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Cao W, Zhao S, Tong X, Dai H, Sun J, Xu J, Qiu G, Zhu J, Tian Y. Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13086-13112. [PMID: 37501479 DOI: 10.3934/mbe.2023583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.
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Affiliation(s)
- Wen Cao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Siqi Zhao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaochong Tong
- School of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China
| | - Haoran Dai
- Northern Information Control Research Institute Group Co. Ltd, Nanjing 211106, China
| | - Jiang Sun
- Beijing QTMap Technology Co. Ltd, Beijing 100192, China
| | - Jiaqi Xu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | | | - Jingwen Zhu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Yuzhen Tian
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
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Liu Y, Wang X, Song C, Chen J, Shu H, Wu M, Guo S, Huang Q, Pei T. Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China. SUSTAINABLE CITIES AND SOCIETY 2023; 89:104314. [PMID: 36438675 PMCID: PMC9676079 DOI: 10.1016/j.scs.2022.104314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/18/2022] [Accepted: 11/19/2022] [Indexed: 05/27/2023]
Abstract
Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.
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Affiliation(s)
- Yaxi Liu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ci Song
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Chen
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Hua Shu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sihui Guo
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiang Huang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Pei
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127045. [PMID: 35742294 PMCID: PMC9222418 DOI: 10.3390/ijerph19127045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 02/05/2023]
Abstract
Medical facilities are an important part of urban public facilities and a vital pillar for the survival of citizens at critical times. During the rapid spread of coronavirus disease (COVID-19), Wuhan was forced into lockdown with a severe shortage of medical resources and high public tension. Adequate allocation of medical facilities is significant to stabilize citizens’ emotions and ensure their living standards. This paper combines text sentiment analysis techniques with geographic information system (GIS) technology and uses a coordination degree model to evaluate the dynamic demand for medical facilities in Wuhan based on social media data and medical facility data. This study divided the epidemic into three phases: latent, outbreak and stable, from which the following findings arise: Public sentiment changed from negative to positive. Over half of the subdistricts in three phases were in a dysfunctional state, with a circular distribution of coordination levels decreasing from the city center to the outer. Thus, when facing major public health emergencies, Wuhan revealed problems of uneven distribution of medical facilities and unreasonable distribution of grades. This study aims to provide a basis and suggestions for the city to respond to major public health emergencies and optimize the allocation of urban medical facilities.
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