Trends in STI testing, diagnoses, and use of online chlamydia self-sampling services among young people during the first year of the COVID-19 pandemic in England (preprint)

Purpose: Measures to control COVID-19 reduced face-to-face appointments and walk-ins at sexual health services (SHSs). Remote access to SHSs through online self-sampling for STIs was increased. This analysis assesses how these changes affected service use and STI testing among young people in England. Methods: Data on all chlamydia, gonorrhoea and syphilis tests from 2019-2020 amongst English-resident 15-24 year olds (hereafter referred to as young people) were obtained from national STI surveillance datasets. We calculated proportional differences in tests and diagnoses for each STI, by demographic characteristics including age and socioeconomic deprivation, between 2019 and 2020. Among those tested for chlamydia, we used binary logistic regression to determine crude and adjusted odds ratios (OR) between demographic characteristics and being tested for chlamydia by an online service. Results: Compared to 2019, there were declines in testing (30% for chlamydia, 26% for gonorrhoea, 36% for syphilis) and diagnoses (31% for chlamydia, 25% for gonorrhoea and 23% for syphilis) among young people in 2020. These reductions were greater amongst 15-19 year-olds (vs. 20-24 year-olds). Among young people tested for chlamydia, those living in the least deprived areas were more likely to be tested using an online self-sampling kit compared to those living in the most deprived areas (males; OR=1.24[1.22-1.26], females; OR=1.28[1.27-1.30]). Conclusion: The first year of the COVID-19 pandemic in England saw declines in STI testing and diagnoses in young people and disparities in the use of online chlamydia self-sampling which risk widening existing health inequalities.

Beyond COVID-19: Network science and sustainable exit strategies (preprint)

On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in Network Science and Epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in Network Science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.

Differentially Private Health Tokens for Estimating COVID-19 Risk (preprint)

In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an `immuno-privileged' class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens -- randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.

TraceSecure: Towards Privacy Preserving Contact Tracing (preprint)

Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns, however, regarding the privacy and security of users data when using these apps. These concerns are paramount for users who contract the virus, as they are generally required to release all their data. Motivated by the need to protect users privacy we propose two solutions to this problem. Our first solution builds on current "message based" methods and our second leverages ideas from secret sharing and additively homomorphic encryption.