Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 89
Filter
Add filters

Document Type
Year range
1.
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
2.
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
3.
Sci Rep ; 11(1): 12110, 2021 06 08.
Article in English | MEDLINE | ID: covidwho-1517640

ABSTRACT

Wearing surgical masks or other similar face coverings can reduce the emission of expiratory particles produced via breathing, talking, coughing, or sneezing. Although it is well established that some fraction of the expiratory airflow leaks around the edges of the mask, it is unclear how these leakage airflows affect the overall efficiency with which masks block emission of expiratory aerosol particles. Here, we show experimentally that the aerosol particle concentrations in the leakage airflows around a surgical mask are reduced compared to no mask wearing, with the magnitude of reduction dependent on the direction of escape (out the top, the sides, or the bottom). Because the actual leakage flowrate in each direction is difficult to measure, we use a Monte Carlo approach to estimate flow-corrected particle emission rates for particles having diameters in the range 0.5-20 µm. in all orientations. From these, we derive a flow-weighted overall number-based particle removal efficiency for the mask. The overall mask efficiency, accounting both for air that passes through the mask and for leakage flows, is reduced compared to the through-mask filtration efficiency, from 93 to 70% for talking, but from only 94-90% for coughing. These results demonstrate that leakage flows due to imperfect sealing do decrease mask efficiencies for reducing emission of expiratory particles, but even with such leakage surgical masks provide substantial control.


Subject(s)
Aerosols , Communicable Disease Control/methods , Cough , Exhalation , Filtration , Masks , Virus Diseases/prevention & control , Adolescent , Adult , COVID-19/prevention & control , Equipment Failure , Female , Humans , Male , Middle Aged , Monte Carlo Method , Particle Size , Probability , Respiration , Sneezing , Young Adult
4.
Comput Intell Neurosci ; 2021: 8640794, 2021.
Article in English | MEDLINE | ID: covidwho-1511540

ABSTRACT

The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall-Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.


Subject(s)
COVID-19 , Humans , Models, Statistical , Monte Carlo Method , Reproducibility of Results , SARS-CoV-2
5.
Sci Rep ; 11(1): 21715, 2021 11 05.
Article in English | MEDLINE | ID: covidwho-1504467

ABSTRACT

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.


Subject(s)
COVID-19/epidemiology , Deep Learning , Neural Networks, Computer , Algorithms , Geography , Humans , Machine Learning , Memory, Short-Term , Models, Statistical , Monte Carlo Method , Population Dynamics , Public Health Informatics , Reproducibility of Results , SARS-CoV-2 , Time Factors , United States/epidemiology
6.
Epidemiol Infect ; 149: e238, 2021 11 04.
Article in English | MEDLINE | ID: covidwho-1500390

ABSTRACT

The effectiveness of screening travellers during times of international disease outbreak is contentious, especially as the reduction in the risk of disease importation can be very small. Border screening typically consists of travellers being thermally scanned for signs of fever and/or completing a survey declaring any possible symptoms prior to admission to their destination country; while more thorough testing typically exists, these would generally prove more disruptive to deploy. In this paper, we describe a simple Monte Carlo based model that incorporates the epidemiology of coronavirus disease-2019 (COVID-19) to investigate the potential decrease in risk of disease importation that might be achieved by requiring travellers to undergo screening upon arrival during the current pandemic. This is a purely theoretical study to investigate the maximum impact that might be attained by deploying a test or testing programme simply at the point of entry, through which we may assess such action in the real world as a method of decreasing the risk of importation. We, therefore, assume ideal conditions such as 100% compliance among travellers and the use of a 'perfect' test. In addition to COVID-19, we also apply the presented model to simulated outbreaks of influenza, severe acute respiratory syndrome (SARS) and Ebola for comparison. Our model only considers screening implemented at airports, being the predominant method of international travel. Primary results showed that in the best-case scenario, screening at the point of entry may detect a maximum of 8.8% of travellers infected with COVID-19, compared to 34.8.%, 9.7% and 3.0% for travellers infected with influenza, SARS and Ebola respectively. While results appear to indicate that screening is more effective at preventing disease ingress when the disease in question has a shorter average incubation period, our results suggest that screening at the point of entry alone does not represent a sufficient method to adequately protect a nation from the importation of COVID-19 cases.


Subject(s)
COVID-19/diagnosis , COVID-19/transmission , Mass Screening , SARS-CoV-2 , Travel , COVID-19/prevention & control , Humans , Models, Biological , Monte Carlo Method , Risk Factors
7.
J Comput Biol ; 28(11): 1113-1129, 2021 11.
Article in English | MEDLINE | ID: covidwho-1483349

ABSTRACT

The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets.


Subject(s)
Cluster Analysis , Computational Biology/methods , Brazil , Databases, Genetic , Entropy , Humans , Monte Carlo Method , South Africa , United Kingdom , United States
8.
Comput Intell Neurosci ; 2021: 5918511, 2021.
Article in English | MEDLINE | ID: covidwho-1463058

ABSTRACT

A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.


Subject(s)
COVID-19 , Models, Statistical , Humans , Likelihood Functions , Monte Carlo Method , SARS-CoV-2
9.
J Mol Graph Model ; 110: 108050, 2022 01.
Article in English | MEDLINE | ID: covidwho-1458690

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the ongoing COVID-19 pandemic. With some notable exceptions, safe and effective vaccines, which are now being widely distributed globally, have largely begun to stabilise the situation. However, emerging variants of concern and vaccine hesitancy are apparent obstacles to eradication. Therefore, the need for the development of potent antivirals is still of importance. In this context, the SARS-CoV-2 main protease (Mpro) is a critical target and numerous clinical trials, predominantly in the private domain, are currently in progress. Here, our aim was to extend our previous studies, with hypericin and cyanidin-3-O-glucoside, as potential inhibitors of the SARS-CoV-2 Mpro. Firstly, we performed all-atom microsecond molecular dynamics simulations, which highlight the stability of the ligands in the Mpro active site over the duration of the trajectories. We also invoked PELE Monte Carlo simulations which indicate that both hypericin and cyanidin-3-O-glucoside preferentially interact with the Mpro active site and known allosteric sites. For further validation, we performed an in vitro enzymatic activity assay that demonstrated that hypericin and cyanidin-3-O-glucoside inhibit Mpro activity in a dose-dependent manner at biologically relevant (µM) concentrations. However, both ligands are much less potent than the well-known covalent antiviral GC376, which was used as a positive control in our experiments. Nevertheless, the biologically relevant activity of hypericin and cyanidin-3-O-glucoside is encouraging. In particular, a synthetic version of hypericin has FDA orphan drug designation, which could simplify potential clinical evaluation in the context of COVID-19.


Subject(s)
COVID-19 , Pandemics , Antiviral Agents/pharmacology , Coronavirus 3C Proteases , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Monte Carlo Method , Protease Inhibitors/pharmacology , SARS-CoV-2
10.
Environ Sci Pollut Res Int ; 28(43): 61853-61859, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1446193

ABSTRACT

Surfaces can be contaminated by droplets produced through coughing or sneezing. In this exploratory work, the UV disinfection results of Bacillus subtilis spores in dried saliva droplets were fitted to a three-parameter kinetic model (R2 ≥ 0.97). This model has a disinfection rate constant for single organisms and a smaller one for aggregates found in droplets. The fraction of organisms found in aggregates (ß) could account for the effects of different-sized droplets in the experimental work. Since a wide spectrum of droplet sizes can be produced, and some of the rate constants were uncertain, Monte Carlo simulation was used to estimate the UV inactivation performance in dried saliva droplets in a variety of conditions. Using conservative distribution for ß, the model was applied to the UV disinfection of SARS-CoV-2 in dried saliva droplets. It was shown that a one-log reduction of SARS-CoV-2 was very likely (p>99.9%) and a two-log reduction was probable (p=75%) at a dose of 60 mJ/cm2. Aggregates tend to be variable and limit the log reductions that can be achieved at high UV doses.


Subject(s)
COVID-19 , Disinfection , Bacillus subtilis , Humans , Kinetics , Monte Carlo Method , SARS-CoV-2 , Saliva , Spores, Bacterial , Ultraviolet Rays
11.
J Math Biol ; 83(4): 34, 2021 09 14.
Article in English | MEDLINE | ID: covidwho-1410027

ABSTRACT

Two stochastic models are proposed to describe the evolution of the COVID-19 pandemic. In the first model the population is partitioned into four compartments: susceptible S, infected I, removed R and dead people D. In order to have a cross validation, a deterministic version of such a model is also devised which is represented by a system of ordinary differential equations with delays. In the second stochastic model two further compartments are added: the class A of asymptomatic individuals and the class L of isolated infected people. Effects such as social distancing measures are easily included and the consequences are analyzed. Numerical solutions are obtained with Monte Carlo simulations. Quantitative predictions are provided which can be useful for the evaluation of political measures, e.g. the obtained results suggest that strategies based on herd immunity are too risky. Finally, the models are calibrated on data referring to the second wave of infection in Italy.


Subject(s)
COVID-19 , Humans , Models, Biological , Monte Carlo Method , Pandemics , SARS-CoV-2 , Stochastic Processes
13.
Stat Med ; 40(27): 6209-6234, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1396957

ABSTRACT

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , Humans , Monte Carlo Method , Workflow
14.
Mol Biol Evol ; 38(4): 1537-1543, 2021 04 13.
Article in English | MEDLINE | ID: covidwho-1387956

ABSTRACT

The rooting of the SARS-CoV-2 phylogeny is important for understanding the origin and early spread of the virus. Previously published phylogenies have used different rootings that do not always provide consistent results. We investigate several different strategies for rooting the SARS-CoV-2 tree and provide measures of statistical uncertainty for all methods. We show that methods based on the molecular clock tend to place the root in the B clade, whereas methods based on outgroup rooting tend to place the root in the A clade. The results from the two approaches are statistically incompatible, possibly as a consequence of deviations from a molecular clock or excess back-mutations. We also show that none of the methods provide strong statistical support for the placement of the root in any particular edge of the tree. These results suggest that phylogenetic evidence alone is unlikely to identify the origin of the SARS-CoV-2 virus and we caution against strong inferences regarding the early spread of the virus based solely on such evidence.


Subject(s)
COVID-19/virology , Genome, Viral , Mutation , Phylogeny , SARS-CoV-2/genetics , Algorithms , Animals , Bayes Theorem , Evolution, Molecular , Humans , Likelihood Functions , Markov Chains , Models, Genetic , Models, Statistical , Monte Carlo Method , Mutation, Missense , RNA, Viral/genetics , Uncertainty
15.
Am J Epidemiol ; 190(7): 1377-1385, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1387704

ABSTRACT

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.


Subject(s)
COVID-19/transmission , Epidemiologic Measurements , Models, Statistical , Uncertainty , Basic Reproduction Number , Communicable Diseases , Humans , Monte Carlo Method , Pandemics , SARS-CoV-2
16.
Sci Rep ; 11(1): 17328, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379336

ABSTRACT

Public health officials discouraged travel and non-household gatherings for Thanksgiving, but data suggests that travel increased over the holidays. The objective of this analysis was to assess associations between holiday gatherings and SARS-CoV-2 positivity in the weeks following Thanksgiving. Using an online survey, we sampled 7770 individuals across 10 US states from December 4-18, 2020, about 8-22 days post-Thanksgiving. Participants were asked about Thanksgiving, COVID-19 symptoms, and SARS-CoV-2 testing and positivity in the prior 2 weeks. Logistic regression was used to identify factors associated with SARS-CoV-2 positivity and COVID-19 symptoms in the weeks following Thanksgiving. An activity score measured the total number of non-essential activities an individual participated in the prior 2 weeks. The probability of community transmission was estimated using Markov Chain Monte Carlo (MCMC) methods. While 47.2% had Thanksgiving at home with household members, 26.9% had guests and 25.9% traveled. There was a statistically significant interaction between how people spent Thanksgiving, the frequency of activities, and SARS-CoV-2 test positivity in the prior 2 weeks (p < 0.05). Those who had guests for Thanksgiving or traveled were only more likely to test positive for SARS-CoV-2 if they also had high activity (e.g., participated in > one non-essential activity/day in the prior 2 weeks). Had individuals limited the number and frequency of activities post-Thanksgiving, cases in surveyed individuals would be reduced by > 50%. As travel continues to increase and the more contagious Delta variant starts to dominate transmission, it is critical to promote how to gather in a "low-risk" manner (e.g., minimize other non-essential activities) to mitigate the need for nationwide shelter-at-home orders.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Travel/statistics & numerical data , Adult , COVID-19 Testing , Female , Holidays , Humans , Male , Markov Chains , Middle Aged , Monte Carlo Method , Public Health , United States/epidemiology
17.
Eur J Clin Invest ; 51(11): e13669, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1354478

ABSTRACT

BACKGROUND: In 2020, early U.S. COVID-19 testing sites offered diagnostic capacity to patients and were important sources of epidemiological data about the spread of the novel pandemic disease. However, little research has comprehensively described American testing sites' distribution by race/ethnicity and sought to identify any relation to known disparities in COVID-19 outcomes. METHODS: Locations of U.S. COVID-19 testing sites were gathered from 16 April to 28 May 2020. Geographic testing disparities were evaluated with comparisons of the demographic makeup of zip codes around each testing site versus Monte Carlo simulations, aggregated to statewide and nationwide levels. State testing disparities were compared with statewide disparities in mortality observed one to 3 weeks later using multivariable regression, controlling for confounding disparities and characteristics. RESULTS: Nationwide, COVID-19 testing sites geographically overrepresented White residents on 7 May, underrepresented Hispanic residents on 16 April, 7 May and 28 May and overrepresented Black residents on 28 May compared with random distribution within counties, with new sites added over time exhibiting inconsistent disparities for Black and Hispanic populations. For every 1 percentage point increase in underrepresentation of Hispanic populations in zip codes with testing, mortality among the state's Hispanic population was 1.04 percentage points more over-representative (SE = 0.415, p = .01). CONCLUSIONS: American testing sites were not distributed equitably by race during this analysis, often underrepresenting minority populations who bear a disproportionate burden of COVID-19 cases and deaths. With an easy-to-implement measure of geographic disparity, these results provide empirical support for the consideration of access when distributing preventive resources.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/diagnosis , Health Facilities/statistics & numerical data , Healthcare Disparities/ethnology , Mortality , African Americans , COVID-19/mortality , Geography , Health Services Accessibility , Humans , Monte Carlo Method , SARS-CoV-2 , United States
18.
J Res Health Sci ; 21(2): e00517, 2021 Jun 28.
Article in English | MEDLINE | ID: covidwho-1326176

ABSTRACT

BACKGROUND: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. STUDY DESIGN: Descriptive study. METHODS: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. RESULTS: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). CONCLUSION: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , Epidemics , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , Cross-Sectional Studies , Humans , Iran/epidemiology , Likelihood Functions , Markov Chains , Monte Carlo Method , Pandemics , SARS-CoV-2
19.
PLoS One ; 16(7): e0254999, 2021.
Article in English | MEDLINE | ID: covidwho-1325438

ABSTRACT

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Models, Statistical , Pandemics , SARS-CoV-2/pathogenicity , COVID-19/diagnosis , COVID-19/physiopathology , China/epidemiology , Cough/diagnosis , Cough/physiopathology , Fatigue/diagnosis , Fatigue/physiopathology , Fever/diagnosis , Fever/physiopathology , Hospitals , Humans , Monte Carlo Method , Survival Analysis
20.
PLoS Comput Biol ; 17(7): e1009211, 2021 07.
Article in English | MEDLINE | ID: covidwho-1325367

ABSTRACT

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , COVID-19/transmission , Pandemics , SARS-CoV-2 , Algorithms , Basic Reproduction Number/statistics & numerical data , Bayes Theorem , Computational Biology , Epidemics/statistics & numerical data , France/epidemiology , Humans , Ireland/epidemiology , Markov Chains , Models, Statistical , Monte Carlo Method , Pandemics/statistics & numerical data , Seroepidemiologic Studies , Stochastic Processes , Time Factors
SELECTION OF CITATIONS
SEARCH DETAIL
...