Background: This study applied the susceptible-exposed-infectious-removed (SEIR) model to analyze and simulate the transmission mechanisms of the coronavirus disease 2019 (COVID-19) in China.
Methods: The population migration was embedded in the SEIR model to simulate and analyze the effects of the amount of population inflow on the number of confirmed cases. Based on numerical simulations, this study used statistical data for the empirical validation of its theoretical deductions and discussed how to improve the effectiveness of epidemic prevention and control considering population migration variables. Statistics regarding the numbers of infected people in various provinces were obtained from the epidemic-related data reported by China's National Health Commission.
Results: This study explored how the epidemic should be prevented and controlled from the perspective of population migration variables. It found that the combination of a susceptible population, an infected population, and transmission media were important routes affecting the number of infections and that the migration of a Hubei-related infected population played a key role in promoting epidemic spread. Epidemic prevention and control should focus on regions with better economic conditions than the epidemic region. Prevention and control efforts should focus on the more populated neighboring provinces having convenient transportation links with the epidemic region. To prevent and control epidemic spread, priority should be given to elucidating the destinations and directions of population migration from the domestic origin of infections, and then controlling population migration or human-to-human contact after such migration.
Conclusions: This study enriched and expanded on simulations of the effects of population migration on the COVID-19 epidemic and China-based empirical studies while offering an epidemic evaluation and warning mechanism to prevent and control similar public health emergencies in the future.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968569 | PMC |
| http://dx.doi.org/10.1186/s12889-021-10605-2 | DOI Listing |
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