Government trust, perceptions of COVID-19 and behaviour change: cohort surveys, Singapore.

OBJECTIVE: To evaluate how public perceptions and trust in government communications affected the adoption of protective behaviour in Singapore during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We launched our community-based cohort to assess public perceptions of infectious disease outbreaks in mid-2019. After the first case of COVID-19 was reported in Singapore on 23 January, we launched a series of seven COVID-19 surveys to both existing and regularly enrolled new participants every 2 weeks. As well as sociodemographic properties of the participants, we recorded changing responses to judge awareness of the situation, trust in various information sources and perceived risk. We used multivariable logistic regression models to evaluate associations with perceptions of risk and self-reported adopted frequencies of protective behaviour. FINDINGS: Our cohort of 633 participants provided 2857 unique responses during the seven COVID-19 surveys. Most agreed or strongly agreed that information from official government sources (99.1%; 528/533) and Singapore-based news agencies (97.9%; 522/533) was trustworthy. Trust in government communication was significantly associated with higher perceived threat (odds ratio, OR: 2.2; 95% confidence interval, CI: 1.6-3.0), but inversely associated with perceived risk of infection (OR: 0.6; 95% CI: 0.4-0.8) or risk of death if infected (OR: 0.6; 95% CI: 0.4-0.9). Trust in government communication was also associated with a greater likelihood of adopting protective behaviour. CONCLUSION: Our findings show that trust is a vital commodity when managing an evolving outbreak. Our repeated surveys provided real-time feedback, allowing an improved understanding of the interplay between perceptions, trust and behaviour.

Identifying COVID-19 cases in primary care settings

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness at point of assessment. Clinical differentiators of cases and controls were used to derive model-based risk scores. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5{degrees}C and 37.9{degrees}C, and temperature above 38{degrees}C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, we firstly used leave-one-out cross-validation, which confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. Secondly, in a separate ongoing prospective study of 237 COVID-19 and 346 primary care patients presenting with symptoms of acute respiratory infection, the baseline model had a sensitivity of 0.57 and specificity of 0.89, and in retrospective notes review of 100 COVID-19 cases diagnosed in primary care, sensitivity was 0.56. A web-app based tool has been developed for easy implementation as an adjunct to laboratory testing to differentiate COVID-19 positive cases among patients presenting in outpatient settings.