The Potential Economic Impact of the Updated COVID-19 mRNA Fall 2023 Vaccines in Japan.

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.

Clinical impact and cost-effectiveness of the updated COVID-19 mRNA Autumn 2023 vaccines in Germany.

OBJECTIVES: To assess the potential clinical impact and cost-effectiveness of coronavirus disease 2019 (COVID-19) mRNA vaccines updated for Autumn 2023 in adults aged ≥60 years and high-risk persons aged 30-59 years in Germany over a 1-year analytic time horizon (September 2023-August 2024). METHODS: A compartmental Susceptible-Exposed-Infected-Recovered model was updated and adapted to the German market. Numbers of symptomatic infections, a number of COVID-19 related hospitalizations and deaths, costs, and quality-adjusted life-years (QALYs) gained were calculated using a decision tree model. The incremental cost-effectiveness ratio of an Autumn 2023 Moderna updated COVID-19 (mRNA-1273.815) vaccine was compared to no additional vaccination. Potential differences between the mRNA-1273.815 and the Autumn Pfizer-BioNTech updated COVID-19 (XBB.1.5 BNT162b2) vaccines, as well as societal return on investment for the mRNA-1273.815 vaccine relative to no vaccination, were also examined. RESULTS: Compared to no autumn vaccination, the mRNA-1273.815 campaign is predicted to prevent approximately 1,697,900 symptomatic infections, 85,400 hospitalizations, and 4,100 deaths. Compared to an XBB.1.5 BNT162b2 campaign, the mRNA-1273.815 campaign is also predicted to prevent approximately 90,100 symptomatic infections, 3,500 hospitalizations, and 160 deaths. Across both analyses we found the mRNA-1273.815 campaign to be dominant. CONCLUSIONS: The mRNA-1273.815 vaccine can be considered cost-effective relative to the XBB.1.5 BNT162b2 vaccine and highly likely to provide more benefits and save costs compared to no vaccine in Germany, and to offer high societal return on investment.

The potential clinical impact and cost-effectiveness of the updated COVID-19 mRNA fall 2023 vaccines in the United States.

AIMS: To assess the potential clinical impact and cost-effectiveness of COVID-19 mRNA vaccines updated for fall 2023 in adults aged ≥18 years over a 1-year analytic time horizon (September 2023-August 2024). MATERIALS AND METHODS: A compartmental Susceptible-Exposed-Infected-Recovered model was updated to reflect COVID-19 cases in summer 2023. The numbers of symptomatic infections, COVID-19-related hospitalizations and deaths, and costs and quality-adjusted life-years (QALYs) gained were calculated using a decision tree model. The incremental cost-effectiveness ratio (ICER) of a Moderna updated mRNA fall 2023 vaccine (Moderna Fall Campaign) was compared to no additional vaccination. Potential differences between the Moderna and the Pfizer-BioNTech fall 2023 vaccines were also examined. RESULTS: Base case results suggest that the Moderna Fall Campaign would decrease the expected 64.2 million symptomatic infections by 7.2 million (11%) to 57.0 million. COVID-19-related hospitalizations and deaths are expected to decline by 343,000 (-29%) and 50,500 (-33%), respectively. The Moderna Fall Campaign would increase QALYs by 740,880 and healthcare costs by $5.7 billion relative to no vaccine, yielding an ICER of $7700 per QALY gained. Using a societal cost perspective, the ICER is $2100. Sensitivity analyses suggest that vaccine effectiveness, COVID-19 incidence, hospitalization rates, and costs drive cost-effectiveness. With a relative vaccine effectiveness of 5.1% for infection and 9.8% for hospitalization for the Moderna vaccine versus the Pfizer-BioNTech vaccine, use of the Moderna vaccine is expected to prevent 24,000 more hospitalizations and 3300 more deaths than the Pfizer-BioNTech vaccine. LIMITATIONS AND CONCLUSIONS: As COVID-19 becomes endemic, future incidence, including patterns of infection, are highly uncertain. The effectiveness of fall 2023 vaccines is unknown, and it is unclear when a new variant that evades natural or vaccine immunity will emerge. Despite these limitations, our model predicts the Moderna Fall Campaign vaccine is highly cost-effective across all sensitivity analyses.

Some principles for using epidemiologic study results to parameterize transmission models.

Background: Infectious disease models, including individual based models (IBMs), can be used to inform public health response. For these models to be effective, accurate estimates of key parameters describing the natural history of infection and disease are needed. However, obtaining these parameter estimates from epidemiological studies is not always straightforward. We aim to 1) outline challenges to parameter estimation that arise due to common biases found in epidemiologic studies and 2) describe the conditions under which careful consideration in the design and analysis of the study could allow us to obtain a causal estimate of the parameter of interest. In this discussion we do not focus on issues of generalizability and transportability. Methods: Using examples from the COVID-19 pandemic, we first identify different ways of parameterizing IBMs and describe ideal study designs to estimate these parameters. Given real-world limitations, we describe challenges in parameter estimation due to confounding and conditioning on a post-exposure observation. We then describe ideal study designs that can lead to unbiased parameter estimates. We finally discuss additional challenges in estimating progression probabilities and the consequences of these challenges. Results: Causal estimation can only occur if we are able to accurately measure and control for all confounding variables that create non-causal associations between the exposure and outcome of interest, which is sometimes challenging given the nature of the variables we need to measure. In the absence of perfect control, non-causal parameter estimates should still be used, as sometimes they are the best available information we have. Conclusions: Identifying which estimates from epidemiologic studies correspond to the quantities needed to parameterize disease models, and determining whether these parameters have causal interpretations, can inform future study designs and improve inferences from infectious disease models. Understanding the way in which biases can arise in parameter estimation can inform sensitivity analyses or help with interpretation of results if the magnitude and direction of the bias is understood.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics.

Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. When vaccine stockpiles are limited, doses should be allocated in locations to maximize their impact. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of characteristics of the population (e.g., size, underlying immunity, heterogeneous risk structure, interaction), vaccine (e.g., vaccine efficacy), pathogen (e.g., transmissibility), and delivery (e.g., varying speed and timing of rollout). Across a wide range of characteristics considered, we find that vaccine allocation proportional to population size (i.e., pro-rata allocation) performs either better or comparably to nonproportional allocation strategies in minimizing the cumulative number of infections. These results may argue in favor of sharing of vaccines between locations in the context of an epidemic caused by an emerging pathogen, where many epidemiologic characteristics may not be known.

Gaussian Accelerated Molecular Dynamics in OpenMM.

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics.

Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. Due to limited vaccine stockpiles, vaccine doses should be allocated in locations where their impact will be maximized. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of population size, underlying immunity, continuous vaccine roll-out, heterogeneous population risk structure, and differences in disease transmissibility. We find that in the context of an emerging pathogen where many epidemiologic characteristics might not be known, equal vaccine allocation between populations performs optimally in most scenarios. In the specific case considering heterogeneous population risk structure, first targeting individuals at higher risk of transmission or death due to infection leads to equal resource allocation across populations.

Correction: Practical considerations for measuring the effective reproductive number, Rt.

[This corrects the article DOI: 10.1371/journal.pcbi.1008409.].

Declining HIV incidence in sub-Saharan Africa: a systematic review and meta-analysis of empiric data.

INTRODUCTION: UNAIDS models suggest HIV incidence is declining in sub-Saharan Africa. The objective of this study was to assess whether modelled trends are supported by empirical evidence. METHODS: We conducted a systematic review and meta-analysis of adult HIV incidence data from sub-Saharan Africa by searching Embase, Scopus, PubMed and OVID databases and technical reports published between 1 January 2010 and 23 July 2019. We included prospective and cross-sectional studies that directly measured incidence from blood samples. Incidence data were abstracted according to population risk group, geographic location, sex, intervention arm and calendar period. Weighted regression models were used to assess incidence trends across general population studies by sex. We also identified studies reporting greater than or equal to three incidence measurements since 2010 and assessed trends within them. RESULTS: Total 291 studies, including 22 sub-Saharan African countries, met inclusion criteria. Most studies were conducted in South Africa (n = 102), Uganda (n = 46) and Kenya (n = 41); there were 26 countries with no published incidence data, most in western and central Africa. Data were most commonly derived from prospective observational studies (n = 163; 56%) and from geographically defined populations with limited demographic or risk-based enrolment criteria other than age (i.e., general population studies; n = 151; 52%). Across general population studies, average annual incidence declines since 2010 were 0.12/100 person-years (95% CI: 0.06-0.18; p = 0.001) among men and 0.10/100 person-years (95% CI: -0.02-0.22; p = 0.093) among women in eastern Africa, and 0.25/100 person-years (95% CI: 0.17-034; p < 0.0001) among men and 0.42/100 person-years (95% CI: 0.23-0.62; p = 0.0002) among women in southern Africa. In nine of 10 studies with multiple measurements, incidence declined over time, including in two studies of key populations. Across all population risk groups, the highest HIV incidence estimates were observed among men who have sex with men, with rates ranging from 1.0 to 15.4/100 person-years. Within general population studies, incidence was typically higher in women than men with a median female-to-male incidence rate ratio of 1.47 (IQR: 1.11 to 1.83) with evidence of a growing sex disparity over time. CONCLUSIONS: Empirical incidence data show the rate of new HIV infections is declining in eastern and southern Africa. However, recent incidence data are non-existent or very limited for many countries and key populations.

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19.

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.

Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from 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 of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, 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 resulting Rt estimates 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 in Rt estimation.

Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials.

To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling - combining projections from independent modeling groups - to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting.

Practical considerations for measuring the effective reproductive number, R t.

Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from 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 of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), 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 spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates 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 in R t estimation.