Back

CoVID-19 in Singapore: Impact of Contact Tracing and Self-awareness on Healthcare Demand

Huang, Q.; Wang, L.; Yang, Y.; Huang, L.; Du, Z.; Xiao, G.

2020-06-05 health policy
10.1101/2020.06.04.20122879 medRxiv
Show abstract

BackgroundA great concern around the globe now is to mitigate the COVID-19 pandemic via contact tracing. Analyzing the control strategies during the first five months of 2020 in Singapore is important to estimate the effectiveness of contacting tracing measures. MethodsWe developed a mathematical model to simulate the COVID-19 epidemic in Singapore, with local cases stratified into 5 categories according to the conditions of contact tracing and self-awareness. Key parameters of each category were estimated from local surveillance data. We also simulated a set of possible scenarios to predict the effects of contact tracing and self-awareness for the following month. FindingsDuring January 23 - March 16, 2020, the success probabilities of contact tracing and self-awareness were estimated to be 31% (95% CI 28%-33%) and 54% (95% CI 51%-57%), respectively. During March 17 - April 7, 2020, several social distancing measures (e.g., limiting mass gathering) were introduced in Singapore, which, however, were estimated with minor contribution to reduce the non-tracing reproduction number per local case (R{iota},2). If contact tracing and self-awareness cannot be further improved, we predict that the COVID-19 epidemic will continue to spread in Singapore if R{iota},2 [≥] 1.5. ConclusionContact tracing and self-awareness can mitigate the COVID-19 transmission, and can be one of the key strategies to ensure a sustainable reopening after lifting the lockdown. SummaryWe evaluate the efficiency of contact tracing and self-awareness in Singapores early-stage control of COVID-19. Then use a branching model to simulate and evaluate the possible prospective outcomes of Singapores COVID-19 control in different scenarios.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.