Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

Abstract

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

Longitudinal Wastewater-Based Surveillance for SARS-CoV-2 in High-Density Student Dormitories in Singapore

“…Throughout the coronavirus disease 2019 (COVID-19) pandemic, environments with a high density of inhabitants, such as hospitals, assisted living facilities, cruise ships, schools, and universities have been identified as particularly prone to outbreaks . Close proximity and frequent interactions in those environments can facilitate the transmission of the etiological agent, that is, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). , Since 2020, various national and international guidance and policy documents have recommended continuous monitoring of such environs to minimize the emergence of local COVID-19 clusters and outbreaks. − Clinical diagnostics using antigen tests and nasopharyngeal swabbing proved successful in mitigating outbreaks by identifying and localizing symptomatic and asymptomatic virus carriers in universities − and hospitals .…”

“…These include the prevalence of the disease agent in a population, population size and density, 25 and ventilation of spaces occupied by infected people. 26,27 The campus hostels/dormitories contain two types of buildings: (1) buildings where residential rooms are connected by interior hallways (some may have windows), which aretermed enclosed corridors, and (2) buildings where residential rooms are connected by open-air covered walkways defined by partial walls or railings, which are termed open corridors. Fully enclosed areas have been shown to have an increased risk for transmitting SARS-CoV-2, while wellventilated areas have a reduced risk for transmission.…”

Uneven geographies: ageing and population dynamics in Latvia

“…Finally, current research on the COVID-19 pandemic shows that the risk of death among infected people increases rapidly with age. Additionally, it was shown that illness incidence is higher in densely inhabited and urbanized areas (Ferguson et al, 2020;Gurram et al, 2022;Nazia, 2022;Pilkington et al, 2021). European regions are ageing unequally; thus, coronavirus SARS-CoV-2 poses particular challenges to regions and populations with greater proportions of vulnerable older people (Kashnitsky & Aburto, 2020).…”

Analyzing the Trends of COVID-19 and Human Activity Intensity in Malaysia

“…This study focuses on two objectives: (1) to demonstrate how the waves of the disease cases and the check-ins can be identified, and (2) if there is a relationship between the disease situation and human activity intensity. Previous studies showed that human activity intensity and disease cases were positively correlated [ 27 , 28 , 29 ]. This means that when a place has a higher intensity of human activity, it may lead to a more serious outbreak.…”