Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 36.049
Filtrar
1.
Artigo em Inglês | MEDLINE | ID: mdl-34063533

RESUMO

Occupational and non-occupational risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported in healthcare workers (HCWs), but studies evaluating risk factors for infection among physician trainees are lacking. We aimed to identify sociodemographic, occupational, and community risk factors among physician trainees during the first wave of coronavirus disease 2019 (COVID-19) in New York City. In this retrospective study of 328 trainees at the Mount Sinai Health System in New York City, we administered a survey to assess risk factors for SARS-CoV-2 infection between 1 February and 30 June 2020. SARS-CoV-2 infection was determined by self-reported and laboratory-confirmed IgG antibody and reverse transcriptase-polymerase chain reaction test results. We used Bayesian generalized linear mixed effect regression to examine associations between hypothesized risk factors and infection odds. The cumulative incidence of infection was 20.1%. Assignment to medical-surgical units (OR, 2.51; 95% CI, 1.18-5.34), and training in emergency medicine, critical care, and anesthesiology (OR, 2.93; 95% CI, 1.24-6.92) were independently associated with infection. Caring for unfamiliar patient populations was protective (OR, 0.16; 95% CI, 0.03-0.73). Community factors were not statistically significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for physician trainees responding to the COVID-19 pandemic.


Assuntos
Médicos , Teorema de Bayes , Pessoal de Saúde , Humanos , Cidade de Nova Iorque/epidemiologia , Pandemias , Estudos Retrospectivos
2.
BMC Genomics ; 22(1): 442, 2021 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-34118867

RESUMO

BACKGROUND: Rutabaga or swede (Brassica napus ssp. napobrassica (L.) Hanelt) varies in root and leaf shape and colour, flesh colour, foliage growth habits, maturity date, seed quality parameters, disease resistance and other traits. Despite these morphological differences, no in-depth molecular analyses of genetic diversity have been conducted in this crop. Understanding this diversity is important for conservation and broadening the use of this resource. RESULTS: This study investigated the genetic diversity within and among 124 rutabaga accessions from five Nordic countries (Norway, Sweden, Finland, Denmark and Iceland) using a 15 K single nucleotide polymorphism (SNP) Brassica array. After excluding markers that did not amplify genomic DNA, monomorphic and low coverage site markers, the accessions were analyzedwith 6861 SNP markers. Allelic frequency statistics, including polymorphism information content (PIC), minor allele frequency (MAF) and mean expected heterozygosity ([Formula: see text]e) and population differentiation statistics such as Wright's F-statistics (FST) and analysis of molecular variance (AMOVA) indicated that the rutabaga accessions from Norway, Sweden, Finland and Denmark were not genetically different from each other. In contrast, accessions from these countries were significantly different from the accessions from Iceland (P < 0.05). Bayesian analysis with the software STRUCTURE placed 66.9% of the rutabaga accessions into three to four clusters, while the remaining 33.1% constituted admixtures. Three multivariate analyses: principal coordinate analysis (PCoA), the unweighted pair group method with arithmetic mean (UPGMA) and neighbour-joining (NJ) clustering methods grouped the 124 accessions into four to six subgroups. CONCLUSION: Overall, the correlation of the accessions with their geographic origin was very low, except for the accessions from Iceland. Thus, Icelandic rutabaga accessions can offer valuable germplasm for crop improvement.


Assuntos
Brassica napus , Polimorfismo de Nucleotídeo Único , Teorema de Bayes , Finlândia , Variação Genética , Biologia Molecular , Noruega , Países Escandinavos e Nórdicos , Suécia
3.
BMJ Glob Health ; 6(6)2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-34103325

RESUMO

INTRODUCTION: COVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions. METHODS: We estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors. RESULTS: The results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda. CONCLUSION: The analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time.


Assuntos
COVID-19/transmissão , Busca de Comunicante , Teorema de Bayes , COVID-19/epidemiologia , Controle de Doenças Transmissíveis , Humanos , Masculino , Ruanda/epidemiologia , SARS-CoV-2
4.
Popul Health Metr ; 19(1): 27, 2021 05 31.
Artigo em Inglês | MEDLINE | ID: mdl-34059063

RESUMO

BACKGROUND: The number of deaths attributable to COVID-19 in Spain has been highly controversial since it is problematic to tell apart deaths having COVID as the main cause from those provoked by the aggravation by the viral infection of other underlying health problems. In addition, overburdening of health system led to an increase in mortality due to the scarcity of adequate medical care, at the same time confinement measures could have contributed to the decrease in mortality from certain causes. Our aim is to compare the number of deaths observed in 2020 with the projection for the same period obtained from a sequence of previous years. Thus, this computed mortality excess could be considered as the real impact of the COVID-19 on the mortality rates. METHODS: The population was split into four age groups, namely: (< 50; 50-64; 65-74; 75 and over). For each one, a projection of the death numbers for the year 2020, based on the interval 2008-2020, was estimated using a Bayesian spatio-temporal model. In each one, spatial, sex, and year effects were included. In addition, a specific effect of the year 2020 was added ("outbreak"). Finally, the excess deaths in year 2020 were estimated as the count of observed deaths minus those projected. RESULTS: The projected death number for 2020 was 426,970 people, the actual count being 499,104; thus, the total excess of deaths was 72,134. However, this increase was very unequally distributed over the Spanish regions. CONCLUSION: Bayesian spatio-temporal models have proved to be a useful tool for estimating the impact of COVID-19 on mortality in Spain in 2020, making it possible to assess how the disease has affected different age groups accounting for effects of sex, spatial variation between regions and time trend over the last few years.


Assuntos
COVID-19/mortalidade , Causas de Morte , Pandemias , Adulto , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Surtos de Doenças , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Biológicos , Mortalidade/tendências , SARS-CoV-2 , Espanha/epidemiologia , Análise Espaço-Temporal
5.
BMC Infect Dis ; 21(1): 533, 2021 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-34098885

RESUMO

BACKGROUND: Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). METHODS: Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. RESULTS: For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. CONCLUSIONS: In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.


Assuntos
COVID-19/epidemiologia , COVID-19/transmissão , Pandemias , Teorema de Bayes , Controle de Doenças Transmissíveis/métodos , Simulação por Computador , Previsões , Humanos , Índia/epidemiologia , Modelos Estatísticos
6.
BMC Infect Dis ; 21(1): 539, 2021 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-34098893

RESUMO

BACKGROUND: In sub-Saharan Africa, acute respiratory infections (ARI), acute gastrointestinal infections (GI) and acute febrile disease of unknown cause (AFDUC) have a large disease burden, especially among children, while respective aetiologies often remain unresolved. The need for robust infectious disease surveillance to detect emerging pathogens along with common human pathogens has been highlighted by the ongoing novel coronavirus disease 2019 (COVID-19) pandemic. The African Network for Improved Diagnostics, Epidemiology and Management of Common Infectious Agents (ANDEMIA) is a sentinel surveillance study on the aetiology and clinical characteristics of ARI, GI and AFDUC in sub-Saharan Africa. METHODS: ANDEMIA includes 12 urban and rural health care facilities in four African countries (Côte d'Ivoire, Burkina Faso, Democratic Republic of the Congo and Republic of South Africa). It was piloted in 2018 in Côte d'Ivoire and the initial phase will run from 2019 to 2021. Case definitions for ARI, GI and AFDUC were established, as well as syndrome-specific sampling algorithms including the collection of blood, naso- and oropharyngeal swabs and stool. Samples are tested using comprehensive diagnostic protocols, ranging from classic bacteriology and antimicrobial resistance screening to multiplex real-time polymerase chain reaction (PCR) systems and High Throughput Sequencing. In March 2020, PCR testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and analysis of full genomic information was included in the study. Standardised questionnaires collect relevant clinical, demographic, socio-economic and behavioural data for epidemiologic analyses. Controls are enrolled over a 12-month period for a nested case-control study. Data will be assessed descriptively and aetiologies will be evaluated using a latent class analysis among cases. Among cases and controls, an integrated analytic approach using logistic regression and Bayesian estimation will be employed to improve the assessment of aetiology and associated risk factors. DISCUSSION: ANDEMIA aims to expand our understanding of ARI, GI and AFDUC aetiologies in sub-Saharan Africa using a comprehensive laboratory diagnostics strategy. It will foster early detection of emerging threats and continued monitoring of important common pathogens. The network collaboration will be strengthened and site diagnostic capacities will be reinforced to improve quality management and patient care.


Assuntos
Doenças Transmissíveis/diagnóstico , Doenças Transmissíveis/epidemiologia , Programas de Rastreamento , Vigilância de Evento Sentinela , Teorema de Bayes , Burkina Faso , Estudos de Casos e Controles , Costa do Marfim , República Democrática do Congo , Febre/epidemiologia , Febre/microbiologia , Gastroenteropatias/epidemiologia , Gastroenteropatias/microbiologia , Humanos , Reação em Cadeia da Polimerase em Tempo Real , Infecções Respiratórias/epidemiologia , África do Sul
7.
Sci Rep ; 11(1): 11400, 2021 05 31.
Artigo em Inglês | MEDLINE | ID: mdl-34059775

RESUMO

An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection-even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay-based on sequential serology-that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines.


Assuntos
Anticorpos Antivirais/imunologia , Linfócitos B/imunologia , COVID-19/imunologia , SARS-CoV-2/imunologia , Linfócitos T/imunologia , Formação de Anticorpos , Teorema de Bayes , Simulação por Computador , Reações Cruzadas/imunologia , Humanos , Modelos Imunológicos , SARS-CoV-2/patogenicidade , Carga Viral
8.
Artigo em Inglês | MEDLINE | ID: mdl-34073448

RESUMO

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.


Assuntos
COVID-19 , Teorema de Bayes , Estudos Transversais , Humanos , SARS-CoV-2 , Rede Social
9.
Nat Commun ; 12(1): 3289, 2021 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078897

RESUMO

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.


Assuntos
Tronco Encefálico/patologia , AVC Isquêmico/patologia , Córtex Sensório-Motor/patologia , Tálamo/patologia , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Mapeamento Encefálico , Tronco Encefálico/irrigação sanguínea , Tronco Encefálico/diagnóstico por imagem , Revascularização Cerebral/métodos , Estudos de Coortes , Feminino , Humanos , Processamento de Imagem Assistida por Computador , AVC Isquêmico/diagnóstico por imagem , AVC Isquêmico/terapia , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Fatores de Risco , Córtex Sensório-Motor/irrigação sanguínea , Córtex Sensório-Motor/diagnóstico por imagem , Índice de Gravidade de Doença , Fatores Sexuais , Tálamo/irrigação sanguínea , Tálamo/diagnóstico por imagem , Resultado do Tratamento
10.
Exp Appl Acarol ; 84(2): 473-484, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34089463

RESUMO

In total, 57 ticks were collected from six white-tailed deer (Odocoileus virginianus) and three mule deer (O. hemionus) in northern Mexico during the 2017, 2018 and 2019 hunting seasons. Morphological features of adult male and female ticks were observed and photographed using a stereo-microscope and scanning electron micrography. The ticks were identified as Dermacentor albipictus based on taxonomic keys. Molecular analysis using DNA amplification of the 16S rDNA and cytochrome oxidase 1 (COI) genes was employed to resolve the phylogenetic relationships from 18 strains of Dermacentor species. Bayesian phylogenetic analysis was performed in order to obtain a phylogenetic tree based on the concatenated sequence in the D. albipictus clade. The geometric morphometric analysis compared the body shape of ticks collected from specimens of two deer species by analyzing nine dorsal and ventral landmarks from both males and females. The results suggest that body shape variation in dorsal structures might be related to the host.


Assuntos
Cervos , Dermacentor , Animais , Teorema de Bayes , Feminino , Masculino , México , Filogenia
11.
Environ Monit Assess ; 193(7): 402, 2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-34109456

RESUMO

The disordered growth of large cities around water bodies causes environmental damage due to discarded plastics and microplastics (MPs) that aquatic organisms can ingest. This study analyzed the occurrence, type, and abundance of MPs in the gastrointestinal contents of four species of commercial fish (120 total specimens), namely, Brazilian mojarra (Eugerres brasilianus) and mullets (Mugil curema, Mugil curvidens, and Mugil liza), obtained in Porto Seguro in Bahia, Brazil, between March and May 2019. A priori probability distributions were generated using a Bayesian approach and simulations to assess MP intake based on varying exposure amounts (θ = 0.2, θ = 0.5, and θ = 0.8). E. brasilianus (53.33%) and Mugil spp. (41.66%) were contaminated with some types of MPs. Black, blue, and green MPs dominated in the extracted samples, and most measured 1.0 mm in length or smaller. The dominant polymers identified using Fourier transform infrared spectroscopy (FTIR) were polyester, polypropylene, semi-synthetic rayon fiber, and polyamide 6 (nylon). The a posteriori probabilities of more than half the E. brasilianus and Mugil spp. ingesting MPs were 0.336 and 0.008, respectively, indicating that E. brasilianus is much more likely to ingest MPs. These simulations can be useful tools for assessing the environmental quality and local anthropic impact of MP ingestion by fish populations.


Assuntos
Microplásticos , Poluentes Químicos da Água , Animais , Teorema de Bayes , Brasil , Monitoramento Ambiental , Plásticos , Poluentes Químicos da Água/análise
12.
Sensors (Basel) ; 21(9)2021 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-34068804

RESUMO

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.


Assuntos
Acidentes por Quedas , Dispositivos Eletrônicos Vestíveis , Acidentes por Quedas/prevenção & controle , Algoritmos , Teorema de Bayes , Humanos , Projetos Piloto , Adulto Jovem
13.
BMC Bioinformatics ; 22(Suppl 6): 194, 2021 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078269

RESUMO

BACKGROUND: Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. RESULTS: In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. CONCLUSIONS: We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus .


Assuntos
Vírus , Algoritmos , Teorema de Bayes , Humanos , Metagenômica
14.
Sci Rep ; 11(1): 11485, 2021 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-34075094

RESUMO

In the wake of the COVID-19 pandemic, it has been mandated to keep enlarged distances from others. We interviewed 136 German subjects over five weeks from the end of March to the end of April 2020 during the first wave of infections about their preferred interpersonal distance (IPD) before, during, and after the COVID-19 pandemic. In response to the pandemic, subjects adapted to distance requirements and preferred a larger IPD. This enlarged IPD was judged to partially persist after the pandemic crisis. People anticipated keeping more IPD to others even if there was no longer any risk of a SARS-CoV-2 infection. We also sampled two follow-up measurements, one in August, after the first wave of infections had been flattened, and one in October 2020, at the beginning of the second wave. Here, we observed that IPD varied with the incidence of SARS-CoV-2 within Germany. Overall, our data indicated that adaptation to distance requirements might happen asymmetrically. Preferred IPD rapidly adapted in response to distance requirements, but an enlargement of IPD may partially linger after the COVID-19 pandemic-crisis. We discuss our findings in light of proxemic theory and as an indicator for socio-cultural adaptation beyond the course of the pandemic.


Assuntos
Adaptação Psicológica , COVID-19/psicologia , Distanciamento Físico , Isolamento Social/psicologia , Adolescente , Adulto , Teorema de Bayes , COVID-19/epidemiologia , COVID-19/prevenção & controle , COVID-19/transmissão , Medo , Feminino , Seguimentos , Alemanha/epidemiologia , Humanos , Incidência , Masculino , Modelos Psicológicos , Pandemias/prevenção & controle , Estigma Social , Inquéritos e Questionários/estatística & dados numéricos , Fatores de Tempo , Adulto Jovem
15.
BMC Public Health ; 21(1): 1037, 2021 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078329

RESUMO

BACKGROUND: To assess if physical distancing measures to control the COVID-19 pandemic can be relaxed, one of the key indicators used is the reproduction number R. Many developing countries, however, have limited capacities to estimate R accurately. This study aims to demonstrate how health production function can be used to assess the state of COVID-19 transmission and to determine a risk-based relaxation policy. METHODS: The author employs a simple "bridge" between epidemiological models and production economics to establish the cumulative number of COVID-19 cases as a short-run total product function and to derive the corresponding marginal product, average product, and production elasticity. Three crucial dates defining the states of transmission, labelled red, yellow, and green zones, are determined. Relaxation policy is illogical in the "red zone" and is not recommended in the "yellow zone". In the "green zone", relaxation can be considered. The Bayesian probability of near term's daily cases meeting a policy target is computed. The method is applied to France, Germany, Italy, the UK, and the US, and to Indonesia as an example of application in developing countries. RESULTS: This study uses data from the WHO COVID-19 Dashboard, beginning from the first recording date for each country until February 28, 2021. As of June 30, 2020, France, Germany, Italy, and the UK had arrived at the "green zone" but with a high risk of transmission re-escalations. In the following weeks, their production elasticities were rising, giving a signal of accelerated transmissions. The signal was corroborated by these countries' rising cases, making them leaving the "green zone" in the later months. By February 28, 2021, the UK had returned to the "green zone", France, Germany, and Italy were still in the "yellow zone", while the US reached the "green zone" at a very high number of cases. Despite being in the "red zone", Indonesia relaxed its distancing measures, causing a sharp rise of cases. CONCLUSIONS: Health production function can show the state of COVID-19 transmission. A rising production elasticity gives an early warning of transmission escalations. The elasticity is a useful parameter for risk-based relaxation policy.


Assuntos
Pandemias , Teorema de Bayes , França , Alemanha , Política de Saúde , Humanos , Indonésia , Itália , Políticas
16.
BMC Public Health ; 21(1): 935, 2021 05 17.
Artigo em Inglês | MEDLINE | ID: mdl-34001089

RESUMO

BACKGROUND: Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food insecurity and the causal relationships between them is an important scientific question. We aim to construct a Bayesian belief network model of food security in rural South Africa to act as a tool for decision support in the design of interventions. METHODS: Here, we use data from the Agincourt Health and Socio-demographic Surveillance System (HDSS) study area, which is close to the Mozambique border in a low-income region of South Africa, together with Bayesian belief network (BBN) methodology to address this question. RESULTS: We find that a combination of expert elicitation and learning from data produces the most credible set of causal relationships, as well as the greatest predictive performance with 10-fold cross validation resulting in a Briers score 0.0846, information reward of 0.5590, and Bayesian information reward of 0.0057. We report the resulting model as a directed acyclic graph (DAG) that can be used to model the expected effects of complex interventions to improve food security. Applications to sensitivity analyses and interventional simulations show ways the model can be applied as tool for decision support for human experts in deciding on interventions. CONCLUSIONS: The resulting models can form the basis of the iterative generation of a robust causal model of household food security in the Agincourt HDSS study area and in other similar populations.


Assuntos
População Rural , Teorema de Bayes , Abastecimento de Alimentos , Humanos , Moçambique , África do Sul
17.
Risk Anal ; 41(5): 710-720, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33942351

RESUMO

Human challenge trials (HCTs) are a potential method to accelerate development of vaccines and therapeutics. However, HCTs for COVID-19 pose ethical and practical challenges, in part due to the unclear and developing risks. In this article , we introduce an interactive model for exploring some risks of a severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) dosing study, a prerequisite for any COVID-19 challenge trials. The risk estimates we use are based on a Bayesian evidence synthesis model which can incorporate new data on infection fatality risks (IFRs) to patients, and infer rates of hospitalization. The model estimates individual risk, which we then extrapolate to overall mortality and hospitalization risk in a dosing study. We provide a web tool to explore risk under different study designs. Based on the Bayesian model, IFR for someone between 20 and 30 years of age is 15.1 in 100,000, with a 95% uncertainty interval from 11.8 to 19.2, while risk of hospitalization is 130 per 100,000 (100-160). However, risk will be reduced in an HCT via screening for comorbidities, selecting lower-risk population, and providing treatment. Accounting for this with stronger assumptions, we project the fatality risk to be as low as 2.5 per 100,000 (1.6-3.9) and the hospitalization risk to be 22.0 per 100,000 (14.0-33.7). We therefore find a 50-person dosing trial has a 99.74% (99.8-99.9%) chance of no fatalities, and a 98.9% (98.3-99.3%) probability of no cases requiring hospitalization.


Assuntos
/transmissão , Medição de Risco , Antivirais/uso terapêutico , Teorema de Bayes , /terapia , /uso terapêutico , Ética em Pesquisa , Humanos , /isolamento & purificação
18.
Dis Markers ; 2021: 5522729, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33968281

RESUMO

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.


Assuntos
/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Aprendizado de Máquina , Tomografia Computadorizada por Raios X/métodos , Teorema de Bayes , Estudos de Casos e Controles , Infecções Comunitárias Adquiridas/diagnóstico por imagem , Árvores de Decisões , Humanos , Modelos Estatísticos , Pneumonia/diagnóstico por imagem , Sensibilidade e Especificidade
19.
BMJ Open ; 11(3): e041418, 2021 03 08.
Artigo em Inglês | MEDLINE | ID: mdl-34006022

RESUMO

INTRODUCTION: Antimicrobial resistance (AMR) is a global health threat that requires urgent research using a multidisciplinary approach. The biological drivers of AMR are well understood, but factors related to treatment seeking and the social contexts of antibiotic (AB) use behaviours are less understood. Here we describe the Holistic Approach to Unravel Antibacterial Resistance in East Africa, a multicentre consortium that investigates the diverse drivers of drug resistance in urinary tract infections (UTIs) in East Africa. METHODS AND ANALYSIS: This study will take place in Uganda, Kenya and Tanzania. We will conduct geospatial mapping of AB sellers, and conduct mystery client studies and in-depth interviews (IDIs) with drug sellers to investigate AB provision practices. In parallel, we will conduct IDIs with doctors, alongside community focus groups. Clinically diagnosed patients with UTI will be recruited from healthcare centres, provide urine samples and complete a questionnaire capturing retrospective treatment pathways, sociodemographic characteristics, attitudes and knowledge. Bacterial isolates from urine and stool samples will be subject to culture and antibiotic sensitivity testing. Genomic DNA from bacterial isolates will be extracted with a subset being sequenced. A follow-up household interview will be conducted with 1800 UTI-positive patients, where further environmental samples will be collected. A subsample of patients will be interviewed using qualitative tools. Questionnaire data, microbiological analysis and qualitative data will be linked at the individual level. Quantitative data will be analysed using statistical modelling, including Bayesian network analysis, and all forms of qualitative data analysed through iterative thematic content analysis. ETHICS AND DISSEMINATION: Approvals have been obtained from all national and local ethical review bodies in East Africa and the UK. Results will be disseminated in communities, with local and global policy stakeholders, and in academic circles. They will have great potential to inform policy, improve clinical practice and build regional pathogen surveillance capacity.


Assuntos
Antibacterianos , Farmacorresistência Bacteriana , Antibacterianos/uso terapêutico , Teorema de Bayes , Estudos Transversais , Humanos , Quênia , Estudos Retrospectivos , Tanzânia , Uganda/epidemiologia
20.
J Anim Sci ; 99(5)2021 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-33939812

RESUMO

Automatic feeding systems in pig production allow for the recording of individual feeding behavior traits, which might be influenced by the social interactions among individuals. This study fitted mixed models to estimate the direct and social effects on visit duration at the feeder of group-housed pigs. The dataset included 74,413 records of each visit duration time (min) event at the automatic feeder from 135 pigs housed in 14 pens. The sequence of visits at the feeder was employed as a proxy for the social interaction between individuals. To estimate animal effects, the direct effect was apportioned to the animal feeding (feeding pig), and the social effect was apportioned to the animal that entered the feeder immediately after the feeding pig left the feeding station (follower). The data were divided into two subsets: "non-immediate replacement" time (NIRT, N = 6,256), where the follower pig occupied the feeder at least 600 s after the feeding pig left the feeder, and "immediate replacement" time (IRT, N = 58,255), where the elapsed time between replacements was less than or equal to 60 s. The marginal posterior distribution of the parameters was obtained by Bayesian method. Using the IRT subset, the posterior mean of the proportion of variance explained by the direct effect (PrpσTemefós) was 18% for all models. The proportion of variance explained by the follower social effect (Prpσ^f2) was 2%, and the residual variance (σ^e2) decreased, suggesting an improved model fit by including the follower effect. Fitting the models with the NIRT subset, the estimate of PrpσTemefós was 20% but the Prpσ^f2 was almost zero and σ^e2 was identical for all models. For the IRT subset, the predicted best linear unbiased predictor (BLUP) of direct (Direct BLUP) and social (Follower BLUP) random effects on visit duration at the feeder of an animal was calculated. Feeder visit duration time was not correlated with traits, such as weight gain or average feed intake (P > 0.05), whereas for the daily feeder occupation time, the estimated correlation was positive with the Direct BLUP (r^ = 0.51, P < 0.05) and negative with the Follower BLUP (r^= -0.26, P < 0.05). The results suggest that the visit duration of an animal at the single-space feeder was influenced by both direct and social effects when the replacement time between visits was less than 1 min. Finally, animals that spent a longer time per day at the feeder seemed to do so by shortening the meal length of the preceding individual at the feeder.


Assuntos
Ingestão de Alimentos , Comportamento Alimentar , Ração Animal/análise , Animais , Teorema de Bayes , Suínos , Ganho de Peso
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...