Revisiting assumptions in test-negative studies for estimating vaccine effectiveness: the need for a clinical case definition (preprint)

Test negative studies have been used extensively for the estimation of COVID-19 vaccine effectiveness (VE). Such studies are able to estimate VE against medically-attended illness under certain assumptions. Selection bias may be present if the probability of participation is associated with vaccination or COVID-19, but this can be mitigated through use of a clinical case definition to screen patients for eligibility, which increases the likelihood that cases and non-cases come from the same source population. We examined the extent to which this type of bias could harm COVID-19 VE through systematic review and simulation. A systematic review of test-negative studies was re-analysed to identify studies ignoring the need for clinical criteria. Studies using a clinical case definition had a lower pooled VE estimate compared with studies that did not. Simulations varied the probability of selection by case and vaccination status. Positive bias away from the null (i.e., inflated VE consistent with the systematic review) was observed when there was a higher proportion of healthy, vaccinated non-cases, which may occur if a dataset contains many results from asymptomatic screening in settings where vaccination coverage is high. We provide an html tool for researchers to explore site-specific sources of selection bias in their own studies. We recommend all group consider the potential for selection bias in their vaccine effectiveness studies, particularly when using administrative data.

Prior infections and effectiveness of SARS-CoV-2 vaccine in test-negative study: A systematic review and meta-analysis (preprint)

Background: Prior infection with SARS-CoV-2 can provide protection against infection and severe COVID-19. In settings with high pre-existing immunity, vaccine effectiveness (VE) should decrease with higher levels of immunity among unvaccinated individuals. Here, we conducted a systematic review and meta-analysis to understand the influence of prior infection on VE. Methods: We included test-negative design (TND) studies that examined VE against infection or severe disease (hospitalization, ICU admission, or death) for primary vaccination series. To determine the impact of prior infections on VE estimates, we compared studies that excluded or included people with prior COVID-19 infection. We also compared VE estimates by the cumulative incidence of cases before the start of and incidence rates during each study in the study locations, as further measures of prior infections in the community. Findings: We identified 67 studies that met inclusion criteria. Pooled VE among studies that included people with prior COVID-19 infection was lower against infection (pooled VE: 77%; 95% confidence interval (CI): 72%, 81%) and severe disease (pooled VE: 86%; 95% CI: 83%, 89%), compared with studies that excluded people with prior COVID-19 infection (pooled VE against infection: 87%; 95% CI: 85%, 89%; pooled VE against severe disease: 93%; 95% CI: 91%, 95%). There was a negative correlation between the cumulative incidence of cases before the start of the study and VE estimates against infection (spearman correlation ({rho}) = -0.32; 95% CI: -0.45, -0.18) and severe disease ({rho} = -0.49; 95% CI: -0.64, -0.30). There was also a negative correlation between the incidence rates of cases during the study period and VE estimates against infection ({rho} = -0.48; 95% CI: -0.59, -0.34) and severe disease ({rho} = -0.42; 95% CI: -0.58, -0.23). Interpretation: Based on a review of published VE estimates we found clear empirical evidence that higher levels of pre-existing immunity in a population were associated with lower VE estimates. Excluding previously infected individuals from VE studies may result in higher VE estimates with limited generalisability to the wider population. Prior infections should be treated as confounder and effect modificatory when the policies were targeted to whole population or stratified by infection history, respectively.

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households (preprint)

Quantifying variation of individual infectiousness is critical to inform disease control. Previous studies reported substantial heterogeneity in transmission of many infectious diseases (including SARS-CoV-2). However, those results are difficult to interpret since the number of contacts is rarely considered in such approaches. Here, we analyze data from 17 SARS-CoV-2 household transmission studies conducted in periods dominated by ancestral strains, in which the number of contacts was known. By fitting individual-based household transmission models to these data, accounting for number of contacts and baseline transmission probabilities, the pooled estimate suggests that the 20% most infectious cases have 3.1-fold (95% confidence interval: 2.2-4.2 fold) higher infectiousness than average cases, which is consistent with the observed heterogeneity in viral shedding. Household data can inform the estimation of transmission heterogeneity, which is important for epidemic management.

Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis (preprint)

Background: Dose fractionation of Coronavirus Disease 2019 (COVID-19) vaccine could effectively accelerate global vaccine coverage, while supporting evidence of efficacy, immunogenicity, and safety are unavailable, especially with emerging variants.Methods: We systematically reviewed clinical trials reported dose-finding results and estimated the dose-response relationship of neutralizing antibodies (nAbs) of COVID-19 vaccines using generalized additive model. We predicted the vaccine efficacy against both ancestral and variants, using previously reported correlates of protection and cross-reactivity. We also reviewed and compared seroconversion to nAbs, T-cell responses and safety profiles between fractional and standard dose groups.Results: We found that dose fractionation of mRNA and protein subunit vaccines could induce SARS-CoV-2 specific nAbs and T-cells that confer a reasonable level of protection (i.e., vaccine efficacy > 50%) against ancestral strains and variants up to Omicron. Safety profiles of fractional doses were non-inferior to the standard dose.Conclusion: Dose fractionation of mRNA and protein subunit vaccines may be safe and effective.

Dynamic analysis and evaluation of asymptomatic infection in the spread of COVID-19 (preprint)

COVID-19 has spread worldwide for nearly two years. Many countries have experienced repeated epidemics, that is, after the epidemic has been controlled for a period of time, the number of new cases per day is low, and the outbreak will occur again a few months later. In order to study the relationship between this low level of infection and the number of asymptomatic infections, and to evaluate the role of asymptomatic infections in the development of the epidemic, we have established an improved infectious disease dynamics model that can be used to evaluate the spread of the COVID-19 epidemic, and fitted the epidemic data in the three flat periods in England. According to the obtained parameters, according to the calculation of the model, the proportion of asymptomatic infections in these three flat periods are 41%, 53% and 58% respectively. After the first flat period, the number of daily newly confirmed cases predicted by the model began to increase around July 1, 2020. After more than four months of epidemic spread, it reached a peak on November 12, which is consistent with the actual case situation. Unanimous. After the second flat period, the model predicts that the number of new confirmed cases per day will increase from about May 7, 2021, and after about 73 days of epidemic development, it will reach a peak on July 20, showing the overall trend of the epidemic. In the above, the predicted results of the model are consistent with the actual cases. After the third flat period, the number of daily newly diagnosed cases predicted by the model began to increase around December 1, 2021, and reached a peak in December, and the number of cases will drop to a very low level after May 2022. According to our research results, due to the large number of asymptomatic infections, the spread of the epidemic is not easy to stop completely in a short time. However, when the epidemic enters a period of flat time, nucleic acid testing is performed, and asymptomatic infections are isolated at home for 14 days (the recovery period of symptomatic infection is about 10 days) may be an option that can be considered to interrupt the transmission of the case.

A kinetic model considering the decline of antibody level and vaccination of COVID-19 (preprint)

In order to evaluate the decline in antibody levels and the impact of vaccination on the spread of the epidemic, we establish COVID-19 dynamic models that consider the decline in antibody levels and the effects of vaccination, and retrospectively evaluate the epidemic situation in England. Based on the epidemic data in England from September 1 to October 31, 2020, considering the continuous decline in the antibody level of COVID-19 recovers, an improved SEIR infectious disease dynamics model that considers the reinfection of recovers due to the decline in antibody levels is established. The kinetic parameters of the SEIR model are obtained by fitting. On this basis, a SEIRV infectious disease dynamic model with vaccination is established to study the impact of different vaccination rates and vaccine failure rates on the development of the epidemic in England. We obtain the lower the vaccine failure rate, the fewer new cases. When the vaccination rate is fixed at 0.005 (equivalent to 250000 people vaccinated every day), the peak of the epidemic will decrease with the decrease of vaccine failure rate. The peak value when the failure rate is 0.001 is 81.4% lower than the peak value when the failure rate is 0.01, and the peak value when the failure rate is 0.01 is 89.5% lower than the peak value when the failure rate is 0.02. When the failure rate is less than 0.01, the peak time will advance with the decrease of failure rate; when the failure rate is greater than 0.01, the peak time will be delayed with the decrease of failure rate; when the failure rate is 0.01, the peak time is 528 days later than that when the failure rate is 0.001 and 295 days later than that when the failure rate is 0.05. On the 60th day of vaccination, the vaccine failure rate of 0.002 decreases the number of cases by 5.8% compared with the vaccine failure rate of 0.01; on the 70th day of vaccination, the vaccine failure rate of 0.002 reduces the number of cases by 9.1% compared with the vaccine failure rate of 0.01. Therefore, with the extension of time, the vaccine with low failure rate has a more obvious effect on reducing the number of cases than the vaccine with high failure rate. When the vaccine failure rate is fixed at 0.005, we study the impact of different vaccination rates on the spread of the epidemic in England, the result shows that the peak of epidemic situation decreases with the increase of vaccination rate, and the peak time advance with the increase of vaccination rate, when the vaccination rate is 0.025, the peak decreases by 74.8% and the peak time was 114 days earlier than that when the vaccination rate is 0.005.Therefore, the higher the vaccine efficiency and vaccination rate, the lower the peak of the epidemic. On the basis of improving the effectiveness of vaccines, increasing the vaccination rate is of practical significance for controlling the spread of the epidemic.