A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917640886288384 |
|---|---|
| author | Xiao, X. Chen, P. Cao, X. Liu, K. Deng, L. Zhao, D. Chen, Z. Deng, Q. Yu, F. Zhang, H. |
| author_facet | Xiao, X. Chen, P. Cao, X. Liu, K. Deng, L. Zhao, D. Chen, Z. Deng, Q. Yu, F. Zhang, H. |
| contents | Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lessons from others with similar risk profiles. A Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile similarity evaluation. There are four steps in this method: (1) The similarity evaluation indicators are determined from three dimensions, i.e., the Basis of National Epidemic Prevention & Control, Social Resilience, and Infection Situation. (2) The data related to the indicators are collected and preprocessed. (3) The first round of screening on the preprocessed dataset is conducted through an improved collaborative filtering algorithm to calculate the preliminary similarity result from the perspective of the infection situation. (4) Finally, the K-Means model is used for the second round of screening to obtain the final similarity values. The approach will be applied to decision-making support in the context of COVID-19. Our results demonstrate that the recommendations generated by the STDSA model are more accurate and aligned better with the actual situation than those produced by pure K-means models. This study will provide new insights into preventing and controlling epidemics in regions that lack experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios Xiao, X. Chen, P. Cao, X. Liu, K. Deng, L. Zhao, D. Chen, Z. Deng, Q. Yu, F. Zhang, H. Machine Learning Physics and Society Applications Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lessons from others with similar risk profiles. A Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile similarity evaluation. There are four steps in this method: (1) The similarity evaluation indicators are determined from three dimensions, i.e., the Basis of National Epidemic Prevention & Control, Social Resilience, and Infection Situation. (2) The data related to the indicators are collected and preprocessed. (3) The first round of screening on the preprocessed dataset is conducted through an improved collaborative filtering algorithm to calculate the preliminary similarity result from the perspective of the infection situation. (4) Finally, the K-Means model is used for the second round of screening to obtain the final similarity values. The approach will be applied to decision-making support in the context of COVID-19. Our results demonstrate that the recommendations generated by the STDSA model are more accurate and aligned better with the actual situation than those produced by pure K-means models. This study will provide new insights into preventing and controlling epidemics in regions that lack experience. |
| title | A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios |
| topic | Machine Learning Physics and Society Applications |
| url | https://arxiv.org/abs/2404.10004 |