Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions
Abstract Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspo...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | International Journal of Health Geographics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12942-023-00355-2 |
| _version_ | 1797397792700760064 |
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| author | Ya-Peng Lee Tzai-Hung Wen |
| author_facet | Ya-Peng Lee Tzai-Hung Wen |
| author_sort | Ya-Peng Lee |
| collection | DOAJ |
| description | Abstract Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks. |
| first_indexed | 2024-03-09T01:15:17Z |
| format | Article |
| id | doaj.art-0f3db49c86ca4e6c84fec0292b0c8536 |
| institution | Directory Open Access Journal |
| issn | 1476-072X |
| language | English |
| last_indexed | 2024-03-09T01:15:17Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Health Geographics |
| spelling | doaj.art-0f3db49c86ca4e6c84fec0292b0c85362023-12-10T12:30:14ZengBMCInternational Journal of Health Geographics1476-072X2023-12-0122111710.1186/s12942-023-00355-2Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regionsYa-Peng Lee0Tzai-Hung Wen1Department of Geography, National Taiwan UniversityDepartment of Geography, National Taiwan UniversityAbstract Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.https://doi.org/10.1186/s12942-023-00355-2Spatial epidemiologyConditional autoregressive modelBayesian areal Wombling methodsSpatial panel modelDisease mapping |
| spellingShingle | Ya-Peng Lee Tzai-Hung Wen Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions International Journal of Health Geographics Spatial epidemiology Conditional autoregressive model Bayesian areal Wombling methods Spatial panel model Disease mapping |
| title | Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions |
| title_full | Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions |
| title_fullStr | Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions |
| title_full_unstemmed | Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions |
| title_short | Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions |
| title_sort | understanding the spread of infectious diseases in edge areas of hotspots dengue epidemics in tropical metropolitan regions |
| topic | Spatial epidemiology Conditional autoregressive model Bayesian areal Wombling methods Spatial panel model Disease mapping |
| url | https://doi.org/10.1186/s12942-023-00355-2 |
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