Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2021
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911665067393024 |
|---|---|
| author | Xia, Mingtao Böttcher, Lucas Chou, Tom |
| author_facet | Xia, Mingtao Böttcher, Lucas Chou, Tom |
| contents | Efficient testing and vaccination protocols are critical aspects of epidemic management. To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies. Within our framework, we find that optimal intervention policies first target high-degree nodes before shifting to lower-degree nodes in a time-dependent manner. Using such optimal policies, it is possible to delay outbreaks and reduce incidence rates to a greater extent than uniform and reinforcement-learning-based interventions, particularly on certain scale-free networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_13709 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Controlling epidemics through optimal allocation of test kits and vaccine doses across networks Xia, Mingtao Böttcher, Lucas Chou, Tom Populations and Evolution Social and Information Networks Optimization and Control Efficient testing and vaccination protocols are critical aspects of epidemic management. To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies. Within our framework, we find that optimal intervention policies first target high-degree nodes before shifting to lower-degree nodes in a time-dependent manner. Using such optimal policies, it is possible to delay outbreaks and reduce incidence rates to a greater extent than uniform and reinforcement-learning-based interventions, particularly on certain scale-free networks. |
| title | Controlling epidemics through optimal allocation of test kits and vaccine doses across networks |
| topic | Populations and Evolution Social and Information Networks Optimization and Control |
| url | https://arxiv.org/abs/2107.13709 |