Abstract
The COVID-19 epidemic has unleashed a trail of health and economic destruction since the first infected patient was reported in Wuhan, China in late 2019. While this disease is seemingly not as deadly compared to SARS, Ebola, or MERS, it is an exceptionally virulent plague. Evidence has suggested that certain segments of the population and environmental attributes are more vulnerable. Specifically, the elderly people and those with pre-existing medical conditions reported the highest morbidity from COVID-19 infection. Places that are densely populated, with voluminous human traffic, and fleeting social interactions are ostensibly most conducive for viral transmission. Geospatial networks with high centrality and transitivity such as public transportations, leisure and recreational spaces, and workplaces, are locations most susceptible to COVID-19. In response to this epidemic, Singapore entered into a lockdown to curb the spread. All but essential workers such as those in healthcare, public services, and critical supply chains, were required to work from home and minimize interpersonal contact. This study aims to understand local vulnerability by introducing changes of risks and human mobilities across space and time. The study develops a socio-ecological framework of epidemiology using a set of social, built, and spatial features known to influence disease transmission. Subzones with higher integrated vulnerabilities could receive greater epidemiological attention and support in future pandemics.
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Notes
- 1.
About 10 subzones make up a planning area, a broader division of regional towns with about 70,000 to 100,000 residents.
- 2.
A “coffee shop” is colloquial term that refers to a place that offers dine-in food, drinks and a place where people gather to interact.
- 3.
Moving distance parameter for sensitivity analysis is different from Table 6.2 as it was meant to measure a more active radius of mobility and interactions.
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Acknowledgements
The preparation of this manuscript by the second author was supported by Singapore University of Technology and Design (Cities Sector: PIE-SGP-CTRS-1803).
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See Fig. S1.
The histograms of: a all local vulnerability values, b–g the local vulnerability values for each month from January to June. All seven sub-plots shared the same set of Jenk’s natural breaks that was calculated based on all local vulnerability values. Jenk’s natural breaks method would generate breaks that aims to minimize the variance within group and maximize the variance between groups. As a result, all the five breaks (excluding the minimum and maximum values) located at the lower point (valley) in the histogram presented in (a). We generated Jenk’s natural breaks on all local vulnerability values so that the six months shared the same set of break values, and thus the results in different months can be compared
See Fig. S2.
The histograms of: a all IV, b–g the IV for each month from January to June. All seven sub-plots shared the same set of Jenk’s natural breaks that was calculated based on all IV. Similar to Figure S1, the Jenk’s natural break values located at the lower points in (a). We generated Jenk’s natural breaks on all IV so that the six months shared the same set of break values, and thus the results in different months can be compared
See Fig. S3.
The boxenplot of the integrated vulnerability for each parameter combination set
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Leong, CH., Chin, W.C.B., Feng, CC., Wang, YC. (2021). A Socio-Ecological Perspective on COVID-19 Spatiotemporal Integrated Vulnerability in Singapore. In: Shaw, SL., Sui, D. (eds) Mapping COVID-19 in Space and Time. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-72808-3_6
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