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Kuo FY, Wen TH. Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 148:102804. [PMID: 36267149 PMCID: PMC9567310 DOI: 10.1016/j.apgeog.2022.102804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
The rapid spread of a (re)emerging pandemic (e.g., COVID-19) is usually attributed to the invisible transmission caused by asymptomatic cases. Health authorities rely on large-scale voluntary screening to identify and isolate invisible spreaders as well as symptomatic people as early as possible to control disease spread. Raising public awareness is beneficial for improving the effectiveness of epidemic prevention because it could increase the usage and demand for testing kits. However, the effectiveness of testing could be influenced by the spatial demand for medical resources in different periods. Spatial demand could also be triggered by public awareness in areas with two geographical factors, including spatial proximity to resources and attractiveness of human mobility. Therefore, it is necessary to explore the spatial variations in raising public awareness on the effectiveness of COVID-19 screening. We implemented spatial simulation models to integrate various levels of public awareness and pandemic dynamics in time and space. Moreover, we also assessed the effects of the spatial proximity of testing kits and the ease of human mobility on COVID-19 testing at various levels of public awareness. Our results indicated that high public awareness promotes high willingness to be tested. This causes the demand to not be fully satisfied at the peak times during a pandemic, yet the shortage of tests does not significantly increase pandemic severity. We also found that when public awareness is low, concentrating on unattractive areas (such as residential or urban fringe areas) could promote a higher benefit of testing. On the other hand, when awareness is high, the factor of distances to testing stations is more important for promoting the benefit of testing; allocating additional testing resources in areas distant from stations could have a higher benefit of testing. This study aims to provide insights for health authorities into the allocation of testing resources against disease outbreaks with respect to various levels of public awareness.
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Affiliation(s)
- Fei-Ying Kuo
- Department of Geography, National Taiwan University, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taiwan
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Singer BJ, Thompson RN, Bonsall MB. Evaluating strategies for spatial allocation of vaccines based on risk and centrality. J R Soc Interface 2022; 19:20210709. [PMID: 35167774 PMCID: PMC8847001 DOI: 10.1098/rsif.2021.0709] [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] [Indexed: 11/12/2022] Open
Abstract
When vaccinating a large population in response to an invading pathogen, it is often necessary to prioritize some individuals to be vaccinated first. One way to do this is to choose individuals to vaccinate based on their location. Methods for this prioritization include strategies that target those regions most at risk of importing the pathogen, and strategies that target regions with high centrality on the travel network. We use a simple infectious disease epidemic model to compare a risk-targeting strategy to two different centrality-targeting strategies based on betweenness centrality and random walk percolation centrality, respectively. We find that the relative effectiveness of these strategies in reducing the total number of infections varies with the basic reproduction number of the pathogen, travel rates, structure of the travel network and vaccine availability. We conclude that when a pathogen has high spreading capacity, or when vaccine availability is limited, centrality-targeting strategies should be considered as an alternative to the more commonly used risk-targeting strategies.
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Affiliation(s)
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Coventry, UK.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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3
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Intracity Pandemic Risk Evaluation Using Mobile Phone Data: The Case of Shanghai during COVID-19. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020. [DOI: 10.3390/ijgi9120715] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the framework: identification of high-risk groups, calculation of dynamic population flow and construction of a human mobility network, exposure and transmission risk assessment, and pandemic prevention guidelines. First, high-risk groups were extracted from mobile phone data based on multi-day activity chains. Second, daily human mobility networks were created by aggregating population and origin-destination (OD) flows. Third, clustering analysis, time series analysis, and network analysis were employed to evaluate pandemic risk. Finally, several solutions are proposed to control the pandemic. The outbreak period of COVID-19 in Shanghai was used to verify the proposed framework and methodology. The results show that the evaluation method is able to reflect the different spatiotemporal patterns of pandemic risk. The proposed framework and methodology may help prevent future public health emergencies and localized epidemics from evolving into global pandemics.
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Savini L, Candeloro L, Calistri P, Conte A. A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy. Microorganisms 2020; 8:microorganisms8060911. [PMID: 32560207 PMCID: PMC7355905 DOI: 10.3390/microorganisms8060911] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/04/2020] [Accepted: 06/15/2020] [Indexed: 11/16/2022] Open
Abstract
In February 2020, Italy became the epicenter for COVID-19 in Europe, and at the beginning of March, the Italian Government put in place emergency measures to restrict population movement. Aim of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy, using commuting data at a high spatial resolution, characterizing the territory in terms of vulnerability. We used a Susceptible–Infectious stochastic model and we estimated a municipality-specific infection contact rate (β) to capture the susceptibility to the disease. We identified in Lombardy, Veneto and Emilia Romagna regions (52% of all Italian cases) significant clusters of high β, due to the simultaneous presence of connections between municipalities and high population density. Local simulated spreading in regions, with different levels of infection observed, showed different disease geographical patterns due to different β values and commuting systems. In addition, we produced a vulnerability map (in the Abruzzi region as an example) by simulating the epidemic considering each municipality as a seed. The result shows the highest vulnerability values in areas with commercial hubs, close to the highest populated cities and the most industrial area. Our results highlight how human mobility can affect the epidemic, identifying particular situations in which the health authorities can promptly intervene to control the disease spread.
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Savini L, Candeloro L, Calistri P, Conte A. A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy. Microorganisms 2020. [PMID: 32560207 DOI: 10.1101/2020.05.12.20100040v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023] Open
Abstract
In February 2020, Italy became the epicenter for COVID-19 in Europe, and at the beginning of March, the Italian Government put in place emergency measures to restrict population movement. Aim of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy, using commuting data at a high spatial resolution, characterizing the territory in terms of vulnerability. We used a Susceptible-Infectious stochastic model and we estimated a municipality-specific infection contact rate () to capture the susceptibility to the disease. We identified in Lombardy, Veneto and Emilia Romagna regions (52% of all Italian cases) significant clusters of high , due to the simultaneous presence of connections between municipalities and high population density. Local simulated spreading in regions, with different levels of infection observed, showed different disease geographical patterns due to different values and commuting systems. In addition, we produced a vulnerability map (in the Abruzzi region as an example) by simulating the epidemic considering each municipality as a seed. The result shows the highest vulnerability values in areas with commercial hubs, close to the highest populated cities and the most industrial area. Our results highlight how human mobility can affect the epidemic, identifying particular situations in which the health authorities can promptly intervene to control the disease spread.
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Affiliation(s)
- Lara Savini
- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis. Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise "G. Caporale", 64100 Teramo, Italy
| | - Luca Candeloro
- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis. Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise "G. Caporale", 64100 Teramo, Italy
| | - Paolo Calistri
- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis. Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise "G. Caporale", 64100 Teramo, Italy
| | - Annamaria Conte
- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis. Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise "G. Caporale", 64100 Teramo, Italy
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Riascos AP, Mateos JL. Networks and long-range mobility in cities: A study of more than one billion taxi trips in New York City. Sci Rep 2020; 10:4022. [PMID: 32132592 PMCID: PMC7055277 DOI: 10.1038/s41598-020-60875-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/07/2020] [Indexed: 11/16/2022] Open
Abstract
We analyze the massive data set of more than one billion taxi trips in New York City, from January 2009 to December 2015. With these records of seven years, we generate an origin-destination matrix that has information of a vast number of trips. The mobility and flow of taxis can be described as a directed weighted network that connects different zones of high demand for taxis. This network has in and out degrees that follow a stretched exponential and a power law with an exponential cutoff distributions, respectively. Using the origin-destination matrix, we obtain a rank, called "OD rank”, analogous to the page rank of Google, that gives the more relevant places in New York City in terms of taxi trips. We introduced a model that captures the local and global dynamics that agrees with the data. Considering the taxi trips as a proxy of human mobility in cities, it might be possible that the long-range mobility found for New York City would be a general feature in other large cities around the world.
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Affiliation(s)
- A P Riascos
- Instituto de Física, Universidad Nacional Autónoma de México, Apartado Postal 20-364, 01000, Ciudad de México, Mexico.
| | - José L Mateos
- Instituto de Física, Universidad Nacional Autónoma de México, Apartado Postal 20-364, 01000, Ciudad de México, Mexico. .,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Apartado Postal 04510, Ciudad de México, Mexico.
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Zhou S, Zhou S, Liu L, Zhang M, Kang M, Xiao J, Song T. Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16245013. [PMID: 31835451 PMCID: PMC6950619 DOI: 10.3390/ijerph16245013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/05/2019] [Accepted: 12/06/2019] [Indexed: 12/25/2022]
Abstract
Environment and human mobility have been considered as two important factors that drive the outbreak and transmission of dengue fever (DF). Most studies focus on the local environment while neglecting environment of the places, especially epidemic areas that people came from or traveled to. Commuting is a major form of interactions between places. Therefore, this research generates commuting flows from mobile phone tracked data. Geographically weighted Poisson regression (GWPR) and analysis of variance (ANOVA) are used to examine the effect of commuting flows, especially those from/to epidemic areas, on DF in 2014 at the Jiedao level in Guangzhou. The results suggest that (1) commuting flows from/to epidemic areas affect the transmission of DF; (2) such effects vary in space; and (3) the spatial variation of the effects can be explained by the environment of the epidemic areas that commuters commuted from/to. These findings have important policy implications for making effective intervention strategies, especially when resources are limited.
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Affiliation(s)
- Shuli Zhou
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
| | - Suhong Zhou
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
- Correspondence: (S.Z.); (T.S.)
| | - Lin Liu
- Center of Geo-Informatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;
- Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China;
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
- Correspondence: (S.Z.); (T.S.)
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