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Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018
INTRODUCTION: Five states in India are reporting sporadic outbreaks of Kyasanur Forest Disease (KFD). Goa experienced an outbreak of KFD in 2015. It remains as an important differential diagnosis for tropical fever in the endemic regions. Few studies among neighboring two states (Karnataka and Keral...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7187534/ https://www.ncbi.nlm.nih.gov/pubmed/32355449 http://dx.doi.org/10.1186/s41182-020-00213-y |
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author | Oliveira, Annet Selvaraj, Kalaiselvi Tripathy, Jaya Prasad Betodkar, Utkarsh Cacodcar, Jagadish Quadros, Nikhita Wadkar, Abhijit |
author_facet | Oliveira, Annet Selvaraj, Kalaiselvi Tripathy, Jaya Prasad Betodkar, Utkarsh Cacodcar, Jagadish Quadros, Nikhita Wadkar, Abhijit |
author_sort | Oliveira, Annet |
collection | PubMed |
description | INTRODUCTION: Five states in India are reporting sporadic outbreaks of Kyasanur Forest Disease (KFD). Goa experienced an outbreak of KFD in 2015. It remains as an important differential diagnosis for tropical fever in the endemic regions. Few studies among neighboring two states (Karnataka and Kerala) have described the epidemiological characteristics of KFD. However, there is no study which describes the same among cases in the state of Goa. Hence, we planned to understand the epidemiology (time, place, and person distribution) of the disease including seasonal pattern with forecasting using zero-inflated negative binomial regression and time series models. We also explored geo-spatial clustering of KFD cases in Goa during 2015–2018 which would help design effective intervention to curb its transmission in Goa. RESULTS: Blood samples of all suspected cases of KFD during 2015 to 2018 were tested using reverse transcriptase-polymerase chain reaction technique. Reports of these results were periodically shared with the state surveillance unit. Records of 448 confirmed cases of KFD available at the State Integrated Disease Surveillance Programme were analyzed. The mean (SD) age of the patients was 41.6 (14.9) years. Of 143 cases with documented travel history, 135 (94.4%) had history of travel to forest for cashew plucking. Two thirds of cases (66.3%) did not receive KFD vaccine prior to the disease. Case fatality rate of 0.9% was reported. Seasonal peaks were observed during January to April, and forecasting demonstrated a peak in cases in the subsequent year also during January–April persisting till May. Around 40 villages located along the Western Ghats had reported KFD, and affected villages continued to report cases in the subsequent years also. Case density-based geographic maps show clustering of cases around the index village. CONCLUSION: Most of the confirmed cases did not receive any vaccination. KFD cases in Goa followed a specific seasonal pattern, and clustering of cases occurred in selected villages located in North Goa. Most of the patients who had suffered from the disease had visited the forest for cashew plucking. Planning for public health interventions such as health education and vaccination campaigns should consider these epidemiological features. |
format | Online Article Text |
id | pubmed-7187534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71875342020-04-30 Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 Oliveira, Annet Selvaraj, Kalaiselvi Tripathy, Jaya Prasad Betodkar, Utkarsh Cacodcar, Jagadish Quadros, Nikhita Wadkar, Abhijit Trop Med Health Research INTRODUCTION: Five states in India are reporting sporadic outbreaks of Kyasanur Forest Disease (KFD). Goa experienced an outbreak of KFD in 2015. It remains as an important differential diagnosis for tropical fever in the endemic regions. Few studies among neighboring two states (Karnataka and Kerala) have described the epidemiological characteristics of KFD. However, there is no study which describes the same among cases in the state of Goa. Hence, we planned to understand the epidemiology (time, place, and person distribution) of the disease including seasonal pattern with forecasting using zero-inflated negative binomial regression and time series models. We also explored geo-spatial clustering of KFD cases in Goa during 2015–2018 which would help design effective intervention to curb its transmission in Goa. RESULTS: Blood samples of all suspected cases of KFD during 2015 to 2018 were tested using reverse transcriptase-polymerase chain reaction technique. Reports of these results were periodically shared with the state surveillance unit. Records of 448 confirmed cases of KFD available at the State Integrated Disease Surveillance Programme were analyzed. The mean (SD) age of the patients was 41.6 (14.9) years. Of 143 cases with documented travel history, 135 (94.4%) had history of travel to forest for cashew plucking. Two thirds of cases (66.3%) did not receive KFD vaccine prior to the disease. Case fatality rate of 0.9% was reported. Seasonal peaks were observed during January to April, and forecasting demonstrated a peak in cases in the subsequent year also during January–April persisting till May. Around 40 villages located along the Western Ghats had reported KFD, and affected villages continued to report cases in the subsequent years also. Case density-based geographic maps show clustering of cases around the index village. CONCLUSION: Most of the confirmed cases did not receive any vaccination. KFD cases in Goa followed a specific seasonal pattern, and clustering of cases occurred in selected villages located in North Goa. Most of the patients who had suffered from the disease had visited the forest for cashew plucking. Planning for public health interventions such as health education and vaccination campaigns should consider these epidemiological features. BioMed Central 2020-04-28 /pmc/articles/PMC7187534/ /pubmed/32355449 http://dx.doi.org/10.1186/s41182-020-00213-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Oliveira, Annet Selvaraj, Kalaiselvi Tripathy, Jaya Prasad Betodkar, Utkarsh Cacodcar, Jagadish Quadros, Nikhita Wadkar, Abhijit Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title | Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title_full | Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title_fullStr | Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title_full_unstemmed | Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title_short | Geospatial clustering, seasonal trend and forecasting of Kyasanur Forest Disease in the state of Goa, India, 2015–2018 |
title_sort | geospatial clustering, seasonal trend and forecasting of kyasanur forest disease in the state of goa, india, 2015–2018 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7187534/ https://www.ncbi.nlm.nih.gov/pubmed/32355449 http://dx.doi.org/10.1186/s41182-020-00213-y |
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