Revisiting COVID-19 policies: 10 evidence-based recommendations for where to go from here

Background Strategies to control coronavirus 2019 disease (COVID-19) have often been based on preliminary and limited data and have tended to be slow to evolve as new evidence emerges. Yet knowledge about COVID-19 has grown exponentially, and the expanding rollout of vaccines presents further opportunity to reassess the response to the pandemic more broadly. Main text We review the latest evidence concerning 10 key COVID-19 policy and strategic areas, specifically addressing: 1) the expansion of equitable vaccine distribution, 2) the need to ease restrictions as hospitalization and mortality rates eventually fall, 3) the advantages of emphasizing educational and harm reduction approaches over coercive and punitive measures, 4) the need to encourage outdoor activities, 5) the imperative to reopen schools, 6) the far-reaching and long-term economic and psychosocial consequences of sustained lockdowns, 7) the excessive focus on surface disinfection and other ineffective measures, 8) the importance of reassessing testing policies and practices, 9) the need for increasing access to outpatient therapies and prophylactics, and 10) the necessity to better prepare for future pandemics. Conclusions While remarkably effective vaccines have engendered great hope, some widely held assumptions underlying current policy approaches call for an evidence-based reassessment. COVID-19 will require ongoing mitigation for the foreseeable future as it transforms from a pandemic into an endemic infection, but maintaining a constant state of emergency is not viable. A more realistic public health approach is to adjust current mitigation goals to be more data-driven and to minimize unintended harms associated with unfocused or ineffective control efforts. Based on the latest evidence, we therefore present recommendations for refining 10 key policy areas, and for applying lessons learned from COVID-19 to prevent and prepare for future pandemics.

Estimating the impact of virus testing strategies on the COVID-19 case fatality rate using fixed-effects models

The SARS-CoV2 has now spread worldwide causing over four million deaths. Testing strategies are highly variable between countries and their impact on mortality is a major issue. Retrospective multicenter study with a prospective database on all inpatients throughout mainland France. Using fixed effects models, we exploit policy discontinuities at region borders in France to estimate the effect of testing on the case fatality rate. In France, testing policies are determined at a regional level, generating exogenous variation in testing rates between departments on each side of a region border. We compared all contiguous department pairs located on the opposite sides of a region border. The increase of one percentage point in the test rate is associated with a decrease of 0.0015 percentage point in the death rate, that is, for each additional 2000 tests, we could observe three fewer deaths. Our study suggests that COVID-19 population testing could have a significant impact on the mortality rate which should be considered in decision-making. As concern grows over the current second wave of COVID-19, our findings support the implementation of large-scale screening strategies in such epidemic contexts.

Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.

HIV testing in termination of pregnancy and colposcopy services: a scoping review

BackgroundWomen and girls are relatively under-represented across the HIV treatment cascade. Two conditions unique to women, pregnancy and cervical cancer/dysplasia, share a common acquisition mode with HIV. This scoping review aimed to explore HIV testing practices in voluntary termination of pregnancy (TOP) and colposcopy services.MethodsThe scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. We searched articles published up to 20 December 2020 using three electronic databases (PubMed/Medline, Embase, Google Scholar) and including the keywords “HIV Testing”, “Abortion, Induced”, “Colposcopy”, “HIV screen*” and “termination of pregnancy”.ResultsA total of 1496 articles were identified, of which 55 met the inclusion criteria. We included studies providing background HIV prevalence in addition to prevalence in the study population and studies of women seeking TOP rather than presenting with TOP complications. This limited our review to high-income, low HIV prevalence settings. We observed two study phases: studies pre-antiretroviral therapy (ART) using unlinked anonymous testing data and examining HIV risk factors associated with positive HIV tests and studies post-ART using routine testing data and exploring HIV testing uptake. HIV prevalence was estimated at >0.2% in most TOP settings and >1% (range 1.7%–11.4%) in colposcopy services. Many TOP providers did not have local HIV testing policies and HIV testing was not mentioned in many specialist guidelines. Testing uptake was 49%–96% in TOP and 23%–75% in colposcopy services.ConclusionGiven the estimated HIV prevalence of >0.1% among women attending TOP and colposcopy services, HIV testing would be economically feasible to perform in high-income settings. Explicit testing policies are frequently lacking in these two settings, both at the local level and in specialist guidelines. Offering HIV testing regardless of risk factors could normalise testing, reduce late HIV presentation and create an opportunity for preventive counselling.

Optimal Testing Strategies to Monitor COVID-19 Traced Contacts.

The quarantine of identified close contacts has been vital to reducing transmission rates and averting secondary infection risk before symptom onset and by asymptomatic cases. The effectiveness of this contact tracing strategy to mitigate transmission is sensitive to the adherence to quarantines, which may be lower for longer quarantine periods or in vaccinated populations (where perceptions of risk are reduced). This study develops a simulation model to evaluate contact tracing strategies based on the sequential testing of identified contacts after exposure as an alternative to quarantines, in which contacts are isolated only after confirmation by a positive test. The analysis considers different number and types of tests (PCR and lateral flow antigen tests (LFA)) to identify the cost-effective testing policies that minimize the expected infecting days post-exposure considering different levels of testing capacity. This analysis suggests that even a limited number of tests can be effective at reducing secondary infection risk: two LFA tests (with optimal timing) avert infectiousness at a level that is comparable to 14-day quarantine with 90% adherence; adding a third test (PCR or LFA) reaches the efficiency of a 95% quarantine adherence. These results are robust to the exposure dates of the contact, which suggests that simple testing rules can be effective for improving contact tracing in settings where strict quarantine adherence is difficult to implement.

On the effectiveness of tracking and testing in SEIR models for improving health vs. economy trade-offs

We study the effectiveness of tracking and testing policies for suppressing epidemic outbreaks. We evaluate the performance of tracking-based intervention methods on a network SEIR model, which we augment with an additional parameter to model pre-symptomatic and asymptomatic individuals, and study the effectiveness of these methods in combination with or as an alternative to quarantine and global lockdown policies. Our focus is on the basic trade-off between human-lives lost and economic costs, and on how this trade-off changes under different quarantine, lockdown, tracking, and testing policies. Our main findings are as follows: (1) Tests combined with patient quarantines reduce both economic costs and mortality, however, an extensive-scale testing capacity is required to achieve a significant improvement. (2) Tracking significantly reduces both economic costs and mortality. (3) Tracking combined with a moderate testing capacity can achieve containment without lockdowns. (4) In the presence of a flow of new incoming infections, dynamic “On–Off” lockdowns are more efficient than fixed lockdowns. In this setting as well, tracking strictly improves efficiency. The results show the extreme usefulness of policies that combine tracking and testing for reducing mortality and economic costs, and their potential to contain outbreaks without imposing any social distancing restrictions. This highlights the difficult social question of trading-off these gains against patient privacy, which is inevitably infringed by tracking.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.