» Articles » PMID: 33108386

Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data

Overview
Journal PLoS One
Date 2020 Oct 27
PMID 33108386
Citations 42
Authors
Affiliations
Soon will be listed here.
Abstract

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

Citing Articles

A novel framework for modeling quarantinable disease transmission.

Liu W, Liu C, Wang D, She Y PLoS One. 2025; 20(2):e0317553.

PMID: 39937828 PMC: 11819513. DOI: 10.1371/journal.pone.0317553.


An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction.

Wang B, Shen Y, Yan X, Kong X PeerJ Comput Sci. 2024; 10:e2046.

PMID: 38855247 PMC: 11157592. DOI: 10.7717/peerj-cs.2046.


Epidemicity indices and reproduction numbers from infectious disease data in connected human populations.

Trevisin C, Mari L, Gatto M, Rinaldo A Infect Dis Model. 2024; 9(3):875-891.

PMID: 38746942 PMC: 11090859. DOI: 10.1016/j.idm.2024.04.011.


Psychometric properties of the COVID-19 safety measures questionnaire in the employees of the radiation therapy center.

Saber K, Madadizadeh F, Abdollahi-Dehkordi S, Azmoonfar R, Hamzian N, Shabani M J Educ Health Promot. 2024; 13:95.

PMID: 38726092 PMC: 11081435. DOI: 10.4103/jehp.jehp_1007_22.


Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.

Wang Y, Yuan F, Song Y, Rao H, Xiao L, Guo H PLoS One. 2024; 19(4):e0301420.

PMID: 38593140 PMC: 11003692. DOI: 10.1371/journal.pone.0301420.


References
1.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y . Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020; 382(13):1199-1207. PMC: 7121484. DOI: 10.1056/NEJMoa2001316. View

2.
Small M, Tse C, Walker D . Super-spreaders and the rate of transmission of the SARS virus. Physica D. 2020; 215(2):146-158. PMC: 7114355. DOI: 10.1016/j.physd.2006.01.021. View

3.
Lai S, Ruktanonchai N, Zhou L, Prosper O, Luo W, Floyd J . Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 2020; 585(7825):410-413. PMC: 7116778. DOI: 10.1038/s41586-020-2293-x. View

4.
Gross T, DLima C, Blasius B . Epidemic dynamics on an adaptive network. Phys Rev Lett. 2006; 96(20):208701. DOI: 10.1103/PhysRevLett.96.208701. View

5.
Pastor-Satorras R, Vespignani A . Epidemic spreading in scale-free networks. Phys Rev Lett. 2001; 86(14):3200-3. DOI: 10.1103/PhysRevLett.86.3200. View