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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.

Estimation of the effective reproductive number R t 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 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 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 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.

Polycystin-1 (PC1) is the protein product of the PKD1 gene whose mutation causes autosomal dominant Polycystic Kidney Disease (ADPKD). PC1 is an atypical G protein-coupled receptor (GPCR) with an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal and membrane-embedded C-terminal (CTF) fragments. Recently, activation of PC1 CTF signaling was shown to be regulated by a stalk tethered agonist (TA), resembling the mechanism observed for adhesion GPCRs. Here, synthetic peptides of the first 9- (p9), 17- (p17), and 21-residues (p21) of the PC1 stalk TA were shown to re-activate signaling by a stalkless CTF mutant in human cell culture assays. Novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) simulations elucidated binding conformations of p9, p17, and p21 and revealed multiple specific binding regions to the stalkless CTF. Peptide agonists binding to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of stalk TA-mediated PC1 CTF activation. Additional sequence coevolution analyses showed the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. These insights into the structural dynamic mechanism of PC1 activation by TA peptide agonists provide an in-depth understanding that will facilitate the development of therapeutics targeting PC1 for ADPKD treatment.

The M 5 muscarinic acetylcholine receptor (M 5 mAChR) represents a promising therapeutic target for neurological disorders. However, the high conservation of its orthosteric binding site poses significant challenges for drug development. While selective positive allosteric modulators (PAMs) offer a potential solution, a structural understanding of the M 5 mAChR and its allosteric binding sites remains limited. Here, we present a 2.8 Å cryo-electron microscopy structure of the M 5 mAChR complexed with heterotrimeric G q protein and the agonist iperoxo, completing the active-state structural characterization of the mAChR family. To identify the binding site of M 5 -selective PAMs, we implement an integrated approach combining mutagenesis, pharmacological assays, structural biology, and molecular dynamics simulations. Our mutagenesis studies reveal that selective M 5 PAMs bind outside previously characterized M 5 mAChR allosteric sites. Subsequently, we obtain a 2.1 Å structure of M 5 mAChR co-bound with acetylcholine and the selective PAM VU6007678, revealing an allosteric pocket at the extrahelical interface between transmembrane domains 3 and 4 that is confirmed through mutagenesis and simulations. These findings demonstrate the diverse mechanisms of allosteric regulation in mAChRs and highlight the value of integrating pharmacological and structural approaches to identify allosteric binding sites. The M 5 muscarinic acetylcholine receptor represents a promising therapeutic target for neurological disorders. Here, the authors reveal a 2.1 Å cryo-EM structure of the M 5 bound to a selective positive allosteric modulator site that enables structure-based drug design.

Background/Objectives: Respiratory syncytial virus (RSV) is a leading cause of severe respiratory disease in older adults. Despite growing recognition of RSV as a public health concern, vaccination options remain limited. This study assessed the potential long-term public health impact of extended mRNA-1345 RSV vaccination campaigns. Methods: A dynamic transmission model, stratified by age, was developed to evaluate the epidemiological and clinical impact of RSV vaccination in the UK over a 20-year time horizon. Eight vaccination strategies were assessed: two reflecting the JCVI recommendation for the 2024–2025 season and its recent extension, and six extended strategies considering broader eligible age groups, higher coverage, and/or revaccination every 2 or 3 years. Two exploratory analyses and extensive model validation versus reported data were also conducted. Results: Strategies combining broader age eligibility (≥60 years), higher coverage (80%), and 2-year revaccination achieved the greatest impact, preventing 310,000 hospitalisations over 20 years in the total UK population. Exploratory analyses showed that the expected public health impact might exceed the estimates presented in this analysis, if an alternative vaccine efficacy profile or the projected demographic shift would be confirmed. Conclusions: Extended RSV vaccination strategies including broader age eligibility and routine revaccination could offer substantial public health benefits in the UK. Targeting adults aged ≥60 years is expected to be particularly efficient in achieving a sustainable reduction in RSV burden. These findings could provide valuable support for national policy discussions on optimising RSV vaccination strategies in older adults, particularly regarding target age groups, revaccination schedules, and long-term programme planning.

Background/Objectives: COVID-19 continues to challenge public health due to emerging variants. To mitigate this, the Korea Disease Control and Prevention Agency (KDCA) recommends annual COVID-19 vaccination, but uptake remains suboptimal. This study evaluates the public health and economic impact of annual mRNA COVID-19 vaccination for adults aged 50 and older in South Korea during the 2024–2025 season, focusing on hospitalizations and costs. Methods: We estimated hospitalizations prevented by the mRNA-1273 XBB.1.5 containing vaccine by calculating symptomatic infection incidence rates, hospitalization rates among unvaccinated individuals, vaccine effectiveness (VE) against hospitalization, and vaccination rates. Incidence rates among the unvaccinated with an annual vaccine were derived by adjusting overall infection rates based on vaccination coverage and VE against COVID-19 hospitalization rates. Hospitalization costs were obtained from a real-world dataset, integrating the KDCA’s COVID-19 confirmed cases with National Health Insurance claims data. Comparative analyses between mRNA-1273 and BNT162b2 used published meta-analysis results. Results: Assuming vaccination rates remain consistent with the 2023–2024 season, mRNA-1273 is projected to prevent 37,200 hospitalizations and save USD 77.2 million in healthcare costs during the 2024–2025 season compared to no annual vaccination. Compared to BNT162b2, it is expected to prevent an additional 13,260 hospitalizations saving USD 27.5 million. If vaccination rates increased to match influenza, hospitalizations prevented by mRNA-1273 could rise to 79,800 with USD 164.2 million in healthcare savings compared to no annual vaccination. Conclusion: Annual mRNA COVID-19 vaccination with mRNA-1273 substantially reduces hospitalizations and healthcare costs. Increasing vaccination rates are essential to maximize public health benefits.

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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.

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

•Vaccine allocation between populations during emerging pandemics is challenging.•Using a SEIR model, we compare the impact of various vaccine allocation strategies.•We vary population size, risk structure, underlying immunity, and roll-out speed.•We show that in most cases, allocation proportional to population size is optimal. 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.