Estimation of the effective reproductive numberR_tis important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are usingR_tto assess the effectiveness of interventions and to inform policy. However, estimation ofR_tfrom available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation ofR_t, we recommend the approach of Cori and colleagues, which uses data from before timetand empirical estimates of the distribution of time between infections. Methods that require data from after timet, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resultingR_testimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems inR_testimation.

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

Approximately 10–20% of individuals suffering from COVID-19 develop prolonged symptoms known as long COVID or post-COVID condition (LC). This review aimed to assess healthcare resource use (HCRU) and healthcare costs associated with LC. Because LC is not clearly defined and often remains undiagnosed, studies reporting on long-term follow-up of individuals with a COVID-19 diagnosis were also included. Among the 41 publications included, 36 reported on HCRU and 16 on costs. Individuals with LC had significantly elevated HCRU and healthcare costs vs. controls without a COVID-19 diagnosis over ≥15 months, with a 7.6–13.1% increase in total healthcare costs per person per month as assessed by difference-in-difference analysis. Among studies that did not specifically refer to LC, having a COVID-19 diagnosis was associated with a significant 4–10% increase in long-term total HCRU over 6–8 months and a 1.3- to 2.9-fold relative increase in total healthcare costs over 6 months. Due to the heterogeneity of the included studies, high-quality evidence is needed to better understand the economic burden of LC. In the absence of effective treatments, prioritizing the prevention of acute COVID-19, e.g., through vaccination, may be crucial for preventing LC and the associated long-term HCRU and medical spending.

Introduction In the post-pandemic era, people with underlying medical conditions continue to be at increased risk for severe COVID-19 disease, yet COVID-19 vaccination uptake remains low. This study estimated the clinical and economic impact of updated 2023/24 Moderna COVID-19 vaccination among high-risk adults versus no 2023/24 vaccination and versus updated Pfizer/BioNTech vaccination. Methods A static Markov model was adapted for high-risk adults, including immunocompromised (IC), chronic lung disease (CLD), chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes mellitus (DM) populations in the United States (US). Results Vaccination with the updated Moderna vaccine at current coverage rates was estimated to prevent considerable COVID-19 hospitalizations in CLD (101,309), DM (97,358), CVD (47,830), IC (14,834), and CKD (13,558) populations versus no 2023/24 vaccination. Vaccination also provided net medical cost-savings of US$399 million (M)–2129M (healthcare payer) and $457M–2531M (societal perspective), depending on population. The return-on-investment was positive across all conditions ($1.10–$2.60 gain for every $1 invested). Healthcare savings increased with a relative 10% increase in current vaccination coverage ($439M–$2342M), and from meeting US 2030 targets of 70% coverage ($1096M–$5707M). Based on higher vaccine effectiveness observed in real-world evidence studies, updated Moderna vaccination was estimated to prevent additional COVID-19 hospitalizations in DM (13,105), CLD (10,359), CVD (6241), IC (1979), and CKD (942) versus Pfizer/BioNTech’s updated vaccine, with healthcare payer and societal cost-savings, making it the dominant strategy. Healthcare savings per patient vaccinated with Moderna versus Pfizer/BioNTech’s updated vaccine were $31.00–$59.00, depending on population. Results were robust across sensitivity/scenario analyses. Conclusions Updated 2023/24 Moderna COVID-19 vaccination was estimated to provide significant health benefits through prevention of COVID-19 in high-risk populations, and cost-savings to healthcare payers and society, versus no 2023/24 vaccination and updated Pfizer/BioNTech vaccination. Increasing current low COVID-19 vaccination coverage rates was estimated to be cost-saving while preventing many more severe infections and hospitalizations in these high-risk populations. A graphical abstract is available with this article. Graphical Abstract

With continued high disease burden in vulnerable groups and fiscal responsibility shifting to Canada's jurisdictions, assessing the economic value of COVID-19 vaccines is critical for optimizing COVID-19 prevention. This study estimated the public health impact and economically justifiable price (EJP) of Moderna's next-generation COVID-19 vaccine (mRNA-1283) versus no vaccination in Canada, and relative to currently authorized vaccines (mRNA-1273; BNT-162b2). The target population included individuals aged ≥65 years and 12-64 years at high-risk of severe COVID-19 outcomes, consistent with 2025/2026 national guidelines. Analyses were conducted using a static decision-analytic model (1-year horizon) from a publicly funded healthcare payer perspective. Vaccine efficacy against infection and hospitalization for mRNA-1283 versus no 2024/2025 vaccination was based on mRNA-1283's pivotal trial and mRNA-1273 real-world data. Clinical outcomes included infections, hospitalizations, deaths, and number needed to vaccinate (NNV); economic outcomes included total costs, quality-adjusted life-years (QALY), and EJP at a $50,000/QALY willingness-to-pay threshold. Sensitivity analyses were performed. Compared to no vaccine, annual vaccination with mRNA-1283 prevented 288,912 symptomatic infections (NNV = 15), 11,710 hospitalizations (NNV = 364), and 2,194 deaths (NNV = 1,944). The EJP for mRNA-1283 was $325 ($230-$771 in sensitivity analyses). Semi-annual dosing (≥65 or ≥80 years) averted additional hospitalizations and deaths compared to annual vaccination. mRNA-1283 prevented an additional 2,873-3,689 hospitalizations and 537-690 deaths compared to currently authorized vaccines. EJPs for mRNA-1283 were $78 and $103 when compared to mRNA-1273 and BNT162b2, respectively. This study does not include indirect effects, and mRNA-1283 effectiveness has not yet been validated in real-world studies. VE waning and incidence estimates are highly uncertain. British and American estimates were used as Canadian data proxies. mRNA-1283 could reduce the COVID-19 clinical burden and provide economic value for the NACI-recommended population, exceeding current mRNA vaccines; COVID-19 program planners may consider supporting access to mRNA-1283 to optimize public health impact.

This analysis estimates the economic and clinical impact of a Moderna updated COVID-19 mRNA Fall 2023 vaccine for adults ≥18 years in Japan. A previously developed Susceptible-Exposed-Infected-Recovered (SEIR) model with a one-year analytic time horizon (September 2023–August 2024) and consequences decision tree were used to estimate symptomatic infections, COVID-19 related hospitalizations, deaths, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER) for a Moderna updated Fall 2023 vaccine versus no additional vaccination, and versus a Pfizer–BioNTech updated mRNA Fall 2023 vaccine. The Moderna vaccine is predicted to prevent 7.2 million symptomatic infections, 272,100 hospitalizations and 25,600 COVID-19 related deaths versus no vaccine. In the base case (healthcare perspective), the ICER was ¥1,300,000/QALY gained ($9400 USD/QALY gained). Sensitivity analyses suggest results are most affected by COVID-19 incidence, initial vaccine effectiveness (VE), and VE waning against infection. Assuming the relative VE between both bivalent vaccines apply to updated Fall 2023 vaccines, the base case suggests the Moderna version will prevent an additional 1,100,000 symptomatic infections, 27,100 hospitalizations, and 2600 deaths compared to the Pfizer–BioNTech vaccine. The updated Moderna vaccine is expected to be highly cost-effective at a ¥5 million willingness-to-pay threshold across a wide range of scenarios.