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1.
PLoS Comput Biol ; 16(9): e1008103, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32956350

RESUMO

Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This "semi-explicit" water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests.


Assuntos
Sítios de Ligação/fisiologia , Simulação de Acoplamento Molecular , Ligação Proteica/fisiologia , Proteínas , Água , Algoritmos , Aminoácidos/química , Aminoácidos/metabolismo , Ligação de Hidrogênio , Ligantes , Método de Monte Carlo , Proteínas/química , Proteínas/metabolismo , Água/química , Água/metabolismo
2.
Nan Fang Yi Ke Da Xue Xue Bao ; 40(5): 713-717, 2020 May 30.
Artigo em Chinês | MEDLINE | ID: mdl-32897205

RESUMO

OBJECTIVE: To explore the relationship between sample size in the groups and statistical power of ANOVA and Kruskal-Wallis H test with an imbalanced design. METHODS: The sample sizes of the two tests were estimated by SAS program with given parameter settings, and Monte Carlo simulation was used to examine the changes in power when the total sample size varied or remained fixed. RESULTS: In ANOVA, when the total sample size was fixed, increasing the sample size in the group with a larger mean square error improved the statistical power, but an excessively large difference in the sample sizes between groups led to reduced power. When the total sample size was not fixed, a larger mean square error in the group with increased sample size was associated with a greater increase of the statistical power. In Kruskal-wallis H test, when the total sample size was fixed, increasing the sample size in groups with large mean square errors increased the statistical power irrespective of the sample size difference between the groups; when total sample size was not fixed, a larger mean square error in the group with increased sample size resulted in an increased statistical power, and the increment was similar to that for a fixed total sample size. CONCLUSIONS: The relationship between statistical power and sample size in groups is affected by the mean square error, and increasing the sample size in a group with a large mean square error increases the statistical power. In Kruskal-Wallis H test, increasing the sample size in a group with a large mean square error is more cost- effective than increasing the total sample size to improve the statistical power.


Assuntos
Modelos Estatísticos , Simulação por Computador , Método de Monte Carlo , Tamanho da Amostra
3.
Math Biosci Eng ; 17(4): 2842-2852, 2020 03 25.
Artigo em Inglês | MEDLINE | ID: mdl-32987501

RESUMO

Since the first case of coronavirus disease (COVID-19) in Wuhan Hubei, China, was reported in December 2019, COVID-19 has spread rapidly across the country and overseas. The first case in Anhui, a province of China, was reported on January 10, 2020. In the field of infectious diseases, modeling, evaluating and predicting the rate of disease transmission is very important for epidemic prevention and control. Different intervention measures have been implemented starting from different time nodes in the country and Anhui, the epidemic may be divided into three stages for January 10 to February 11, 2020, namely. We adopted interrupted time series method and develop an SEI/QR model to analyse the data. Our results displayed that the lockdown of Wuhan implemented on January 23, 2020 reduced the contact rate of epidemic transmission in Anhui province by 48.37%, and centralized quarantine management policy for close contacts in Anhui reduced the contact rate by an additional 36.97%. At the same time, the estimated basic reproduction number gradually decreased from the initial 2.9764 to 0.8667 and then to 0.5725. We conclude that the Wuhan lockdown and the centralized quarantine management policy in Anhui played a crucial role in the timely and effective mitigation of the epidemic in Anhui. One merit of this work is the adoption of morbidity data which may reflect the epidemic more accurately and promptly. Our estimated parameters are largely in line with the World Health Organization estimates and previous studies.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Biológicos , Pandemias , Pneumonia Viral/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , China/epidemiologia , Simulação por Computador , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Humanos , Análise de Séries Temporais Interrompida/estatística & dados numéricos , Cadeias de Markov , Conceitos Matemáticos , Método de Monte Carlo , Morbidade/tendências , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Quarentena/estatística & dados numéricos
4.
Math Biosci Eng ; 17(4): 3052-3061, 2020 04 08.
Artigo em Inglês | MEDLINE | ID: mdl-32987516

RESUMO

The novel coronavirus disease 2019 (COVID-19) infection broke out in December 2019 in Wuhan, and rapidly overspread 31 provinces in mainland China on 31 January 2020. In the face of the increasing number of daily confirmed infected cases, it has become a common concern and worthy of pondering when the infection will appear the turning points, what is the final size and when the infection would be ultimately controlled. Based on the current control measures, we proposed a dynamical transmission model with contact trace and quarantine and predicted the peak time and final size for daily confirmed infected cases by employing Markov Chain Monte Carlo algorithm. We estimate the basic reproductive number of COVID-19 is 5.78 (95%CI: 5.71-5.89). Under the current intervention before 31 January, the number of daily confirmed infected cases is expected to peak on around 11 February 2020 with the size of 4066 (95%CI: 3898-4472). The infection of COVID-19 might be controlled approximately after 18 May 2020. Reducing contact and increasing trace about the risk population are likely to be the present effective measures.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Biológicos , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Algoritmos , Número Básico de Reprodução/estatística & dados numéricos , China/epidemiologia , Simulação por Computador , Busca de Comunicante/estatística & dados numéricos , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Epidemias/prevenção & controle , Epidemias/estatística & dados numéricos , Mapeamento Geográfico , Humanos , Cadeias de Markov , Conceitos Matemáticos , Método de Monte Carlo , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Quarentena/estatística & dados numéricos
5.
Math Biosci Eng ; 17(4): 3618-3636, 2020 05 13.
Artigo em Inglês | MEDLINE | ID: mdl-32987547

RESUMO

A new COVID-19 epidemic model with media coverage and quarantine is constructed. The model allows for the susceptibles to the unconscious and conscious susceptible compartment. First, mathematical analyses establish that the global dynamics of the spread of the COVID-19 infectious disease are completely determined by the basic reproduction number R0. If R0 ≤ 1, then the disease free equilibrium is globally asymptotically stable. If R0 > 1, the endemic equilibrium is globally asymptotically stable. Second, the unknown parameters of model are estimated by the MCMC algorithm on the basis of the total confirmed new cases from February 1, 2020 to March 23, 2020 in the UK. We also estimate that the basic reproduction number is R0 = 4.2816(95%CI: (3.8882, 4.6750)). Without the most restrictive measures, we forecast that the COVID-19 epidemic will peak on June 2 (95%CI: (May 23, June 13)) (Figure 3a) and the number of infected individuals is more than 70% of UK population. In order to determine the key parameters of the model, sensitivity analysis are also explored. Finally, our results show reducing contact is effective against the spread of the disease. We suggest that the stringent containment strategies should be adopted in the UK.


Assuntos
Betacoronavirus , Meios de Comunicação , Infecções por Coronavirus/epidemiologia , Pandemias , Pneumonia Viral/epidemiologia , Quarentena , Algoritmos , Número Básico de Reprodução/estatística & dados numéricos , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Humanos , Cadeias de Markov , Conceitos Matemáticos , Modelos Biológicos , Método de Monte Carlo , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Fatores de Tempo , Reino Unido/epidemiologia
6.
Math Biosci Eng ; 17(4): 3637-3648, 2020 05 18.
Artigo em Inglês | MEDLINE | ID: mdl-32987548

RESUMO

Based on the reported data from February 16, 2020 to March 9, 2020 in South Korea including confirmed cases, death cases and recovery cases, the control reproduction number was estimated respectively at different control measure phases using Markov chain Monte Carlo method and presented using the resulting posterior mean and 95% credible interval (CrI). At the early phase from February 16 to February 24, we estimate the basic reproduction number R0 of COVID-19 to be 4.79(95% CrI 4.38 - 5.2). The estimated control reproduction number dropped rapidly to Rc ≈ 0.32(95% CrI 0.19 - 0.47) at the second phase from February 25 to March 2 because of the voluntary lockdown measures. At the third phase from March 3 to March 9, we estimate Rc to be 0.27 (95% CrI 0.14 - 0.42). We predict that the final size of the COVID-19 outbreak in South Korea is 9661 (95% CrI 8660 - 11100) and the whole epidemic will be over by late April. It is found that reducing contact rate and enhancing the testing speed will have the impact on the peak value and the peak time.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Pandemias , Pneumonia Viral/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , Simulação por Computador , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Surtos de Doenças/prevenção & controle , Surtos de Doenças/estatística & dados numéricos , Humanos , Cadeias de Markov , Conceitos Matemáticos , Modelos Biológicos , Método de Monte Carlo , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , República da Coreia/epidemiologia , Fatores de Tempo
7.
Math Biosci Eng ; 17(4): 3710-3720, 2020 05 21.
Artigo em Inglês | MEDLINE | ID: mdl-32987551

RESUMO

Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22, 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation. In this paper, we developed a SEIQR difference-equation model of COVID-19 that took into account the transmission with discrete time imported cases, to perform assessment and risk analysis. Our findings suggest that if the lock-down date in Wuhan is earlier, the infectious cases are fewer. Moreover, we reveal the effects of city lock-down date on the final scale of cases: if the date is advanced two days, the cases may decrease one half (67, 95% CI: 66-68); if the date is delayed for two days, the cases may reach about 196 (95% CI: 193-199). Our investigation model could be potentially helpful to study the transmission of COVID-19, in other provinces of China except Hubei. Especially, the method may also be used in countries with the first confirmed case is imported.


Assuntos
Betacoronavirus , Infecções por Coronavirus/transmissão , Modelos Biológicos , Pandemias , Pneumonia Viral/transmissão , Número Básico de Reprodução/estatística & dados numéricos , China/epidemiologia , Simulação por Computador , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Humanos , Cadeias de Markov , Conceitos Matemáticos , Método de Monte Carlo , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Quarentena/estatística & dados numéricos , Fatores de Tempo , Viagem/estatística & dados numéricos
8.
Comput Math Methods Med ; 2020: 9391251, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32908584

RESUMO

In this paper, a utility-based multicriteria model is proposed to support the physicians to deal with an important medical decision-the screening decision problem-given the squeeze put on resources due to the COVID-19 pandemic. Since the COVID-19 emerged, the number of patients with an acute respiratory failure has increased in the health units. This chaotic situation has led to a deficiency in health resources. Thus, this study, using the concepts of the multiattribute utility theory (MAUT), puts forward a mathematical model to aid physicians in the screening decision problem. The model is used to generate which of the three alternatives is the best one for where patients with suspected COVID-19 should be treated, namely, an intensive care unit (ICU), a hospital ward, or at home in isolation. Also, a decision information system, called SIDTriagem, is constructed and illustrated to operate the mathematical model proposed.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/diagnóstico , Pandemias , Pneumonia Viral/diagnóstico , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Cuidados Críticos , Tomada de Decisões Assistida por Computador , Técnicas de Apoio para a Decisão , Serviços de Assistência Domiciliar , Hospitalização , Humanos , Programas de Rastreamento , Conceitos Matemáticos , Método de Monte Carlo , Isolamento de Pacientes , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Triagem/métodos
9.
PLoS One ; 15(9): e0237627, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32877420

RESUMO

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.


Assuntos
Infecções por Coronavirus/patologia , Modelos Teóricos , Pneumonia Viral/patologia , Fatores Etários , Betacoronavirus/isolamento & purificação , Brasil/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Infecções por Coronavirus/virologia , Humanos , Cadeias de Markov , Método de Monte Carlo , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Pneumonia Viral/virologia , Quarentena
10.
J Korean Med Sci ; 35(35): e321, 2020 Sep 07.
Artigo em Inglês | MEDLINE | ID: mdl-32893522

RESUMO

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed significant global public health challenges and created a substantial economic burden. Korea has experienced an extensive outbreak, which was linked to a religion-related super-spreading event. However, the implementation of various non-pharmaceutical interventions (NPIs), including social distancing, spring semester postponing, and extensive testing and contact tracing controlled the epidemic. Herein, we estimated the effectiveness of each NPI using a simulation model. METHODS: A compartment model with a susceptible-exposed-infectious-quarantined-hospitalized structure was employed. Using the Monte-Carlo-Markov-Chain algorithm with Gibbs' sampling method, we estimated the time-varying effective contact rate to calibrate the model with the reported daily new confirmed cases from February 12th to March 31st (7 weeks). Moreover, we conducted scenario analyses by adjusting the parameters to estimate the effectiveness of NPI. RESULTS: Relaxed social distancing among adults would have increased the number of cases 27.4-fold until the end of March. Spring semester non-postponement would have increased the number of cases 1.7-fold among individuals aged 0-19, while lower quarantine and detection rates would have increased the number of cases 1.4-fold. CONCLUSION: Among the three NPI measures, social distancing in adults showed the highest effectiveness. The substantial effect of social distancing should be considered when preparing for the 2nd wave of COVID-19.


Assuntos
Controle de Doenças Transmissíveis/métodos , Busca de Comunicante/métodos , Infecções por Coronavirus/transmissão , Programas de Rastreamento/métodos , Pneumonia Viral/transmissão , Distância Social , Betacoronavirus , Simulação por Computador , Exposição Ambiental/prevenção & controle , Humanos , Cadeias de Markov , Modelos Teóricos , Método de Monte Carlo , Pandemias , Prática de Saúde Pública/legislação & jurisprudência , República da Coreia
11.
Clin Imaging ; 67: 226-236, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32871427

RESUMO

PURPOSE: Digital radiography has the potential to improve the practice of radiography but it also has the potential to significantly increase patient doses. Considering rapidly growing digital radiography in many centers, concerns rise about increasing the collective dose of the human population and following health effects. This study aimed to estimate organ and effective doses and calculate the lifetime attributable risk (LAR) of cancer incidence and mortality in digital radiography procedures in Iran. METHODS: Organ and effective doses of 12 routine digital radiography examinations including the skull, cervical spine, chest, thoracic spine, lumbar spine, pelvic and abdomen were estimated using PCXMC software based on Monte Carlo simulation method. Then, LARs of cancer incidence and mortality were estimated using the BEIR VII method. RESULTS: Organ doses ranged from 0.01 to a maximum of 2.5 mGy while effective doses ranged from 0.01 to 0.7 mSv. Radiation risk showed dependence on the X-ray examination type and the patient's sex and age. In skull and cervical X-rays, the thyroid; in the chest and thoracic spine X-rays, the lung, and breast; and in the lumbar spine, pelvic and abdominal X-rays, the colon and bladder had the highest LAR of cancer incidence and mortality. Furthermore, younger patients and also females were at higher radiation risk. CONCLUSION: The lifetime attributable risk of cancer incidence and mortality due to radiation exposure is not trivial. Therefore efforts should be made to reduce patient doses while maintaining image quality.


Assuntos
Neoplasias Induzidas por Radiação/epidemiologia , Abdome , Mama , Feminino , Humanos , Incidência , Masculino , Método de Monte Carlo , Pescoço , Neoplasias Induzidas por Radiação/etiologia , Pelve , Doses de Radiação , Intensificação de Imagem Radiográfica , Radiografia , Fatores de Risco , Software , Coluna Vertebral , Tórax
12.
SAR QSAR Environ Res ; 31(9): 697-715, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32878494

RESUMO

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF1) and balance of correlation with IIC (TF2), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF2). The model developed with TF2 for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r 2 = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.


Assuntos
Compostos Azo/química , Celulose/química , Corantes/química , Relação Quantitativa Estrutura-Atividade , Adsorção , Simulação por Computador , Método de Monte Carlo
13.
BMC Bioinformatics ; 21(1): 375, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32859148

RESUMO

BACKGROUND: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. RESULTS: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. CONCLUSIONS: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.


Assuntos
RNA/química , Análise de Sequência de RNA/métodos , Interface Usuário-Computador , Teorema de Bayes , Humanos , Método de Monte Carlo , RNA/genética , RNA/metabolismo , Tuberculose/genética , Tuberculose/patologia
14.
Aquat Toxicol ; 227: 105589, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32841884

RESUMO

Pesticides have an impact on the aquatic environment, with ecological effects. The regulation of this impact is of key importance. One of the components of the planning of agricultural and industrial activities is the development of databases and models in order to identify substances that may cause damage. In this study, a quantitative structure-activity relationship (QSAR) approach was established for the prediction of acute toxicity toward rainbow trout of various pesticides. The so-called index of ideality of correlation is the main component of this approach. The validation of this approach has been carried out with three random splits into the training and validation sets. The range of statistical quality of models obtained here for the validation set is R2 = [0.81-0.86] and RMSE = [0.55-0.65].


Assuntos
Modelos Teóricos , Oncorhynchus mykiss , Praguicidas/toxicidade , Poluentes Químicos da Água/toxicidade , Animais , Bases de Dados Factuais , Método de Monte Carlo , Praguicidas/química , Relação Quantitativa Estrutura-Atividade , Poluentes Químicos da Água/química
15.
PLoS One ; 15(8): e0237462, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32853259

RESUMO

In the present study, a new class of heavy tailed distributions using the T-X family approach is introduced. The proposed family is called type-I heavy tailed family. A special model of the proposed class, named Type-I Heavy Tailed Weibull (TI-HTW) model is studied in detail. We adopt the approach of maximum likelihood estimation for estimating its parameters, and assess the maximum likelihood performance based on biases and mean squared errors via a Monte Carlo simulation framework. Actuarial quantities such as value at risk and tail value at risk are derived. A simulation study for these actuarial measures is conducted, proving that the proposed TI-HTW is a heavy-tailed model. Finally, we provide a comparative study to illustrate the proposed method by analyzing three real data sets from different disciplines such as reliability engineering, bio-medical and financial sciences. The analytical results of the new TI-HTW model are compared with the Weibull and some other non-nested distributions. The Baysesian analysis is discussed to measure the model complexity based on the deviance information criterion.


Assuntos
Modelos Estatísticos , Economia , Engenharia , Funções Verossimilhança , Método de Monte Carlo , Farmacologia , Projetos de Pesquisa
16.
Sci Rep ; 10(1): 13120, 2020 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-32753639

RESUMO

The coronavirus disease 2019 (COVID-19) has now spread throughout most countries in the world causing heavy life losses and damaging social-economic impacts. Following a stochastic point process modelling approach, a Monte Carlo simulation model was developed to represent the COVID-19 spread dynamics. First, we examined various expected performances (theoretical properties) of the simulation model assuming a number of arbitrarily defined scenarios. Simulation studies were then performed on the real COVID-19 data reported (over the period of 1 March to 1 May) for Australia and United Kingdom (UK). Given the initial number of COVID-19 infection active cases were around 10 for both countries, the model estimated that the number of active cases would peak around 29 March in Australia (≈ 1,700 cases) and around 22 April in UK (≈ 22,860 cases); ultimately the total confirmed cases could sum to 6,790 for Australia in about 75 days and 206,480 for UK in about 105 days. The results of the estimated COVID-19 reproduction numbers were consistent with what was reported in the literature. This simulation model was considered an effective and adaptable decision making/what-if analysis tool in battling COVID-19 in the immediate need, and for modelling any other infectious diseases in the future.


Assuntos
Infecções por Coronavirus/patologia , Método de Monte Carlo , Pneumonia Viral/patologia , Austrália/epidemiologia , Betacoronavirus/isolamento & purificação , Betacoronavirus/fisiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Infecções por Coronavirus/virologia , Humanos , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Pneumonia Viral/virologia , Reino Unido/epidemiologia
17.
Spat Spatiotemporal Epidemiol ; 34: 100354, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32807396

RESUMO

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.


Assuntos
Doenças Transmissíveis Emergentes/epidemiologia , Infecções por Coronavirus/epidemiologia , Surtos de Doenças/estatística & dados numéricos , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Síndrome Respiratória Aguda Grave/epidemiologia , Infecções por Coronavirus/diagnóstico , Bases de Dados Factuais , Feminino , Humanos , Masculino , Programas de Rastreamento/métodos , Modelos Estatísticos , Método de Monte Carlo , Pneumonia Viral/diagnóstico , Distribuição de Poisson , Prevalência , Estudos Prospectivos , Saúde Pública , Síndrome Respiratória Aguda Grave/diagnóstico , Conglomerados Espaço-Temporais , Estados Unidos/epidemiologia
18.
J Infect Dev Ctries ; 14(7): 713-720, 2020 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-32794459

RESUMO

INTRODUCTION: There are significant differences in the active cases and fatality rates of Covid-19 for different European countries. METHODOLOGY: The present study employs Monte Carlo based transmission growth simulations for Italy, Germany and Turkey. The probabilities of transmission at home, work and social networks and the number of initial cases have been calibrated to match the basic reproduction number and the reported fatality curves. Parametric studies were conducted to observe the effect of social distancing, work closure, testing and quarantine of the family and colleagues of positively tested individuals. RESULTS: It is observed that estimates of the number of initial cases in Italy compared to Turkey and Germany are higher. Turkey will probably experience about 30% less number of fatalities than Germany due its smaller elderly population. If social distancing and work contacts are limited to 25% of daily routines, Germany and Turkey may limit the number of fatalities to a few thousands as the reproduction number decreases to about 1.3 from 2.8. Random testing may reduce the number of fatalities by 10% upon testing least 5/1000 of the population. Quarantining of family and workmates of positively tested individuals may reduce the total number of fatalities by about 50%. CONCLUSIONS: The fatality rate of Covid-19 is estimated to be about 1.5% based on the simulation results. This may further be reduced by limiting the number of non-family contacts to two, conducting tests more than 0.5% of the population and immediate quarantine of the contacts for positively tested individuals.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Quarentena , Adolescente , Adulto , Distribuição por Idade , Idoso , Número Básico de Reprodução , Criança , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/mortalidade , Características da Família , Alemanha/epidemiologia , Humanos , Itália/epidemiologia , Pessoa de Meia-Idade , Método de Monte Carlo , Pandemias , Pneumonia Viral/diagnóstico , Pneumonia Viral/mortalidade , Isolamento Social , Rede Social , Turquia/epidemiologia , Adulto Jovem
19.
PLoS One ; 15(8): e0236954, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32760106

RESUMO

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms.


Assuntos
Algoritmos , Teorema de Bayes , Biometria/métodos , Fenômenos Bioquímicos , Simulação por Computador , Funções Verossimilhança , Cadeias de Markov , Redes e Vias Metabólicas , Modelos Biológicos , Modelos Estatísticos , Método de Monte Carlo , Análise de Regressão , Processos Estocásticos
20.
PLoS One ; 15(8): e0236466, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32764764

RESUMO

AIM: The present work concerns the comparison of the performances of three systems for dosimetry in RPT that use different techniques for absorbed dose calculation (organ-level dosimetry, voxel-level dose kernel convolution and Monte Carlo simulations). The aim was to assess the importance of the choice of the most adequate calculation modality, providing recommendations about the choice of the computation tool. METHODS: The performances were evaluated both on phantoms and patients in a multi-level approach. Different phantoms filled with a 177Lu-radioactive solution were used: a homogeneous cylindrical phantom, a phantom with organ-shaped inserts and two cylindrical phantoms with inserts different for shape and volume. A total of 70 patients with NETs treated by PRRT with 177Lu-DOTATOC were retrospectively analysed. RESULTS: The comparisons were performed mainly between the mean values of the absorbed dose in the regions of interest. A general better agreement was obtained between Dose kernel convolution and Monte Carlo simulations results rather than between either of these two and organ-level dosimetry, both for phantoms and patients. Phantoms measurements also showed the discrepancies mainly depend on the geometry of the inserts (e.g. shape and volume). For patients, differences were more pronounced than phantoms and higher inter/intra patient variability was observed. CONCLUSION: This study suggests that voxel-level techniques for dosimetry calculation are potentially more accurate and personalized than organ-level methods. In particular, a voxel-convolution method provides good results in a short time of calculation, while Monte Carlo based computation should be conducted with very fast calculation systems for a possible use in clinics, despite its intrinsic higher accuracy. Attention to the calculation modality is recommended in case of clinical regions of interest with irregular shape and far from spherical geometry, in which Monte Carlo seems to be more accurate than voxel-convolution methods.


Assuntos
Lutécio/química , Imagens de Fantasmas/estatística & dados numéricos , Radioisótopos/química , Radiometria/estatística & dados numéricos , Receptores de Peptídeos/isolamento & purificação , Algoritmos , Humanos , Método de Monte Carlo , Doses de Radiação , Receptores de Peptídeos/química , Estudos Retrospectivos
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