Volatility and heterogeneity of vaccine sentiments means continuous monitoring is needed when measuring message effectiveness (preprint)

Background The success of vaccination programs often depends on the effectiveness of the vaccine messages, particularly during emergencies such as the COVID-19 pandemic. The current suboptimal uptake of COVID-19 vaccines across many parts of the world highlights the tremendous challenges in overcoming vaccine hesitancy and refusal even in the context of a world-devastating pandemic. Methods We conducted a randomized controlled trial in Hong Kong to evaluate the impact of seven vaccine messages on COVID-19 vaccine uptake (with the government slogan as the control). The participants included 127,000 individuals who googled COVID-19-related information during July-October 2021. Results The impact of vaccine messages on uptake varied substantially over time and among different groups of users. For example, the message that emphasized the indirect protection of vaccination on family members (i) increased overall uptake by 30% (6-59%) in July but had no effect afterwards for English language users; and (ii) had no effect on overall uptake for Chinese language users throughout the study. Such volatility and heterogeneity in message effectiveness highlight the limitations of one-size-fits-all and static vaccine communication. Conclusions Epidemic nowcasting should include real-time monitoring of vaccine hesitancy and message effectiveness, in order to adapt messaging appropriately. This dynamic dimension of surveillance has so far been underinvested. Trial registration The study was registered at ClinicalTrials.gov (NCT05499299).

COVID-19 Datathon Based on Deidentified Governmental Data as an Approach for Solving Policy Challenges, Increasing Trust, and Building a Community: Case Study (preprint)

Triggered by the COVID-19 crisis, Israel's Ministry of Health (MoH) held a virtual Datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel's research community was invited to offer insights to COVID-19 policy challenges. The Datathon was designed to (1) develop operationalizable data-driven models to address COVID-19 health-policy challenges and (2) build a community of researchers from academia, industry, and government and rebuild their trust in the government. Three specific challenges were defined based on their relevance (significance, data availability, and potential to anonymize the data): immunization policies, special needs of the young population, and populations whose rate of compliance with COVID-19 testing is low. The MoH team extracted diverse, reliable, up-to-date, and deidentified governmental datasets for each challenge. Secure remote-access research environments with relevant data science tools were set on Amazon Web. The MoH screened the applicants and accepted around 80 participants, teaming them to balance areas of expertise as well as represent all sectors of the community. One week following the event, anonymous surveys for participants and mentors were distributed to assess overall usefulness and points for improvement. The 48-hour Datathon and pre-event sessions included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the 3 winning teams are currently considered by the MoH as potential data science methods relevant for national policies. The most positive results were increased trust in the MoH and greater readiness to work with the government on these or future projects. Detailed feedback offered concrete lessons for improving the structure and organization of future government-led datathons.

Association of COVID19-induced Anosmia and Ageusia with Depression and Suicidal Ideation (preprint)

Background. Clinical reports from patients suffering from the novel coronavirus (COVID-19) reflect a high prevalence of sensory deprivation or loss pertaining to smell (dysosmia/anosmia) and/or taste (dysgeusia/ageusia). Given the importance of the senses to daily functioning and personal experience, the mental health consequences of these symptoms warrant further attention. Methods. A cohort of Reddit users posting within the /r/covid19positive subforum (N=15,821) was leveraged to analyze instantaneous risk of transition to a state of suicidal ideation or depression using Cox proportional-hazards models. Risk transition was defined by posts made in suicide- or depression-related forums, or mentions of relevant phrases with and without mention of anosmia/ageusia in /r/covid19positive. Self-diagnosis of COVID-19 was also modeled as a separate and simultaneous predictor of mental health risk. Results. Mention of anosmia/ageusia was significantly associated with transition to a risk state. Users with a history of anosmia/ageusia-related posts and who self-identified as COVID-19 positive had 30% higher instantaneous risk relative to others. The highest increase in instantaneous risk of suicidal ideation or depression occurred more than 100 days after first posting in /r/covid19positive. Limitations. Use of self-diagnosed disease as well as a broad array of anosmia/ageusia-related terminology may entail both information bias and overestimates of symptom incidence. Conclusions. The specific effects of COVID-19 on the senses may have long-term implications for patient mental health well-being beyond the primary recovery period. Future work is needed to investigate the longitudinal mental health burden of residual COVID-19 symptom presentation.

Tracking COVID-19 using online search (preprint)

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest -- as opposed to infections -- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.