How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission
Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and under...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Tropical Medicine and Infectious Disease |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2414-6366/7/8/164 |
| _version_ | 1797431601363156992 |
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| author | Chia-Hsien Lin Tzai-Hung Wen |
| author_facet | Chia-Hsien Lin Tzai-Hung Wen |
| author_sort | Chia-Hsien Lin |
| collection | DOAJ |
| description | Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions. |
| first_indexed | 2024-03-09T09:47:32Z |
| format | Article |
| id | doaj.art-111fcf6245004bdc9c2bab427eee9f8d |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-03-09T09:47:32Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-111fcf6245004bdc9c2bab427eee9f8d2023-12-02T00:23:55ZengMDPI AGTropical Medicine and Infectious Disease2414-63662022-08-017816410.3390/tropicalmed7080164How Spatial Epidemiology Helps Understand Infectious Human Disease TransmissionChia-Hsien Lin0Tzai-Hung Wen1Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City 10610, TaiwanDepartment of Geography, National Taiwan University, Taipei City 10617, TaiwanBoth directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.https://www.mdpi.com/2414-6366/7/8/164hot spotneighbourhood effectspatial-temporal analysisspatial epidemiologyspatial heterogeneityvisualization |
| spellingShingle | Chia-Hsien Lin Tzai-Hung Wen How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission Tropical Medicine and Infectious Disease hot spot neighbourhood effect spatial-temporal analysis spatial epidemiology spatial heterogeneity visualization |
| title | How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission |
| title_full | How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission |
| title_fullStr | How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission |
| title_full_unstemmed | How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission |
| title_short | How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission |
| title_sort | how spatial epidemiology helps understand infectious human disease transmission |
| topic | hot spot neighbourhood effect spatial-temporal analysis spatial epidemiology spatial heterogeneity visualization |
| url | https://www.mdpi.com/2414-6366/7/8/164 |
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