1
|
He C, Wu Y, Zhou X, Huang Y, Shui A, Liu S. The heterogeneous impact of population mobility on the influent characteristics of wastewater treatment facilities. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 366:121672. [PMID: 38991349 DOI: 10.1016/j.jenvman.2024.121672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/17/2024] [Accepted: 06/30/2024] [Indexed: 07/13/2024]
Abstract
Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change. Beyond physical damages, non-physical shocks induced by disasters warrant attention. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics. However, the impact path remains inadequately explored. Leveraging the stay-at-home orders during the COVID-19 pandemic as a natural experiment, this study aims to quantify and interpret the heterogeneous impacts of mobility reduction on the influent characteristics of WWTFs with different socio-economic, infrastructural, and climatic conditions. To achieve this goal, we developed a research framework integrating causal inference and interpretable machine learning techniques. Based on the empirical data from China, we find that 79.1% of the studied WWTFs, typically located in cities with well-developed drainage infrastructures and low per capita water usage, exhibited resilience against drastic mobility reduction. In contrast, 20.9% of the studied WWTFs displayed significant variations in influent characteristics. Large-capacity WWTFs in subtropical regions encountered challenges with low-load operations, and small-capacity facilities in suburban areas grappled with nutrient imbalances. This study provides valuable insights to equip WWTFs in anticipating and adapting potential variations in influent characteristics triggered by mobility reduction.
Collapse
Affiliation(s)
- Chengyu He
- School of Environment, Tsinghua University, 100084, Beijing, China
| | - Yipeng Wu
- School of Environment, Tsinghua University, 100084, Beijing, China
| | - Xiao Zhou
- Hefei University of Technology, School of Civil and Hydraulic Engineering, 230009, Hefei, China
| | - Yujun Huang
- School of Environment, Tsinghua University, 100084, Beijing, China
| | - Ailun Shui
- School of Environment, Tsinghua University, 100084, Beijing, China
| | - Shuming Liu
- School of Environment, Tsinghua University, 100084, Beijing, China.
| |
Collapse
|
2
|
Liu C, Holme P, Lehmann S, Yang W, Lu X. Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation. JMIR Form Res 2024; 8:e55013. [PMID: 38941609 PMCID: PMC11245661 DOI: 10.2196/55013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/31/2024] [Accepted: 04/19/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. OBJECTIVE The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. METHODS To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. RESULTS We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. CONCLUSIONS The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.
Collapse
Affiliation(s)
- Chuchu Liu
- School of Economics and Management, Changsha University of Science and Technology, Changsha, China
- College of Systems Engineering, National University of Defense Technology, Changsha, China
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, Finland
- Center for Computational Social Science, Kobe University, Kobe, Japan
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark
| | - Wenchuan Yang
- College of Systems Engineering, National University of Defense Technology, Changsha, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha, China
| |
Collapse
|
3
|
Qiao J, Nishiura H. Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study. Epidemiol Health 2024; 46:e2024025. [PMID: 38317530 PMCID: PMC11099593 DOI: 10.4178/epih.e2024025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/06/2024] [Indexed: 02/07/2024] Open
Abstract
OBJECTIVES Although the role of specific holidays in modifying transmission dynamics of infectious diseases has received some research attention, the epidemiological impact of public holidays on the transmission of coronavirus disease 2019 (COVID-19) remains unclear. METHODS To assess the extent of increased transmission frequency during public holidays, we collected COVID-19 incidence and mobility data in Hokkaido, Tokyo, Aichi, and Osaka from February 15, 2020 to September 30, 2021. Models linking the estimated effective reproduction number (Rt) with raw or adjusted mobility, public holidays, and the state of emergency declaration were developed. The best-fit model included public holidays as an essential input variable, and was used to calculate counterfactuals of Rt in the absence of holidays. RESULTS During public holidays, on average, Rt increased by 5.71%, 3.19%, 4.84%, and 24.82% in Hokkaido, Tokyo, Aichi, and Osaka, respectively, resulting in a total increase of 580 (95% confidence interval [CI], 213 to 954), 2,209 (95% CI, 1,230 to 3,201), 1,086 (95% CI, 478 to 1,686), and 5,211 (95% CI, 4,554 to 5,867) cases that were attributable to the impact of public holidays. CONCLUSIONS Public holidays intensified the transmission of COVID-19, highlighting the importance of considering public holidays in designing appropriate public health and social measures in the future.
Collapse
Affiliation(s)
- Jiaying Qiao
- School of Public Health, Kyoto University, Kyoto, Japan
| | | |
Collapse
|
4
|
Zhang X, Chen B, Le J, Hu Y. Impact of different nucleic acid testing scenarios on COVID-19 transmission. Heliyon 2024; 10:e23700. [PMID: 38187298 PMCID: PMC10767492 DOI: 10.1016/j.heliyon.2023.e23700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
In the past three years, waves of COVID-19 infections have emerged one after another, and may enter a small-scale wave-like recurrent epidemic pattern in the future. When COVID-19 infections occur in small-scale, how to efficiently detect and prevent the disease has become the main problem. In this study, based on the characteristics of the Omicron variant and China's pandemic prevention and control strategies, the following three nucleic acid testing scenarios were simulated: scenario 1 (baseline scenario) included conducting nucleic acid testing at administrative region; scenario 2 included conducting nucleic acid testing at the community; and scenario 3 included conducting nucleic acid testing at the health facility closest to households. The model calibration showed that the baseline scenario was consistent with the actual transmission scenario of the disease. The simulation results revealed that compared with scenario 1, the cumulative cases in scenarios 2 and 3 were reduced by 9.52 % and 46.83 %, respectively. Compared with scenario 2, the cumulative cases in scenario 3 were reduced by 41.23 %. Thus, adopting nucleic acid testing measures at the household level can effectively limit the spread of COVID-19 and should be given a priority when local emergency occurs in the future.
Collapse
Affiliation(s)
- Xuedong Zhang
- School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102627, China
- Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China
| | - Bo Chen
- School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102627, China
| | - Jiaxu Le
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China
| | - Yi Hu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China
| |
Collapse
|
5
|
Liao J, Liu XF, Xu XK, Zhou T. COVID-19 spreading patterns in family clusters reveal gender roles in China. J R Soc Interface 2023; 20:20230336. [PMID: 38086400 PMCID: PMC10715915 DOI: 10.1098/rsif.2023.0336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Understanding different gender roles forms part of the efforts to reduce gender inequality. This paper analyses COVID-19 family clusters outside Hubei Province in mainland China during the 2020 outbreak, revealing significant differences in spreading patterns across gender and family roles. Results show that men are more likely to be the imported cases of a family cluster, and women are more likely to be infected within the family. This finding provides new supportive evidence of the 'men as breadwinner and women as homemaker' (MBWH) gender roles in China. Further analyses reveal that the MBWH pattern is stronger in eastern than in western China, stronger for younger than for elder people. This paper offers not only valuable references for formulating gender-differentiated epidemic prevention policies but also an exemplification for studying group differences in similar scenarios.
Collapse
Affiliation(s)
- Jingyi Liao
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Xiao-Ke Xu
- Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, People's Republic of China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, People's Republic of China
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, People's Republic of China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| |
Collapse
|
6
|
Li W, Yao Y. The spatiotemporal analysis of the population migration network in China, 2021. Infect Dis Model 2023; 8:1117-1126. [PMID: 37915999 PMCID: PMC10616395 DOI: 10.1016/j.idm.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 11/03/2023] Open
Abstract
Population migration is a critical component of large-scale spatiotemporal models of infectious disease transmission. Identifying the most influential spreaders in networks is vital to controlling and understanding the spreading process of infectious diseases. We used Baidu Migration data for the whole year of 2021 to build mobility networks. The nodes of the network represent cities, and the edges represent the population flow between cities. By applying the k-shell decomposition and the Louvain algorithm, we could get the k-shell values for each city and community partition. Then, we identified the most efficient nodes or pathways in a complex network by generating random networks. Furthermore, we analyzed the eigenvalue of the migration matrix to find the nodes that have the most impact on the network. We also found the consistency between k-shell value and eigenvalue through Kendall's τ test. The main result is that in Spring Festival and National Day, the network is at higher risk of an infectious disease outbreak and the Yangtze River Delta is at the highest risk of an epidemic all year around. Shanghai is the most significant node in both k-shell value and eigenvalue analysis. The spatiotemporal property of the network should be taken into account to model the transmission of infectious diseases more accurately.
Collapse
Affiliation(s)
- Wenjie Li
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Ye Yao
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| |
Collapse
|
7
|
Zhao Y, Zhu Z, Chen B, Qiu S, Huang J, Lu X, Yang W, Ai C, Huang K, He C, Jin Y, Liu Z, Wang FY. Toward parallel intelligence: An interdisciplinary solution for complex systems. Innovation (N Y) 2023; 4:100521. [PMID: 37915363 PMCID: PMC10616416 DOI: 10.1016/j.xinn.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/03/2023] [Indexed: 11/03/2023] Open
Abstract
The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in the artificial systems, computational experiments, and parallel execution (ACP) approach has been developed. The method cultivates a cycle termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.
Collapse
Affiliation(s)
- Yong Zhao
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Zhengqiu Zhu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Bin Chen
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Sihang Qiu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Jincai Huang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Weiyi Yang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Chuan Ai
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Kuihua Huang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Cheng He
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai 200032, China
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Yucheng Jin
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Zhong Liu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Fei-Yue Wang
- State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| |
Collapse
|
8
|
Xu RH, Shi L, Shi Z, Li T, Wang D. Investigating Individuals' Preferences in Determining the Functions of Smartphone Apps for Fighting Pandemics: Best-Worst Scaling Survey Study. J Med Internet Res 2023; 25:e48308. [PMID: 37581916 PMCID: PMC10466146 DOI: 10.2196/48308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/12/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Smartphone apps have been beneficial in controlling and preventing the COVID-19 pandemic. However, there is a gap in research surrounding the importance of smartphone app functions from a user's perspective. Although the insights and opinions of different stakeholders, such as policymakers and medical professionals, can influence the success of a public health policy, any strategy will face difficulty in achieving the expected effect if it is not based on a method that users can accept. OBJECTIVE This study aimed to assess the importance of a hypothetical smartphone app's functions for managing health during a pandemic based on the perspective of user preferences. METHODS A cross-sectional and web-based survey using the best-worst scaling (BWS) method was used to investigate the general population's preferences for important smartphone app functions. Participants were recruited from a professional surveying company's web-based surveying panel. The attributes of the BWS questionnaire were developed based on a robust process, including literature review, interviews, and expert discussion. A balanced incomplete block design was used to construct the choice task to ensure the effectiveness of the research design. Count analysis, conditional logit model analysis, and mixed logit analysis were used to estimate preference heterogeneity among respondents. RESULTS The responses of 2153 participants were eligible for analysis. Nearly 55% (1192/2153) were female, and the mean age was 31.4 years. Most participants (1765/2153, 81.9%) had completed tertiary or higher education, and approximately 70% (1523/2153) were urban residents. The 3 most vital functions according to their selection were "surveillance and monitoring of infected cases," "quick self-screening," and "early detection of infected cases." The mixed logit regression model identified significant heterogeneity in preferences among respondents, and stratified analysis showed that some heterogeneities varied in respondents by demographics and COVID-19-related characteristics. Participants who preferred to use the app were more likely to assign a high weight to the preventive functions than those who did not prefer to use it. Conversely, participants who showed lower willingness to use the app tended to indicate a higher preference for supportive functions than those who preferred to use it. CONCLUSIONS This study ranks the importance of smartphone app features that provide health care services during a pandemic based on the general population's preferences in China. It provides empirical evidence for decision-makers to develop eHealth policies and strategies that address future public health crises from a person-centered care perspective. Continued use of apps and smart investment in digital health can help improve health outcomes and reduce the burden of disease on individuals and communities.
Collapse
Affiliation(s)
- Richard Huan Xu
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Lushaobo Shi
- School of Health Management, Southern Medical University, Guangzhou, China
| | - Zengping Shi
- School of Health Management, Southern Medical University, Guangzhou, China
| | - Ting Li
- School of Health Management, Southern Medical University, Guangzhou, China
| | - Dong Wang
- School of Health Management, Southern Medical University, Guangzhou, China
| |
Collapse
|
9
|
Wei R, Zhang Y, Gao S, Brown BJ, Hu S, Link BG. Health disparity in the spread of COVID-19: Evidence from social distancing, risk of interactions, and access to testing. Health Place 2023; 82:103031. [PMID: 37120950 PMCID: PMC10126219 DOI: 10.1016/j.healthplace.2023.103031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/27/2023] [Accepted: 04/17/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE - To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing. METHODS Analysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth. RESULTS Results identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19. CONCLUSION - This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.
Collapse
Affiliation(s)
- Ran Wei
- School of Public Policy, University of California, Riverside, CA, 92521, USA.
| | - Yujia Zhang
- School of Public Policy, University of California, Riverside, CA, 92521, USA.
| | - Song Gao
- GeoDS Lab, Department of Geography, University of Wisconsin, Madison, WI, 53706, USA.
| | - Brandon J Brown
- Department of Social Medicine, Population and Public Health, University of California, Riverside, CA, USA.
| | - Songhua Hu
- Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, 20742, USA.
| | - Bruce G Link
- School of Public Policy, University of California, Riverside, CA, 92521, USA.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Yu L, Zhao P, Tang J, Pang L. Changes in tourist mobility after COVID-19 outbreaks. ANNALS OF TOURISM RESEARCH 2023; 98:103522. [PMID: 36474961 PMCID: PMC9715491 DOI: 10.1016/j.annals.2022.103522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 11/07/2022] [Accepted: 11/13/2022] [Indexed: 06/17/2023]
Abstract
We comparatively examined tourist mobility changes in the entire country and explicitly covered two distinct waves of COVID-19 outbreaks, based on mobile phone data from 277.15 million tourists from 2019 to 2021 in China. The results show that domestic tourism in Beijing was even higher after the pandemic than prior to it. In addition, we found that female and elderly groups had a slower recovery after the first wave, whereas this was the opposite one year later, after the second wave. Additionally, wealthier, larger cities were notably hit the hardest. Overall, our findings provide a better understanding of tourism management in public health crises and policy-making during post-pandemic recovery and for future outbreaks.
Collapse
Affiliation(s)
- Ling Yu
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
| | - Pengjun Zhao
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
- School of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Junqing Tang
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
| | - Liang Pang
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
| |
Collapse
|
12
|
Bai Y, Xu M, Liu C, Shen M, Wang L, Tian L, Tan S, Zhang L, Holme P, Lu X, Lau EHY, Cowling BJ, Du Z. Travel-related Importation and Exportation Risks of SARS-CoV-2 Omicron Variant in 367 Prefectures (Cities) - China, 2022. China CDC Wkly 2022; 4:885-889. [PMID: 36285319 PMCID: PMC9579982 DOI: 10.46234/ccdcw2022.184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/21/2022] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTION Minimizing the importation and exportation risks of coronavirus disease 2019 (COVID-19) is a primary concern for sustaining the "Dynamic COVID-zero" strategy in China. Risk estimation is essential for cities to conduct before relaxing border control measures. METHODS Informed by the daily number of passengers traveling between 367 prefectures (cities) in China, this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks. RESULTS Under the transmission scenario (R0 =5.49), this study estimated the cumulative case incidence of Changchun City, Jilin Province as 3,233 (95% confidence interval: 1,480, 4,986) before a lockdown on March 14, 2022, which is close to the 3,168 cases reported in real life by March 16, 2022. In a total of 367 prefectures (cities), 127 (35%) had high exportation risks according to the simulation and could transmit the disease to 50% of all other regions within a period from 17 to 94 days. The average time until a new infection arrives in a location in 1 of the 367 prefectures (cities) ranged from 26 to 101 days. CONCLUSIONS Estimating COVID-19 importation and exportation risks is necessary for preparedness, prevention, and control measures of COVID-19 - especially when new variants emerge.
Collapse
Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mingda Xu
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Caifen Liu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an City, Shaanxi Province, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Linwei Tian
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Suoyi Tan
- College of Systems Engineering, National University of Defense Technology, Changsha City, Hunan Province, China
| | - Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an City, Shaanxi Province, China
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, Finland,Center for Computational Social Science, Kobe University, Kobe, Japan
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha City, Hunan Province, China
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong Special Administrative Region, China,Benjamin J. Cowling,
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
| |
Collapse
|
13
|
Lai S, Bogoch II, Ruktanonchai NW, Watts A, Lu X, Yang W, Yu H, Khan K, Tatem AJ. Assessing spread risk of COVID-19 within and beyond China in early 2020. DATA SCIENCE AND MANAGEMENT 2022. [PMCID: PMC9411104 DOI: 10.1016/j.dsm.2022.08.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
14
|
Zhang Y, Wang L, Zhu JJH, Wang X. The spatial dissemination of COVID-19 and associated socio-economic consequences. J R Soc Interface 2022; 19:20210662. [PMID: 35167771 PMCID: PMC8847004 DOI: 10.1098/rsif.2021.0662] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.
Collapse
Affiliation(s)
- Yafei Zhang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, People's Republic of China.,Department of Media and Communication, and School of Data Science, City University of Hong Kong, Hong Kong S.A.R., People's Republic of China
| | - Lin Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, People's Republic of China
| | - Jonathan J H Zhu
- Department of Media and Communication, and School of Data Science, City University of Hong Kong, Hong Kong S.A.R., People's Republic of China
| | - Xiaofan Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, People's Republic of China.,Department of Automation, Shanghai University, Shanghai 200444, People's Republic of China
| |
Collapse
|
15
|
Lai S, Sorichetta A, Steele J, Ruktanonchai CW, Cunningham AD, Rogers G, Koper P, Woods D, Bondarenko M, Ruktanonchai NW, Shi W, Tatem AJ. Global holiday datasets for understanding seasonal human mobility and population dynamics. Sci Data 2022; 9:17. [PMID: 35058466 PMCID: PMC8776767 DOI: 10.1038/s41597-022-01120-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 12/10/2021] [Indexed: 11/17/2022] Open
Abstract
Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.
Collapse
Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Jessica Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Corrine W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Alexander D Cunningham
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Grant Rogers
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Patrycja Koper
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Dorothea Woods
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Weifeng Shi
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| |
Collapse
|
16
|
Affiliation(s)
- Sune Lehmann
- DTU Compute, Technical University of Denmark, Denmark
- Center for Social Data Science, University of Copenhagen, Denmark
| |
Collapse
|