全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Assessing the Spatial Equality of COVID Testing Sites Maintaining Zero COVID Policy

DOI: 10.4236/jgis.2024.163012, PP. 183-200

Keywords: COVID-19, Testing Sites, Spatial Disparities, Spatial Equality, Guangzhou City, Accessibility

Full-Text   Cite this paper   Add to My Lib

Abstract:

Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.

References

[1]  Tao, R., Downs, J., Beckie, T.M., Chen, Y. and McNelley, W. (2020) Examining Spatial Accessibility to COVID-19 Testing Sites in Florida. Annals of GIS, 26, 319-327.
https://doi.org/10.1080/19475683.2020.1833365
[2]  Grigsby-Toussaint, D.S., Shin, J.C. and Jones, A. (2021) Disparities in the Distribution of COVID-19 Testing Sites in Black and Latino Areas in New York City. Preventive Medicine, 147, Article ID: 106463.
https://doi.org/10.1016/j.ypmed.2021.106463
[3]  Ma, Y., Xu, S., An, Q., Qin, M., Li, S., Lu, K., et al. (2022) Coronavirus Disease 2019 Epidemic Prediction in Shanghai under the “Dynamic Zero-Covid Policy” Using Time-Dependent SEAIQR Model. Journal of Biosafety and Biosecurity, 4, 105-113.
https://doi.org/10.1016/j.jobb.2022.06.002
[4]  Silver, A. (2021) Covid-19: Why China Is Sticking to “Zero Tolerance” Public Health Measures. BMJ, 375, n2756.
https://doi.org/10.1136/bmj.n2756
[5]  Marshall, A.T., Hackman, D.A., Kan, E., Abad, S., Baker, F.C., Baskin-Sommers, A., et al. (2022) Location Matters: Regional Variation in Association of Community Burden of COVID-19 with Caregiver and Youth Worry. Health & Place, 77, Article ID: 102885.
https://doi.org/10.1016/j.healthplace.2022.102885
[6]  Lau, S.S.S., Ho, C.C.Y., Pang, R.C.K., Su, S., Kwok, H., Fung, S., et al. (2022) COVID-19 Burnout Subject to the Dynamic Zero-COVID Policy in Hong Kong: Development and Psychometric Evaluation of the COVID-19 Burnout Frequency Scale. Sustainability, 14, Article No. 8235.
https://doi.org/10.3390/su14148235
[7]  Bai, W., Sha, S., Cheung, T., Su, Z., Jackson, T. and Xiang, Y. (2022) Optimizing the Dynamic Zero-COVID Policy in China. International Journal of Biological Sciences, 18, 5314-5316.
https://doi.org/10.7150/ijbs.75699
[8]  Liu, J., Liu, M. and Liang, W. (2022) The Dynamic COVID-Zero Strategy in China. China CDC Weekly, 4, 74-75.
https://doi.org/10.46234/ccdcw2022.015
[9]  Cheshmehzangi, A., Zou, T. and Su, Z. (2022) Commentary: China’s Zero-COVID Approach Depends on Shanghai’s Outbreak Control. Frontiers in Public Health, 10, Article ID: 912992.
https://doi.org/10.3389/fpubh.2022.912992
[10]  Kim, S., Watson, K., Khare, N., Shastri, S., Goia Pintpo, C.D. and Nazir, N. (2021) Addressing Racial/Ethnic Equity in Access to COVID-19 Testing through Drive-Thru and Walk-In Testing Sites in Chicago. Medical Research Archives, 9, Article No. 2430.
https://doi.org/10.18103/mra.v9i5.2430
[11]  Shea, S., Nguyen, T., Kim, D.H., Gee, G.C., Wang, M.C. and Umemoto, K. (2024) Lessons Learned from TranslateCovid, a Multilingual Online Resource Hub for Asian American and Pacific Islander Communities and Beyond. Public Health Reports.
https://doi.org/10.1177/00333549241236092
[12]  Hernandez, J.H., Karletsos, D., Avegno, J. and Reed, C.H. (2021) Is Covid-19 Community Level Testing Effective in Reaching At-Risk Populations? Evidence from Spatial Analysis of New Orleans Patient Data at Walk-Up Sites. BMC Public Health, 21, Article No. 632.
https://doi.org/10.1186/s12889-021-10717-9
[13]  Qi, F., Barragan, D., Rodriguez, M.G. and Lu, J. (2022) Evaluating Spatial Accessibility to COVID-19 Vaccine Resources in Diversely Populated Counties in the United States. Frontiers in Public Health, 10, Article ID: 895538.
https://doi.org/10.3389/fpubh.2022.895538
[14]  Hendricks, B., Price, B.S., Dotson, T., Kimble, W., Davis, S., Khodaverdi, M., et al. (2023) If You Build It, Will They Come? Is Test Site Availability a Root Cause of Geographic Disparities in COVID-19 Testing? Public Health, 216, 21-26.
https://doi.org/10.1016/j.puhe.2022.09.009
[15]  Xu, J. and Yeh, A.G.O. (2003) Guangzhou. Cities, 20, 361-374.
https://doi.org/10.1016/s0264-2751(03)00056-8
[16]  Niu, M. and Wu, Y. (2019) Financing Urban Growth in China: A Case Study of Guangzhou. Australian Journal of Social Issues, 55, 141-161.
https://doi.org/10.1002/ajs4.87
[17]  Li, L., Dong, H., Han, D., et al. (2021) Temporal Dynamic in the Impact of COVID-19 Outbreak on Cause-Specific Mortality in Guangzhou, China. BMC Public Health, 21, Article No. 883.
https://doi.org/10.1186/s12889-021-10771-3
[18]  Liu, K., Yin, L., Lu, F. and Mou, N. (2020) Visualizing and Exploring POI Configurations of Urban Regions on Poi-Type Semantic Space. Cities, 99, Article ID: 102610.
https://doi.org/10.1016/j.cities.2020.102610
[19]  Guangzhou Statistics Bureau (2019) Guangzhou Statistical Yearbook 2019. China Statistics Press.
[20]  Xie, Z. and Yan, J. (2008) Kernel Density Estimation of Traffic Accidents in a Network Space. Computers, Environment and Urban Systems, 32, 396-406.
https://doi.org/10.1016/j.compenvurbsys.2008.05.001
[21]  Mehmood, M.S., Jin, A., Rehman, A., Ahamad, M.I. and Li, G. (2022) Spatial Variability and Accessibility of Collection and Delivery Points in Nanjing, China. Computational Urban Science, 2, Article No. 27.
https://doi.org/10.1007/s43762-022-00054-x
[22]  Haq, M.I.U., Li, Q. and Hassan, S. (2019) Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing. IEEE Access, 7, 162254-162267.
https://doi.org/10.1109/access.2019.2950045
[23]  Abousaeidi, M., Fauzi, R. and Muhamad, R. (2016) Geographic Information System (GIS) Modeling Approach to Determine the Fastest Delivery Routes. Saudi Journal of Biological Sciences, 23, 555-564.
https://doi.org/10.1016/j.sjbs.2015.06.004
[24]  Xia, S., Xiong, Z., Luo, Y., WeiXu, and Zhang, G. (2015) Effectiveness of the Euclidean Distance in High Dimensional Spaces. Optik, 126, 5614-5619.
https://doi.org/10.1016/j.ijleo.2015.09.093
[25]  Sherali, H.D. and Tuncbilek, C.H. (1992) A Squared-Euclidean Distance Location-Allocation Problem. Naval Research Logistics, 39, 447-469.
https://doi.org/10.1002/1520-6750(199206)39:4<447::aid-nav3220390403>3.0.co;2-o
[26]  Okabe, A., Satoh, T. and Sugihara, K. (2009) A Kernel Density Estimation Method for Networks, Its Computational Method and a GIS-Based Tool. International Journal of Geographical Information Science, 23, 7-32.
https://doi.org/10.1080/13658810802475491
[27]  Asabor, E.N., Warren, J.L. and Cohen, T. (2022) Racial/Ethnic Segregation and Access to COVID-19 Testing: Spatial Distribution of COVID-19 Testing Sites in the Four Largest Highly Segregated Cities in the United States. American Journal of Public Health, 112, 518-526.
https://doi.org/10.2105/ajph.2021.306558
[28]  Chen, Y., Tao, R. and Downs, J. (2022) Location Optimization of COVID-19 Vaccination Sites: Case in Hillsborough County, Florida. International Journal of Environmental Research and Public Health, 19, Article No. 12443.
https://doi.org/10.3390/ijerph191912443
[29]  Baser, O. (2021) Population Density Index and Its Use for Distribution of Covid-19: A Case Study Using Turkish Data. Health Policy, 125, 148-154.
https://doi.org/10.1016/j.healthpol.2020.10.003
[30]  Seto, E., Min, E., Ingram, C., Cummings, B. and Farquhar, S.A. (2020) Community-Level Factors Associated with COVID-19 Cases and Testing Equity in King County, Washington. International Journal of Environmental Research and Public Health, 17, Article No. 9516.
https://doi.org/10.3390/ijerph17249516
[31]  Ahmad, R.A., Indriani, C., Arisanti, R.R., Nanda, R.O., Mahendradhata, Y. and Wibawa, T. (2023) Seroprevalence of SARS-CoV-2 and Risk Factors in Bantul Regency in March-April 2021, Yogyakarta, Indonesia. PLOS Global Public Health, 3, e0000698.
https://doi.org/10.1371/journal.pgph.0000698
[32]  Moreno, C., Allam, Z., Chabaud, D., Gall, C. and Pratlong, F. (2021) Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities, 4, 93-111.
https://doi.org/10.3390/smartcities4010006
[33]  Huang, Q., Jackson, S., Derakhshan, S., Lee, L., Pham, E., Jackson, A., et al. (2021) Urban-Rural Differences in COVID-19 Exposures and Outcomes in the South: A Preliminary Analysis of South Carolina. PLOS ONE, 16, e0246548.
https://doi.org/10.1371/journal.pone.0246548
[34]  Callaghan, T., Lueck, J.A., Trujillo, K.L. and Ferdinand, A.O. (2021) Rural and Urban Differences in COVID-19 Prevention Behaviors. The Journal of Rural Health, 37, 287-295.
https://doi.org/10.1111/jrh.12556
[35]  Zhang, X., Dupre, M.E., Qiu, L., Zhou, W., Zhao, Y. and Gu, D. (2017) Urban-Rural Differences in the Association between Access to Healthcare and Health Outcomes among Older Adults in China. BMC Geriatrics, 17, Article No. 151.
https://doi.org/10.1186/s12877-017-0538-9

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413