Uncertainties in the Assessment of COVID-19 Risk: A Study of People’s Exposure to High-Risk Environments Using Individual-Level Activity Data

References

• Adam, D., P. Wu, J. Wong, E. Lau, T. Tsang, S. Cauchemez, G. Leung, and B. Cowling. 2020. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine 26 (11):1714–19. doi: https://doi.org/10.1038/s41591-020-1092-0.
   PubMed Web of Science ®Google Scholar
• Alkhamis, M. A., S. Al Youha, M. M. Khajah, N. B. Haider, S. Alhardan, A. Nabeel, S. Al Mazeedi, and S. K. Al-Sabah. 2020. Spatiotemporal dynamics of the COVID-19 pandemic in the State of Kuwait. International Journal of Infectious Diseases: Official Publication of the International Society for Infectious Diseases 98:153–60. doi: https://doi.org/10.1016/j.ijid.2020.06.078.
   Web of Science ®Google Scholar
• Amram, O., S. Amiri, R. B. Lutz, B. Rajan, and P. Monsivais. 2020. Development of a vulnerability index for diagnosis with the novel coronavirus, COVID-19, in Washington State, USA. Health & Place 64:102377. doi: https://doi.org/10.1016/j.healthplace.2020.102377.
   PubMed Web of Science ®Google Scholar
• Azuma, K., N. Kagi, H. Kim, and M. Hayashi. 2020. Impact of climate and ambient air pollution on the epidemic growth during COVID-19 outbreak in Japan. Environmental Research 190:110042. doi: https://doi.org/10.1016/j.envres.2020.110042.
   PubMed Web of Science ®Google Scholar
• Badr, H. S., H. Du, M. Marshall, E. Dong, M. M. Squire, and L. M. Gardner. 2020. Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. The Lancet Infectious Diseases 20 (11):1247–54. doi: https://doi.org/10.1016/S1473-3099(20)30553-3.
   PubMed Web of Science ®Google Scholar
• Baker, M. G., T. K. Peckham, and N. S. Seixas. 2020. Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection. PLoS ONE 15 (4):e0232452. doi: https://doi.org/10.1371/journal.pone.0232452.
   PubMed Web of Science ®Google Scholar
• Bian, L. 2004. A conceptual framework for an individual-based spatially explicit epidemiological model. Environment and Planning B: Planning and Design 31 (3):381–95. doi: https://doi.org/10.1068/b2833.
   Web of Science ®Google Scholar
• Bian, L., Y. Huang, L. Mao, E. Lim, G. Lee, Y. Yang, M. Cohen, and D. Wilson. 2012. Modeling individual vulnerability to communicable diseases: A framework and design. Annals of the Association of American Geographers 102 (5):1016–25. doi: https://doi.org/10.1080/00045608.2012.674844.
   Web of Science ®Google Scholar
• Brunsdon, C., J. Corcoran, and G. Higgs. 2007. Visualising space and time in crime patterns: A comparison of methods. Computers, Environment and Urban Systems 31 (1):52–75. doi: https://doi.org/10.1016/j.compenvurbsys.2005.07.009.
   Google Scholar
• Chang, S., E. Pierson, P. W. Koh, J. Gerardin, B. Redbird, D. Grusky, and J. Leskovec. 2021. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589 (7840):82–87. doi: https://doi.org/10.1038/s41586-020-2923-3.
   PubMed Web of Science ®Google Scholar
• Cheng, T., and M. Adepeju. 2014. Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection. PLoS ONE 9 (6):e100465. doi: https://doi.org/10.1371/journal.pone.0100465.
   PubMed Web of Science ®Google Scholar
• Coccia, M. 2020. Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Science of the Total Environment 729:138474. doi: https://doi.org/10.1016/j.scitotenv.2020.138474.
   PubMed Web of Science ®Google Scholar
• Crawford, T. W., S. B. J. Pitts, J. T. McGuirt, T. C. Keyserling, and A. S. Ammerman. 2014. Conceptualizing and comparing neighborhood and activity space measures for food environment research. Health & Place 30:215–25. doi: https://doi.org/10.1016/j.healthplace.2014.09.007.
   PubMed Web of Science ®Google Scholar
• Das, A., S. Ghosh, K. Das, T. Basu, M. Das, and I. Dutta. 2020. Modelling the effect of area-deprivation on COVID-19 incidences: A study of Chennai Megacity, India. Public Health 185:266–69. doi: https://doi.org/10.1016/j.puhe.2020.06.011.
   Web of Science ®Google Scholar
• Desjardins, M. R., A. Hohl, and E. M. Delmelle. 2020. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Applied Geography 118:102202. doi: https://doi.org/10.1016/j.apgeog.2020.102202.
   PubMed Web of Science ®Google Scholar
• Durfey, S. N., A. J. Kind, W. R. Buckingham, E. H. DuGoff, and A. N. Trivedi. 2019. Neighborhood disadvantage and chronic disease management. Health Services Research 54:206–16. doi: https://doi.org/10.1111/1475-6773.13092.
   PubMed Web of Science ®Google Scholar
• Epanechnikov, V. A. 1969. Non-parametric estimation of a multivariate probability density. Theory of Probability & Its Applications 14 (1):153–58. doi: https://doi.org/10.1137/1114019.
   Web of Science ®Google Scholar
• Fattorini, D., and F. Regoli. 2020. Role of the chronic air pollution levels in the COVID-19 outbreak risk in Italy. Environmental Pollution 264:114732. doi:https://doi.org/10.1016/j.envpol.2020.114732Get.
   PubMed Web of Science ®Google Scholar
• Gao, S., J. Rao, Y. Kang, Y. Liang, J. Kruse, D. Dopfer, A. K. Sethi, J. F. M. Reyes, B. S. Yandell, and J. A. Patz. 2020. Association of mobile phone location data indications of travel and stay-at-home mandates with COVID-19 infection rates in the U.S. JAMA Network Open 3 (9):e2020485. doi: https://doi.org/10.1001/jamanetworkopen.2020.20485.
   PubMed Web of Science ®Google Scholar
• Google LLC. 2020. Google COVID-19 community mobility reports. Accessed January 7, 2020. https://www.google.com/covid19/mobility/.
   Google Scholar
• Gozzi, N., M. Tizzoni, M. Chinazzi, L. Ferres, A. Vespignani, and N. Perra. 2021. Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile. Nature Communications 12 (1):2429. doi: https://doi.org/10.1038/s41467-021-22601-6.
   PubMed Web of Science ®Google Scholar
• Hamidi, S., S. Sabouri, and R. Ewing. 2020. Does density aggravate the COVID-19 pandemic? Early findings and lessons for planners. Journal of the American Planning Association 86 (4):495–509. doi: https://doi.org/10.1080/01944363.2020.1777891.
   Web of Science ®Google Scholar
• Harapan, H., N. Itoh, A. Yufika, W. Winardi, S. Keam, H. Te, D. Megawati, Z. Hayati, A. L. Wagner, and M. Mudatsir. 2020. Coronavirus disease 2019 (COVID-19): A literature review. Journal of Infection and Public Health 13 (5):667–73. doi: https://doi.org/10.1016/j.jiph.2020.03.019.
   PubMed Web of Science ®Google Scholar
• Helbich, M., M. H. M. Browning, and M.-P. Kwan. 2021. Time to address the spatiotemporal uncertainties in COVID-19 research: Concerns and challenges. The Science of the Total Environment 764:142866. doi: https://doi.org/10.1016/j.scitotenv.2020.142866.
   Web of Science ®Google Scholar
• Hohl, A., E. Delmelle, W. Tang, and I. Casas. 2016. Accelerating the discovery of space–time patterns of infectious diseases using parallel computing. Spatial and Spatiotemporal Epidemiology 19:10–20. doi: https://doi.org/10.1016/j.sste.2016.05.002.
   Web of Science ®Google Scholar
• Holsten, J. E. 2009. Obesity and the community food environment: A systematic review. Public Health Nutrition 12 (3):397–405. doi: https://doi.org/10.1017/S1368980008002267.
   PubMed Web of Science ®Google Scholar
• Huang, J., M.-P. Kwan, Z. Kan, M. S. Wong, C. Y. T. Kwok, and X. Yu. 2020. Investigating the relationship between the built environment and relative risk of COVID-19 in Hong Kong. ISPRS International Journal of Geo-Information 9 (11):624. doi: https://doi.org/10.3390/ijgi9110624.
   Web of Science ®Google Scholar
• Hung, W. T., A. Manandhar, and S. A. Ranasinghege. 2010. A walkability survey in Hong Kong. In Proceedings of TRANSED 2010: 12th International Conference on Mobility and Transport for Elderly and Disabled Persons, June 1–4, 2010, Hong Kong, China. https://trid.trb.org/view/1126939.
   Google Scholar
• Kan, Z., M.-P. Kwan, M. S. Wong, J. Huang, and D. Liu. 2021. Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong. The Science of the Total Environment 772 (10):145379. doi: https://doi.org/10.1016/j.scitotenv.2021.145379.
   Google Scholar
• Kim, J., and M.-P. Kwan. 2019. Beyond commuting: Ignoring individuals’ activity-travel patterns may lead to inaccurate assessments of their exposure to traffic congestion. International Journal of Environmental Research and Public Health 16 (1):89. doi: https://doi.org/10.3390/ijerph16010089.
   Web of Science ®Google Scholar
• Kim, J., and M.-P. Kwan. 2021a. Assessment of sociodemographic disparities in environmental exposure might be erroneous due to neighborhood effect averaging: Implications for environmental inequality research. Environmental Research 195:110519. doi: https://doi.org/10.1016/j.envres.2020.110519.
   PubMed Web of Science ®Google Scholar
• Kim, J., and M.-P. Kwan. 2021b. How neighborhood effect averaging might affect assessment of individual exposures to air pollution: A study of ozone exposures in Los Angeles. Annals of the American Association of Geographers 111 (1):121–40. doi: https://doi.org/10.1080/24694452.2020.1756208.
   Web of Science ®Google Scholar
• Kim, J., and M.-P. Kwan. 2021c. The impact of the COVID-19 pandemic on people’s mobility: A longitudinal study of the U.S. from March to September of 2020. Journal of Transport Geography 93:103039. doi: https://doi.org/10.1016/j.jtrangeo.2021.103039.
   PubMed Web of Science ®Google Scholar
• Kodera, S., E. A. Rashed, and A. Hirata. 2020. Correlation between COVID-19 morbidity and mortality rates in Japan and local population density, temperature, and absolute humidity. International Journal of Environmental Research and Public Health 17 (15):5477. doi: https://doi.org/10.3390/ijerph17155477.
   PubMed Web of Science ®Google Scholar
• Kou, L., M.-P. Kwan, and Y. Chai. 2020. The effects of activity-related contexts on individual sound exposures: A time–geographic approach to soundscape studies. Environment and Planning B: Urban Analytics and City Science. Advance online publication. doi: https://doi.org/10.1177/2399808320965243.
   Google Scholar
• Kraemer, M. U. G., C.-H. Yang, B. Gutierrez, C.-H. Wu, B. Klein, D. M. Pigott, L. Du Plessis, N. R. Faria, R. Li, W. P. Hanage, et al. 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368 (6490):493–97. doi: https://doi.org/10.1126/science.abb4218.
   PubMed Web of Science ®Google Scholar
• Kwan, M.-P. 2012. The uncertain geographic context problem. Annals of the Association of American Geographers 102 (5):958–68. doi: https://doi.org/10.1080/00045608.2012.687349.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2013. Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility: Space–time integration in geography and GIScience. Annals of the Association of American Geographers 103 (5):1078–86. doi: https://doi.org/10.1080/00045608.2013.792177.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2018a. The limits of the neighborhood effect: Contextual uncertainties in geographic, environmental health, and social science research. Annals of the American Association of Geographers 108 (6):1482–90. doi: https://doi.org/10.1080/24694452.2018.1453777.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2018b. The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health 15 (9):1841. doi: https://doi.org/10.3390/ijerph15091841.
   PubMed Web of Science ®Google Scholar
• Kwan, M.-P., J. Wang, M. Tyburski, D. H. Epstein, W. J. Kowalczyk, and K. L. Preston. 2019. Uncertainties in the geographic context of health behaviors: A study of substance users’ exposure to psychosocial stress using GPS data. International Journal of Geographical Information Science 33 (6):1176–95. doi: https://doi.org/10.1080/13658816.2018.1503276.
   Web of Science ®Google Scholar
• Kwok, C. Y. T., M. S. Wong, K. L. Chan, M.-P. Kwan, J. E. Nichol, C. H. Liu, J. Y. H. Wong, A. K. C. Wai, L. W. C. Chan, Y. Xu, et al. 2021. Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19: A study in Hong Kong. Science of the Total Environment 764:144455. doi: https://doi.org/10.1016/j.scitotenv.2020.144455.
   Web of Science ®Google Scholar
• Lai, S., A. Farnham, N. W. Ruktanonchai, and A. J. Tatem. 2019. Measuring mobility, disease connectivity and individual risk: A review of using mobile phone data and mHealth for travel medicine. Journal of Travel Medicine 26 (3):1–9. doi: https://doi.org/10.1093/jtm/taz019.
   Google Scholar
• Law, S., A. W. Leung, and C. Xu. 2020. “Third wave” of COVID-19 pandemic in Hong Kong. Bangladesh Journal of Infectious Diseases 7:S61–S62. doi: https://doi.org/10.3329/bjid.v7i00.50165.
   Google Scholar
• Lee, W. D., M. Qian, and T. Schwanen. 2021. The association between socioeconomic status and mobility reductions in the early stage of England’s COVID-19 epidemic. Health & Place 69:102563. doi: https://doi.org/10.1016/j.healthplace.2021.102563.
   Web of Science ®Google Scholar
• Li, M., X. Shi, X. Li, W. Ma, J. He, and T. Liu. 2019. Epidemic forest: A spatiotemporal model for communicable diseases. Annals of the American Association of Geographers 109 (3):812–36. doi: https://doi.org/10.1080/24694452.2018.1511413.
   Web of Science ®Google Scholar
• Ma, J., Y. Tao, M.-P. Kwan, and Y. Chai. 2020. Assessing mobility-based real-time air pollution exposure in space and time using smart sensors and GPS trajectories in Beijing. Annals of the American Association of Geographers 110 (2):434–48. doi: https://doi.org/10.1080/24694452.2019.1653752.
   Web of Science ®Google Scholar
• Ma, S., L. Yang, M.-P. Kwan, Z. Zuo, H. Qian, and M. Li. 2021. Do individuals’ activity structures influence their PM2.5 exposure levels? Evidence from human trajectory data in Wuhan city. International Journal of Environmental Research and Public Health 18 (9):4583. doi: https://doi.org/10.3390/ijerph18094583.
   Web of Science ®Google Scholar
• Ma, X., X. Li, M.-P. Kwan, and Y. Chai. 2020. Who could not avoid exposure to high levels of residence-based pollution by daily mobility? Evidence of air pollution exposure from the perspective of the neighborhood effect averaging problem (NEAP). International Journal of Environmental Research and Public Health 17 (4):1223. doi: https://doi.org/10.3390/ijerph17041223.
   Web of Science ®Google Scholar
• Nakaya, T., and K. Yano. 2010. Visualising crime clusters in a space‐time cube: An exploratory data‐analysis approach using space‐time kernel density estimation and scan statistics. Transactions in GIS 14 (3):223–39. doi: https://doi.org/10.1111/j.1467-9671.2010.01194.x.
   Web of Science ®Google Scholar
• Park, Y. M., and M.-P. Kwan. 2017. Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health Place 43:85–94. doi: https://doi.org/10.1016/j.healthplace.2016.10.002.
   PubMed Web of Science ®Google Scholar
• Pullano, G., E. Valdano, N. Scarpa, S. Rubrichi, and V. Colizza. 2020. Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: A population-based study. The Lancet Digital Health 2 (12):e638–e649. doi: https://doi.org/10.1016/S2589-7500(20)30243-0.
   PubMedGoogle Scholar
• Shi, X., M. Li, O. Hunter, B. Guetti, A. Andrew, E. Stommel, W. Bradley, and M. Karagas. 2019. Estimation of environmental exposure: Interpolation, kernel density Estimation, or snapshotting. Annals of GIS 25 (1):1–8. doi: https://doi.org/10.1080/19475683.2018.1555188.
   PubMed Web of Science ®Google Scholar
• St‐Denis. X. 2020. Sociodemographic determinants of occupational risks of exposure to COVID‐19 in Canada. Canadian Review of Sociology/Revue Canadienne de Sociologie 57 (3):399–452. doi: https://doi.org/10.1111/cars.12288.
   Web of Science ®Google Scholar
• Stoddard, S. T., A. C. Morrison, G. M. Vazquez-Prokopec, V. P. Soldan, T. J. Kochel, U. Kitron, J. P. Elder, and T. W. Scott. 2009. The role of human movement in the transmission of vector-borne pathogens. PLoS Neglected Tropical Diseases 3 (7):e481. doi: https://doi.org/10.1371/journal.pntd.0000481.
   PubMed Web of Science ®Google Scholar
• Tan, Y., M.-P. Kwan, and Z. Chen. 2020. Examining ethnic exposure through the perspective of the neighborhood effect averaging problem: A case study of Xining, China. International Journal of Environmental Research and Public Health 17 (8):2872. doi: https://doi.org/10.3390/ijerph17082872.
   PubMed Web of Science ®Google Scholar
• Thornton, L. E., J. R. Pearce, and A. M. Kavanagh. 2011. Using geographic information systems (GIS) to assess the role of the built environment in influencing obesity: A glossary. International Journal of Behavioral Nutrition and Physical Activity 8 (1):71. doi: https://doi.org/10.1186/1479-5868-8-71.
   PubMedGoogle Scholar
• Tian, H., Y. Liu, Y. Li, C.-H. Wu, B. Chen, M. U. G. Kraemer, B. Li, J. Cai, B. Xu, Q. Yang, et al. 2020. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368 (6491):638–42. doi: https://doi.org/10.1126/science.abb6105.
   PubMed Web of Science ®Google Scholar
• van Doremalen, N., T. Bushmaker, D. H. Morris, M. G. Holbrook, A. Gamble, B. N. Williamson, A. Tamin, J. L. Harcourt, N. J. Thornburg, S. I. Gerber, et al. 2020. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine 382 (16):1564–67. doi: https://doi.org/10.1056/NEJMc2004973.
   PubMed Web of Science ®Google Scholar
• Viboud, C., O. N. Bjørnstad, D. L. Smith, L. Simonsen, M. A. Miller, and B. T. Grenfell. 2006. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312 (5772):447–51. doi: https://doi.org/10.1126/science.1125237.
   PubMed Web of Science ®Google Scholar
• Wang, J., and M.-P. Kwan. 2018. An analytical framework for integrating the spatiotemporal dynamics of environmental context and individual mobility in exposure assessment: A study on the relationship between food environment exposures and body weight. International Journal of Environmental Research and Public Health 15 (9):2022. doi: https://doi.org/10.3390/ijerph15092022.
   Web of Science ®Google Scholar
• Wang, J., M.-P. Kwan, and Y. Chai. 2018. An innovative context-based crystal-growth activity space method for environmental exposure assessment: A study using GIS and GPS trajectory data collected in Chicago. International Journal of Environmental Research and Public Health 15 (4):703. doi: https://doi.org/10.3390/ijerph15040703.
   PubMed Web of Science ®Google Scholar
• Wang, V., and T. May. 2021. In “coffin homes” and “cages,” Hong Kong lockdown exposes inequality. New York Times, January 26. Accessed May 10, 2021. https://www.nytimes.com/2021/01/26/world/asia/hong-kong-coronavirus-lockdown-inequality.html?_ga=2.185757673.1763379367.1617417488-133072336.1607935695.
   Google Scholar
• Wesolowski, A., N. Eagle, A. J. Tatem, D. L. Smith, A. M. Noor, R. W. Snow, and C. O. Buckee. 2012. Quantifying the impact of human mobility on malaria. Science 338 (6104):267–70. doi: https://doi.org/10.1126/science.1223467.
   PubMed Web of Science ®Google Scholar
• Willberg, E., O. Järv, T. Väisänen, and T. Toivonen. 2021. Escaping from cities during the COVID-19 crisis: Using mobile phone data to trace mobility in Finland. ISPRS International Journal of Geo-Information 10 (2):103. doi: https://doi.org/10.3390/ijgi10020103.
   Web of Science ®Google Scholar
• World Health Organization. 2021. Coronavirus disease (COVID-19): Weekly epidemiological, update—30 March 2021. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19—31-march-2021.
   Google Scholar
• Wu, X., R. C. Nethery, M. B. Sabath, D. Braun, and F. Dominici. 2020. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science Advances 6 (45):eabd4049. doi: https://doi.org/10.1126/sciadv.abd4049.
   PubMed Web of Science ®Google Scholar
• Xiong, C., S. Hu, M. Yang, W. Luo, and L. Zhang. 2020. Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections. Proceedings of the National Academy of Sciences of the United States of America 117 (44):27087–89. doi: https://doi.org/10.1073/pnas.2010836117.
   PubMed Web of Science ®Google Scholar
• Xiong, Y., Y. Wang, F. Chen, and M. Zhu. 2020. Spatial statistics and influencing factors of the COVID-19 epidemic at both prefecture and county levels in Hubei Province, China. International Journal of Environmental Research and Public Health 17 (11):3903. doi: https://doi.org/10.3390/ijerph17113903.
   Web of Science ®Google Scholar
• Yu, X., M. S. Wong, M.-P. Kwan, J. E. Nichol, R. Zhu, J. Heo, P. W. Chan, D. C. W. Chin, C. Y. T. Kwok, and Z. Kan. 2021. COVID-19 infection and mortality: Association with PM2.5 concentration and population size—An exploratory study. ISPRS International Journal of Geo-Information 10 (3):123. doi: https://doi.org/10.3390/ijgi10030123.
   Web of Science ®Google Scholar
• Zhang, L., S. Zhou, M.-P. Kwan, F. Chen, and R. Lin. 2018. Impacts of individual daily greenspace exposure on health based on individual activity space and structural equation modeling. International Journal of Environmental Research and Public Health 15 (10):2323. doi: https://doi.org/10.3390/ijerph15102323.
   PubMed Web of Science ®Google Scholar
• Zhao, P., M.-P. Kwan, and S. Zhou. 2018. The uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in Guangzhou. International Journal of Environmental Research and Public Health 15 (2):308. doi: https://doi.org/10.3390/ijerph15020308.
   Web of Science ®Google Scholar