Contrasting SARS-CoV-2 Epidemics in Singapore: Cohort Studies in Migrant Workers and the General Population (preprint)

Background: From January 2020, Singapore implemented comprehensive measures to suppress SARS-CoV-2. Community transmission has been limited, although explosive outbreaks have occurred in migrant worker dormitories.Methods: We conducted longitudinal SARS-CoV-2 serology studies among 478 residents of a SARS-CoV-2 affected migrant worker dormitory between May and July 2020, and 937 community-dwelling adult Singapore residents with sera collected before September 2019 and in November/December 2020. We tested sera for SARS-CoV-2 neutralising antibodies and used Bayesian methods to estimate infection incidence accounting for test sensitivity and specificity.Findings: We estimated that <2 per 1000 adult residents in the community were infected with SARS-CoV-2 in 2020 (cumulative seroprevalence: 0.16% (95% CrI: 0.008% - 0.72%), approximately 4 times higher than the national notified case incidence. In contrast, in the migrant worker cohort, nearly two-thirds had been infected by July 2020 (cumulative seroprevalence: 63.8% (95% CrI: 57.9% - 70.3%); no symptoms were reported in almost all of these infections.Conclusions: Our findings demonstrate that SARS-CoV-2 suppression is possible with rapid implementation of border restrictions, case isolation, contact tracing, quarantining and social distancing measures. However, the risk of large-scale epidemics in densely-populated environments requires specific consideration in preparedness planning. Prioritising these settings in vaccination strategies should minimise risk of future resurgences and potential for spillover to the wider community.Funding: This work was funded by the Wellcome Trust grant (221013/Z/20/Z), the NUS Efforts Against COVID-19 fund and the Singapore National Medical Research Council (STPRG-FY19-001 and COVID19RF-003).Declaration of Interests: L-FW and WNC are co-inventors of a patent application for the cPass test kit. The remaining authors declare no competing interests in relation to this work.Ethics Approval Statement: The community cohort study was approved by the National University of Singapore Institutional Review Board (reference H-20-032).

Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network (preprint)

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying "influential passengers" using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.