Generalizable principles underly the evolution and shaping of population level SARS-CoV-2 immune markers (preprint)

While individual level immune responses to SARS-CoV-2 are well characterized, population immunity and the factors that drive population immune markers are largely undescribed. In this study, we examined spike antibody responses that track with infection risk amongst a household cohort in the northwest and southeast of the Dominican Republic. Our sampling period was from Aug 2021 to Nov 2022, capturing sequential waves of Delta, BA.1, BA.2, and BA.4/5 transmission. We show that population antibody levels normalized from a highly irregular to a Gaussian distribution, driven by accrued infections and antibody boosting among individuals with lower baseline immunity and waning among those with higher immunity, irrespective of interval vaccination. Using a limited number of predictor variables and out-of-sample validation methods we were able to predict S-antibody changes at the Nov 2022 timepoint with a high degree of accuracy (Pearson’s correlation coefficient 0.95 for predicted vs observed change). S-antibody level at the baseline sampling timepoint was by far the most influential predictor, demonstrated by a strong association when used as the only predictor variable (Pearson’s correlation coefficient 0.92). Findings were stable across geographically distinct study regions, suggesting drivers of immune dynamics apply equally across the Dominican Republic, and likely other countries with comparable transmission profiles. Our results suggest that given sufficient transmission, generalizable and discernable principles underly population immune dynamics. We propose that these findings can be used to delineate immune dynamics in other settings, inform transmission modeling, and guide public health priorities for SARS-CoV-2, and potentially other non-immune sterilizing emerging pathogens.

Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic (preprint)

The COVID-19 pandemic both relied and placed significant burdens on the experts involved from research and public health sectors. The sustained high pressure of a pandemic on responders, such as healthcare workers, can lead to lasting psychological impacts including acute stress disorder, post-traumatic stress disorder, burnout, and moral injury, which can impact individual wellbeing and productivity. As members of the infectious disease modelling community, we convened a reflective workshop to understand the professional and personal impacts of response work on our community and to propose recommendations for future epidemic responses. The attendees represented a range of career stages, institutions, and disciplines. This piece was collectively produced by those present at the session based on our collective experiences. Key issues we identified at the workshop were lack of institutional support, insecure contracts, unequal credit and recognition, and mental health impacts. Our recommendations include rewarding impactful work, fostering academia-public health collaboration, decreasing dependence on key individuals by developing teams, increasing transparency in decision-making, and implementing sustainable work practices. Despite limitations in representation, this workshop provided valuable insights into the UK COVID-19 modelling experience and guidance for future public health crises. Recognising and addressing the issues highlighted here is crucial, in our view, for ensuring the effectiveness of epidemic response work in the future.

Within-host SARS-CoV-2 viral kinetics informed by complex life course exposures reveals different intrinsic properties of Omicron and Delta variants (preprint)

The emergence of successive SARS-CoV-2 variants of concern (VOC) during 2020-22, each exhibiting increased epidemic growth relative to earlier circulating variants, has created a need to understand the drivers of such growth. However, both pathogen biology and changing host characteristics - such as varying levels of immunity - can combine to influence replication and transmission of SARS-CoV-2 within and between hosts. Disentangling the role of variant and host in individual-level viral shedding of VOCs is essential to inform COVID-19 planning and response, and interpret past epidemic trends. Using data from a prospective observational cohort study of healthy adult volunteers undergoing weekly occupational health PCR screening, we developed a Bayesian hierarchical model to reconstruct individual-level viral kinetics and estimate how different factors shaped viral dynamics, measured by PCR cycle threshold (Ct) values over time. Jointly accounting for both inter-individual variation in Ct values and complex host characteristics - such as vaccination status, exposure history and age - we found that age and number of prior exposures had a strong influence on peak viral replication. Older individuals and those who had at least five prior antigen exposures to vaccination and/or infection typically had much lower levels of shedding. Moreover, we found evidence of a correlation between the speed of early shedding and duration of incubation period when comparing different VOCs and age groups. Our findings illustrate the value of linking information on participant characteristics, symptom profile and infecting variant with prospective PCR sampling, and the importance of accounting for increasingly complex population exposure landscapes when analysing the viral kinetics of VOCs.

Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia (preprint)

Estimation of the impact of vaccination and non-pharmaceutical interventions (NPIs) on COVID-19 incidence is complicated by several factors, including the successive emergence of SARS-CoV-2 variants of concern and changing population immunity resulting from vaccination and previous infection. We developed an age-structured multi-strain COVID-19 transmission model framework that could estimate the impact of vaccination and NPIs while accounting for these factors. We applied this framework to French Polynesia, which unlike many countries experienced multiple large COVID-19 waves from multiple variants over the course of the pandemic, interspersed with periods of elimination. We estimated that the vaccination programme averted 54.3% (95% CI 54.0-54.6%) of the 6840 hospitalisations and 60.2% (95% CI 59.9-60.5%) of the 1280 hospital deaths that would have occurred in a baseline scenario without any vaccination up to May 2022. Vaccination also averted an estimated 28.4% (95% CI 28.2-28.7%) of 193,000 symptomatic cases in the baseline scenario. We estimated the booster campaign contributed 3.4%, 2.9% and 3.3% to overall reductions in cases, hospitalisations and hospital deaths respectively. Our results suggested that removing, or altering the timings of, the lockdowns during the first two waves had non-linear effects on overall incidence owing to the resulting effect on accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.

Seroprevalence of SARS-CoV-2 Antibodies in French Polynesia and Perspective for Vaccine Strategies (preprint)

In French Polynesia, Wuhan, Delta and Omicron SARS-CoV-2 variants-of-concern (VOCs) caused epidemics with variable severities. We assessed the prevalence and titers of anti-SARS-CoV-2 antibodies related to natural infection and/or vaccination, from a representative sample (N=673) of the adult population of Tahiti recruited during November-December 2021 (after the Delta outbreak and just before the Omicron epidemic). Of the 673 participants tested, 644 (95.7%) had detectable antibodies against SARS-CoV-2-S and/or -N proteins resulting from natural infection and/or vaccination, and 388 (57.7%) were positive only for the detection of anti-N antibodies indicating natural infection. SARS-CoV-2 seroprevalence extrapolated to the adult population of Tahiti was estimated at 95.9%. Concentrations of anti-SARS-CoV-2-S antibodies significantly increased with age, number of self-reported SARS-CoV-2 infections (0 or ≥1), and number of COVID-19 vaccine doses (0, 1, 2, or 3) received by the participants. Elderly people, who are at higher risk of severe outcomes, had received more vaccine doses than younger individuals both in our sample and in the general population. The high level of antibody responses related to past infections and vaccination, especially booster doses, has likely contributed to reducing the severity of the Omicron outbreak in French Polynesia.

Real-time surveillance of international SARS-CoV-2 prevalence using systematic traveller arrival screening (preprint)

Background: Effective COVID-19 response relies on good knowledge of infection dynamics, but owing to under-ascertainment and delays in symptom-based reporting, obtaining reliable infection data has typically required large dedicated local population studies. Although many countries implemented SARS-CoV-2 testing among travellers, interpretation of arrival testing data has typically been challenging because arrival testing data were rarely reported systematically, and pre-departure testing was often in place as well, leading to non-representative infection status among arrivals. Methods: In French Polynesia, testing data were reported systematically with enforced pre-departure testing type and timing, making it possible to adjust for non-representative infection status among arrivals. Combining statistical models of PCR positivity with data on international travel protocols, we reconstructed estimates of prevalence at departure using only testing data from arrivals. We then applied this estimation approach to the USA and France, using data from over 220,000 tests from travellers arriving into French Polynesia between July 2020 and March 2022. Findings: We estimated a peak infection prevalence at departure of 2.8% (2.3-3.6%) in France and 1.1% (0.81-3.1%) in the USA in late 2020/early 2021, with prevalence of 5.4% (4.8-6.1%) and 5.5% (4.6-6.6%) respectively estimated for the Omicron BA.1 waves in early 2022. We found that our infection estimates were a leading indicator of later reported case dynamics, as well as being consistent with subsequent observed changes in seroprevalence over time. Interpretation: As well as elucidating previously unmeasured infection dynamics in these countries, our analysis provides a proof-of-concept for scalable tracking of global infections during future pandemics.

Estimating the increase in reproduction number associated with the Delta variant using local area dynamics in England (preprint)

Background: Local estimates of the time-varying effective reproduction number (Rt) of COVID-19 in England became increasingly heterogeneous during April and May 2021. This may have been attributable to the spread of the Delta SARS-CoV-2 variant. This paper documents real-time analysis that aimed to investigate the association between changes in the proportion of positive cases that were S-gene positive, an indicator of the Delta variant against a background of the previously predominant Alpha variant, and the estimated time-varying Rt at the level of upper-tier local authorities (UTLA). Method: We explored the relationship between the proportion of samples that were S-gene positive and the Rt of test-positive cases over time from the 23 February 2021 to the 25 May 2021. Effective reproduction numbers were estimated using the EpiNow2 R package independently for each local authority using two different estimates of the generation time. We then fit a range of regression models to estimate a multiplicative relationship between S-gene positivity and weekly mean Rt estimate. Results: We found evidence of an association between increased mean Rt estimates and the proportion of S-gene positives across all models evaluated with the magnitude of the effect increasing as model flexibility was decreased. Models that adjusted for either national level or NHS region level time-varying residuals were found to fit the data better, suggesting potential unexplained confounding. Conclusions: Our results indicated that even after adjusting for time-varying residuals between NHS regions, S-gene positivity was associated with an increase in the effective reproduction number of COVID-19. These findings were robust across a range of models and generation time assumptions, though the specific effect size was variable depending on the assumptions used. The lower bound of the estimated effect indicated that the reproduction number of Delta was above 1 in almost all local authorities throughout the period of investigation.

Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021 (preprint)

We estimate the potential remaining COVID-19 burden in 19 European countries by estimating the proportion of each country’s population that has acquired immunity to severe disease through infection or vaccination. Our results suggest that many European countries could still face a substantial burden of hospitalisations and deaths, particularly those with lower vaccination coverage, less historical transmission, and/or older populations. Continued non-pharmaceutical interventions and efforts to achieve high vaccination coverage are required in these countries to limit severe COVID-19 outcomes.

Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission (preprint)

Background Several countries have controlled the spread of COVID-19 through varying combinations of border restrictions, case finding, contact tracing and careful calibration on the resumption of domestic activities. However, evaluating the effectiveness of these measures based on observed cases alone is challenging as it does not reflect the transmission dynamics of missed infections. Methods Combining data on notified local COVID-19 cases with known and unknown sources of infections (i.e. linked and unlinked cases) in Singapore in 2020 with a transmission model, we reconstructed the incidence of missed infections and estimated the relative effectiveness of different types of outbreak control. We also examined implications for estimation of key real-time metrics — the reproduction number and ratio of unlinked to linked cases, using observed data only as compared to accounting for missed infections. Findings Prior to the partial lockdown in Singapore, initiated in April 2020, we estimated 89% (95%CI 75–99%) of the infections caused by notified cases were contact traced, but only 12.5% (95%CI 2–69%) of the infections caused by missed infectors were identified. We estimated that the reproduction number was 1.23 (95%CI 0.98–1.54) after accounting for missed infections but was 0.90 (95%CI 0.79-1.1) based on notified cases alone. At the height of the outbreak, the ratio of missed to notified infections was 34.1 (95%CI 26.0–46.6) but the ratio of unlinked to linked infections was 0.81 (95%CI 0.59–1.36). Our results suggest that when case finding and contact tracing identifies at least 50% and 20% of the infections caused by missed and notified cases respectively, the reproduction number could be reduced by more than 14%, rising to 20% when contact tracing is 80% effective. Interpretation Depending on the relative effectiveness of border restrictions, case finding and contact tracing, unobserved outbreak dynamics can vary greatly. Commonly used metrics to evaluate outbreak control — typically based on notified data — could therefore misrepresent the true underlying outbreak. Funding Ministry of Health, Singapore. Research in context Evidence before this study We searched PubMed, BioRxiv and MedRxiv for articles published in English up to Mar 20, 2021 using the terms: (2019-nCoV OR “novel coronavirus” OR COVID-19 OR SARS-CoV-2) AND (border OR travel OR restrict* OR import*) AND (“case finding” OR surveillance OR test*) AND (contact trac*) AND (model*). The majority of modelling studies evaluated the effectiveness of various combinations of interventions in the absence of outbreak data. For studies that reconstructed the initial spread of COVID-19 with outbreak data, they further simulated counterfactual scenarios in the presence or absence of these interventions to quantify the impact to the outbreak trajectory. None of the studies disentangled the effects of case finding, contact tracing, introduction of imported cases and the reproduction number, in order to reproduce an observed SARS-CoV-2 outbreak trajectory. Added value of this study Notified COVID-19 cases with unknown and known sources of infection are identified through case finding and contact tracing respectively. Their respective daily incidence and the growth rate over time may differ. By capitalising on these differences in the outbreak data and the use of a mathematical model, we could identify the key drivers behind the growth and decline of both notified and missed COVID-19 infections in different time periods — e.g. domestic transmission vs external introductions, relative role of case finding and contact tracing in domestic transmission. Estimating the incidence of missed cases also allows us to evaluate the usefulness of common surveillance metrics that rely on observed cases. Implications of all the available evidence Comprehensive outbreak investigation data integrated with mathematical modelling helps to quantify the strengths and weaknesses of each outbreak control intervention during different stages of the pandemic. This would allow countries to better allocate limited resources to strengthen outbreak control. Furthermore, the data and modelling approach allows us to estimate the extent of missed infections in the absence of population wide seroprevalence surveys. This allows us to compare the growth dynamics of notified and missed infections as reliance on the observed data alone may create the illusion of a controlled outbreak.

Epidemiological and public health requirements for COVID-19 contact tracing apps and their evaluation (preprint)

Digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. Real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust.

The Effect of Control Strategies that Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China (preprint)

Background: In December 2019, a novel strain of SARS-CoV-2 emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures and efforts in response to the outbreak. Methods: We quantified the effects of control measures on population contact patterns in Wuhan, China, to assess their effects on the progression of the outbreak. We included the latest estimates of epidemic parameters from a transmission model fitted to data on local and internationally exported cases from Wuhan in the age-structured epidemic framework. Further, we looked at the age-distribution of cases. Lastly, we simulated lifting of the control measures by allowing people to return to work in a phased-in way, and looked at the effects of returning to work at different stages of the underlying outbreak. Findings: Changes in mixing patterns may have contributed to reducing the number of infections in mid-2020 by 92% (interquartile range: 66–97%). There are benefits to sustaining these measures until April in terms of reducing the height of the peak, overall epidemic size in mid-2020 and probability that a second peak may occur after return to work. However, the modelled effects of social distancing measures vary by the duration of infectiousness and the role school children play in the epidemic. Interpretation: Restrictions on activities in Wuhan, if maintained until April, would likely contribute to the reduction and delay the epidemic size and peak, respectively. However, there are some limitations to the analysis, including large uncertainties around estimates of R0 and the duration of infectiousness.Funding: KP, YL, MJ, and PK were funded by the Bill & Melinda Gates Foundation (grant number INV003174), YL and MJ were funded by the National Institute for Health Research (NIHR) (16/137/109), TWR and AJK were funded by the Wellcome Trust (grant number 206250/Z/17/Z), RME was funded by HDR UK (grant number MR/S003975/1), and ND was funded by NIHR (HPRU-2012-10096).This research was partly funded by the National Institute for Health Research (NIHR) (16/137/109) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care. We would like to acknowledge (in a randomised order) the other members of the London School of Hygiene & Tropical Medicine COVID-19 modelling group, who contributed to this work: Stefan Flasche, Samuel Clifford, Carl A B Pearson, James D Munday, Sam Abbott, Hamish Gibbs, Alicia Rosello, Billy J Quilty, Thibaut Jombart, Fiona Sun, Charlie Diamond, Amy Gimma, Kevin van Zandvoort, Sebastian Funk, Christopher I Jarvis, W John Edmunds, Nikos I Bosse, and Joel Hellewell. Their funding sources are as follows: Stefan Flasche and Sam Clifford (Sir Henry Dale Fellowship [grant number 208812/Z/17/Z]); Billy J Quilty, Fiona Sun, and Charlie Diamond (NIHR [grant number 16/137/109]); Joel Hellewell, Sam Abbott, James D Munday, and Sebastian Funk (Wellcome Trust [grant number 210758/Z/18/Z] ); Amy Gimma and Christopher I Jarvis (Global Challenges Research Fund [grant number ES/P010873/1]); Hamish Gibbs (Department of Health and Social Care [grant number ITCRZ 03010]); Alicia Rosello (NIHR [grant number PROD-1017-20002]); Thibaut Jombart (RCUK/ESRC [grant number ES/P010873/1], UK PH RST, NIHR HPRU Modelling Methodology); Kevin van Zandvoort (Elrha’s Research for Health in Humanitarian Crises (R2HC) Programme, UK Government (DFID), Wellcome Trust, NIHR).Declaration of Interest: The authors declare no competing interests.