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1.
Chem Res Toxicol ; 35(11): 2097-2106, 2022 Nov 21.
Article in English | MEDLINE | ID: covidwho-2050239

ABSTRACT

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.


Subject(s)
COVID-19 , Humans , Computer Simulation , Monte Carlo Method , Lung , Computers
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 167-170, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018758

ABSTRACT

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.


Subject(s)
COVID-19 , Pandemics , Bayes Theorem , COVID-19/epidemiology , Humans , Monte Carlo Method , Reproduction
3.
Comput Math Methods Med ; 2022: 1444859, 2022.
Article in English | MEDLINE | ID: covidwho-2001938

ABSTRACT

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.


Subject(s)
COVID-19 , Bayes Theorem , Humans , Markov Chains , Monte Carlo Method , Statistical Distributions
4.
Philos Trans R Soc Lond B Biol Sci ; 377(1861): 20210242, 2022 10 10.
Article in English | MEDLINE | ID: covidwho-2001544

ABSTRACT

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'.


Subject(s)
COVID-19 , Software , Algorithms , Bayes Theorem , Humans , Markov Chains , Monte Carlo Method , Phylogeny , SARS-CoV-2/genetics
5.
Biomed Phys Eng Express ; 8(6)2022 09 05.
Article in English | MEDLINE | ID: covidwho-1992047

ABSTRACT

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.


Subject(s)
COVID-19 , Radiotherapy Planning, Computer-Assisted , COVID-19/radiotherapy , Humans , Lung/diagnostic imaging , Monte Carlo Method , Radiology, Interventional , Radiotherapy Planning, Computer-Assisted/methods
6.
Int J Environ Res Public Health ; 19(16)2022 08 16.
Article in English | MEDLINE | ID: covidwho-1987807

ABSTRACT

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.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Humans , Markov Chains , Monte Carlo Method , Pandemics/prevention & control
7.
Appl Radiat Isot ; 188: 110364, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1982566

ABSTRACT

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.


Subject(s)
COVID-19 , Radiopharmaceuticals , Computer Simulation , Humans , Monte Carlo Method , Radiometry/methods , Radiopharmaceuticals/therapeutic use
8.
Chaos ; 32(7): 073123, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1978070

ABSTRACT

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.


Subject(s)
Epidemics , Resource Allocation , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Markov Chains , Models, Biological , Monte Carlo Method , Resource Allocation/statistics & numerical data , Resource Allocation/trends
9.
Antimicrob Agents Chemother ; 66(6): e0025422, 2022 06 21.
Article in English | MEDLINE | ID: covidwho-1874495

ABSTRACT

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.


Subject(s)
COVID-19 , Adenosine/analogs & derivatives , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/therapeutic use , Alanine/analogs & derivatives , Anti-Bacterial Agents/pharmacokinetics , COVID-19/drug therapy , Critical Illness/therapy , Furans , Humans , Monte Carlo Method , Triazines
10.
J Biol Dyn ; 16(1): 412-438, 2022 12.
Article in English | MEDLINE | ID: covidwho-1868208

ABSTRACT

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.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Humans , Models, Biological , Monte Carlo Method , Pandemics/prevention & control
11.
Biosystems ; 218: 104708, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1866917

ABSTRACT

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.


Subject(s)
COVID-19 , Computer Simulation , COVID-19/epidemiology , Humans , Monte Carlo Method , Pandemics , Probability
12.
Stat Med ; 41(16): 3131-3148, 2022 07 20.
Article in English | MEDLINE | ID: covidwho-1850242

ABSTRACT

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.


Subject(s)
COVID-19 , Bayes Theorem , Humans , Markov Chains , Monte Carlo Method
13.
Comput Intell Neurosci ; 2022: 5134507, 2022.
Article in English | MEDLINE | ID: covidwho-1799192

ABSTRACT

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.


Subject(s)
COVID-19 , Bayes Theorem , Computer Simulation , Humans , Likelihood Functions , Monte Carlo Method , Reproducibility of Results
14.
Environ Sci Technol ; 56(9): 5641-5652, 2022 05 03.
Article in English | MEDLINE | ID: covidwho-1783919

ABSTRACT

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.


Subject(s)
COVID-19 , Clothing , Humans , Inhalation Exposure , Monte Carlo Method , SARS-CoV-2
15.
Chaos ; 32(2): 023127, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1721773

ABSTRACT

During the COVID-19 pandemic, many institutions have announced that their counterparties are struggling to fulfill contracts. Therefore, it is necessary to consider the counterparty default risk when pricing options. After the 2008 financial crisis, a variety of value adjustments have been emphasized in the financial industry. The total value adjustment (XVA) is the sum of multiple value adjustments, which is also investigated in many stochastic models, such as the Heston [B. Salvador and C. W. Oosterlee, Appl. Math. Comput. 391, 125489 (2020)] and Bates [L. Goudenège et al., Comput. Manag. Sci. 17, 163-178 (2020)] models. In this work, a widely used pure jump Lévy process, the Carr-Geman-Madan-Yor process has been considered for pricing a Bermudan option with various value adjustments. Under a pure jump Lévy process, the value of derivatives satisfies a fractional partial differential equation (FPDE). Therefore, we construct a method that combines Monte Carlo with a finite difference of FPDE to find the numerical approximation of exposure and compare it with the benchmark Monte Carlo simulation and Fourier-cosine series method. We use the discrete energy estimate method, which is different from the existing works, to derive the convergence of the numerical scheme. Based on the numerical results, the XVA is computed by the financial exposure of the derivative value.


Subject(s)
COVID-19 , Pandemics , Computer Simulation , Humans , Monte Carlo Method , SARS-CoV-2
16.
Syst Biol ; 71(6): 1549-1560, 2022 10 12.
Article in English | MEDLINE | ID: covidwho-1713733

ABSTRACT

We present a two-headed approach called Bayesian Integrated Coalescent Epoch PlotS (BICEPS) for efficient inference of coalescent epoch models. Firstly, we integrate out population size parameters, and secondly, we introduce a set of more powerful Markov chain Monte Carlo (MCMC) proposals for flexing and stretching trees. Even though population sizes are integrated out and not explicitly sampled through MCMC, we are still able to generate samples from the population size posteriors. This allows demographic reconstruction through time and estimating the timing and magnitude of population bottlenecks and full population histories. Altogether, BICEPS can be considered a more muscular version of the popular Bayesian skyline model. We demonstrate its power and correctness by a well-calibrated simulation study. Furthermore, we demonstrate with an application to SARS-CoV-2 genomic data that some analyses that have trouble converging with the traditional Bayesian skyline prior and standard MCMC proposals can do well with the BICEPS approach. BICEPS is available as open-source package for BEAST 2 under GPL license and has a user-friendly graphical user interface.[Bayesian phylogenetics; BEAST 2; BICEPS; coalescent model.].


Subject(s)
COVID-19 , Software , Algorithms , Bayes Theorem , Humans , Markov Chains , Models, Genetic , Monte Carlo Method , Phylogeny , SARS-CoV-2
17.
MAbs ; 14(1): 2021601, 2022.
Article in English | MEDLINE | ID: covidwho-1625321

ABSTRACT

Coronavirus disease 2019, caused by SARS-CoV-2, remains an on-going pandemic, partly due to the emergence of variant viruses that can "break-through" the protection of the current vaccines and neutralizing antibodies (nAbs), highlighting the needs for broadly nAbs and next-generation vaccines. We report an antibody that exhibits breadth and potency in binding the receptor-binding domain (RBD) of the virus spike glycoprotein across SARS coronaviruses. Initially, a lead antibody was computationally discovered and crystallographically validated that binds to a highly conserved surface of the RBD of wild-type SARS-CoV-2. Subsequently, through experimental affinity enhancement and computational affinity maturation, it was further developed to bind the RBD of all concerning SARS-CoV-2 variants, SARS-CoV-1 and pangolin coronavirus with pico-molar binding affinities, consistently exhibited strong neutralization activity against wild-type SARS-CoV-2 and the Alpha and Delta variants. These results identify a vulnerable target site on coronaviruses for development of pan-sarbecovirus nAbs and vaccines.


Subject(s)
Antibodies, Viral/immunology , Antigens, Viral/immunology , Broadly Neutralizing Antibodies/immunology , COVID-19/immunology , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Antibodies, Viral/genetics , Antibodies, Viral/metabolism , Antibody Affinity , Antibody Specificity , Antigen-Antibody Reactions , Antigens, Viral/chemistry , Antigens, Viral/genetics , Broadly Neutralizing Antibodies/genetics , Broadly Neutralizing Antibodies/metabolism , Crystallography, X-Ray , Epitopes/chemistry , Epitopes/immunology , Humans , Immunoglobulin Fragments/immunology , Molecular Docking Simulation , Monte Carlo Method , Neutralization Tests , Peptide Fragments/chemistry , Peptide Fragments/metabolism , Protein Domains , Recombinant Fusion Proteins/genetics , Recombinant Fusion Proteins/immunology , Recombinant Fusion Proteins/metabolism , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics
18.
Emerg Microbes Infect ; 11(1): 168-171, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1623181

ABSTRACT

HCoV-OC43 is one of the mildly pathogenic coronaviruses with high infection rates in common population. Here, 43 HCoV-OC43 related cases with pneumonia were reported, corresponding genomes of HCoV-OC43 were obtained. Phylogenetic analyses based on complete genome, orf1ab and spike genes revealed that two novel genotypes of HCoV-OC43 have emerged in China. Obvious recombinant events also can be detected in the analysis of the evolutionary dynamics of novel HCoV-OC43 genotypes. Estimated divergence time analysis indicated that the two novel genotypes had apparently independent evolutionary routes. Efforts should be conducted for further investigation of genomic diversity and evolution analysis of mildly pathogenic coronaviruses.


Subject(s)
Common Cold/epidemiology , Coronavirus Infections/epidemiology , Coronavirus OC43, Human/genetics , Genome, Viral , Genotype , Pneumonia, Viral/epidemiology , Base Sequence , Bayes Theorem , Child , Child, Hospitalized , Child, Preschool , China/epidemiology , Common Cold/pathology , Common Cold/transmission , Common Cold/virology , Coronavirus Infections/pathology , Coronavirus Infections/transmission , Coronavirus Infections/virology , Coronavirus OC43, Human/classification , Coronavirus OC43, Human/pathogenicity , Epidemiological Monitoring , Female , Humans , Infant , Male , Monte Carlo Method , Mutation , Phylogeny , Pneumonia, Viral/pathology , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Recombination, Genetic
19.
PLoS One ; 17(1): e0260836, 2022.
Article in English | MEDLINE | ID: covidwho-1613339

ABSTRACT

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.


Subject(s)
COVID-19/epidemiology , Humans , Japan/epidemiology , Markov Chains , Models, Statistical , Monte Carlo Method , Normal Distribution , Poisson Distribution , Regression Analysis , SARS-CoV-2/pathogenicity , Spatial Analysis , Spatio-Temporal Analysis
20.
PLoS Comput Biol ; 17(12): e1009652, 2021 12.
Article in English | MEDLINE | ID: covidwho-1546836

ABSTRACT

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.


Subject(s)
Communicable Diseases/transmission , Disease Outbreaks , Endemic Diseases , Epidemics , Animals , Bayes Theorem , Communicable Diseases/physiopathology , Computational Biology/methods , Computer Simulation , Environment , Humans , Models, Biological , Models, Theoretical , Monte Carlo Method , Probability , Stochastic Processes
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