ABSTRACT
Background: Population-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic. Methods: Household members ≥5 years were enrolled in a three-stage national household cluster serosurvey in the Dominican Republic. We measured pan-immunoglobulin antibodies against the SARS-CoV-2 spike (anti-S) and nucleocapsid glycoproteins, and pseudovirus neutralising activity against the ancestral and B.1.617.2 (Delta) strains. Seroprevalence and cumulative prior infections were weighted and adjusted for assay performance and seroreversion. Binary classification machine learning methods and pseudovirus neutralising correlates of protection were used to estimate 50% and 80% protection against symptomatic infection. Findings: Between 30 Jun and 12 Oct 2021 we enrolled 6683 individuals from 3832 households. We estimate that 85.0% (CI 82.1-88.0) of the ≥5 years population had been immunologically exposed and 77.5% (CI 71.3-83) had been previously infected. Protective immunity sufficient to provide at least 50% protection against symptomatic SARS-CoV-2 infection was estimated in 78.1% (CI 74.3-82) and 66.3% (CI 62.8-70) of the population for the ancestral and Delta strains respectively. Younger (5-14 years, OR 0.47 [CI 0.36-0.61]) and older (≥75-years, 0.40 [CI 0.28-0.56]) age, working outdoors (0.53 [0.39-0.73]), smoking (0.66 [0.52-0.84]), urban setting (1.30 [1.14-1.49]), and three vs no vaccine doses (18.41 [10.69-35.04]) were associated with 50% protection against the ancestral strain. Interpretation: Cumulative infections substantially exceeded prior estimates and overall immunological exposure was high. After controlling for confounders, markedly lower immunological protection was observed to the ancestral and Delta strains across certain subgroups, findings that can guide public health interventions and may be generalisable to other settings and viral strains. Funding: This study was funded by the US CDC.
ABSTRACT
The Pfizer COVID-19 vaccine is associated with increased myocarditis incidence. Constantly evolving evidence regarding incidence and case fatality of COVID-19 and myocarditis related to infection or vaccination, creates challenges for risk-benefit analysis of vaccination. Challenges are complicated further by emerging evidence of waning vaccine effectiveness, and variable effectiveness against variants. Here, we build on previous work on the COVID-19 Risk Calculator (CoRiCal) by integrating Australian and international data to inform a Bayesian network that calculates probabilities of outcomes for the delta variant under different scenarios of Pfizer COVID-19 vaccine coverage, age groups (≥12 years), sex, community transmission intensity and vaccine effectiveness. The model estimates that in a population where 5% were unvaccinated, 5% had one dose, 60% had two doses and 30% had three doses, there was a substantially greater probability of developing (239-5847 times) and dying (1430-384,684 times) from COVID-19-related than vaccine-associated myocarditis (depending on age and sex). For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over 2 months, 68,813 symptomatic COVID-19 cases and 981 deaths would be prevented, with 42 and 16 expected cases of vaccine-associated myocarditis in males and females, respectively. These results justify vaccination in all age groups as vaccine-associated myocarditis is generally mild in the young, and there is unequivocal evidence for reduced mortality from COVID-19 in older individuals. The model may be updated to include emerging best evidence, data pertinent to different countries or vaccines and other outcomes such as long COVID.
ABSTRACT
Uncertainty surrounding the risk of developing and dying from Thrombosis and Thrombocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the model structure, relevant variables, data for inclusion, and how these data were used to inform the model. The model can be used as a decision-support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be adapted to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can be used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.
Subject(s)
COVID-19 , Thrombocytopenia , Bayes Theorem , COVID-19/complications , COVID-19/prevention & control , COVID-19 Vaccines/adverse effects , ChAdOx1 nCoV-19 , Humans , Post-Acute COVID-19 SyndromeABSTRACT
Thrombosis and Thrombocytopenia Syndrome (TTS) has been associated with the AstraZencea (AZ) COVID-19 vaccine (Vaxzevria). Australia has reported low TTS incidence of < 3/100,000 after the first dose, with case fatality rate (CFR) of 5-6%. Risk-benefit analysis of vaccination has been challenging because of rapidly evolving data, changing levels of transmission, and variation in rates of TTS, COVID-19, and CFR between age groups. We aim to optimise risk-benefit analysis by developing a model that enables inputs to be updated rapidly as evidence evolves. A Bayesian network was used to integrate local and international data, government reports, published literature and expert opinion. The model estimates probabilities of outcomes under different scenarios of age, sex, low/medium/high transmission (0.05%/0.45%/5.76% of population infected over 6 months), SARS-CoV-2 variant, vaccine doses, and vaccine effectiveness. We used the model to compare estimated deaths from AZ vaccine-associated TTS with i) COVID-19 deaths prevented under different scenarios, and ii) deaths from COVID-19 related atypical severe blood clots (cerebral venous sinus thrombosis & portal vein thrombosis). For a million people aged ≥ 70 years where 70% received first dose and 35% received two doses, our model estimated < 1 death from TTS, 25 deaths prevented under low transmission, and > 3000 deaths prevented under high transmission. Risks versus benefits varied significantly between age groups and transmission levels. Under high transmission, deaths prevented by AZ vaccine far exceed deaths from TTS (by 8 to > 4500 times depending on age). Probability of dying from COVID-related atypical severe blood clots was 58-126 times higher (depending on age and sex) than dying from TTS. To our knowledge, this is the first example of the use of Bayesian networks for risk-benefit analysis for a COVID-19 vaccine. The model can be rapidly updated to incorporate new data, adapted for other countries, extended to other outcomes (e.g., severe disease), or used for other vaccines.
Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19 Vaccines , Humans , Infant, Newborn , Vaccine EfficacyABSTRACT
INTRODUCTION: The increase in international travel brought about by globalisation has enabled the rapid spread of emerging pathogens with epidemic and pandemic potential. While travel connectivity-based assessments may help understand patterns of travel network-mediated epidemics, such approaches are rarely carried out in sufficient detail for Oceania where air travel is the dominant method of transportation between countries. DESIGN: Travel data from the Australian Bureau of Statistics, Stats NZ and the United Nations World Tourism Organization websites were used to calculate travel volumes in 2018 within Oceania and between Oceania and the rest of the world. The Infectious Disease Vulnerability Index (IDVI) was incorporated into the analysis as an indicator of each country's capacity to contain an outbreak. Travel networks were developed to assess the spread of infectious diseases (1) into and from Oceania, (2) within Oceania and (3) between each of the Pacific Island Countries and Territories (PICTs) and their most connected countries. RESULTS: Oceania was highly connected to countries in Asia, Europe and North America. Australia, New Zealand and several PICTs were highly connected to the USA and the UK (least vulnerable countries for outbreaks based on the IDVI), and to China (intermediate low vulnerable country). High variability was also observed between the PICTs in the geographical distribution of their international connections. The PICTs with the highest number of international connections were Fiji, French Polynesia, Guam and Papua New Guinea. CONCLUSION: Travel connectivity assessments may help to accurately stratify the risk of infectious disease importation and outbreaks in countries depending on disease transmission in other parts of the world. This information is essential to track future requirements for scaling up and targeting outbreak surveillance and control strategies in Oceania.