Risk-based prediction for optimal timing of booster vaccination for COVID-19 to prevent severe disease (preprint)

While waning protection from vaccination and natural infection against SARS-CoV-2 infection is well-documented, recent analyses have also found waning of protection against severe COVID-19. This highlights a broader need to understand the optimal timing of COVID-19 booster vaccines specific to an individual to mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age group and immune status. Here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively mitigate the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit. Analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, we estimated that in persons 75+ years, annual and semiannual bivalent boosters would reduce annual absolute risk of severe COVID-19 by 311 (277-369) and 578 (494-671) cases, respectively, compared to a one-time bivalent booster dose. In contrast, for persons 18-49 years, the model estimated that annual and semiannual bivalent boosters would reduce annual absolute risk of severe COVID-19 by 20 (13-26) and 37 (24-50) cases per 100,000 persons, respectively, compared to a one-time bivalent booster dose. Persons with prior infection had a much lower benefit of more frequent boosting, while immunocompromised persons had larger benefit. This study underscores the benefit of customizing timing of COVID-19 booster vaccines based on individual risk.

Determining population-level allocation strategies for COVID-19 treatments in the United States using a quantitative framework, a case study using nirmatrelvir/ritonavir (preprint)

BackgroundNew COVID-19 medications force decision makers to weigh limited evidence of efficacy and cost in determining which patient populations to target for treatment. A case in point is nirmatrelvir/ritonavir, a drug that has been recommended for elderly, high-risk individuals, regardless of vaccination status, even though clinical trials have only evaluated it in unvaccinated patients. A simple optimization framework might inform a more reasoned approach to the tradeoffs implicit in the treatment allocation decision. MethodsWe used a mathematical model to analyze the cost-effectiveness of four nirmatrelvir/ritonavir allocation strategies, stratified by vaccination status and risk for severe disease. We considered treatment effectiveness at preventing hospitalization ranging from 21% to 89%. Sensitivity analyses were performed on major parameters of interest. A web-based tool was developed to permit decision-makers to tailor the analysis to their settings and priorities. ResultsProviding nirmatrelvir/ritonavir to unvaccinated patients at high-risk for severe disease was cost-saving when effectiveness against hospitalization exceeded 33% and cost-effective under all other data scenarios we considered. The cost-effectiveness of other allocation strategies, including those for vaccinated adults and those at lower-risk for severe disease, depended on willingness-to-pay thresholds, treatment cost and effectiveness, and the likelihood of severe disease. ConclusionsPriority for nirmatrelvir/ritonavir treatment should be given to unvaccinated persons at high-risk of severe disease from COVID-19. Further priority may be assigned by weighing treatment effectiveness, disease severity, drug cost, and willingness to pay for deaths averted.

Model-based estimates of deaths averted and cost per life saved by scaling-up mRNA COVID-19 vaccination in low and lower-middle income countries in the COVID-19 Omicron variant era (preprint)

Background: While almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries has received a full primary vaccine series, compared to over 70% of the population of high-income nations. Methods: We used economic and epidemiologic models, parameterized with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programs in low and lower-middle income countries (LIC/LMIC) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2. Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up, and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs. Findings: Global scale up of vaccination to provide two doses of mRNA vaccine to everyone in LIC/LMIC would cost $35.5 billion and avert 1.3 million deaths from COVID-19, at a cost of $26,900 per death averted. Scaling up vaccination to provide three doses of mRNA vaccine to everyone in LIC/LMIC would cost $61.2 billion and avert 1.5 million deaths from COVID-19 at a cost of $40,800 per death averted. Lower estimated infection fatality ratios, higher cost-per-dose, and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis. Interpretation: Scaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life (VSL). Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Understanding the barriers to pooled SARS-CoV-2 testing in the United States (preprint)

Pooled testing for SARS-CoV-2 detection is instrumental for increasing test capacity while decreasing test cost, key factors for sustainable, long-term surveillance measures. While numerous pooled approaches have been described, uptake by labs has been limited. We surveyed 90 US labs to understand the barriers to implementing pooled testing.