Publication: A Spatial Model of Socioeconomic and Demographic Determinants of Dengue Hemorrhagic Fever in Nepal
| creativeworkseries.issn | 1812-2027 | |
| dc.contributor.author | Mahato, RK | |
| dc.contributor.author | Htike, KM | |
| dc.contributor.author | Yadav, A | |
| dc.contributor.author | Baral, S | |
| dc.contributor.author | Yadav, RK | |
| dc.contributor.author | Kafle, A | |
| dc.contributor.author | Sharma, V | |
| dc.date.accessioned | 2026-01-18T06:20:40Z | |
| dc.date.available | 2026-01-18T06:20:40Z | |
| dc.date.issued | 2025 | |
| dc.description | Mahato RK,1 Htike KM,1 Yadav A,2 Baral S,3 Yadav RK,3,4 Kafle A,5 Sharma V6 1Faculty of Public Health Khon Kaen University Khon Kaen, Thailand 2Ministry of Health and Population Kathmandu, Nepal 3School of Health and Allied Sciences, Pokhara University, Pokhara, Nepal 4LA GRANDEE International College Department of Public Health Pokhara University, Nepal 5Tropical Medicine, Faculty of Medicine Khon Kaen University, Thailand 6Kathmandu University School of Medical sciences Dhulikhel, Nepal | |
| dc.description.abstract | ABSTRACT Background Dengue hemorrhagic fever (DHF) has re-emerged across the global South, particularly in tropical and subtropical urban areas, driven by environmental changes alongside local demographic and socioeconomic factors. Objective To investigate the spatial patterns and socioeconomic determinants of dengue fever in Nepal from 2020 to 2023. Method Using Geographic Information Systems (GIS), Gi* cluster analysis, and Local Moran’s I statistics, the study examined the relationship between socio-economic variables and dengue incidence across districts. Key factors analyzed included population density, urbanization, and night-time light (NTL) intensity. Result Bivariate Local Indicators of Spatial Association (LISA) analysis showed fluctuating correlations between dengue hemorrhagic fever incidence and factors such as population density, urbanization, and night-time light intensity. Moran’s I value for population density were -0.083 in 2020, -0.082 in 2021, 0.526 in 2022, and -0.020 in 2023. Similarly, for urbanization, Moran’s I values shifted from -0.103 in 2020 to -0.090 in 2021, 0.458 in 2022, and 0.007 in 2023. Night-time light intensity also demonstrated changing correlations, with Moran’s I values of -0.091 in 2020, -0.102 in 2021, 0.415 in 2022, and -0.068 in 2023. A notable shift from negative to positive correlations occurred between 2020 and 2022. In 2022, high-incidence dengue hemorrhagic fever clusters emerged in densely populated areas, while distinct spatial patterns were observed in 2020 and 2021. Conclusion Dengue hemorrhagic fever risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. The study emphasized the importance of dynamic, targeted public health interventions based on spatial and socio-economic factors to effectively manage evolving dengue outbreak patterns. KEY WORDS Dengue, Gi* statistics, Local indicators of spatial association, Socio-economic status, Spatial analysis | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14572/4305 | |
| dc.language.iso | en_US | |
| dc.publisher | Kathmandu University | |
| dc.subject | Dengue | |
| dc.subject | Gi* statistics | |
| dc.subject | Local indicators of spatial association | |
| dc.subject | Socio-economic status | |
| dc.subject | Spatial analysis | |
| dc.title | A Spatial Model of Socioeconomic and Demographic Determinants of Dengue Hemorrhagic Fever in Nepal | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| local.article.type | Original Article | |
| oaire.citation.endPage | 34 | |
| oaire.citation.startPage | 25 | |
| relation.isJournalIssueOfPublication | 5b7ce504-7ff0-4b0a-ade4-234af6118bc1 | |
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