Publication date: Jul 10, 2023
COVID-19 has dramatically changed people’s mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it’s pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.
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| Concepts | Keywords |
|---|---|
| Covid | Based |
| Malls | Continuing |
| Reopening | Control |
| Restaurants | Covid |
| Data | |
| Epidemic | |
| Functional | |
| Interest | |
| Levels | |
| Perpetration | |
| Points | |
| Reopening | |
| Risk | |
| Spots | |
| Transmission |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | MESH | infections |
| drug | DRUGBANK | Pentaerythritol tetranitrate |
| disease | IDO | infection |
| drug | DRUGBANK | Coenzyme M |
| disease | MESH | emergency |
| disease | VO | population |
| disease | VO | effective |
| disease | IDO | process |
| disease | MESH | infectious diseases |
| disease | MESH | Pneumonia |
| disease | VO | efficient |
| disease | MESH | malaria |
| pathway | KEGG | Malaria |
| drug | DRUGBANK | Serine |
| drug | DRUGBANK | Medical air |
| disease | MESH | water quality |
| disease | IDO | blood |
| disease | IDO | infectivity |
| disease | VO | effectiveness |
| disease | VO | time |
| disease | IDO | contact tracing |
| disease | IDO | susceptible population |
| disease | IDO | infected population |
| disease | IDO | site |
| drug | DRUGBANK | Methionine |
| disease | VO | vaccinated |
| drug | DRUGBANK | Trestolone |
| disease | IDO | production |
| drug | DRUGBANK | Etoperidone |
| disease | MESH | death |
| disease | MESH | seizure |
| disease | VO | USA |