Modelling the impact of population mobility, post-infection immunity and vaccination on SARS-CoV-2 transmission in the Dominican Republic (preprint)

COVID-19 epidemic dynamics are driven by a complex interplay of factors including population behaviour, government interventions, new variants, vaccination campaigns and immunity from prior infections. We aimed to quantify the epidemic drivers of SARS-CoV-2 dynamics in the Dominican Republic, an upper-middle income country of 10.8 million people, and assess the impact of the vaccination campaign implemented in February 2021 in saving lives and averting hospitalisations. We used an age-structured, multi-variant transmission dynamic model to characterise epidemic drivers in the Dominican Republic and explore counterfactual scenarios around vaccination coverage and population mobility. We fit the model to reported deaths, hospital bed occupancy, ICU bed occupancy and seroprevalence data until December 2021 and simulated epidemic trajectories under different counterfactual vaccination scenarios. We estimate that vaccination averted 5040 hospital admissions (95% CrI: 4750 - 5350), 1500 ICU admissions (95% CrI: 1420 - 1590) and 544 deaths (95% CrI: 488 - 606) in the first 6 months of the campaign. We also found that early vaccination with Sinovac-CoronaVac was preferable to delayed vaccination using a product with higher efficacy. We investigated the trade-off between changes in vaccination coverage and population mobility to understand how much relaxation of social distancing measures vaccination was able to 'buy' in the later stages of a pandemic. We found that if no vaccination had occurred, an additional decrease of 10-20% in population mobility would have been required to maintain the same death and hospitalisation outcomes. We found SARS-CoV-2 transmission dynamics in the Dominican Republic were driven by substantial accumulation of immunity during the first two years of the pandemic but that, despite this, vaccination was essential in enabling a return to pre-pandemic mobility levels without incurring considerable additional morbidity and mortality.

Predicting subnational incidence of COVID-19 cases and deaths in EU countries (preprint)

Background: Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. Methods: We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (test number, vaccine coverage..). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. Results: At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts remained stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. Discussion: Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

Comparative B cell and antibody responses induced by adenoviral vectored and mRNA vaccines against COVID-19 (preprint)

Both vector and mRNA vaccines were an important part of the response to the COVID-19 pandemic and may be required in future outbreaks and pandemics. However, adenoviral vectored (AdV) vaccines may be less immunogenic than mRNA vaccines against SARS-CoV-2. We assessed anti-spike and anti-vector immunity among infection-naive Health Care Workers (HCW) following two doses of AdV (AZD1222) versus mRNA (BNT162b2) vaccine. 183 AdV and 274 mRNA vaccinees enrolled between April and October 2021. Median ages were 42 and 39 years, respectively. Blood was collected at least once, 10-48 days after vaccine dose 2. Surrogate virus neutralization test (sVNT) and spike binding antibody titres were a median of 4.2 and 2.2 times lower, respectively, for AdV compared to mRNA vaccinees (p<0.001). Median percentages of memory B cells that recognized fluorescent-tagged spike and RBD were 2.9 and 8.3 times lower, respectively for AdV compared to mRNA vaccinees. Titres of IgG reactive with human Adenovirus type 5 hexon protein rose a median of 2.2-fold after AdV vaccination but were not correlated with anti-spike antibody titres. Together the results show that mRNA induced substantially more sVNT antibody than AdV vaccine due to greater B cell expansion and targeting of the RBD. Pre-existing AdV vector cross-reactive antibodies were boosted following AdV vaccination but had no detectable effect on immunogenicity. Key pointsO_LImRNA SARS-CoV-2 vaccine induced higher surrogate neutralizing antibody titres than adenoviral vaccine C_LIO_LImRNA vaccine induced a more potent, RBD-targeted B cell response than AdV vaccine C_LIO_LIAdenoviral vaccine boosted antibodies against human Adenovirus, but titres dont correlate with anti-spike titres C_LI

Key challenges for the surveillance of respiratory viruses: transitioning out of the acute phase of the SARS-CoV-2 pandemic (preprint)

To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.

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 approach 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.

Integrated SARS-CoV-2 serological and virological screening across an acute fever surveillance platform to monitor temporal changes in anti-spike antibody levels and risk of infection during sequential waves of variant transmission - Dominican Republic, March 2021 to August 2022 (preprint)

We monitored SARS-CoV-2 antibody changes following implementation of a national COVID-19 vaccination campaign and assessed implications for immunological protection against variants of concern. Between March 2021 and August 2022, we prospectively enrolled 2,300 patients seeking care for undifferentiated febrile illnesses across two study sites in the Dominican Republic. Sera was tested for total anti-spike antibodies (anti-S) and simultaneously collected nasopharyngeal samples by RT-PCR for acute SARS-CoV-2 infection. Geometric mean anti-S titers increased from 6.6 BAU/ml (95% CI 5.1-8.7) to 1,332 BAU/ml (CI 1055-1,682) during the study period. Multivariable binomial odds ratios for acute SARS-CoV-2 infection were 0.55 (0.40-0.74), 0.38 (0.27-0.55), and 0.27 (0.18-0.40) for the second, third, and fourth versus the first anti-S quartile, with similar findings by viral strain. Integrated serological and virological screening present an opportunity to rethink existing surveillance platforms by simultaneously monitoring population-level immunological markers and implications for emerging variant transmission.

Non-hospitalised, vaccinated adults with COVID-19 caused by Omicron BA.1 and BA.2 present with changing symptom profiles compared to those with Delta despite similar viral kinetics (preprint)

Introduction The impact of COVID-19 vaccination on disease in the community has been limited, as a result of both SARS-CoV-2 Variants of Concern that partially escape vaccine-induced immunity. We sought to characterise symptoms and viral loads over the course of COVID-19 infection in otherwise-healthy vaccinated adults, representative of the general population, to assess whether current self-isolation guidance remains justified. Methods In a prospective, observational cohort study, healthy vaccinated UK adults who reported a positive PCR or lateral flow test, self-swabbed on alternate days until day 10. We compared symptoms and viral kinetics between infections caused by VOCs Delta and Omicron (sub-variants BA.1 and BA.2) and investigated applicability of UK NHS isolation guidelines to these newer VOCs. Results 373 infection episodes were reported among 349 participants. Across VOCs, symptom duration was similar, however symptom profiles differed significantly among infections caused by Delta, Omicron BA.1 and BA.2. Anosmia was reported in <10% of participants with BA.1 and BA.2, compared to 42% with Delta infection, coryza fatigue and myalgia predominated. Most notably, viral load trajectories and peaks did not differ between Delta, BA.1 and BA.2, irrespective of symptom severity, VOC or vaccination status. Conclusion COVID-19 isolation guidance should not differ based on symptom severity or febrile illness and must remain under review as new SARS-CoV-2 VOCs emerge and population immunity changes. Our study emphasises the ongoing transmission risk of Omicron sub-variants in vaccinated adults with mild symptoms that may extend beyond current isolation periods.

Using high-resolution contact networks to evaluate SARS-CoV-2 transmission and control in large-scale multi-day events (preprint)

The emergence of the highly transmissible SARS-CoV-2 Delta variant has created a need to reassess the risk posed by increasing social contacts as countries resume pre-pandemic activities, particularly in the context of resuming large-scale events over multiple days. To examine how social contacts formed in different activity settings influences interventions required to control outbreaks, we combined high-resolution data on contacts among passengers and crew on cruise ships with network transmission models. We found passengers had a median of 20 (IQR 10-36) unique close contacts per day, and over 60% of their contact episodes were made in dining or sports areas where mask wearing is typically limited. In simulated outbreaks, we found that vaccination coverage and rapid antigen tests had a larger effect than mask mandates alone, indicating the importance of combined interventions against Delta to reduce event risk in the vaccine era.

Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust (preprint)

BackgroundWe aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. MethodsHCWs at Sheffield Teaching Hospitals NHS Foundation Trust (STH) were prospectively enrolled and sampled at two time points. SARS-CoV-2 antibodies were tested using an in-house assay for IgG and IgA reactivity against Spike and Nucleoprotein (sensitivity 99{middle dot}47%, specificity 99{middle dot}56%). Data were analysed using three statistical models: a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. FindingsAs of 12th June 2020, 24{middle dot}4% (n=311/1275) HCWs were seropositive. Of these, 39{middle dot}2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41{middle dot}1%, 95% CrI 30{middle dot}0-52{middle dot}9) and in Physiotherapists and Occupational Therapists (39{middle dot}2%, 95% CrI 24{middle dot}4-56{middle dot}5). Older age groups showed overall higher median antibody titres. Further modelling suggests that, for a serological assay with an overall sensitivity of 80%, antibody titres may be markedly affected by differences in age, with sensitivity estimates of 89% in those over 60 years but 61% in those [≤]30 years. InterpretationHCWs in acute medical units working closely with COVID-19 patients were at highest risk of infection, though whether these are infections acquired from patients or other staff is unknown. Current serological assays may underestimate seroprevalence in younger age groups if validated using sera from older and/or more symptomatic individuals. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies published up to March 6th 2021, using the terms "COVID", "SARS-CoV-2", "seroprevalence", and "healthcare workers", and in addition for articles of antibody titres in different age groups against coronaviruses using "coronavirus", "SARS-CoV-2, "antibody", "antibody tires", "COVID" and "age". We included studies that used serology to estimate prevalence in healthcare workers. SARS-CoV-2 seroprevalence has been shown to be greater in healthcare workers working on acute medical units or within domestic services. Antibody levels against seasonal coronaviruses, SARS-CoV and SARS-CoV-2 were found to be higher in older adults, and patients who were hospitalised. Added value of this studyIn this healthcare worker seroprevalence modelling study at a large NHS foundation trust, we confirm that those working on acute medical units, COVID-19 "Red Zones" and within domestic services are most likely to be seropositive. Furthermore, we show that physiotherapists and occupational therapists have an increased risk of COVID-19 infection. We also confirm that antibody titres are greater in older individuals, even in the context of non-hospitalised cases. Importantly, we demonstrate that this can result in age-specific sensitivity in serological assays, where lower antibody titres in younger individuals results in lower assay sensitivity. Implications of all the available evidenceThere are distinct occupational roles and locations in hospitals where the risk of COVID-19 infection to healthcare workers is greatest, and this knowledge should be used to prioritise infection prevention control and other measures to protect healthcare workers. Serological assays may have different sensitivity profiles across different age groups, especially if assay validation was undertaken using samples from older and/or hospitalised patients, who tend to have higher antibody titres. Future seroprevalence studies should consider adjusting for age-specific assay sensitivities to estimate true seroprevalence rates. Author Contributions O_TBL View this table: org.highwire.dtl.DTLVardef@77acb4org.highwire.dtl.DTLVardef@eb9b35org.highwire.dtl.DTLVardef@1af298org.highwire.dtl.DTLVardef@12cf3e1org.highwire.dtl.DTLVardef@3f6476_HPS_FORMAT_FIGEXP M_TBL C_TBL

Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic (preprint)

Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This can involve sub-national redistribution, short-term relocations as well as international migration. In this paper, we combine detailed location data from Facebook measuring the location of approximately 6 million daily active Facebook users in 5km2 tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (First lockdown, End of term, Beginning of term, Christmas). We also show how the timing and magnitude of observed population changes can impact the size of epidemics using a deterministic model of COVID-19 transmission. We estimate that between March 2020 and March 2021, the total population of the UK has declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Further, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates. The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear how these changes may persist after the COVID-19 pandemic.

Serial intervals observed in SARS-CoV-2 B.1.617.2 variant cases (preprint)

Rapid growth of the B.1.617.2 variant of SARS-CoV-2 has been observed in many countries. Broadly, the factors driving the recent rapid growth of COVID-19 cases could be attributed to shorten generation intervals or higher transmissibility (effective reproduction number, R), or both. As such, establishing reasons for the observed rapid growth will allow countries to know how best to enhance their outbreak control measures. In this study, we analysed the serial interval of household transmission pairs infected with SARS-CoV-2 B.1.617.2 variant and compared with those who were infected prior to the occurrence of the major global SARS-CoV-2 variants. After controlling for confounding factors, our findings suggest no significant changes in the serial intervals for SARS-CoV-2 cases infected with the B.1.617.2 variant. This in turn lends support for a hypothesis of a higher R for B.1.617.2 cases.

SARS-CoV-2 infection and reinfection in a seroepidemiological workplace cohort in the United States (preprint)

Identifying the extent of SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4411 US employees in four states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an adjusted odds ratio of 0.09 (95% CI: 0.005 - 0.48) for reinfection, implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 91% reduced odds of a subsequent PCR positive test. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a sixth month time period. We also highlight two major sources of bias and uncertainty to be considered when estimating reinfection risk, confounders and the choice of baseline time point, and show how to account for both in our analysis.

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.

Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections (preprint)

BackgroundRoutine asymptomatic testing using RT-PCR of people who interact with vulnerable populations, such as medical staff in hospitals or care workers in care homes, has been employed to help prevent outbreaks among vulnerable populations. Although the peak sensitivity of RT-PCR can be high, the probability of detecting an infection will vary throughout the course of an infection. The effectiveness of routine asymptomatic testing will therefore depend on testing frequency and how PCR detection varies over time. MethodsWe fitted a Bayesian statistical model to a dataset of twice weekly PCR tests of UK healthcare workers performed by self-administered nasopharyngeal swab, regardless of symptoms. We jointly estimated times of infection and the probability of a positive PCR test over time following infection, we then compared asymptomatic testing strategies by calculating the probability that a symptomatic infection is detected before symptom onset and the probability that an asymptomatic infection is detected within 7 days of infection. FindingsWe estimated that the probability that the PCR test detected infection peaked at 77% (54 - 88%) 4 days after infection, decreasing to 50% (38 - 65%) by 10 days after infection. Our results suggest a substantially higher probability of detecting infections 1-3 days after infection than previously published estimates. We estimated that testing every other day would detect 57% (33-76%) of symptomatic cases prior to onset and 94% (75-99%) of asymptomatic cases within 7 days if test results were returned within a day. InterpretationOur results suggest that routine asymptomatic testing can enable detection of a high proportion of infected individuals early in their infection, provided that the testing is frequent and the time from testing to notification of results is sufficiently fast. FundingWellcome Trust, National Institute for Health Research (NIHR) Health Protection Research Unit, Medical Research Council (UKRI)

Short-term forecasts to inform the response to the COVID-19 epidemic in the UK (preprint)

Background: Short-term forecasts of infectious disease can create situational awareness and inform planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time. Methods: We evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models to ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We further compared model performance to a null model of no change. Results: In most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble. Conclusions: Ensembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.

Epidemiological and immunological features of obesity and SARS-CoV-2 (preprint)

Background: Obesity is established as a key correlate of severe SARS-CoV-2 outcomes. Multiple other epidemiological and immunological features are less well-defined including whether obesity increases susceptibility to SARS-CoV-2, influences symptom phenotype, or impedes or alters the immune response to infection. Given the substantial global burden of obesity and given these uncertainties, we examined the epidemiology and immunology of obesity and SARS-CoV-2. Methods: Industry employees were invited to participate in a prospective SARS-CoV-2 serology-based cohort study. Blood and baseline survey measures that included demographics, comorbidities, and prior COVID-19 compatible symptoms were collected. Serological testing and interim symptom reporting were conducted monthly. SARS-CoV-2 immunoassays included an IgG ELISA targeting the spike RBD, multiarray Luminex targeting 20 viral antigens, pseudovirus neutralization, and T cell ELISPOT assays. Unadjusted and adjusted analyses were used to identify differences in seroprevalence, clinical features, and immune parameters by BMI. Results: Of 4469 individuals enrolled, 322 (7.21%) were seropositive. Adjusted seroprevalence was non-significantly lower with higher BMI. Obesity was associated with increased reporting of fever (OR 3.43 [95% CI 1.58-7.60]) and multiple other symptoms and aggregate measures. There were no identifiable differences in immune response between obese and non-obese individuals. Discussion: We present benchmark data that obesity is not linked to increased risk of SARS-CoV-2 infection; that symptom phenotype is strongly influenced by obesity; and that despite evidence of obesity-associated immune dysregulation in severe infections, there is no evidence of muted or dysfunctional immune response across multiple immune measures among non-severe infections.