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.

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.

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 Local Burden of Disease During the First Wave of the COVID-19 Epidemic in England, Using Different Data Sources from Changing Surveillance Practices (preprint)

Background: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. MethodsWe fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January - 30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.ResultsA model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. ConclusionsLimitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

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.

Estimated transmissibility and severity of novel SARS-CoV-2 Variant of Concern 202012/01 in England (preprint)

A novel SARS-CoV-2 variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in November 2020 and is rapidly spreading towards fixation. Using a variety of statistical and dynamic modelling approaches, we estimate that this variant has a 43-90% (range of 95% credible intervals 38-130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine roll-out, COVID-19 hospitalisations and deaths across England in 2021 will exceed those in 2020. Concerningly, VOC 202012/01 has spread globally and exhibits a similar transmission increase (59-74%) in Denmark, Switzerland, and the United States.

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.