HLA Alleles, COVID-19 Vaccine Antibody Response and Real-World Breakthrough Outcomes (preprint)

The rapid development, approval, and global distribution of COVID-19 vaccines represent an unprecedented intervention in public health history, with over 13 billion doses administered worldwide in two years. However, our understanding of the HLA genetic underpinnings of COVID-19 vaccine-induced antibody responses and their clinical implications for breakthrough outcomes remain limited. To bridge this knowledge gap, we designed and performed a series of genetic and epidemiological analyses among 368,098 vaccinated individuals, and a subset of 194,371 participants who had antibody serology tests. Firstly, we corroborated earlier findings that SNPs associated with antibody response were predominantly located in Major Histocompatibility Complex region, and that the expansive HLA-DQB1*06 allele family was linked to better antibody responses. However, our findings contest the claim that DQB1*06 alleles alone significantly impact breakthrough risks. Additionally, our results suggest that the specific DQB1*06:04 subtype could be the true causal allele, as opposed to the previously reported DQB1*06:02. Secondly, we identified and validated six new functional HLA alleles that independently contribute to vaccine-induced antibody responses. Moreover, we unravelled additive effects of variations across multiple HLA genes that, concurrently, change the risk of clinically relevant breakthrough COVID-19 outcomes. Finally, we detangled the overall vaccine effectiveness and showed that antibody positivity accounts for approximately 20% protection against breakthrough infection and 50% against severe outcomes. These novel findings provide robust population evidence demonstrating how variations within HLA genes strongly, collectively, and causally influence vaccine-induced antibody responses, and the risk of COVID-19 breakthrough infection and related outcomes, with implications for subsequent functional research and personalised vaccination.

The effect of COVID-19 lockdowns on fertility in the Democratic Republic of the Congo (preprint)

Most countries implemented public health measures, including lockdowns, during the COVID-19 pandemic. It has been speculated that the pandemic will affect fertility, but the direction, magnitude, and mechanisms of these effects are not well understood. Using data from the national health management information system and an augmented synthetic control methodology, we examined the impact of a lockdown of Kinshasa in April 2020 on the subsequent fertility of women, which we proxy by the number of births in health facilities months after the policy was implemented. Seven months after the lockdown, we see a large increase in births in Kinshasa, as compared to control areas, which at its peak represents an additional 5000 monthly births, or a 45% increase relative to baseline. We also observe increases in complimentary maternal health services but not in other health services. Increased births were observed among women both older and younger than 20. Lockdown policies have likely affected fertility and future pandemic preparedness plans should anticipate the effects find strategies to mitigate any negative unintended effects.

The enteric virome of cats with feline panleukopenia differs in abundance and diversity from healthy cats (preprint)

Feline panleukopenia (FPL) is a severe, often fatal disease caused by feline parvovirus (FPV). How infection with FPV might impact the composition of the entire eukaryotic enteric virome in cats has not been characterized. We used metatranscriptomic and viral particle enrichment metagenomic approaches to characterize the enteric viromes of 23 cats naturally infected with FPV (FPV-cases) and 36 age-matched healthy shelter cats (healthy controls). Sequencing reads were detected from 11 mammalian infecting viral families mostly belonging to Coronaviridae, Parvoviridae and Astroviridae . Among the healthy control cats the most abundant viruses were Feline coronavirus, Mamastrovirus 2 and Carnivore bocaparvovirus 3 (Feline bocavirus 1) with frequent co-infections of all three. Feline chaphamaparvovirus was only detected in healthy controls (6/36, 16.7%). Among the FPV-cases, in addition to FPV, the most abundant viruses were Mamastrovirus 2 , Feline coronavirus and Carnivore bocaparvovirus 4 (Feline bocaparvovirus 2). The latter and Feline bocaparvovirus 3 were detected significantly more frequently in FPV-cases than in healthy controls. Feline calicivirus was present in a high proportion of FPV-cases (11/23, 47.8%) compared to healthy controls (5/36, 13.9%, p=0.0067). Feline kobuvirus infections were also common among FPV-cases (9/23, 39.1%) and were not detected in any healthy control cats (p<0.0001). While abundant in both groups, astroviruses were more frequently present in FPV-cases (19/23, 82.6%) than in healthy controls (18/36, p=0.0142). The differences in eukaryotic virome composition found in this study indicate that further investigations to determine associations between enteric viral co-infections on clinical disease severity in cats with FPL are warranted.

Comparative effectiveness of the BNT162b2 vs ChAdOx1 vaccine against Covid-19 (preprint)

Although pivotal trials with varying populations and study methods suggest higher efficacy for mRNA than adenoviral Covid-19 vaccines, no direct evidence is available. Here, we conducted a head-to-head comparison of BNT162b2 versus ChAdOx1 against Covid-19. We analysed 235,181 UK Biobank participants aged 50 years or older and vaccinated with one or two doses of BNT162b2 or ChAdOx1. People were followed from the vaccination date until 18/10/2021. Inverse probability weighting was used to minimise confounding and the Cox models to derive hazard ratio. We found that, compared with two doses of ChAdOx1, vaccination with BNT162b2 was associated with 30% lower risks of both SARS-CoV-2 infection and related hospitalisation during the period dominated by the delta variant. Also, this comparative effectiveness was consistent across several subgroups and persisted for at least six months, suggesting no differential waning between the two vaccines. Our findings can inform evidence-based Covid-19 vaccination campaigns and booster strategies.

Comparative effectiveness and safety of homologous two-dose ChAdOx1 versus heterologous vaccination with ChAdOx1 and BNT162b2: a cohort analysis (preprint)

Small trials have suggested that heterologous vaccination with first-dose ChAdOx1 and second-dose BNT162b2 may generate a better immune response than homologous vaccination with two doses of ChAdOx1. We used linked data from Catalonia (Spain), where those aged <60 who received a first dose of ChAdOx1 could choose between ChAdOx1 and BNT162b2 for their second dose. Comparable cohorts were obtained after exact-matching 14,325/17,849 (80.3%) people receiving heterologous vaccination to 14,325/149,386 (9.6%) receiving homologous vaccination by age, sex, region, and date of second dose. Of these, 238 (1.7%) in the heterologous and 389 (2.7%) in the homologous groups developed COVID-19 between 1st June 2021 and 11th October 2021. The resulting hazard ratio (95% confidence interval) was 0.61 [ 0.52-0.71 ], favouring heterologous vaccination, with a Number Needed to Treat of 94.9 [ 71.8 - 139.8 ]. The two groups had similar testing rates and safety outcomes. Sensitivity and negative control outcome analyses confirmed these findings. In conclusion, we demonstrate that a heterologous vaccination schedule with ChAdOx1 followed by BNT162b2 was more efficacious than and similarly safe to homologous vaccination with two doses of ChAdOx1. Most of the infections in our study occurred when Delta was the predominant SARS-CoV-2 variant in Spain. These data agree with previous phase 2 randomised trials.

Correlates of Protection against symptomatic and asymptomatic SARS-CoV-2 infection (preprint)

Background: Although 6 COVID-19 vaccines have been approved by the World Health Organisation as of 7th June 2021, global supply remains limited. An understanding of the immune response associated with protection could facilitate rapid licensure of new vaccines. Methods: Data from a randomised efficacy trial of ChAdOx1 nCoV-19 (AZD1222) vaccine in the UK was analysed to determine the antibody levels associated with protection against SARS-CoV-2. Anti-spike and anti-RBD IgG by multiplex immunoassay, pseudovirus and live neutralizing antibody at 28 days after the second dose were measured in infected and non-infected vaccine recipients. Weighted generalised additive models for binary data were applied to outcome. Cubic spline smoothed log antibody levels, and baseline risk of exposure were the predictor variables with weights applied to account for selection bias in sample processing. Results: Higher levels of all immune markers were correlated with a reduced risk of symptomatic infection. Vaccine efficacy of 80% against primary symptomatic COVID-19 was achieved with antibody level of 40923 (95% CI: 16748, 125017) and 63383 (95% CI: 16903, not computed (NC)) for anti-spike and anti-RBD, and 185 (95% CI: NC, NC) and 247 (95% CI: 101, NC) for pseudo- and live-neutralisation assays respectively. Antibody responses did not correlate with overall protection against asymptomatic infection. Conclusions: Correlates of protection can be used to bridge to new populations using validated assays. The data can be used to extrapolate efficacy estimates for new vaccines where large efficacy trials cannot be conducted. More work is needed to assess correlates for emerging variants.

Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic (preprint)

Background : Poor data quality is limiting the greater use of data sourced from routine health information systems (RHIS), especially in low and middle-income countries. An important part of this issue comes from missing values, where health facilities, for a variety of reasons, miss their reports into the central system.  Methods : Using data from the Health Management Information System (HMIS) and the advent of COVID-19 pandemic in the Democratic Republic of the Congo (DRC) as an illustrative case study, we implemented six commonly-used imputation methods using the DRC’s HMIS datasets and evaluated their performance through various statistical techniques, i.e., simple linear regression, segmented regression which is widely used in interrupted time series studies, and parametric comparisons through t-tests and non-parametric comparisons through Wilcoxon Rank-Sum tests. We also examined the performance of these six imputation methods under different missing mechanisms and tested their stability to changes in the data. Results : For regression analyses, there was no substantial difference found in the results generated from all methods except mean imputation and exclusion & interpolation when the RHIS dataset contained less than 20% missing values. However, as the missing proportion grew, machine learning methods such as  missForest and  k -NN started to produce biased estimates, and they were found to be also lack of robustness to minimal changes in data or to consecutive missingness. On the other hand, multiple imputation generated the overall most unbiased estimates and was the most robust to all changes in data. For comparing group means through t-tests, the results from mean imputation and exclusion & interpolation disagreed with the true inference obtained using the complete data, suggesting that these two methods would not only lead to biased regression estimates but also generate unreliable t-test results. Conclusions : We recommend the use of multiple imputation in addressing missing values in RHIS datasets. In cases necessary computing resources are unavailable to multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion & interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus their use in the handling of missing values should be discouraged.   Keywords : Missing Data; Routine Health Information Systems (RHIS); Health Management Information System (HMIS); Health Services Research; Low and middle-income countries (LMICs); Multiple imputation

Single Dose Administration, And The Influence Of The Timing Of The Booster Dose On Immunogenicity and Efficacy Of ChAdOx1 nCoV-19 (AZD1222) Vaccine (preprint)

Background: The ChAdOx1 nCoV-19 (AZD1222) vaccine has been approved for emergency use by the UK regulatory authority, MHRA, with a regimen of two standard doses given with an interval of between 4 and 12 weeks. The planned rollout in the UK will involve vaccinating people in high risk categories with their first dose immediately, and delivering the second dose 12 weeks later.Here we provide both a further prespecified pooled analysis of trials of ChAdOx1 nCoV-19 and exploratory analyses of the impact on immunogenicity and efficacy of extending the interval between priming and booster doses. In addition, we show the immunogenicity and protection afforded by the first dose, before a booster dose has been offered.Methods: We present data from phase III efficacy trials of ChAdOx1 nCoV-19 in the United Kingdom and Brazil, and phase I/II clinical trials in the UK and South Africa, against symptomatic disease caused by SARS-CoV-2. The data cut-off date for these analyses was 7th December 2020. The accumulated cases of COVID-19 disease at this cut-off date exceeds the number required for a pre-specified final analysis, which is also presented. As previously described, individuals over 18 years of age were randomised 1:1 to receive two standard doses (SD) of ChAdOx1 nCoV-19 (5x1010 viral particles) or a control vaccine/saline placebo. In the UK trial efficacy cohort a subset of participants received a lower dose (LD, 2.2x1010 viral particles) of the ChAdOx1 nCoV-19 for the first dose. All cases with a nucleic acid amplification test (NAAT) were adjudicated for inclusion in the analysis, by a blinded independent endpoint review committee. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov; NCT04324606, NCT04400838, and NCT04444674.Findings: 17,177 baseline seronegative trial participants were eligible for inclusion in the efficacy analysis, 8948 in the UK, 6753 in Brazil and 1476 in South Africa, with 619 documented NAAT +ve infections of which 332 met the primary endpoint of symptomatic infection >14 days post dose 2.The primary analysis of overall vaccine efficacy >14 days after the second dose including LD/SD and SD/SD groups, based on the prespecified criteria was 66.7% (57.4%, 74.0%). There were no hospitalisations in the ChAdOx1 nCoV-19 group after the initial 21 day exclusion period, and 15 in the control group.Vaccine efficacy after a single standard dose of vaccine from day 22 to day 90 post vaccination was 76% (59%, 86%), and modelled analysis indicated that protection did not wane during this initial 3 month period. Similarly, antibody levels were maintained during this period with minimal waning by day 90 day (GMR 0.66, 95% CI 0.59, 0.74).In the SD/SD group, after the second dose, efficacy was higher with a longer prime-boost interval: VE 82.4% 95%CI 62.7%, 91.7% at 12+ weeks, compared with VE 54.9%, 95%CI 32.7%, 69.7% at <6 weeks. These observations are supported by immunogenicity data which showed binding antibody responses more than 2-fold higher after an interval of 12 or more weeks compared with and interval of less than 6 weeks GMR 2.19 (2.12, 2.26) in those who were 18-55 years of age.Interpretation: ChAdOx1 nCoV-19 vaccination programmes aimed at vaccinating a large proportion of the population with a single dose, with a second dose given after a 3 month period is an effective strategy for reducing disease, and may be the optimal for rollout of a pandemic vaccine when supplies are limited in the short term.Trial Registration: Studies are registered at ISRCTN89951424 and ClinicalTrials.gov; NCT04324606, NCT04400838, and NCT04444674.Funding: UKRI, NIHR, CEPI, the Bill & Melinda Gates Foundation, the Lemann Foundation, Rede D’OR, the Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and Astra Zeneca.Conflict of Interest: Oxford University has entered into a partnership with Astra Zeneca for further development of ChAdOx1 nCoV-19. SCG is co-founder of Vaccitech (collaborators in the early development of this vaccine candidate) and named as an inventor on a patent covering use of ChAdOx1-vectored vaccines and a patent application covering this SARS-CoV-2 vaccine. TL is named as aninventor on a patent application covering this SARS-CoV-2 vaccine and was a consultant to Vaccitech for an unrelated project. PMF is a consultant to Vaccitech. AJP is Chair of UK Dept.Health and Social Care’s (DHSC) Joint Committee on Vaccination & Immunisation (JCVI), but does not participate in discussions on COVID-19 vaccines, and is a member of the WHO’sSAGE. AJP and SNF are NIHR Senior Investigator. The views expressed in this article do not necessarily represent the views of DHSC, JCVI, NIHR or WHO. AVSH reports personal feesfrom Vaccitech, outside the submitted work and has a patent on ChAdOx1 licensed to Vaccitech, and may benefit from royalty income to the University of Oxford from sales of this vaccine by AstraZeneca and sublicensees. MS reports grants from NIHR, non-financial support fromAstraZeneca, during the conduct of the study; grants from Janssen, grants fromGlaxoSmithKline, grants from Medimmune, grants from Novavax, grants and non-financialsupport from Pfizer, grants from MCM, outside the submitted work. CG reports personal fees from the Duke Human Vaccine Institute, outside of the submitted work. SNF reports grants from Janssen and Valneva, outside the submitted work. ADD reports grants and personal fees from AstraZeneca, outside of the submitted work. In addition, ADD has a patent manufacturingprocess for ChAdOx vectors with royalties paid to AstraZeneca, and a patent ChAdOx2 vector with royalties paid to AstraZeneca. The other authors declare no competing interests.

Gravitational and Autoregressive Analysis Spatial Diffusion of COVID-19 in Hubei Province, China (preprint)

The spatial diffusion of epidemic disease follows distance decay law in geography, but different diffusion processes may be modeled by different mathematical functions under different spatio-temporal conditions. This paper is devoted to modeling spatial diffusion patterns of COVID-19 stemming from Wuhan city to Hubei province. The methods include gravity and spatial auto-regression analyses. The local gravity model is derived from allometric scaling and global gravity model, and then the parameters of the local gravity model are estimated by observational data and linear regression. The main results are as below. The local gravity model based on power law decay can effectively describe the diffusion patterns and process of COVID-19 in Hubei Province, and the goodness of fit of the gravity model based on negative exponential decay to the observation data is not satisfactory. Further, the goodness of fit of the model to data entirely became better and better over time, the size elasticity coefficient increases first and then decreases, and the distance attenuation exponent decreases first and then increases. Moreover, the significance of spatial autoregressive coefficient in the model is low, and the confidence level is less than 80%. The conclusions can be reached as follows. (1) The spatial diffusion of COVID-19 of Hubei bears long range effect, and the size of a city and the distance of the city to Wuhan affect the total number of confirmed cases. (2) Wuhan direct transmission is the main process in the spatial diffusion of COVID-19 in Hubei at the early stage, and the horizontal transmission between regions is not significant. (3) The effect of spatial isolation measures taken by Chinese government against the transmission of COVID-19 is obvious. This study suggests that the role of gravity should be taken into account to prevent and control epidemic disease.

Household transmission of COVID-19, Shenzhen, January-February 2020 (preprint)

Coronavirus disease 2019 has led to more than three million cases globally. Since the first family cluster of COVID-19 cases identified in Shenzhen in early January, most of the local transmission occurred within household contacts. Identifying the factors associated with household transmission is of great importance to guide preventive measures.

Prediction of the COVID-19 Epidemic Trends Based on SEIR and AI Models (preprint)

The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. To contain its spread, China adopted unprecedented nationwide interventions on January 23. We sought to show how these control measures impacted the containment of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23, 2020, was indispensable in reducing the eventual COVID-19 epidemic size.

Epidemiological and clinical characteristics of COVID-19 in Shenzhen, the largest migrant city of China (preprint)

We conducted a retrospective study among 417 confirmed COVID-19 cases from Jan 1 to Feb 28, 2020 in Shenzhen, the largest migrant city of China, to identify the epidemiological and clinical features in settings of high population mobility. We estimated the median incubation time to be 5.0 days. 342 (82.0%) cases were imported, 161 (38.6%) cases were identified by surveillance, and 247 (59.2%) cases were reported from cluster events. The main symptoms on admission were fever and dry cough. Most patients (91.4%) had mild or moderate illnesses. Age of 50 years or older, breathing problems, diarrhea, and longer time between the first medical visit and admission were associated with higher level of clinical severity. Surveillance-identified cases were much less likely to progress to severe illness. Although the COVID-19 epidemic has been contained in Shenzhen, close monitoring and risk assessments are imperative for prevention and control of COVID-19 in future.