Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm.

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.

Statistical modeling of hormesis quantities in inverted U-shaped dose-response relationships by reparameterization of a bilogistic model.

Statistical procedures that allow quantitative determination of hormesis features are required for quantitative characterization of hormesis to provide information on the biphasic dose-response phenomenon and its variability. Only a direct estimate of individual effective doses in hormetic dose-response relationships is possible using prior extensions of the bilogistic model of Beckon and coworkers. This study presented further extensions of the model to determine the toxic potency and hormetic dose zone by estimating two effective doses simultaneously. In addition, the extended models allow for partitioning the hormetic dose zone through the dose of maximum stimulation. This study demonstrated a 4-step statistical modeling approach to quantify 20 hormesis quantities. The applicability and challenges of the mathematical procedures are discussed based on a few examples of hormetic dose-response relationships. The syntaxes for the analyses were provided as Appendix to demonstrate its implementation in SAS® statistical software. Given the variability of hormetic dose-responses generated from toxicological studies in many disciplines, the proposed approach cannot apply to all dose-response patterns. However, we hope the proposed extensions could provide versatile statistical tools for quantitatively exploring a variety of biphasic dose-response curves.

Placental growth factor for the prognosis of women with preeclampsia (fullPIERS model extension): context matters.

BACKGROUND: The fullPIERS risk prediction model was developed to identify which women admitted with confirmed diagnosis of preeclampsia are at highest risk of developing serious maternal complications. The model discriminates well between women who develop (vs. those who do not) adverse maternal outcomes. It has been externally validated in several populations. We assessed whether placental growth factor (PlGF), a biomarker associated with preeclampsia risk, adds incremental value to the fullPIERS model. METHODS: Using a cohort of women admitted into tertiary hospitals in well-resourced settings (the USA and Canada), between May 2010 to February 2012, we evaluated the incremental value of PlGF added to fullPIERS for prediction of adverse maternal outcomes within 48 h after admission with confirmed preeclampsia. The discriminatory performance of PlGF and the fullPIERS model were assessed in this cohort using the area under the receiver's operating characteristic curve (AUROC) while the extended model (fullPIERS +PlGF) was assessed based on net reclassification index (NRI) and integrated discrimination improvement (IDI) performances. RESULTS: In a cohort of 541 women delivered shortly (< 1 week) after presentation, 8.1% experienced an adverse maternal outcome within 48 h of admission. Prediction of adverse maternal outcomes was not improved by addition of PlGF to fullPIERS (NRI: -8.7, IDI - 0.06). Discriminatory performance (AUROC) was 0.67 [95%CI: 0.59-0.75] for fullPIERS only and 0.67 [95%CI: 0.58-0.76]) for fullPIERS extended with PlGF, a performance worse than previously documented in fullPIERS external validation studies (AUROC > 0.75). CONCLUSIONS: While fullPIERS model performance may have been affected by differences in healthcare context between this study cohort and the model development and validation cohorts, future studies are required to confirm whether PlGF adds incremental benefit to the fullPIERS model for prediction of adverse maternal outcomes in preeclampsia in settings where expectant management is practiced.

The ASACUSA antihydrogen and hydrogen program: results and prospects.

The goal of the ASACUSA-CUSP collaboration at the Antiproton Decelerator of CERN is to measure the ground-state hyperfine splitting of antihydrogen using an atomic spectroscopy beamline. A milestone was achieved in 2012 through the detection of 80 antihydrogen atoms 2.7 m away from their production region. This was the first observation of 'cold' antihydrogen in a magnetic field free region. In parallel to the progress on the antihydrogen production, the spectroscopy beamline was tested with a source of hydrogen. This led to a measurement at a relative precision of 2.7×10-9 which constitutes the most precise measurement of the hydrogen hyperfine splitting in a beam. Further measurements with an upgraded hydrogen apparatus are motivated by CPT and Lorentz violation tests in the framework of the Standard Model Extension. Unlike for hydrogen, the antihydrogen experiment is complicated by the difficulty of synthesizing enough cold antiatoms in the ground state. The first antihydrogen quantum states scan at the entrance of the spectroscopy apparatus was realized in 2016 and is presented here. The prospects for a ppm measurement are also discussed.This article is part of the Theo Murphy meeting issue 'Antiproton physics in the ELENA era'.

A Flexible Fringe Projection Vision System with Extended Mathematical Model for Accurate Three-Dimensional Measurement.

In order to acquire an accurate three-dimensional (3D) measurement, the traditional fringe projection technique applies complex and laborious procedures to compensate for the errors that exist in the vision system. However, the error sources in the vision system are very complex, such as lens distortion, lens defocus, and fringe pattern nonsinusoidality. Some errors cannot even be explained or rendered with clear expressions and are difficult to compensate directly as a result. In this paper, an approach is proposed that avoids the complex and laborious compensation procedure for error sources but still promises accurate 3D measurement. It is realized by the mathematical model extension technique. The parameters of the extended mathematical model for the 'phase to 3D coordinates transformation' are derived using the least-squares parameter estimation algorithm. In addition, a phase-coding method based on a frequency analysis is proposed for the absolute phase map retrieval to spatially isolated objects. The results demonstrate the validity and the accuracy of the proposed flexible fringe projection vision system on spatially continuous and discontinuous objects for 3D measurement.

Modifications to the anaerobic digestion model no. 1 (ADM1) for enhanced understanding and application of the anaerobic treatment processes - A comprehensive review.

Anaerobic digestion (AD) is a promising method for the recovery of resources and energy from organic wastes. Correspondingly, AD modelling has also been developed in recent years. The International Water Association (IWA) Anaerobic Digestion Model No. 1 (ADM1) is currently the most commonly used structured AD model. However, as substrates become more complex and our understanding of the AD mechanism grows, both systematic and specific modifications have been applied to the ADM1. Modified models have provided a diverse range of application besides AD processes, such as fermentation and biogas upgrading processes. This paper reviews research on the modification of the ADM1, with a particular focus on processes, kinetics, stoichiometry and parameters, which are the major elements of the model. The paper begins with a brief introduction to the ADM1, followed by a summary of modifications, including extensions to the model structure, modifications to kinetics (including inhibition functions) and stoichiometry, as well as simplifications to the model. The paper also covers kinetic parameter estimation and validation of the model, as well as practical applications of the model to a variety of scenarios. The review highlights the need for improvements in simulating AD and biogas upgrading processes, as well as the lack of full-scale applications to other substrates besides sludge (such as food waste and agricultural waste). Future research directions are suggested for model development based on detailed understanding of the anaerobic treatment mechanisms, and the need to recover of valuable products.

Adoption mechanism of telemedicine in underdeveloped country.

The purpose of this study is to reveal the influential mechanism on patients' adoption intention of telemedicine in the underdeveloped areas. Based on 896 patients' data collected in Pakistan, we found that patients' adoption intention is a function of traveling cost, attitudes, and perceived usefulness. High traveling cost is found to have the most significant negative influence on adoption intention. Patients with shorter distance prefer more to use telemedicine. Traveling cost, traveling time, and traveling distance have indirect influences on adoption intention through their significant impacts on perceived usefulness and perceived ease of use. Our findings indicate that geographic locations does matter during promotion of telemedicine. This study also helps identify the true barriers and facilitators to large-scale adoption of telemedicine in developing countries and reduce the gap of healthcare equity as concerned by both UN Millennium Development Goals and UN Sustainable Development Goals.

A robust score test of homogeneity for zero-inflated count data.

In many applications of zero-inflated models, score tests are often used to evaluate whether the population heterogeneity as implied by these models is consistent with the data. The most frequently cited justification for using score tests is that they only require estimation under the null hypothesis. Because this estimation involves specifying a plausible model consistent with the null hypothesis, the testing procedure could lead to unreliable inferences under model misspecification. In this paper, we propose a score test of homogeneity for zero-inflated models that is robust against certain model misspecifications. Due to the true model being unknown in practical settings, our proposal is developed under a general framework of mixture models for which a layer of randomness is imposed on the model to account for uncertainty in the model specification. We exemplify this approach on the class of zero-inflated Poisson models, where a random term is imposed on the Poisson mean to adjust for relevant covariates missing from the mean model or a misspecified functional form. For this example, we show through simulations that the resulting score test of zero inflation maintains its empirical size at all levels, albeit a loss of power for the well-specified non-random mean model under the null. Frequencies of health promotion activities among young Girl Scouts and dental caries indices among inner-city children are used to illustrate the robustness of the proposed testing procedure.

Predicting Mortality in Nursing Home Residents With Dementia and Pneumonia Treated With Antibiotics: Validation of a Prediction Model in a More Recent Population.

BACKGROUND: We aimed to find the best predictive model for 14-day mortality in antibiotic-treated nursing home residents with dementia and pneumonia by first applying an existing model to the recent PneuMonitor study. Second, we evaluated whether model performance improved by revising variables or adding variables related to recent changes in the care for older people. METHODS: The original prognostic model included gender, respiratory rate, respiratory difficulty, pulse rate, decreased alertness, fluid intake, eating dependency, and pressure sores. This model was applied to 380 recent pneumonia episodes in nursing home residents with dementia, updated by considering revising and/or adding variables, internally validated using bootstrapping, and transformed into a simplified risk score that can be used in clinical practice. Model performance was evaluated by Hosmer-Lemeshow statistics and calibration graphs to assess calibration; and area under the receiver operating characteristic curve (AUC) to assess discrimination. RESULTS: The newer cohort had lower 14-day mortality and was less often dehydrated or malnourished. Median AUC of the original model over the imputed datasets was 0.76 (interquartile range: 0.76-0.77), compared to 0.80 in the old cohort. Extending the model with dehydration, bowel incontinence, increase in eating dependency and cardiovascular history, while removing pressure sores, improved AUC: 0.80 (interquartile range: 0.80-0.81) after internal validation. Calibration remained adequate (Hosmer-Lemeshow statistic: p = .67). CONCLUSIONS: In the newer cohort with less severe illness, model performance of the existing model was adequate, but a new extended model distinguished better between residents at low and high mortality risk.

Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data.

OBJECTIVE: To compare weighting methods for Cox regression and multiple imputation (MI) in a case-cohort study in the context of risk prediction modeling. STUDY DESIGN AND SETTING: Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case-cohort data assuming missing information on waist circumference outside the case-cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C-index, the net reclassification improvement, and calibration. RESULTS: The full cohort comprised 21,845 participants, and the case-cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. CONCLUSIONS: MI seems to be a valid method for deriving or extending a risk prediction model from case-cohort data and might be superior for absolute risk calculation when compared to weighted approaches.