ArticleParasite epidemiology and control2018
Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011-2013.
Article in Parasite epidemiology and control, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of geographical information system and spatial analysis in Indian health research: a systematic review.BMC health services research · 2024Pooled it
- Clinical Profile, Syndromic Classification, and Studying the Seasonal Variation and Geo-mapping of the Patients with Febrile Illness Reporting to a Primary Health Center in Puducherry.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Article
- Spatio-temporal distribution of dengue cases and vectors along with the interrelationship of environmental and climatic factors in the metropolitan city, Kolkata, India, for 2017-2022: calling implications for vector control.Emerging microbes & infections · 2025Article
- Spatial Analysis of Dengue Clusters at Department, Municipality and Local Scales in the Southwest of Colombia, 2014-2019.Tropical medicine and infectious disease · 2023Article
- Association of Dengue Virus Serotypes 1&2 with Severe Dengue Having Deletions in Their 3'Untranslated Regions (3'UTRs).Microorganisms · 2023Article
- Efficacy Assessment of Autodissemination Using Pyriproxyfen-Treated Ovitraps in the Reduction of Dengue Incidence in Parañaque City, Philippines: A Spatial Analysis.Tropical medicine and infectious disease · 2023Article
- The application of geographic information system for dengue epidemic in Southeast Asia: A review on trends and opportunity.Journal of public health research · 2022Article
- Using self-organising maps to predict and contain natural disasters and pandemics.International journal of intelligent systems · 2022Article
- Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means.International journal of environmental research and public health · 2021Article
- Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk.Tropical medicine and health · 2021Article
- Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.BMJ global health · 2020Article
- Article
- Applications of machine learning techniques to predict filariasis using socio-economic factors.Epidemiology and infection · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesDengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011-2013. MATERIAL AND
methodsDistrict-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas.
resultsThe analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas.
conclusionThis visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management.
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