The first infection of COVID-19 in Singapore was reported on 4 February, 2020. However, scientific knowledge about the COVID-19 pandemic is still limited, particularly on how the social attributes (population, ethnicity composition, average income, social cohesiveness etc.) and human-made environment (roads, buildings etc.) of a neighbourhood contribute to epidemiological risk.

‘A Socio-Ecological Perspective on COVID-19 Spatiotemporal Integrated Vulnerability in Singapore’ (in Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic (2021)), by Dr Chan-Hoong Leong (SUSS), Dr Wei Chien Benny Chin (SUTD), Associate Professor Chen-Chieh Feng, and Associate Professor Yi-Chen Wang (both NUS Geography), develops a measure of COVID-19 vulnerability in space and time in Singapore. Specifically, the authors use the data on movement restrictions from the COVID-19 outbreak to highlight how an epidemic may evolve over space and time due to the inherent risks to certain subgroups and the type of amenities that attract human congregation and transmission.

Spatiotemporal vulnerability refers to a particular location’s vulnerability to devastations from hazards. To measure spatiotemporal vulnerability to COVID-19 across Singapore, the authors developed an Integrated Vulnerability (IV) model for each subzone. Monthly data on public transport utilisation, housing resale prices, and land use categories were used to study demographic profiles in each subzone. The authors also developed a metric to account for the additional effects arising from human mobility and risk exposures by different social groups on each subzone’s vulnerability (known as ‘spatial lag effects’).

The authors found that the central business district had slightly lower vulnerability relative to other regions, which could be due to its lower population and density. After the Circuit Breaker measures kicked in, the IV of most subzones fell, with only a few subzones remaining at a relatively high level. In particular, Lorong 8 Toa Payoh’s IV and Pei Chun’s IV remained high, the cause of which requires further research.

The study also revealed that although young and elderly residents were confined at home during the lockdown, they might still be vulnerable because some of their family members may have had to travel to work. As the spatial lag effect increases, the more mobile youth and elderly would have an impact on regional IV.  However, this impact was less significant, which could be because the population of these two groups was lower than that of working adults. Alternatively, it could also be because different age groups were facing different epidemiological risks, which were not incorporated into the model.

The study’s limitations are as follows: the time period covered could be extended; the IV model made various assumptions about the demographic profiles, e.g., that elderly residents are more vulnerable; other population parameters, such as ethnicity and gender, could have been considered. Nevertheless, the authors conclude that spatiotemporal vulnerability could be shaped by the urban environment, local demographic characteristics, spatial lag effects, and other forces.

Read the chapter here: https://www.springer.com/gp/book/9783030728076