Disease burden due to COVID-19 in Taiwan: Disability-adjusted life years (DALYs) with implication of Monte Carlo simulations.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has affected a large number of countries. Informing the public and decision makers of the COVID-19's economic burdens is essential for understanding the real pandemic impact. METHODS: COVID-19 premature mortality and disability impact in Taiwan was analyzed using the Taiwan National Infectious Disease Statistics System (TNIDSS) by estimating the sex/age-specific years of life lost through death (YLLs), the number of years lived with disability (YLDs), and the disability-adjusted life years (DALYs) from January 2020 to November 2021. RESULTS: Taiwan recorded 1004.13 DALYs (95% CI: 1002.75-1005.61) per 100,000 population for COVID-19, with YLLs accounting for 99.5% (95% CI: 99.3%99.6%) of all DALYs, with males suffering more from the disease than females. For population aged ≥ 70 years, the disease burdens of YLDs and YLLs were 0.1% and 99.9%, respectively. Furthermore, we found that duration of disease in critical state contributed 63.9% of the variance in DALY estimations. CONCLUSIONS: The nationwide estimation of DALYs in Taiwan provides insights into the demographic distributions and key epidemiological parameter for DALYs. The essentiality of enforcing protective precautions when needed is also implicated. The higher YLLs percentage in DALYs also revealed the fact of high confirmed death rates in Taiwan. To reduce infection risks and disease, it is crucial to maintain moderate social distancing, border control, hygiene measures, and increase vaccine coverage levels.

Modeling the spread of COVID-19 in spatio-temporal context.

This study aims to use data provided by the Virginia Department of Public Health to illustrate the changes in trends of the total cases in COVID-19 since they were first recorded in the state. Each of the 93 counties in the state has its COVID-19 dashboard to help inform decision makers and the public of spatial and temporal counts of total cases. Our analysis shows the differences in the relative spread between the counties and compares the evolution in time using Bayesian conditional autoregressive framework. The models are built under the Markov Chain Monte Carlo method and Moran spatial correlations. In addition, Moran's time series modeling techniques were applied to understand the incidence rates. The findings discussed may serve as a template for other studies of similar nature.

Possibilities of simulation of coronavirus SARS-CoV-2 by using light scattering approach.

Issues related to human coronavirus (SARS CoV-2) are a burning topic of research in present times. Due to its easily contagious nature, real experimentation under laboratory conditions requires a high level of biosafety. A powerful algorithm serves as a potential tool for the analysis of these particles. We attempted to simulate the light scattering from coronavirus (SARS CoV-2) model. Different images were modelled using a modified version of a Monte Carlo code. The results indicate that spikes on the viruses exhibit a significant scattering profile and that the presence of spikes during modelling contributes to the distinctiveness of the scattering profiles.

Quantifying Uncertainty in Mechanistic Models of Infectious Disease.

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

Evaluating a SARS-CoV-2 screening strategy based on serological tests.

BACKGROUND: facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. Although RT-PCR is the most reliable technique to detect ongoing infections, serological tests are frequently proposed as tools in heterogeneous screening strategies. OBJECTIVES: to analyse the performance of a screening strategy proposed by the local government of Tuscany (Central Italy), which first uses qualitative rapid tests for antibody detection, and then RT-PCR tests on the positive subjects. METHODS: a simulation study is conducted to investigate the number of RT-PCR tests required by the screening strategy and the undetected ongoing infections in a pseudo-population of 500,000 subjects, under different prevalence scenarios and assuming a sensitivity of the serological test ranging from 0.50 to 0.80 (specificity 0.98). A compartmental model is used to predict the number of new infections generated by the false negatives two months after the screening, under different values of the infection reproduction number. RESULTS: assuming a sensitivity equal to 0.80 and a prevalence of 0.3%, the screening procedure would require on average 11,167 RT-PCR tests and would produce 300 false negatives, responsible after two months of a number of contagions ranging from 526 to 1,132, under the optimistic scenario of a reproduction number between 0.5 to 1. Resources and false negatives increase with the prevalence. CONCLUSIONS: the analysed screening procedure should be avoided unless the prevalence and the rate of contagion are very low. The cost and effectiveness of the screening strategies should be evaluated in the actual context of the epidemic, accounting for the fact that it may change over time.

TailCoR: A new and simple metric for tail correlations that disentangles the linear and nonlinear dependencies that cause extreme co-movements.

Economic and financial crises are characterised by unusually large events. These tail events co-move because of linear and/or nonlinear dependencies. We introduce TailCoR, a metric that combines (and disentangles) these linear and non-linear dependencies. TailCoR between two variables is based on the tail inter quantile range of a simple projection. It is dimension-free, and, unlike competing metrics, it performs well in small samples and no optimisations are needed. Indeed, TailCoR requires a few lines of coding and it is very fast. A Monte Carlo analysis confirms the goodness of the metric, which is illustrated on a sample of 21 daily financial market indexes across the globe and for 20 years. The estimated TailCoRs are in line with the financial and economic events, such as the 2008 great financial crisis and the 2020 pandemic.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space.

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling.

Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation. Recently, the estimation of the reproduction number, a measure of the pandemic intensity, was formulated as an inverse problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that formulation lacks robustness against the limited quality of the Covid19 data and confidence assessment. The present work aims to address both limitations: First, it discusses solutions to produce a robust assessment of the pandemic intensity by accounting for the low quality of the data directly within the inverse problem formulation. Second, exploiting a Bayesian interpretation of the inverse problem formulation, it devises a Monte Carlo sampling strategy, tailored to a nonsmooth log-concave a posteriori distribution, to produce relevant credibility interval-based estimates for the Covid19 reproduction number. Clinical relevance Applied to daily counts of new infections made publicly available by the Health Authorities for around 200 countries, the proposed procedures permit robust assessments of the time evolution of the Covid19 pandemic intensity, updated automatically and on a daily basis.

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data.

In this work, we presented the type I half logistic Burr-Weibull distribution, which is a unique continuous distribution. It offers several superior benefits in fitting various sorts of data. Estimates of the model parameters based on classical and nonclassical approaches are offered. Also, the Bayesian estimates of the model parameters were examined. The Bayesian estimate method employs the Monte Carlo Markov chain approach for the posterior function since the posterior function came from an uncertain distribution. The use of Monte Carlo simulation is to assess the parameters. We established the superiority of the proposed distribution by utilising real COVID-19 data from varied countries such as Saudi Arabia and Italy to highlight the relevance and flexibility of the provided technique. We proved our superiority using both real data.

Scalable Bayesian phylogenetics.

Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Low-dose radiotherapy to the lungs using an interventional radiology C-arm fluoroscope: Monte Carlo treatment planning and dose measurements in a postmortem subject.

Objective.The goal of this study was to use Monte Carlo (MC) simulations and measurements to investigate the dosimetric suitability of an interventional radiology (IR) c-arm fluoroscope to deliver low-dose radiotherapy to the lungs.Approach.A previously-validated MC model of an IR fluoroscope was used to calculate the dose distributions in a COVID-19-infected patient, 20 non-infected patients of varying sizes, and a postmortem subject. Dose distributions for PA, AP/PA, 3-field and 4-field treatments irradiating 95% of the lungs to a 0.5 Gy dose were calculated. An algorithm was created to calculate skin entrance dose as a function of patient thickness for treatment planning purposes. Treatments were experimentally validated in a postmortem subject by using implanted dosimeters to capture organ doses.Main results.Mean doses to the left/right lungs for the COVID-19 CT data were 1.2/1.3 Gy, 0.8/0.9 Gy, 0.8/0.8 Gy and 0.6/0.6 Gy for the PA, AP/PA, 3-field, and 4-field configurations, respectively. Skin dose toxicity was the highest probability for the PA and lowest for the 4-field configuration. Dose to the heart slightly exceeded the ICRP tolerance; all other organ doses were below published tolerances. The AP/PA configuration provided the best fit for entrance skin dose as a function of patient thickness (R2 = 0.8). The average dose difference between simulation and measurement in the postmortem subject was 5%.Significance.An IR fluoroscope should be capable of delivering low-dose radiotherapy to the lungs with tolerable collateral dose to nearby organs.

Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China.

The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function ß(t) to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in ß(t) and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.

Pre-clinic study of radiopharmaceutical for Covid-19 inactivation: Dose distribution with Monte Carlo Simulation.

Monte Carlo simulation method and Nuclear Medicine MIRD method were used to evaluate the effect of radiopharmaceuticals on Covid-19 disease. The mean absorbed organ dose in the target organ and gamma radiation emitter attenuation properties such as linear attenuation coefficients, energy absorption build-up factors (EABF), exposure build-up factors (EBF), and relative dose distributions (RDD) were examined. The results showed that radiopharmaceuticals containing gamma radiation emitters which are densely ionizing charged particles induced membrane damage and produced protein damage.

Effects of the information-driven awareness on epidemic spreading on multiplex networks.

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a "double-edged sword" effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

Population Pharmacokinetics of Remdesivir and GS-441524 in Hospitalized COVID-19 Patients.

The objective of this study was to describe the population pharmacokinetics of remdesivir and GS-441524 in hospitalized coronavirus disease 2019 (COVID-19) patients. A prospective observational pharmacokinetic study was performed in non-critically ill hospitalized COVID-19 patients with hypoxemia. For evaluation of the plasma concentrations of remdesivir and its metabolite GS-441524, samples were collected on the first day of therapy. A nonlinear mixed-effects model was developed to describe the pharmacokinetics and identify potential covariates that explain variability. Alternative dosing regimens were evaluated using Monte Carlo simulations. Seventeen patients were included. Remdesivir and GS-441524 pharmacokinetics were best described by a one-compartment model. The estimated glomerular filtration rate (eGFR) on GS-441524 clearance was identified as a clinically relevant covariate. The interindividual variability in clearance and volume of distribution for both remdesivir and GS-441524 was high (remdesivir, 38.9% and 47.9%, respectively; GS-441525, 47.4% and 42.9%, respectively). The estimated elimination half-life for remdesivir was 0.48 h, and that for GS-441524 was 26.6 h. The probability of target attainment (PTA) of the in vitro 50% effective concentration (EC50) for GS-441524 in plasma can be improved by shortening the dose interval of remdesivir and thereby increasing the total daily dose (PTA, 51.4% versus 94.7%). In patients with reduced renal function, the metabolite GS-441524 accumulates. A population pharmacokinetic model for remdesivir and GS-441524 in COVID-19 patients was developed. Remdesivir showed highly variable pharmacokinetics. The elimination half-life of remdesivir in COVID-19 patients is short, and the clearance of GS-441524 is dependent on the eGFR. Alternative dosing regimens aimed at optimizing the remdesivir and GS-441524 concentrations may improve the effectiveness of remdesivir treatment in COVID-19 patients.

Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic.

We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.

Monte Carlo simulation of COVID-19 pandemic using Planck's probability distribution.

We present a Monte Carlo simulation model of an epidemic spread inspired on physics variables such as temperature, cross section and interaction range, which considers the Plank distribution of photons in the black body radiation to describe the mobility of individuals. The model consists of a lattice of cells that can be in four different states: susceptible, infected, recovered or death. An infected cell can transmit the disease to any other susceptible cell within some random range R. The transmission mechanism follows the physics laws for the interaction between a particle and a target. Each infected particle affects the interaction region a number n of times, according to its energy. The number of interactions is proportional to the interaction cross section σ and to the target surface density ρ. The discrete energy follows a Planck distribution law, which depends on the temperature T of the system. For any interaction, infection, recovery and death probabilities are applied. We investigate the results of viral transmission for different sets of parameters and compare them with available COVID-19 data. The parameters of the model can be made time dependent in order to consider, for instance, the effects of lockdown in the middle of the pandemic.

Bayesian inference for asymptomatic COVID-19 infection rates.

To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.

Asymmetric Power Hazard Distribution for COVID-19 Mortality Rate under Adaptive Type-II Progressive Censoring: Theory and Inferences.

This article investigates the estimation of the parameters for power hazard function distribution and some lifetime indices such as reliability function, hazard rate function, and coefficient of variation based on adaptive Type-II progressive censoring. From the perspective of frequentism, we derive the point estimations through the method of maximum likelihood estimation. Besides, delta method is implemented to construct the variances of the reliability characteristics. Markov chain Monte Carlo techniques are proposed to construct the Bayes estimates. To this end, the results of the Bayes estimates are obtained under squared error and linear exponential loss functions. Also, the corresponding credible intervals are constructed. A simulation study is utilized to assay the performance of the proposed methods. Finally, a real data set of COVID-19 mortality rate is analyzed to validate the introduced inference methods.

Modeling Clothing as a Vector for Transporting Airborne Particles and Pathogens across Indoor Microenvironments.

Evidence suggests that human exposure to airborne particles and associated contaminants, including respiratory pathogens, can persist beyond a single microenvironment. By accumulating such contaminants from air, clothing may function as a transport vector and source of "secondary exposure". To investigate this function, a novel microenvironmental exposure modeling framework (ABICAM) was developed. This framework was applied to a para-occupational exposure scenario involving the deposition of viable SARS-CoV-2 in respiratory particles (0.5-20 µm) from a primary source onto clothing in a nonhealthcare setting and subsequent resuspension and secondary exposure in a car and home. Variability was assessed through Monte Carlo simulations. The total volume of infectious particles on the occupant's clothing immediately after work was 4800 µm3 (5th-95th percentiles: 870-32 000 µm3). This value was 61% (5-95%: 17-300%) of the occupant's primary inhalation exposure in the workplace while unmasked. By arrival at the occupant's home after a car commute, relatively rapid viral inactivation on cotton clothing had reduced the infectious volume on clothing by 80% (5-95%: 26-99%). Secondary inhalation exposure (after work) was low in the absence of close proximity and physical contact with contaminated clothing. In comparison, the average primary inhalation exposure in the workplace was higher by about 2-3 orders of magnitude. It remains theoretically possible that resuspension and physical contact with contaminated clothing can occasionally transmit SARS-CoV-2 between humans.