Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China

Abstract

Background: This study applied the 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 a combination of a susceptible population, an infected population, and transmission media was an important route 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 stemming 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.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…Transportation infrastructures mainly appeared in papers on a lower scale such as urban districts. On the state scale, the number of high-speed railways in China increased the number of cases ( Z. Hu et al, 2021 ), while Bayode et al (2022) reported no significant relationship between COVID-19 cases and international airports. On the county scale, in Zambia, the number of cases increased due to closeness to airports ( Phiri et al, 2021 ) but in the case of Germany and South Korea, the physical characteristics of transportation infrastructures were not significant factors ( Scarpone et al, 2020 ; Jo et al, 2021 ).…”

“…Furthermore, commuting to work by bicycle and walking are also correlated with a lower number of cases in those neighborhoods ( Tribby and Hartmann, 2021 ; Guo et al, 2021 ; Wali and Frank, 2021 ). Worthy to mention that studies conducted in China showed that distance and connectivity to the pandemic epicenters, specifically Wuhan, are strong, significant and positive predictors of COVID-19 cases ( Z. Hu et al, 2021 ; Qiu et al, 2020 , Qiu et al, 2021 ; Lin et al, 2020 ; X.-D. Yang et al, 2021 ).…”

“…All of them found overcrowding as a positive and significant predictor of COVID-19 cases ( Lee et al, 2021 ; Wang et al, 2021 ; Gaglioti et al, 2021 ; Credit, 2020 ; Ghosh et al, 2021 ). Even some studies found that overcrowding has significant effects on the death rate in the county ( Lee et al, 2021 ; Nguyen et al, 2021 ; Ahmad et al, 2020 ) and urban district scales ( Urban and Nakada, 2021 ; Silva and Ribeiro-Alves, 2021 ; Z. Hu et al, 2021 ). The main reason for this result is that in overcrowded households people have limited space to follow the protocols and avoid having contact with other family members ( Consolazio et al, 2021 ; Kashem et al, 2021 ).…”

Analysis of the Government and Capital Market’s Response to Normalized Epidemic Prevention and Control Based on the Development of Smart Health Care

“…In material and equipment security analysis, Ahmed et al constructed a decision-making model for major public health emergencies based on the spatial and temporal characteristics, key events, transmission dynamics, spatial distribution, infection scale, information characteristics, and medical resource dimensions [17]. Hu et al established observation indicators that signifcantly promoted the clarifcation of prevention and control priorities, achieved early warning efects, clarifed the reserve direction of emergency medical resources, and prevented the recurrence and spread of COVID-19 [18]. In terms of medical information system application, Asadzadeh et al proposed the diferent efects and intrinsic mechanisms of direct and indirect applications of medical information systems on the quality of clinical treatment services and the quality of doctor-patient communication [19].…”

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

“…As to environmental factors, a study indicated temperature and the columnar density of total atmospheric ozone had a strong association with the tendency of COVID-19 spreading in almost all states in the USA [54]. As for regulations mainly including mobility restrictions and other non-pharmacological interventions, ill-prepared work [55], facemask shortage [56], poor traveller screening [57], forgone care [58], and population migration [59] could lead to ineffective prevention and controlling COVID-19. Regarding progressing stages, changes of COVID-19 ND, NH, NR, and NC might be caused by COVID-19 epidemic progressing laws differentially in various countries.…”