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

Abstract

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.

Influence of epidemic situation on COVID-19 vaccination between urban and rural residents in China-Vietnam border area: A cross-sectional survey

“…China’s epidemic prevention and control and economic recovery have achieved positive results [ 12 , 41 ]. China has a population of over 1.4 billion.…”

Epidemiological Characteristics of Infectious Diseases Among Travelers Between China and Foreign Countries Before and During the Early Stage of the COVID-19 Pandemic

“…However, as our knowledge about COVID-19 has increased with time, it can be used as a reference for the prevention and control of other infectious diseases in the future. Second, there were not enough months of monitoring data since the COVID-19 pandemic started because China has taken strict prevention and control measures to greatly reduce the traffic of international travelers after May 2020 ( 9 ). In addition, there were no data of the COVID-19 infection in people having common infectious diseases because we had no authority to detect the infection of common infectious diseases of travelers transferred to the hospital immediately due to the diagnosis of COVID-19.…”

“…This can be done effectively as the pathogens of these diseases are clear and established detection methods are available ( 7 , 8 ). Although the source and route of transmission of different infectious diseases from infected people are different, the human-to-human spread of viruses usually occurs via droplets, sweat, blood, and other body fluids through contact or airborne transmission ( 9 , 10 ). In particular, understanding the epidemic characteristics of infectious diseases that are common among international travelers will help understand the regional and population differences thereof, understand the demographic and geographical characteristics of infectious patients leaving and entering China, and define the infectious disease prevention and control level of different countries.…”

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.…”