Statistical Model Research Articles

Theoretical study of a putative adsorption mechanism of arginine and glutamate on goldfish 5.24 and zebrafish Z06: Statistical physics modeling, thermodynamic study, and docking simulation

An adsorption phenomenon putatively involved in the sense of smell of arginine and glutamate amino acids in the goldfish olfactory receptor 5.24 and the zebrafish olfactory receptor Z06 was applied in this study to deeply interpret and characterize the olfaction process at a molecular level. As a result, the experimental concentration-olfactory response curves were adjusted using the mono-layer model with one energy (MLM1E). On the one hand, the physicochemical parameters involved in the chosen model were utilized to microscopically investigate the four olfactory systems. Indeed, the two amino acids were adsorbed via a non-parallel configuration, and the process of adsorption was multi-molecular. The molar adsorption energies, which ranged from 22.09 to 35.97 kJ/mol, could be utilized to define the energetic interaction types between the investigated amino acids and the two tested fish olfactory receptors. The size and adsorption energy heterogeneity of 5.24 and Z06 binding pockets were measured by estimating the pocket site size distributions or PSDs (stereographic characterization) and the adsorption energy distributions or AEDs (energetic characterization). On the other hand, these physicochemical parameters could also be applied to macroscopically investigate the four olfactory systems. Hence, three thermodynamic functions including the adsorption entropy Sa, the internal energy Eint, and the Gibbs free energy G were computed in order to investigate the disorder, the exothermicity, and the spontaneity of the studied systems, respectively. The docking modeling analysis of arginine and glutamate amino acids with goldfish 5.24 and zebrafish Z06 revealed simulated binding energies that closely matched the average adsorption energies (peaks of AEDs). These affinities corresponded to the adsorption energy bands known as olfactory bands or spectrums of vibration modes.

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Determinants of Risk Factors Associated with Low Birth Weight in Nigeria

Low birth weight (LBW) remains a significant public health challenge with profound implications for neonatal and child health, particularly in low-income countries. Defined by the World Health Organization as a birth weight of less than 2.5 kilograms, LBW contributes to increased neonatal mortality and long-term developmental issues. This study examines the prevalence and determinants of LBW in Nigeria, leveraging data from the 2018 Nigeria Demographic and Health Survey (NDHS). The study employs a cross-sectional design and a stratified two-stage sampling technique, analyzing 7,728 recorded birth weights. Key findings indicate that maternal age, education, and socio-economic status significantly influence birth weight. Optimal reproductive ages (25-34 years) and higher educational attainment are associated with healthier birth weights, whereas younger (below 20 years) and older mothers (above 40 years), and those with lower education levels, face higher LBW risks. Employment and wealth are positively correlated with better birth outcomes, underscoring the importance of financial stability. Environmental factors such as urban residence, access to improved water sources, and sanitation facilities also play crucial roles in determining birth weight. The study compares frequentist logistic regression and Bayesian structured additive logistic regression models to identify and predict LBW risk factors, highlighting regional disparities within Nigeria. The findings emphasize the need for targeted interventions addressing socio-demographic, socio-economic, and environmental determinants to reduce the prevalence of LBW and improve maternal and child health outcomes. Enhanced understanding of these factors through advanced statistical modeling can inform policy and health interventions, ultimately contributing to achieving global health targets and improving neonatal health in Nigeria.

Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking.

Statistical Mobility of Multicellular Colonies of Flagellated Swimming Cells.

We study the stochastic hydrodynamics of colonies of flagellated swimming cells, typified by multicellular choanoflagellates, which can form both rosette and chainlike shapes. The objective is to link cell-scale dynamics to colony-scale dynamics for various colonial morphologies. Via autoregressive stochastic models for the cycle-averaged flagellar force dynamics and statistical models for demographic cell-to-cell variability in flagellar properties and placement, we derive effective transport properties of the colonies, including cell-to-cell variability. We provide the most quantitative detail on disclike geometries to model rosettes, but also present formulas for the dynamics of general planar colony morphologies, which includes planar chain-like configurations.

NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.

Statistical analysis of low-density and high-density polyethylene modified asphalt mixes using the response surface method

The common use of plastics in daily life has engendered gigantic amounts of waste plastics, which have a detrimental influence on the environment. Researchers have proposed several strategies for the recycling of waste plastics and their products. Among these, one strategy is to use them in the construction industry, particularly in asphaltic pavements. This study thus focused on the utilization of waste plastics, specifically Low-Density Polyethylene (LDPE) and High-Density Polyethylene (HDPE), as an additive in Asphalt Concrete (AC) mixes. Indirect Tensile Strength (ITS), Flow Time (FT), and Dynamic Modulus (DM) tests were used to investigate the moisture damage, permanent deformation, fatigue performance, and DM of the LDPE and HDPE modified AC mixes. According to the laboratory test results, the LDPE and HDPE modified AC mixes offer more resistance to moisture damage compared to the Control AC mixes. It was also concluded that asphalt mixes modified with the LDPE and HDPE attained 2.07 times and 1.26 times superior resistance to permanent deformation than the Control mixes. Furthermore, the LDPE and HDPE modified AC mixes outperformed the DM values of the Control mixes by 2.06 times and 1.41 times, respectively. In addition, it was determined that the LDPE and HDPE modified AC mixes exhibit 1.72 times and 1.39 times greater fatigue parameter values than their counterpart, suggesting superior fatigue resistance performance. The statistical models developed using Response Surface Methodology (RSM) revealed the highest values of the Coefficient of Determination (>0.80), adequate precision (>4.0), and lack-of-fit tests, indicating the model’s adequacy for predicting the response.

Artificial intelligence studies to improve the performance of statistical models in anaerobic codigestion

This paper seeks to apply artificial intelligence to improve the results by modelsbased on statistical systems such as response surface methodology (RMS) inliterature, using AI -based computational techniques, which have proven to bevaluable for the design and prediction of the behavior of anaerobic digestionsystems, a matrix of 3 by 3 was used to find the best architecture for the neuronalnetwork, improving the performance of the model proposed in the literature thatachieves R² of 0.90 and with this methodology used in this research an R²adjustment of 0.99 is achieved .

Molecular dynamics simulations of neutron induced collision cascades in Zr — Statistical modelling of irradiation damage and potential applications

Understanding irradiation damage involves a multi-scale and multi-physics approach, integrating data from experiments, simulations and phenomenological models. This paper focuses on its early stages, specifically neutron-induced collision cascades in zirconium, as nuclear-grade zirconium alloys are widely used in fuel assemblies. We have gathered and analysed a significant sample of results from high-fidelity, large-scale molecular dynamics (MD) simulations, employing existing interatomic potentials and the two-temperature model to account for electron–phonon coupling. Our data can be directly applied to higher-scale methods.Furthermore, we carried out a comprehensive statistical analysis of the features associated with the defect production, including the number of defects, their distribution and size of the affected area. As a result, we developed a generative model of collision cascades that is parametric, hierarchical and stochastic, i.e. it takes into account a statistical nature of the phenomenon, is interpretable and shareable. This model has been developed with three primary objectives: to provide a sufficient descriptor of a cascade, to interpolate data obtained from high-fidelity simulations, and to demonstrate that the statistical model can produce representative distributions of primary irradiation defects. The results can be used to generate synthetic inputs for models at longer length and time scales, to provide fast approximations that take into account the morphology of introduced defects, and in general to serve as a powerful analytical tool.

Bronchiectasis Occurs Independently of Chronic Obstructive Pulmonary Disease in Alpha-1 Antitrypsin Deficiency.

Bronchiectasis occurs in patients with alpha-1 antitrypsin deficiency (AATD), but it is unknown whether an association exists independently of chronic obstructive pulmonary disease (COPD). We assessed whether bronchiectasis was associated with COPD in our cohort, and whether it has clinical significance for lung function decline, exacerbation rate, or symptoms. PiZZ, PiSZ and PiMZ patients from the Birmingham AATD Research Database were studied. Demographics were recorded, along with the outcomes of symptoms, FEV1, TLCO, KCO, and annualised exacerbation rate. Lung function decline was calculated for those with ≥3 measurements. Multivariate regression analyses were conducted to assess for associations of bronchiectasis with each outcome. A further binomial logistic regression model assessed for predictors of bronchiectasis diagnosis, including COPD. Those with alternative bronchiectasis causes were excluded from statistical models. 1290 patients were eligible. PiZZ patients with bronchiectasis were older at presentation (54 vs 49 years, p<0.001), less likely to have smoked (65 vs 76.1%, p = 0.001), and had higher mMRC scores (mMRC 2 vs 0 OR 1.97, 95% CI 1.20 - 3.25, p = 0.008; mMRC 3 vs 0 OR 2.58 95% CI 1.59 - 4.19, p<0.001; mMRC 4 vs 0 OR 2.2 95% CI 1.23 - 3.92; p=0.008) than those without. The odds ratio of bronchiectasis diagnosis was not associated with COPD diagnosis in any phenotype. Bronchiectasis was associated with lower serum alpha-1 antitrypsin levels in PiZZ patients (p = 0.012). Bronchiectasis was not associated with a difference in FEV1 pp/year decline, KCO pp/year, TLCO pp/year decline, or exacerbation rate in multivariate analysis. Bronchiectasis exists in a significant minority of AATD patients independently of COPD, and is associated with more severe shortness of breath. Appropriate treatment of bronchiectasis in AATD is essential.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at herd-level using mid-infrared spectral information from routine milk control

As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

Evidence for modern extinction in plants and animals

We summarise evidence of extinction reported in Red List accounts of plants (170 species) and animals (816 species) listed as extinct (EX) or extinct in the wild (EW) under different versions of the Red List. We find that types of evidence and their frequency of use are remarkably consistent across plants, animals and Red List versions. The most common evidence is ‘long time missing, fewer than half of species accounts discuss search effort, and accounts of 15 % of EX/EW plants and 27 % of animals provide no reason for the EX/EW listing. We review use of extinction probability models for EX, EW and Critically Endangered species flagged as possibly extinct, and find that 155 species have been subjects of extinction probability models. Nearly all models applied to birds and mammals agree that the species are extinct. Seventeen statistical models for estimating the probability that a species is extinct based on search effort, traits and detection rate have been published since 1993 and applied to EX, EW, or CR(PE) species. A tiny fraction (0.6 %) of Red List accounts currently include published model results; thus extinction probability modelling has been largely independent of Red Listing. This is likely to change, because since 2019, Red List guidelines provide templates for quantitative estimates of extinction likelihood. Combining statistical models with assessments of species' probability of extinction based on other criteria can strengthen extinction listings of plants and animals, and add to our understanding of the scale of modern species loss.

New discrete fractional accumulation Grey Gompertz model for predicting carbon dioxide emissions

Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated.

Statistical modeling and optimization of volatile fatty acids production by anaerobic digestion of municipal wastewater sludge.

Obtaining value-added products from renewable resources is limited by the lack of specific operating conditions optimized for the physico-chemical characteristics of the biomass and the desired end product. A mathematical model and statistical optimization were developed for the production of volatile fatty acids (VFAs) by anaerobic digestion of municipal sewage sludge. The experimental tests were carried out in triplicate and investigated a wide range of conditions: pH 9.5, 10.5, and 11.5; temperatures 25°C, 35°C, 45°C, and 55°C; primary sludge with organic loading (OL) of 10 and 14g VS (volatile solids); and digested sludge with 4 and 6g VS. Subsequently, a statistical search was performed to obtain optimal production conditions, then a statistical model of VFA production was developed and the optimal conditions were validated at pilot plant scale. The maximum VFA concentration predicted was 6975mg COD (chemical oxygen demand)/L using primary sludge at 25°C, initial OL of 14g VS, and pH 10.5. The obtained third-degree model (r2 = 0.83) is a powerful tool for bioprocess scale-up, offering a promising avenue for sustainable waste management and biorefinery development.

Preparation of sludge-derived spinel ferrite nanoparticles for highly efficient adsorption of Pb(II) from water: Adsorption behaviour and mechanism interpretation via advanced statistical physics model

Because proper disposals of municipal sewage sludge and lead-containing wastewater are still open questions, the novel sludge-derived mesoporous nanospinel ferrite adsorbents were prepared from municipal sewage sludge (S-MZF) and its incineration ash (A-MZF) for Pb(II) removal from water. S-MZF and A-MZF had similar mesoporous structures with an average pore size at around 4.13–5.51 nm. A-MZF displayed a larger specific surface area (254 m2/g) and pore volume (0.3 m3/g) but inferior magnetic property, while S-MZF showed abundant surface functional groups and higher saturation magnetization (12.3 emu/g). The adsorption capacity of S-MZF and A-MZF for Pb(II) was close to each other at 50 °C, reaching 160 mg/g. Initial adsorption of A-MZF for Pb(II) was dominated by intraparticle diffusion, while the evident liquid film diffusion existed in that of S-MZF. Advanced statistical physics modeling found that multiple divalent lead ions were anchored simultaneously by each site of S-MZF mainly via multi-interactions where physical force and precipitation played a key role, while each site of A-MZF anchored only one Pb(II) ion by the physical interaction. As a result, the adsorption capacity of S-MZF mainly depended upon the number of Pb(II) ions adsorbed by each active site, while the available density of active sites on the surface of A-MZF primarily controlled its adsorption capacity for Pb(II).

The Propagation and Reduction of Uncertainty Left Unquantified by Confidence Intervals, P-values, Neural Network Predictions, Posterior Distributions, and Other Statistical Results

Abstract In the use of statistical models to analyze data, there is not only the uncertainty quantified by the models but also uncertainty about which models are adequate for some purpose, such as weighing the evidence for or against a hypothesis of scientific interest. This paper provides methods for propagating such unquantified uncertainty to the results under a unified framework of adequate model averaging. Specifically, the weight of each model used in the average is the probability that it is the most useful model. To allow for the case that none of the models considered would be useful, a catch-all model is included in the model average at a different level of the hierarchy. The catch-all model is the vacuous model in imprecise probability theory, the model that puts no restrictions on the probabilities of statements about the unknown values of interest. That enables defining the proportion of the uncertainty left unquantified by a model as the probability that it is inadequate in the sense of being less useful than the catch-all model. A lower bound for the proportion of unquantified uncertainty of the averaged model decreases as more models are added to the average.

The Processing Spatial Data for Statistical Modeling and Visualization Case study: INLA model for COVID-19 in Alabama, USA

This research emphasizes the visualization of spatial data for statistical modelling and analysis of the relative risk associated with the COVID-19 pandemic in Alabama, USA. We used Bayesian analysis and the Integrated Nested Laplace Approximation (INLA) approach on data ranging from March 11, 2020, to December 31, 2022, which included observed COVID-19 cases, the population for each of the Alabama counties, and a Geographical map of the state. The geographical distribution of COVID-19’s relative risk was determined using various spatial statistical techniques, indicating high-risk locations. The study used Besag-York-Mollié (BYM) models to assess the posterior relative risk of COVID-19, and it found a statistically significant average decrease in COVID-19 case rates across the 67 counties evaluated. These findings have practical implications for evidence-based policymaking in pandemic prevention, mitigation, and preparation.

Relationship of litterfall anomalies with climatic anomalies in a mangrove swamp of the Yucatan Peninsula, Mexico.

Among the set of phenological traits featuring mangrove ecosystems, litterfall production stands out with marked intra-annual and longer-term variation. Furthermore, mangrove forests resilience is one of the most important ecological attribute, reconciling the juxtaposed terrestrial and marine environment such transitional systems occupy. However, world's mangroves are nowadays facing recurrent climatic events, reflected in anomalies depicted by major drivers, including temperature and precipitation. This physical-environmental setting may either constrain or favor overall forest productivity. A combination of time series analysis (spectral density and cross-correlation techniques) and statistical model fitting (General additive model) was implemented to explore trends in total litterfall of a well-developed mangrove forest in southeastern Gulf of Mexico (Celestun Lagoon, SE Mexico) and potential association with the varying behavior of temperature (°C) and precipitation (mm month-1), highlighting their anomalies. The results are consistent with a synchronous response between litterfall production and climatic variables (mean monthly temperature and total monthly precipitation). Concurrent peak litterfall production in Celestun lagoon with high temperatures and precipitation occurred during June and October, featuring a two-month time lag for the response time. More than half of the litterfall anomalies (53.5%) could be reflecting either multiple sources of climatic anomalies (maximum, minimum, and monthly average temperature and monthly total precipitation) or single point events (cyclone landfall). This relationship dynamics showed an interannual persistence (1999-2010). The structure portrayed by the litterfall time-series was not unequivocally related to climatic anomalies. Arguably, climatic anomalies behave with different intensities and even may exhibit complex interactions among them. The study of anomalies provides a baseline for a better grasp of: i) mangrove anomalies responses and ii) their vulnerability to these extremes.

Reconstructing the age-structured case count of COVID-19 from sentinel surveillance data in Japan: a modeling study

ObjectivesTo reconstruct age-structured case counts of COVID-19 using sentinel reporting, which replaced universal reporting of COVID-19 from May 2023 in Japan. MethodsUsing COVID-19 sentinel data stratified by discrete age groups in selected prefectures and referring to universal case count data up to May 8, 2023, we fitted a statistical model to handle weekly growth rates as a function of age and time so as to convert sentinel data to case counts after cessation of universal reporting. ResultsThe age distribution of cases in sentinel reporting was significantly biased toward younger age groups compared to universal reporting. When comparing the epidemic size of the 9th wave (May 8 to September 18, 2023) to the 8th wave (October 3, 2022 to April 10, 2023), using the wave-on-wave ratio of total cumulative sentinel cases led to a significant underestimation of the wave-on-wave in Tokyo (0.975, vs 1.461 by universal reporting) and Okinawa (1.299, vs 1.472). The estimates of growth rates, scaling factors between universal and sentinel cases, and expected universal case count showed robustness to changes in the ending week of the data period. ConclusionsOur model quantified COVID-19 dynamics, comparably to universal reporting that ended in May 2023, enabling detailed and up-to-date health burden analysis using sentinel reports. The cumulative incidence was greater than that suggested from sentinel data in Tokyo, Nara, and Okinawa. Per-population burdens among children were particularly high in Osaka and Nara, indicating a strong bias in sentinel reporting toward pediatric cases.