysed together to identify...

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Main Authors: M Siva Durga Prasad Nayak, KA Narayan
Format: Article
Language:English
Published: JCDR Research and Publications Private Limited 2020-04-01
Series:Journal of Clinical and Diagnostic Research
Subjects:
Online Access:https://jcdr.net/articles/PDF/13650/43472_CE[Ra1]_F(SHU)_PF1(ShG_KM)_PN(SL)_PFA2(OM)_PF2_(MG_OM).pdf
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spelling doaj-27e8e148b1724cd2b04bfe2c4dae76e02020-11-25T02:08:02ZengJCDR Research and Publications Private LimitedJournal of Clinical and Diagnostic Research2249-782X0973-709X2020-04-01144LC05LC0810.7860/JCDR/2020/43472.13650Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective StudyM Siva Durga Prasad Nayak0KA Narayan1Tutor, Department of Community Medicine, Government Medical College, Ongole, Andhra Pradesh, India.Professor, Department of Community Medicine, Mahatma Gandhi Medical College and Research Institute, Pondicherry, India.Introduction: In India, during the last ten years, the dengue fever increased in incidence and in territorial extent. Several studies already described the dengue fever incidence in terms of time, place and person But in spatio-temporal analysis, space and time will be analysed together to identify the high priority areas and during which time period there is chance for occurrence of outbreaks in these high priority districts. Aim: To identify the districts with high log likelihood ratio for high dengue fever incidence. Materials and Methods: The current study design was a retrospective observational study conducted using the secondary data provided by Director of Public Health of Kerala state on a public domain. All the suspected dengue fever cases reported for a period of six months from 1st May 2017 to 4th November 2017 was used for the study. Weekly incidence rates of dengue fever cases and moving averages of dengue fever cases were calculated using MS Excel software. Retrospective spatio-temporal analysis was done with SaT Scan software using discrete poisson model. Results: Total number of dengue fever cases reported was 52371. The incidence of dengue fever rose from May 1-7 to June 26-July 02 and later showed declining trend. Moving averages graph of actual data shows peaks and troughs with an approximate 7 day pattern and the troughs coincided with public holidays. Spatio-temporal analysis revealed that, ten districts out of fourteen districts were identified having significantly (p<0.005) high relative risk and high likelihood ratio for dengue fever cases. Conclusion: Epidemic of dengue fever cases will start in the month of May, reaches peak in June and July, declines thereafter and reaches to normal by September. Time series analysis revealed the lacunae in surveillance system. Spatio-temporal analysis identified ten high priority districts out of which top four are in south Kerala.https://jcdr.net/articles/PDF/13650/43472_CE[Ra1]_F(SHU)_PF1(ShG_KM)_PN(SL)_PFA2(OM)_PF2_(MG_OM).pdfdisease mappingdiscrete poisson modelrelative risk
collection DOAJ
language English
format Article
sources DOAJ
author M Siva Durga Prasad Nayak
KA Narayan
spellingShingle M Siva Durga Prasad Nayak
KA Narayan
Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
Journal of Clinical and Diagnostic Research
disease mapping
discrete poisson model
relative risk
author_facet M Siva Durga Prasad Nayak
KA Narayan
author_sort M Siva Durga Prasad Nayak
title Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
title_short Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
title_full Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
title_fullStr Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
title_full_unstemmed Spatio-temporal Analysis of Dengue Fever Cases: A Retrospective Study
title_sort spatio-temporal analysis of dengue fever cases: a retrospective study
publisher JCDR Research and Publications Private Limited
series Journal of Clinical and Diagnostic Research
issn 2249-782X
0973-709X
publishDate 2020-04-01
description Introduction: In India, during the last ten years, the dengue fever increased in incidence and in territorial extent. Several studies already described the dengue fever incidence in terms of time, place and person But in spatio-temporal analysis, space and time will be analysed together to identify the high priority areas and during which time period there is chance for occurrence of outbreaks in these high priority districts. Aim: To identify the districts with high log likelihood ratio for high dengue fever incidence. Materials and Methods: The current study design was a retrospective observational study conducted using the secondary data provided by Director of Public Health of Kerala state on a public domain. All the suspected dengue fever cases reported for a period of six months from 1st May 2017 to 4th November 2017 was used for the study. Weekly incidence rates of dengue fever cases and moving averages of dengue fever cases were calculated using MS Excel software. Retrospective spatio-temporal analysis was done with SaT Scan software using discrete poisson model. Results: Total number of dengue fever cases reported was 52371. The incidence of dengue fever rose from May 1-7 to June 26-July 02 and later showed declining trend. Moving averages graph of actual data shows peaks and troughs with an approximate 7 day pattern and the troughs coincided with public holidays. Spatio-temporal analysis revealed that, ten districts out of fourteen districts were identified having significantly (p<0.005) high relative risk and high likelihood ratio for dengue fever cases. Conclusion: Epidemic of dengue fever cases will start in the month of May, reaches peak in June and July, declines thereafter and reaches to normal by September. Time series analysis revealed the lacunae in surveillance system. Spatio-temporal analysis identified ten high priority districts out of which top four are in south Kerala.
topic disease mapping
discrete poisson model
relative risk
url https://jcdr.net/articles/PDF/13650/43472_CE[Ra1]_F(SHU)_PF1(ShG_KM)_PN(SL)_PFA2(OM)_PF2_(MG_OM).pdf
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