Model Assumptions Research Articles

Review of the target trial methodological approach on treatment effect estimates in kidney failure: protocol for a systematic assessment

BackgroundPatients with kidney failure often lack robust evidence because they are excluded from randomized trials. Trial emulation provides an alternative approach to derive treatment effect estimates when randomized trials cannot be conducted. Critical questions about the comparative efficacy and safety of interventions in kidney failure are now being answered using this approach or parts of it. However, variations and inconsistencies in reporting cast doubt on the reliability and validity of effect estimates not derived from randomized trials. The aim of this methodological systematic review is to understand the extent to which the target study approach is used in kidney failure and the appropriateness of this approach. By identifying and evaluating studies that qualify as emulating a target trial, compared with studies that did not apply the principles. We aim to provide more specific methodological guidance to increase the clarity and reliability of reporting treatment effect estimates when running a trial in kidney failure is not feasible.MethodsThis protocol is developed in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols (PRISMA-P) statement. MEDLINE, Embase, and reference lists (backwards citation chasing) will be searched up until 1st July 2023 and the search updated prior to publication to identify all studies evaluating patient outcomes in late-stage kidney disease and failure that use target trial emulation as the primary approach for analysis. Two authors (A. A., P. K.) will select articles based on title and abstract and then full text, with a third reviewer settling disagreements (J. P.). The prespecified variables will be extracted, and the risk of bias will be assessed by at least two authors (A. A., P. K., A. N.) using prespecified data forms. This will enable the determination of the robustness of the methodological quality of observational studies in using the whole or elements of the target trial approach. We will thereby assess their ability to reliably report treatment effect estimates.DiscussionWe will provide specific methodological recommendations on how to design target trials and model assumptions for emulation to get reliable treatment effect estimates for therapeutic interventions in kidney failure.Methodological systematic review registrationOpen Science Framework: Identifier https://doi.org/10.17605/OSF.IO/Z4Y29.

Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids.

The domestication of wild canids led to dogs no longer living in the wild but instead residing alongside humans. Extreme changes in behavior and diet associated with domestication may have led to the relaxation of the selective pressure on traits that may be less important in the domesticated context. Thus, here we hypothesize that strongly deleterious mutations may have become less deleterious in domesticated populations. We test this hypothesis by estimating the distribution of fitness effects (DFE) for new amino acid changing mutations using whole-genome sequence data from 24 gray wolves and 61 breed dogs. We find that the DFE is strikingly similar across canids, with 26-28% of new amino acid changing mutations being neutral/nearly neutral (|s| < 1e-5), and 41-48% under strong purifying selection (|s| > 1e-2). Our results are robust to different model assumptions suggesting that the DFE is stable across short evolutionary timescales, even in the face of putative drastic changes in the selective pressure caused by artificial selection during domestication and breed formation. On par with previous works describing DFE evolution, our data indicate that the DFE of amino acid changing mutations depends more strongly on genome structure and organismal characteristics, and less so on shifting selective pressures or environmental factors. Given the constant DFE and previous data showing that genetic variants that differentiate wolf and dog populations are enriched in regulatory elements, we speculate that domestication may have had a larger impact on regulatory variation than on amino acid changing mutations.

Evaluation of traditional and bootstrapped methods for assessing data-poor fisheries: a case study on tropical seabob shrimp (Xiphopenaeus kroyeri) with an improved length-based mortality estimation method.

Unrealistic model assumptions or improper quantitative methods reduce the reliability of data-limited fisheries assessments. Here, we evaluate how traditional length-based methods perform in estimating growth and mortality parameters in comparison with unconstrained bootstrapped methods, based on a virtual population and a case study of seabob shrimp (Xiphopenaeus kroyeri, Heller, 1862). Size data were obtained for 5,725 seabob shrimp caught in four distinct fishing grounds in the Southwestern Atlantic. Also, a synthetic population with known parameter values was simulated. These datasets were analyzed using different length-based methods: the traditional Powell-Wetheral plot method and novel bootstrapped methods. Analysis with bootstrapped ELEFAN (fishboot package) resulted in considerably lower estimates for asymptotic size (L ∞), instantaneous growth rate (K), total mortalities (Z) and Z/K values compared to traditional methods. These parameters were highly influenced by L ∞ estimates, which exhibited median values far below maximum lengths for all samples. Contrastingly, traditional methods (PW method and L max approach) resulted in much larger L ∞ estimates, with average bias >70%. This caused multiplicative errors when estimating both Z and Z/K, with an astonishing average bias of roughly 200%, with deleterious consequences for stock assessment and management. We also present an improved version of the length-converted catch-curve method (the iLCCC) that allows for populations with L ∞ > L max and propagates the uncertainty in growth parameters into mortality estimates. Our results highlight the importance of unbiased growth estimates to robustly evaluate mortality rates, with significant implications for length-based assessments of data-poor stocks. Thus, we underscore the call for standardized, unconstrained use of fishboot routines.

Estimating production functions using costs when outputs are restricted

Conventional proxy-variable approaches to production-function identification assume that output quantities are flexibly adjustable, with firms endogenously choosing them in pursuit of profits. But in not so few industries outputs are exogenously restricted, constraining input allocations by producers. Model assumptions of most proxy estimators become untenable in such settings. We provide a new structural methodology based on a dual formulation of firm production, indirectly identifying technology and productivity from cost data while appropriately accommodating exogenous output restrictions. We showcase our methodology on a panel of U.S. natural gas-fired power-generation plants in 2005–2017 facing price-inelastic demand that restricts their outputs in the short run. We find that power plants were scale-efficient, with (short-run) marginal costs of net electricity generation declining over time. With little evidence of productivity growth in the industry during our sample period, we conclude that the reduction in marginal costs was fueled mainly by diminishing input prices and, potentially, convergence to the optimal long-run input allocation.

Getting started with the graded response model: An introduction and tutorial in R.

This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert-style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right-wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Analysis and optimization of the efficient press shop operation

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.

Dynamic spatial network simulation accounting for multiple ecological factors provides practical recommendations for biosecurity early detection and rapid response (EDRR) strategies.

Globally the spread of invasive pests is being facilitated by increased human mobility and climate change. Simulation modelling can help assess biosecurity strategies for early detection and rapid response (EDRR), but has struggled to account for important factors in the invasion process, such as spatial and temporal variability in habitat suitability and connectivity; population dynamics; and multiple dispersal pathways. We developed a novel dynamic spatial network simulation approach based on spatial network theory that enables integration of a wider range of spatio-temporal factors than previous studies, calibrated it against extensive historical trapping data, and applied it to comprehensively analyse the EDRR strategy for Oriental fruit fly (Bactrocera dorsalis; OFF) in northern Australia. Simulations indicated that the chance of OFF reaching the mainland in the next 20 years could be up to 20% under the current EDRR strategy, depending on how optimistic or pessimistic model assumptions are, and highlighted possible improvements to the EDRR strategy for further consideration. Simulations under optimistic assumptions indicate that transport via wind is most important in OFF reaching the mainland, but under pessimistic assumptions transport via people carrying infected fruit becomes more important. Our new dynamic spatial network simulation approach can account for a wide range of spatio-temporal ecological factors to provide practical real-world recommendations. At a minimum, this approach only requires weather and population data, both of which are available globally from a variety of free and open sources, making it broadly applicable to assessing the EDRR strategies in place for different species in other locations. © 2024 Society of Chemical Industry.

Epistemic commitments have no “Off” button: on the embodiment of commitments by way of model formulation

The current paper examines how a commitment to a principle, adhered to by an individual agent, becomes an accepted standard of an epistemic community. Addressing this question requires three steps: first, to define the terms used throughout the paper, and especially the characteristics of commitments to a principle. The second step is to find a mechanism through which such epistemic commitments are introduced to an epistemic community and in certain cases are adopted as the standard by the community. While there could be several such mechanisms, the current paper focuses on the practice of model formulation. The third step is to demonstrate the analytical framework developed in the first two steps in a case study. The case study chosen for this paper is the unique approach to feedback analysis adopted by the ecologist and population geneticist Richard Levins. In what follows I will show that part of the features that made Levins’ approach unique was his Marxist commitments, and his attempt to embody those commitments in feedback analysis by formally representing them as modeling assumptions.

Search for Pauli Exclusion Principle violations with Gator at LNGS

The Pauli Exclusion Principle (PEP) appears from fundamental symmetries in quantum field theories, but its physical origin is still to be understood. High-precision experimental searches for small PEP violations permit testing key assumptions of the Standard Model with high sensitivity. We report on a dedicated measurement with Gator, a low-background, high-purity germanium detector operated at the Laboratori Nazionali del Gran Sasso, aimed at testing PEP-violating atomic transitions in lead. The experimental technique, relying on forming a new symmetry state by introducing electrons into the pre-existing electron system through a direct current, satisfies the conditions of the Messiah-Greenberg superselection rule. No PEP violation has been observed, and an upper limit on the PEP violation probability of β2/2<4.8·10-29\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta ^2/2 < 4.8 \\cdot 10^{-29}$$\\end{document} (90% CL) is set. This improves the previous constraint from a comparable measurement by more than one order of magnitude.

Multiwavelength Study of Extreme High-energy Peaked BL Lac Source 1ES 0229+200 Using Ultraviolet, X-Ray, and γ-Ray Observations

We present a comprehensive analysis of the broadband spectral energy distribution (SED) of the extreme high-energy peaked BL Lac source, 1ES 0229+200. Our study utilizes near-simultaneous data collected at various epochs between 2017 September and 2021 August (MJD 58119−59365) from different instruments, including AstroSat−UVIT, Soft X-ray focusing Telescope, LAXPC, Swift−UVOT, Fermi-LAT, and MAGIC. We investigate the one-zone synchrotron and synchrotron self-Compton (SSC) model, employing diverse particle distributions such as the log parabola, broken power law, power law with a maximum electron energy γ, energy-dependent diffusion (EDD), and energy-dependent acceleration (EDA) models to fit the broadband SED of the source. Our findings indicate that both peaks in the SED are well described by the one-zone SSC model across all particle distribution models. We estimate the jet power for different particle distributions. The estimated jet power for broken power law particle distributions is found to be on the order of 1047 (1044) erg s−1 for a minimum electron energy γmin ∼10 (104). However, for intrinsically curved particle energy distributions (e.g., log parabola, EDD, and EDA models), the estimated jet power is ∼1044 erg s−1. The SED fitting at five epochs enables us to explore the correlation between the derived spectral parameters of various particle distribution models. Notably, the observed correlations are inconsistent with the predictions in the power-law with a maximum γ model, although the EDD and EDA models yield the correlations as expected. Moreover, the estimated physical parameter values are consistent with the model assumptions.

Sustainability and efficiency assessment of routes for long-term energy storage in chemicals

Renewable power plays a prominent role in the decarbonization of energy generation, particularly wind and solar energy sources. However, the intermittency of these renewable sources calls for energy storage, where hydrogen, ammonia, and methanol have emerged as potential chemical energy vectors. Such alternatives are often evaluated without modelling all the phases in detail of the storage process, i.e., (1) power-to-chemicals (P2C), (2) storage, and (3) chemicals-to-power (C2P), which can lead to limited insights. This work evaluates hydrogen, ammonia, and methanol as chemical energy vectors considering their economic and environmental performance using detailed simulations for all phases of the process based on harmonized assumptions and consistent datasets. Moreover, process simulation and life cycle assessment (LCA) are coupled with data envelopment analysis (DEA) to identify the most efficient alternatives and determine improvement targets for the inefficient ones. Hydrogen is found to have the lowest costs and environmental impacts, while methanol-based scenarios are moderately more expensive, and ammonia routes are the costliest. Furthermore, based on our modelling assumptions, methanol routes outperform ammonia routes in both economic and environmental terms. This work sheds light on the potential of chemical energy storage applications, and aims to open new avenues for holistic assessments of power generation and storage technologies under multiple sustainability and economic indicators.

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

An improved smoothed particle hydrodynamics method for the coupled simulation of fixed net structures in currents and waves

In the present paper, a numerical model, named smoothed particle hydrodynamics (SPH)-net model, is proposed for the coupled simulation of fixed net structures in currents and waves based on the coupling between the SPH method and screen model. The fluid is solved by the SPH method and the net structures are handled by the screen model, both of which are represented by a number of Lagrangian particles. A coupling algorithm between the SPH method and screen model is derived based on the momentum disturbance, which allows for accurate simulation of interactions between net structures and fluid. Thanks to the quasi-static assumption of the screen model, the proposed model can both handle the net structures in currents and waves. To validate the SPH-net model, numerical simulations were conducted on three distinct cases: fixed net panels in currents, a fixed net cage in currents, and a fixed net panel in the regular wave. The comparison of hydrodynamic forces on the net between the numerical results and experimental data demonstrates that the proposed SPH-net model has accuracy and reliability in predicting the hydrodynamic forces acting on fixed net structures in currents and regular waves.

Risk assessment of belt conveyor belt in thermal power plant based on Bow-tie model and uncertainty Fuzzy Dynamic Bayesian Network

Abstract In thermal power plants, coal conveyor belts pose significant risks that jeopardize the stability of the energy supply, underscoring the need for effective risk management. To address the complexity, uncertainty, and polymorphism issues in belt conveyor systems, we introduce a BT-UFDBN risk analysis method specifically for coal conveyor belts. This method develops a typical Bow-tie model, identifies potential risk factors for unplanned stoppages, and utilizes fuzzy evaluation methods and an improved SAM method to determine prior probabilities. The Bow-tie (BT) model is then mapped into a Dynamic Bayesian Network (DBN). To manage uncertainties within the DBN, the Leaky Noisy-OR gate model, stationarity, and first-order Markov assumptions are employed to ensure the model’s validity and practical relevance. This paper uses a belt conveyor system from a thermal power plant as a case study to validate the model’s effectiveness in predicting accident consequences, diagnosing fault causes, and proposing targeted preventive measures for identified weak points. The study provides theoretical guidance for risk management of coal conveyor belts in power plants.

An Absolute Mass, Precise Age, and Hints of Planetary Winds for WASP-121A and b from a JWST NIRSpec Phase Curve

We have conducted a planetary radial velocity measurement of the ultrahot Jupiter WASP-121b using JWST NIRSpec phase curve data. Our analysis reveals the Doppler shift of the planetary spectral lines across the full orbit, which shifts considerably across the detector (∼10 pixels). Using cross-correlation techniques, we have determined an overall planetary velocity amplitude of K p = 215.7 ± 1.1 km s−1, which is in good agreement with the expected value. We have also calculated the dynamical mass for both components of the system by treating it as an eclipsing double-line spectroscopic binary, with WASP-121A having a mass of M ⋆ = 1.330 ± 0.019 M ⊙, while WASP-121b has a mass of M p = 1.170 ± 0.043 M Jup. These dynamical measurements are ∼3× more precise than previous estimates and do not rely on any stellar modeling assumptions that have a ∼5% systematic floor mass uncertainty. Additionally, we used stellar evolution modeling constrained with a stellar density and parallax measurement to determine a precise age for the system, found to be 1.11 ± 0.14 Gyr. Finally, we observed potential velocity differences between the two NIRSpec detectors, with NRS1 lower by 5.5 ± 2.2 km s−1. We suggest that differences can arise from day/night asymmetries in the thermal emission, which can lead to a sensitivity bias favoring the illuminated side of the planet, with planetary rotation and winds both acting to lower a measured K P. The planet’s rotation can account for 1 km s−1 of the observed velocity difference, with 4.5 ± 2.2 km s−1 potentially attributable to vertical differences in wind speeds.

Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow.

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for nonlocal pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations resemble the time series from DNS statistically closely.

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

Long live the cat: Ocelot population viability in a planned reintroduced population in Texas, USA

AbstractReintroductions are often needed to recover carnivore populations and restore ecological processes. Felids are common subjects of reintroduction efforts, but published population models informing felid reintroduction plans are uncommon, and poor planning has sometimes caused issues in felid reintroduction programs. In the United States, ocelots (Leopardus pardalis pardalis) are classified as endangered, and recovery requires population expansion into historic habitat. A multi‐organization effort is underway to establish a new ocelot population in Texas by releasing ocelots into an area of 478 km2 of suitable habitat in ocelots' historic but now unoccupied range. In this study, we used population viability analyses to compare different ocelot reintroduction strategies for the identified reintroduction area. Based on a potential ocelot breeding program's limitations, we modeled reintroduction using a founding population of no more than six ocelots and no more than four ocelots released per year for no more than 15 subsequent years. Within these limitations, we assessed projected population abundances and extinction risks after 30 years for 20 different reintroduction strategies. We found that long‐term releases are necessary to establish a viable population; under conservative model assumptions, releasing six ocelots in the initial year and then releasing four individuals annually for an additional 10–15 years is necessary for attaining a projected population greater than 36.62 ocelots (baseline) with &lt;6% extinction risk. We also found that ocelot population abundance is about equally sensitive to post‐release mortality and inbreeding depression. This highlights the importance of not only supporting reintroduced ocelots' survival but also managing for high genetic diversity in the reintroduction program. Further, we found that realistic but more liberal assumptions on the carrying capacity of the reintroduction area and the age of first reproduction for ocelots increase projected population abundances (53.95 individuals and 61.26 individuals, respectively), and thus reintroduction success. The model's sensitivity to carrying capacity suggests that long‐term habitat protection and expansion are among the most important management actions to support ocelot reintroduction. Our study establishes the first population viability model for an ocelot reintroduction plan anywhere across the species' wide geographic range, and it reinforces several key considerations for wildlife reintroduction efforts worldwide.

Population-level impact of switching to 1-dose human papillomavirus vaccination in high-income countries: examining uncertainties using mathematical modeling.

A concern in high-income countries is that switching to 1-dose human papillomavirus (HPV) vaccination could cause a rebound in HPV infection and cervical cancer if 1-dose efficacy or duration were inferior to 2 doses. Using mathematical modeling and up-to-date trial-based data, we projected the population-level effectiveness of switching from 2-dose to 1-dose vaccination under different vaccine efficacy and duration assumptions in high-income countries. We used HPV-ADVISE (Agent-based Dynamic model for VaccInation and Screening Evaluation), a transmission-dynamic model of HPV infection and cervical cancer, varying key model assumptions to identify those with the greatest impact on projections of HPV-16 and cervical cancer incidence over time: 1) 1-dose vaccine efficacy and vaccine duration, 2) mechanisms of vaccine efficacy and duration over time, 3) midadult (>30 years of age) sexual behavior, 4) progression to cervical cancer among midadults, and 5) vaccination coverage and programs. In high-income countries, 1-dose vaccination would cause no appreciable rebound in HPV-16 infection, except for a limited rebound under the most pessimistic assumptions of vaccine duration (average, 25 years), because 1) the switch would occur when HPV prevalence is low because of high 2-dose vaccination coverage and 2) individuals would be protected during their peak ages of sexual activity (<35 to 40 years of age). Our model projects a more limited rebound in cervical cancer because of a shift to older age at infection, resulting in fewer life-years left to potentially develop cancer. Projections were robust when varying key model assumptions. High protection during peak ages of sexual activity in high-income countries would likely mitigate any potential rebounds in HPV infection and cervical cancer under the most pessimistic assumptions of 1-dose efficacy and duration.