Use of a digital contact tracing system in Singapore to mitigate COVID-19 spread.

BACKGROUND: Contact tracing has been essential to reducing spread of COVID-19. Singapore leveraged technology to assist with contact tracing efforts using a Bluetooth-based app and token platform called 'TraceTogether'. METHODS: We reviewed the impact of this system during the country's Delta and Omicron waves (24 August 2021 to 17 February 2022) to identify differences in number of close contacts and time savings between full automation using TraceTogether alone as compared to manual contact tracing supplemented by TraceTogether. Characteristics of digital contact tracing app or token users were reviewed. Thereafter, the number of close contacts identified by manual and digital contact tracing methods, and the number of confirmed COVID-19 cases among contacts were analysed. The difference in time taken for identification of close contacts was also determined. FINDINGS: Adoption rate for TraceTogether was high, with 93.3% of cases having a registered device. There was a 9.8 h (34.9%) reduction in time savings for close contacts to be informed using TraceTogether alone compared to manual contact tracing supplemented by TraceTogether. The proportion of close contacts automatically identified through TraceTogether alone and turned positive was 3.6%. For those identified through manual contact tracing supplemented by TraceTogether, this proportion was 12.5% and 6.2% for those served quarantine orders and health risk warnings respectively. INTERPRETATION: The high adoption rate of 'TraceTogether' suggest that digital solutions remain a promising option to improve contact tracing in future epidemics. This may have been through its concurrent use with vaccine differentiated public health measures and policies which engender public trust. There is future potential for utilising such technology in managing communicable diseases to achieve good public health outcomes.

Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore-a spatio-temporal model.

Singapore experienced its first known Zika outbreak in 2016. Given the lack of herd immunity, the suitability of the climate for pathogen transmission, and the year-round presence of the vector- Aedes aegypti-Zika had the potential to become endemic, like dengue. Guillain-Barré syndrome and microcephaly are severe complications associated elsewhere with Zika and the risk of these complications makes understanding its spread imperative. We investigated the spatio-temporal spread of locally transmitted Zika in Singapore and assessed the relevance of non-residential transmission of Zika virus infections, by inferring the possible infection tree (i.e. who-infected-whom-where) and comparing inferences using geographically resolved data on cases' home, their work, or their home and work. We developed a spatio-temporal model using time of onset and both addresses of the Zika-confirmed cases between July and September 2016 to estimate the infection tree using Bayesian data augmentation. Workplaces were involved in a considerable fraction (64.2%) of infections, and homes and workplaces may be distant relative to the scale of transmission, allowing ambulant infected persons may act as the 'vector' infecting distant parts of the country. Contact tracing is a challenge for mosquito-borne diseases, but inferring the geographically structured transmission tree sheds light on the spatial transmission of Zika to immunologically naive regions of the country.