Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease

Zhengyan Li,Chengli Zhao,Huichun Li,Xue Zhang

doi:10.3390/healthcare9091224

Zhengyan Li, Chengli Zhao + Show 2 more

Open Access

https://doi.org/10.3390/healthcare9091224

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Journal: Healthcare	Publication Date: Sep 16, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: National University of Defense Technology

Abstract

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

Highlights

Human mobility has become a hot research topic in the scientific community in recent years because of its application value in many fields [1,2,3,4,5,6]
In order to estimate the human mobility patterns in mainland China and build a human mobility network between all prefectures, we propose a data fusion algorithm model based on the network science approach, which can estimate travel flow personal information
In order to estimate the human mobility patterns in mainland China and build a hu-3 of 16 man mobility network between all prefectures, we propose a data fusion algorithm model based on the network science approach, which can estimate travel flow data in mainland China

Summary

Introduction

Human mobility has become a hot research topic in the scientific community in recent years because of its application value in many fields [1,2,3,4,5,6]. The accuracy of the prediction is limited to international spread [8,9,10,11,12,13] because international travel is dominated by air travel, and the airline operation data are accessible. To cope with the lack of human mobility data, researchers have established spatial interaction models to estimate travel flow by using local statistical survey data. The main spatial interaction models are gravity models and radiation models [14,15], which were the main research methods used for obtaining human mobility data in the past. Huang and Mao et al used publicly available airline operation history data to build a gravity model and estimated the number of passengers between airports around the world [16,17]

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