 Research article
 Open Access
 Published:
Effectiveness of the BNT162b2 (PfizerBioNTech) and the ChAdOx1 nCoV19 (OxfordAstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARSCoV2
BMC Infectious Diseases volume 22, Article number: 270 (2022)
Abstract
Background
From January to May 2021 the alpha variant (B.1.1.7) of SARSCoV2 was the most commonly detected variant in the UK. Following this, the Delta variant (B.1.617.2) then became the predominant variant. The UK COVID19 vaccination programme started on 8th December 2020. Prior to the Delta variant, most vaccine effectiveness studies focused on the alpha variant. We therefore aimed to estimate the effectiveness of the BNT162b2 (PfizerBioNTech) and the ChAdOx1 nCoV19 (OxfordAstraZeneca) vaccines in preventing symptomatic and asymptomatic infection with respect to the Delta variant in a UK setting.
Methods
We used anonymised public health record data linked to infection data (PCR) using the Combined Intelligence for Population Health Action resource. We then constructed an SIR epidemic model to explain SARSCoV2 infection data across the Cheshire and Merseyside region of the UK. Vaccines were assumed to be effective after 21 days for 1 dose and 14 days for 2 doses.
Results
We determined that the effectiveness of the OxfordAstraZeneca vaccine in reducing susceptibility to infection is 39% (95% credible interval [34, 43]) and 64% (95% credible interval [61, 67]) for a single dose and a double dose respectively. For the PfizerBioNTech vaccine, the effectiveness is 20% (95% credible interval [10, 28]) and 84% (95% credible interval [82, 86]) for a singledose and a double dose respectively.
Conclusion
Vaccine effectiveness for reducing susceptibility to SARSCoV2 infection shows noticeable improvement after receiving two doses of either vaccine. Findings also suggest that a full course of the PfizerBioNTech provides the optimal protection against infection with the Delta variant. This reinforces the need to complete the full course programme to maximise individual protection and reduce transmission.
Background
The UK COVID19 vaccination programme started on the 8th December 2020 and, by 19 September 2021, the overall vaccine uptake for 1 dose was 89.3% and 83.9% for 2 doses in England for adults aged 18 and over [1]. Assessing the effectiveness of the vaccines is important for government policy, and particularly so as more transmissible variants of SARSCoV2 emerge [2]. Delta (B.1.617.2) was the dominant SARSCoV2 variant in the UK from June 2021 to midDecember 2021 [3, 4], and vaccine effectiveness studies at the time focused on this variant [5,6,7].
Direct vaccine effectiveness is often estimated using a testnegative casecontrol design, which compares the odds of vaccination in a group of symptomatic individuals that test positive for COVID19 with the control group who are defined as individuals showing symptoms of COVID19 but test negative. This methodology was employed in two recent COVID19 vaccine effectiveness studies conducted in England and Scotland [5, 7]. A study by Pouwels et al., 2021 used a more traditional casecontrol design with survey data from randomly selected households across the UK [6]. Here, the control group consisted of randomly selected individuals who did not contract COVID19. Testnegative designs are often logistically beneficial and costeffective and can help to minimise selection bias because the cases and controls are assumed to have similar healthseeking behaviour. But, one of the issues raised with testnegative casecontrol designs is the lack of generalisability [8, 9]; that is, it only considers individuals who have sought to get tested and, therefore, findings may not be generalisable to those individuals who did not access testing services.
Vaccine effectiveness can also be estimated using a compartmental epidemic model that accounts for vaccination. Various methods of forecasting SARSCOV2, including compartmental models, have thus far have been used to study hypothetical scenarios [10]. For example, Wong et al., 2021 used the SIR (susceptible, infected or removed/recovered) model to consider a single dose vaccination program and used it to project new SARSCoV2 cases based on different vaccine effectiveness levels [11]. Another example is the modified version of the SEIR (where E represents a compartment for exposed individuals) model with a single dose vaccine program, and waning natural and vaccineinduced immunity, which is used to study different vaccination policies [12].
Here we consider a modified SIR model with a multidose vaccine program to estimate vaccine effectiveness for the BNT162b2 (hereafter referred to as PfizerBioNTech) and ChAdOx1 nCoV19 (hereafter referred to as OxfordAstraZeneca) vaccines for reducing susceptibility to infection with respect to the Delta variant in England, UK. The method we consider here is not restricted to those individuals who have been tested but considers the entire resident population, therefore providing a more generalisable population estimate for vaccine effectiveness against infection. The method also explicitly accounts for the temporal variation in vaccination levels as well as levels of infection in the population, removing these as a potential source of bias. We exploit a specific time window where initially low levels of infection are being driven rapidly upwards by the emergence of the Delta variant, justifying the use of a simple SIR model.
Methods
Data
We used data from the Combined Intelligence for Population Health Action (CIPHA; www.cipha.nhs.uk) data resource. CIPHA covers the population health management of over 2.6M General Practice registered individuals of Cheshire and Merseyside, UK. It includes personlevel linked anonymised records across the National Health Service (NHS), local government, social care, administrative and public health information systems. From CIPHA we have detailed case data for SARSCoV2 PCR positive individuals together with individuallevel vaccination data. For demographics of data see Additional file 1: Table S1.1.
Demographic data and our denominator population was for Cheshire and Merseyside and was taken from the general practice registered population, sourced from the Spine Demographics service in North West England. SARSCoV2 PCR testing data came from the Public Health England (PHE) Second Generation Surveillance System (SGSS) feeds. For this work this consisted of all Pillar 1 (swab testing in PHE labs and NHS hospitals) and Pillar 2 (swab testing for the wider population, as set out in government guidance) tests taken by individuals whose home address was registered within Cheshire and Merseyside [13]. We considered only SARSCoV2 PCR positive cases in this study and vaccination status data came from the National Immunisation Management System (NIMS). All of these data feeds came via the CIPHA platform. To reduce testing exclusion, the UK government provided free PCR tests where individuals could order a PCR test kit to be sent to their home. Alternatively they could book an appointment at a walkin or drivethrough test site. However, underreporting of SARSCoV2 cases is likely, and could be up to 50% [14]. We have therefore taken this into account in our modelling approach.
SIR model with vaccination
We use an SIR model [15, 16] where individuals are also classified according to their vaccination status. We do not consider an age stratified model to keep parameters to a manageable level. There is some difference in age distribution between the PfizerBioNTech and OxfordAstraZeneca vaccines (Fig. 1) but we consider this sufficiently small for this simplification to be used, particularly for two doses. Susceptible, infected and removed individuals are respectively denoted S, I, and R when unvaccinated, and \(S_{ij}\), \(I_{ij}\), and \(R_{ij}\) when vaccinated, where i is the number of doses and j is the type of vaccine. Considering a twodose vaccine program, i.e., \(i\in \{1,2\}\), the flows between the various classes are shown in Fig. 2 and the system of differential equations is given by:
As shown in Fig. 2, the flow of individuals in this model is not only from susceptible to infected to removed, but also unvaccinated to onedose to twodoses within the susceptible and removed classes. Birth and death processes are neglected in this model. The transmission rate, \(\beta\), is the rate at which an unvaccinated infected individual (I) transmits the virus to an unvaccinated susceptible individual (S). For vaccinated infected individuals (\(I_{ij}\)), their infectiveness, i.e., how likely they are to infect a susceptible individual, is assumed to be reduced by a factor \(\mu _i\) after i doses and we make no distinction between the vaccine types; this parameter is shown to be nonidentifiable [17], see “Results” section for more details. In vaccinated susceptible individuals (\(S_{ij}\)), the effectiveness of dose i of vaccine j in preventing infection is \(e_{ij}\). The recovery rate, \(\gamma\), of an infected individual is assumed to be the same regardless of their vaccination status. As can be seen from the form of the equations for infectious individuals, we expect this parameter to be highly correlated with \(\beta\) and so not wellconstrained by the data. We investigate sensitivity to this parameter over a wide range of plausible values and show that our conclusions on vaccine effectiveness are not sensitive to this. The quantity \(V_{ij}\) is the rate of vaccination with dose i of vaccine j and is determined from the CIPHA data to give a daily vaccination rate. We assume that \(V_{1j}\) is evenly distributed to individuals in classes S and R, \(V_{2j}\) is evenly distributed to the individuals in classes \(S_{1j}\) and \(R_{1j}\), and that infected individuals do not receive the vaccine. The basic SIR model is recovered by initialising all vaccinated populations at zero and setting \(V_{ij}=0\) for all i, j. A summary of the notation used is given in Table 1.
For simplicity, we do not include an exposed state in our model since this would increase the number of fit parameters, lose information and increase instability. Adding a short delay between infection and infectiousness is unlikely to impact the parameters of interest which only concern the rate of exponential growth of the infectious population and previous analysis has shown that the type of data we investigate is more reliably analysed using an SIR model rather than an SEIR model [18].
Model fitting
On 17th May 2021, indoor hospitality was reopened in England. There was a spike in the number of covid19 cases due to the dominance of the Delta variant in the Cheshire and Merseyside NHS region (see Fig. 3a) and across England [3] in combination with the lifting of restrictions. To construct Fig. 3a, a cycle threshold (Ct) cutoff of \(\le 35\) [19] for Sgene, Ngene and ORF1ab (see [20] for details) is required to determine whether it is the Delta or another variant. Only infection data from processing labs that routinely looked at the 3 genes was used. At this point in time, 49% of the population had received 1 dose and 24% had received 2 doses in this region (see Fig. 3b). We use the rapid growth in infections during the period following this date to estimate the effectiveness of the OxfordAstraZeneca and PfizerBioNTech vaccines in our model. During the period of our analysis, use of other vaccines was negligible (of the total vaccines administered, less than 2% of dose 1 and less than 1% of dose 2 were the Moderna vaccine in the CIPHA dataset and no other vaccine types were used) and there were no instances of individuals receiving two different vaccines.
The fitting window used is shown in Fig. 4a. The fitting window starts on 24th May 2021, 7 days after indoor hospitality was reopened. This accounts for the delay in symptoms emerging, which is when people are likely to get tested for COVID19 [21, 22] and also accounts for the 7 day symmetric rolling average, where the number of cases on a given day, 3 days before and 3 days after are averaged. We do this to smooth out the pronounced variations in reporting rates over the course of a week. We can have a symmetric rolling average since this is historic data but note that this differs from rolling averages computed for current data which necessarily involves the 6 preceding days [23].
Markov Chain Monte Carlo (MCMC) methods were used to fit to the incidence and vaccination time series data using the Rstatistical package BayesianTools [24]. The values used to initialise the model fits are shown in Table 2. For parameters that are estimated by model fitting, a value was randomly chosen from their prior distribution to initiate the fits. An adaptive MetropolisHastings algorithm was used where the parameter covariance was updated every 500 iterations after a burnin of 2000 iterations. The algorithm was run for total of \(8\times 10^5\) iterations excluding burnin. The final \(3\times 10^5\) iterations were used to construct the posterior distributions of the parameters, which are plotted in the Additional file 1.
The likelihood function used for MCMC fitting is the Negative Binomial function as in [25]. The Negative Binomial probability mass function is
The following parameterisation is used:
where \(\mu\) is the mean of the distribution and the variance is \(\mu \theta\). Let \({\mathbf {I}}(t) = [I(t),I_{ij}(t)]\) for all i, j be the observed daily incidence on day t and \(\hat{{\mathbf {I}}}(t,x)\) be the incidence generated by the model on day t for a given set of model parameters x. It is assumed that
The log likelihood function is then given by
The SIR model with multidose vaccines is fitted to the data to estimate the posterior distributions of the transmission rate (\(\beta\)), the effectiveness of the vaccines (\(e_{ij}\)), the initial values of the infected classes (I and \(I_{ij}\)), and infectiousness (\(\mu _i\)). We have \(i\in \{1,2\}\) and \(j\in \{{A,P}\}\) for OxfordAstraZeneca and PfizerBioNTech respectively, and therefore posterior distributions for 12 parameters are estimated by the model fit. For all model fits we use uniformly distributed priors for all 12 parameters estimated as shown in Table 2a. For the impact of vaccines on infectiousness (\(\mu _i\)), which implements the reduction in the ability of an individual to transmit the virus, we have no data for the Delta variant of COVID19. For the Alpha variant, this has been estimated to be 0.45–0.50 for one dose of PfizerBioNTech and 0.35–0.50 for one dose of OxfordAstraZeneca, with no data available for the second dose [26]. We therefore make no a priori assumption about infectiousness and consider its full range for the prior distribution; i.e., \(\text {Unif}[0,1]\), allowing for analysis of sensitivity to these parameters. For vaccine effectiveness (\(e_{ij}\)), we also consider the full viable range for the prior distribution; i.e., \(\text {Unif}[0,1]\). The prior distribution for the transmission rate (\(\beta\)) is \(\text {Unif}[0,10]\). This takes into account that \(\beta\) cannot be negative and we expect it to be lower than 10 as this is an extreme scenario where all individuals could get infected due to a high transmission rate. The prior distribution for initial I and initial \(I_{ij}\) is \(\text {Unif}[0,100]\). This takes into account that they cannot be negative and we expect the initial number of infected individuals in each group to be within 100 as the start of our fitting window is shortly after indoor hospitality was reopened (see Fig. 4a).
The infectious period in days is given by \(1/\gamma\), with estimates ranging between 3 and 11 days according to [27]. We therefore consider three model fits with regards to \(\gamma\) (see Table 2a). For our ‘Core Model’ fit we use a median infectious period of 7 days, i.e., \(\gamma =1/7\). To account for sensitivity to the infectious period, we also fit the model for \(\gamma =1/3\) and \(\gamma =1/11\); these fits are called ‘Sensitivity to \(\gamma\)’. For these three fits, the fixed parameters (\(V_{ij}\), N) and the initial values of \(S,S_{ij},R,R_{ij}\), are obtained from CIPHA data. For \(V_{ij}\) we take into account a lag for the vaccine to come into effect. An individual is assumed to move into the relevant vaccinated category after a delay of 21 days postvaccination for dose 1 and 14 days postvaccination for dose 2 of either vaccine [28]. From the CIPHA database, the population size covering Cheshire and Merseyside is 2,730,111 (Additional file 1: Table S1.1). On 24th May 2021, we obtain \(N=2,691,418\) after removing deceased individuals from any cause. We assume this number is fixed for the duration of the fit window. The initial values \(S_{ij}\) and \(R_{ij}\) are shown in Table 2b. The number removed are taken from all individuals who have ever been recorded as infected since the beginning of the pandemic, except for those that died. We are therefore assuming infected individuals retain immunity for the remaining duration of the pandemic, and we are assuming that there is no underreporting of cases (which is certainly not true, especially during the first wave of the pandemic) [14, 29]. The first of these assumptions leads to an overestimate of the removed category on 24th May 2021, and the second (likely more questionable) assumption leads to an underestimate of the removed category. We demonstrate insensitivity of our conclusions to these assumptions by rerunning our analysis assuming that only half of all the infected were actually detected overall, leading to a doubling of the initial removed category on 24th May 2021. This fit is called ‘Double Removed’ (see Table 2c).
Results
Our model used a total population of 2,691,418. From the Core Model, the effectiveness against infection obtained for one dose is 38.5% (95% credible interval [34.3, 42.6]) for OxfordAstraZeneca and 19.5% (95% credible interval [10.4, 28.1]) for PfizerBioNTech. For two doses, we obtained an effectiveness against infection of 64.0% (95% credible interval [61.4, 66.5]) for OxfordAstraZeneca and 83.9% (95% credible interval [82.1, 85.6]) for PfizerBioNTech. The median value and and 95% credible interval for all fitted parameters are shown in Additional file 1: Table S2.1 for all model fits. In Table 3, these values are shown for the Core Model except for the infectiveness of an infected individual with i doses of either vaccine (\(\mu _i\)) as they are nonidentifiable [17]. In particular, the trace plots for all parameters except \(\mu _i\) converge (the MCMC trace plots and posterior distributions for all model fits are shown in the Additional file 1: Figs. S2.1–S2.4). We therefore constructed the log likelihood profiles for \(\mu _i\) and they were flat (see Additional file 1: Fig. S3.1). This means they do not provide any information, confirming nonidentifiability.
Figure 4b–f shows the Core Model fit to data. The incidence curves and 95% confidence interval (CI) bands are plotted using the Core Model median parameter values (Table 3) together with the incidence curves given by the data. The 95% CI is generated by using the fact that the likelihood function used for MCMC fitting is the Negative Binomial function.
For all model fits (see Table 2a), the parameters of direct interest (the vaccine effectiveness parameters) are reproduced in Table 4 where other results from existing studies are reproduced for comparison. For our results we have stated the median value together with the 95% credible interval, where as for the results in [5,6,7] the mean value together with the 95% confidence interval is given.
Discussion
We assessed the effectiveness of the OxfordAstraZeneca and PfizerBioNTech vaccines in reducing the susceptibility of individuals to symptomatic and asymptomatic infection with respect to the SARSCoV2 Delta variant of COVID19 using data from the Cheshire and Merseyside NHS region of the UK. We confirmed that both vaccines provide good protection after two doses but substantially less protection after one dose. The one dose effectiveness against infection was greater for OxfordAstraZeneca (39%) compared to PfizerBioNTech (20%), however the PfizerBioNTech vaccine provides greater protection against infection with the Delta variant after two doses (84% compared to OxfordAstraZeneca vaccine 64%). Our estimates of vaccine effectiveness against infection for one dose of OxfordAstraZeneca and twodoses of either vaccine are consistent with those reported by [5,6,7] (Table 4). Even after changing assumptions, which include the infectious period and the number of removed individuals, the results are still consistent. Furthermore, all studies in Table 4 report a lower effectiveness when compared to the efficacy reported in clinical trials [30, 31] prior to the Delta variant being detected. This suggests that the Delta variant is better at evading vaccine induced immunity.
However, for one dose of the PfizerBioNTech vaccine, our estimate is slightly lower than that reported in these studies. One of the reasons for this is that in our study this group of individuals is much smaller than those groups who have received one dose of OxfordAstraZeneca or two doses. The estimate is still comparable to those reported by Bernal and et al., 2021 and Sheikh et al., 2021 [5, 7]. Similar to our work, these studies included cases who had actively sought COVID19 testing. In contrast, the study conducted by Pouwels et al., 2021 used a community household testing survey to identify cases and controls and notably reported higher 1st dose effect estimates for the PfizerBioNTech vaccine [6]. Note that this is also the case when compared to the clinical trial results reported in Polack et al., 2020 [30], where the efficacy reported is lower for the 1st dose of the PfizerBioNTech vaccine.
Here the effectiveness of the OxfordAstraZeneca and PfizerBioNTech vaccines was estimated by fitting an SIR model where each class of individual was stratified by the number of doses and type of vaccine received. We identified a unique time period in May and June 2021 where the epidemic was undergoing exponential growth from a very low level due to the emergence of the Delta variant and lifting of restrictions, enabling the use of a simple SIR model. During this same period, substantial numbers of vaccines were being administered and this enabled us to extract strong signals for the effectiveness of single and double doses of the PfizerBioNTech and OxfordAstraZeneca vaccines.
The temporal dynamics in each vaccination category are fully accounted for, removing biases caused by the interaction of vaccination rates/ types and the level of infection in the community. For studies employing casecontrol methodology, these biases are harder to account for, for example, there is an assumption that the vaccine under study has no effect on disease incidence in the control population (i.e., the herd effects) [7]. In addition, our study is not restricted to individuals who have sought to get tested and so minimises the issue of population level generalisability [8, 9].
A major assumption in our model is that the number of removed individuals at the start of the fitting window is given by the actual number of recorded infections throughout the whole pandemic. This could be problematic for two reasons. Firstly, there may be waning immunity of previously infected individuals. Secondly, there may be underreporting of infections, particularly in the early stages of the pandemic. This could be due to asymptomatic infection, choosing not to get tested, due to lack of availability of testing or the change over time in the uptake of lateral flow testing as the preferred asymptomatic testing route. The two effects act in opposite directions and the second is likely to be the most dominant on the present timescale. This means that the number of susceptible individuals is likely to be overestimated. To account for this, we considered the case where the number of removed individuals in each class are doubled, representing an underreporting of 50% [14]. This reduces the number in each respective susceptible class for the initial conditions of our model. We make no similar assumption for the dynamics of infection during our fitting window, since detection rates were likely to be high and the fit parameters of interest (exponential rates) are insensitive to reporting rates provided that these rates are constant during the fit window. The vaccine effectiveness estimates are found to be quite insensitive to even this significant modification.
Limitations
It is important to note that our model does not stratify the population by age and therefore it does not take into account the effects of age on vaccine effectiveness. In the UK, vaccines were initially prioritised for the most vulnerable people and then distributed in decreasing order of age [32]. In the fitting window we have used to estimate vaccine effectiveness, unvaccinated individuals or individuals with one dose are much younger. In particular, there is a greater distribution of one dose amongst those ≤ 50 years, and a greater distribution of 2 doses amongst those ≥ 70 years. This means that the single dose vaccine effectiveness is likely to be biased towards the younger population, whereas those with two doses towards the older population. There may be an effect due to variation in immunity across age groups, where younger individuals are likely to have a better immune response to vaccines. The effectiveness of a given vaccine would therefore also depend upon how it is distributed across different age groups. However, Cheshire and Merseyside has had slower population level COVID19 vaccine uptake compared to other areas of the UK [1], which has benefits for estimating vaccine effectiveness in postlicensure studies as this has resulted in a more heterogeneous age distribution.
Conclusion
Vaccine effectiveness for reducing susceptibility to SARSCoV2 Delta variant infection shows noticeable improvement after receiving two doses of either vaccine. Our findings also suggest that a full course of the PfizerBioNTech vaccine provides the optimal protection against infection with the Delta variant. These findings advocate for completion of the full course to maximise individual protection and reduce transmission.
Availability of data and materials
Pseudonymised data are accessible via Combined Intelligence for Population Health Action (CIPHA). Requests can be made to the Data Asset and Access Group for extracts of the largerscale data which cannot be released openly due to information governance requirements. All R code is accessible from the corresponding author.
Abbreviations
 AIC:

Akaike information criterion
 CI:

Confidence interval
 CIPHA:

Combined intelligence for population health action
 MCMC:

Markov chain Monte Carlo
 NHS:

National Health Service
 NIMS:

National immunisation management system
 PCR:

Polymerase chain reaction
 RNA:

Ribose nucleic acid
 SARSCoV2:

Severe acute respiratory syndrome coronavirus 2
 SIR:

Susceptible, infectious or recovered/removed
 SEIR:

Susceptible, exposed, infectious or recovered/removed
 UK:

United Kingdom
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Acknowledgements
We thank CIPHA for providing access to the data used in this study.
Funding
KJS and KP acknowledge support from the EPSRC (Grant EP/T031727/1). DH is funded by a National Institute for Health Research (NIHR) Postdoctoral Fellowship (PDF201811ST2006). IB is funded by NIHR Senior Investigator award. IB and DH are funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Gastrointestinal Infections, a partnership between Public Health England (now UK Health Security Agency, UKHSA), the University of Liverpool and the University of Warwick. Grant No: NIHR ref NIHR200910. The views expressed are those of the authors and not necessarily those of the NIHR, UKHSA or DHSC. The funding bodies were not involved in the study design, data collection, data analysis, data interpretation or writing of the manuscript.
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KJS conceptualised and designed the study and was responsible for supervision. KP was responsible for visualisation. KJS, CEO, IH and KP did the formal analysis. KJS, DH and KP wrote the original draft. SA, CC, MGF, DMH and XZ were responsible for accessing and verifying the data. DH, IB, MGF, DMH and KJS revised the manuscript for important intellectual content. All authors read and approved the final manuscript.
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The University of Liverpool provided secondary data analysis on fully anonymised data. As per the National Health Service (NHS) Health Research Authority guidelines, this work did not require ethical approval. Cheshire & Merseyside Health & Care Partnership Combined Intelligence for Population Health Action (CIPHA) Data Asset and Access Group approved access to the anonomysed data contained in the study. MAST (mass, asymptomatic, serial testing)/SMART (systematic, meaningful, asymptomatic/agile, repeated testing) was defined as ‘an emergency public health intervention during an extraordinary event’ which were subject to the legal and ethical provisions of a health protection activity and COVID19 specifically. The secondary analysis of data provided in a health protection activity is not classified as research, and so does not require research ethics committee review (see http://www.hradecisiontools.org.uk/research/docs/DefiningResearchTable_Oct20171.pdf).
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Not applicable.
Competing interests
IB is Chief Data Scientist Advisor for AstraZeneca.
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Supplementary Information
Additional file 1.
Population demographics, MCMC output (parameter estimates, trace plots and posterior distributions) for all models and log likelihood profile for infectiveness.
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Pattni, K., Hungerford, D., Adams, S. et al. Effectiveness of the BNT162b2 (PfizerBioNTech) and the ChAdOx1 nCoV19 (OxfordAstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARSCoV2. BMC Infect Dis 22, 270 (2022). https://doi.org/10.1186/s1287902207239z
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DOI: https://doi.org/10.1186/s1287902207239z
Keywords
 SARSCoV2
 COVID19
 Vaccine
 SIR
 Modelling
 Infectious disease
 Effectiveness