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1.
Sci Rep ; 11(1): 17744, 2021 09 07.
Artigo em Inglês | MEDLINE | ID: mdl-34493760

RESUMO

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.


Assuntos
COVID-19/epidemiologia , Previsões/métodos , Modelos Estatísticos , Pandemias/estatística & dados numéricos , Interpretação Estatística de Dados , Estudos de Viabilidade , Carga Global da Doença , Produto Interno Bruto/estatística & dados numéricos , Humanos , Distribuição Normal , Densidade Demográfica
2.
Recurso na Internet em Inglês | LIS - Localizador de Informação em Saúde | ID: lis-48404

RESUMO

Advice on what to do during an earthquake varies depending on the country. For example, evacuation is not recommended within the United States. According to Geohazards International, if you currently live in Haiti and are inside your house when you feel an earthquake, this is what to do: If you are inside and can easily get out, evacuate to a safe open place covering your head and your neck. Head to an open space where walls and electric poles cannot fall on you.    If you can’t evacuate, drop where you are, cover your head and neck with one arm and get under a sturdy table, and then hold on to the table legs until the shaking stops. Stay away from landslide areas and hillsides with cracks, as aftershocks can cause new landslides and existing landslides to move again.


Assuntos
Previsões/métodos , Escala Richter , Haiti , Terremotos
3.
Sci Rep ; 11(1): 16587, 2021 08 16.
Artigo em Inglês | MEDLINE | ID: mdl-34400735

RESUMO

The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people's lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.


Assuntos
Algoritmos , COVID-19/mortalidade , Previsões/métodos , SARS-CoV-2/patogenicidade , Adolescente , Adulto , Distribuição por Idade , Idoso , Idoso de 80 Anos ou mais , COVID-19/diagnóstico , COVID-19/virologia , Teste para COVID-19/estatística & dados numéricos , Criança , Pré-Escolar , Simulação por Computador , Feminino , Humanos , Lactente , Recém-Nascido , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Pandemias/estatística & dados numéricos , Catar/epidemiologia , Medição de Risco/métodos , Medição de Risco/estatística & dados numéricos , SARS-CoV-2/isolamento & purificação , Adulto Jovem
4.
Nat Commun ; 12(1): 4720, 2021 08 05.
Artigo em Inglês | MEDLINE | ID: mdl-34354055

RESUMO

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.


Assuntos
COVID-19/epidemiologia , Controle de Doenças Transmissíveis/métodos , Aprendizado Profundo , Surtos de Doenças/prevenção & controle , Previsões/métodos , Humanos , Modelos Teóricos , SARS-CoV-2 , Espanha/epidemiologia
5.
JMIR Public Health Surveill ; 7(8): e28195, 2021 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-34346897

RESUMO

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.


Assuntos
COVID-19/terapia , Censos , Previsões/métodos , Hospitais , Modelos Teóricos , COVID-19/epidemiologia , Humanos , Incidência , Análise Multivariada , North Carolina/epidemiologia
6.
Medicine (Baltimore) ; 100(31): e26776, 2021 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-34397825

RESUMO

ABSTRACT: The coronavirus (COVID-19) disease outbreak was a public health emergency of international concern which eventually evolved into a pandemic. Nigeria was locked down in March, 2020 as the country battled to contain the spread of the disease. By August 2020, phase-by-phase easing of the lockdown was commenced and university students will soon return for academic activities. This study undertakes some epidemiological analysis of the Nigerian COVID-19 data to help the government and university administrators make informed decisions on the safety of personnel and students.The COVID-19 data on confirmed cases, deaths, and recovered were obtained from the website of the Nigerian Centre for Disease Control (NCDC) from April 2, 2020 to August 24, 2020. The infection rate, prevalence, ratio, cause-specific death rate, and case recovery rate were used to evaluate the epidemiological characteristics of the pandemic in Nigeria. Exponential smoothing was adopted in modeling the time series data and forecasting the pandemic in Nigeria up to January 31, 2021.The results indicated that the pandemic had infection rate of at most 3 infections per 1 million per day from April to August 2020. The death rate was 5 persons per 1 million during the period of study while recovery rate was 747 persons per 1000 infections. Analysis of forecast data showed steady but gradual decrease in the daily infection rate and death rate and substantial increase in the recovery rate, 975 recoveries per 1000 infections.In general, the epidemiological attributes of the pandemic from the original data and the forecast data indicated optimism in the decrease in the rate of infection and death in the future. Moreover, the infection rate, prevalence and death rate in January 2021 coincided with the predictions based on the analysis. Therefore, the Nigerian government is encouraged to allow universities in the country to reopen while university administrators set up the necessary protocols for strict adherence to safety measures.


Assuntos
Pessoal Administrativo , COVID-19/mortalidade , Previsões/métodos , COVID-19/epidemiologia , Humanos , Nigéria , Prevalência , Universidades/organização & administração , Universidades/estatística & dados numéricos
7.
J Infect Dev Ctries ; 15(7): 918-924, 2021 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-34343116

RESUMO

INTRODUCTION: The ongoing COVID-19 pandemic has claimed hundreds of thousands of lives around the world. Health planners are seeking ways to forecast the evolution of the pandemic. In this study, a mathematical model was proposed for Saudi Arabia, the country with the highest reported number of COVID-19 cases in the Arab world. METHODOLOGY: The proposed model was adapted from the model used for the Middle East respiratory syndrome outbreak in South Korea. Using time-dependent parameters, the model incorporated the effects of both population-wide self-protective measures and government actions. Data before and after the government imposed control policies on 3 March 2020 were used to validate the model. Predictions for the disease's progression were provided together with the evaluation of the effectiveness of the mitigation measures implemented by the government and self-protective measures taken by the population. RESULTS: The model predicted that, if the government had continued to implement its strong control measures, then the scale of the pandemic would have decreased by 99% by the end of June 2020. Under the current relaxed policies, the model predicted that the scale of the pandemic will have decreased by 99% by 10 August 2020. The error between the model's predictions and actual data was less than 6.5%. CONCLUSIONS: Although the proposed model did not capture all of the effects of human behaviors and government actions, it was validated as a result of its time-dependent parameters. The model's accuracy indicates that it can be used by public health policymakers.


Assuntos
COVID-19/epidemiologia , Modelos Teóricos , Saúde Pública/métodos , Previsões/métodos , Implementação de Plano de Saúde/legislação & jurisprudência , Implementação de Plano de Saúde/normas , Humanos , Saúde Pública/legislação & jurisprudência , Saúde Pública/estatística & dados numéricos , Arábia Saudita/epidemiologia
8.
PLoS One ; 16(7): e0255342, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34324554

RESUMO

INTRODUCTION: Suicide is a major social and health issue in India. Yearly statistics show a concerning increasing pattern of suicidal deaths in India which is higher in comparison to the global trend. There is limited evidence regarding historical analysis of suicide or any forecasting for suicide in India towards predicting the possible risks of death due to suicide. METHODS: This paper examines the trend of suicide rate and characteristics of suicide victims in India, based on the longitudinal time series data over the last 50 years-collected from the National Crime Record Bureau Reports (1969 to 2018) of the Government of India. In our analysis, we have used the time series model to forecast the suicide rates in India for the next decade. ARIMA (4,1,0) model is found to be the best fit model for forecasting the data. FINDINGS: There has been an observable and rising trend of suicide rates in India over the last five decades. The forecast indicates a continuance of rising suicide cases for an upcoming couple of years in India with a limited decline in the following years. The prediction model indicates a future relatively consistent pattern of suicide in India which does not seem to be a very encouraging trend. As we have not included the period staring the year 2020 onwards affected by Covid-19 and which has several disruptions in personal and family spaces, the projected suicide trend during the period of next two to three years (2020-22) may rise far high and then it may show a declining path. Along with this, there is a shift in means of suicide in the last couple of decades. Constituting the second-highest number of cases, Illness associated suicide was visibly a serious concern. CONCLUSION: The present analysis finds that there is no visible substantial relief for suicide deaths during the coming years in India. On the other hand, more extensive exploration of sample cases may provide important information for suicide prevention. Availability of detailed and more inclusive data will be highly useful for analysis and suicide preventive policies. Investment in public health care and other welfare activities like education and employment generation will yield visible positive results in suicide control.


Assuntos
Suicídio/estatística & dados numéricos , Adolescente , Adulto , COVID-19/psicologia , Escolaridade , Emprego/estatística & dados numéricos , Feminino , Previsões/métodos , Humanos , Índia , Masculino , Pessoa de Meia-Idade , Adulto Jovem
9.
Nature ; 595(7866): 181-188, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34194044

RESUMO

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.


Assuntos
Simulação por Computador , Ciência de Dados/métodos , Previsões/métodos , Modelos Teóricos , Ciências Sociais/métodos , Objetivos , Humanos
10.
Nat Commun ; 12(1): 4192, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234142

RESUMO

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.


Assuntos
Previsões/métodos , Modelos Genéticos , Herança Multifatorial , Medicina de Precisão/métodos , Característica Quantitativa Herdável , Estudos de Casos e Controles , Conjuntos de Dados como Assunto , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Tamanho da Amostra , Software
11.
Nat Commun ; 12(1): 4430, 2021 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-34285206

RESUMO

The standard measure of distance in social networks - average shortest path length - assumes a model of "simple" contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are "complex" contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.


Assuntos
Previsões/métodos , Disseminação de Informação , Modelos Teóricos , Rede Social , Simulação por Computador , Conjuntos de Dados como Assunto , Feminino , Humanos , Índia , Masculino , Grupo Associado , População Rural
12.
Sci Rep ; 11(1): 14558, 2021 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-34267263

RESUMO

Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.


Assuntos
COVID-19/virologia , SARS-CoV-2/genética , Análise de Sequência de DNA/métodos , COVID-19/epidemiologia , COVID-19/transmissão , Biologia Computacional/métodos , DNA Viral/genética , Bases de Dados Genéticas , Previsões/métodos , Genoma Viral , Humanos , Aprendizado de Máquina , Mutação , Filogenia , SARS-CoV-2/classificação , SARS-CoV-2/patogenicidade , Sequenciamento Completo do Genoma/métodos
13.
Sci Rep ; 11(1): 14523, 2021 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-34267295

RESUMO

The COVID-19 pandemic (SARS-CoV-2) has revealed the need for proactive protocols to react and act, imposing preventive and restrictive countermeasures on time in any society. The extent to which confirmed cases can predict the morbidity and mortality in a society remains an unresolved issue. The research objective is therefore to test a generic model's predictability through time, based on percentage of confirmed cases on hospitalized patients, ICU patients and deceased. This study reports the explanatory and predictive ability of COVID-19-related healthcare data, such as whether there is a spread of a contagious and virulent virus in a society, and if so, whether the morbidity and mortality can be estimated in advance in the population. The model estimations stress the implementation of a pandemic strategy containing a proactive protocol entailing what, when, where, who and how countermeasures should be in place when a virulent virus (e.g. SARS-CoV-1, SARS-CoV-2 and MERS) or pandemic strikes next time. Several lessons for the future can be learnt from the reported model estimations. One lesson is that COVID-19-related morbidity and mortality in a population is indeed predictable. Another lesson is to have a proactive protocol of countermeasures in place.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Previsões/métodos , Hospitalização/estatística & dados numéricos , Hospitalização/tendências , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Unidades de Terapia Intensiva/tendências , Modelos Estatísticos , Morbidade , Pandemias , Saúde Pública/estatística & dados numéricos , Política Pública/tendências , SARS-CoV-2/isolamento & purificação
14.
Int J Mol Sci ; 22(14)2021 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-34299341

RESUMO

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.


Assuntos
Biomarcadores Farmacológicos/análise , Inibidores do Crescimento/análise , Redes Neurais de Computação , Farmacogenética/métodos , Antineoplásicos/farmacologia , Aprendizado Profundo , Previsões/métodos , Humanos , Concentração Inibidora 50 , Modelos Teóricos , Medicina de Precisão , Curva ROC
15.
Sci Rep ; 11(1): 15343, 2021 07 28.
Artigo em Inglês | MEDLINE | ID: mdl-34321491

RESUMO

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.


Assuntos
COVID-19/diagnóstico , COVID-19/mortalidade , Diagnóstico por Computador/métodos , Previsões/métodos , Redes Neurais de Computação , Algoritmos , Aprendizado Profundo , Humanos , Probabilidade , SARS-CoV-2/isolamento & purificação
16.
Sci Rep ; 11(1): 15271, 2021 07 27.
Artigo em Inglês | MEDLINE | ID: mdl-34315932

RESUMO

COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries' movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.


Assuntos
Inteligência Artificial , COVID-19/epidemiologia , Previsões/métodos , Análise por Conglomerados , Bases de Dados Factuais , Humanos , Pandemias
17.
Value Health ; 24(7): 917-924, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34243834

RESUMO

OBJECTIVES: Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making. METHODS: We identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards. RESULTS: We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent. CONCLUSIONS: There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.


Assuntos
Doenças Transmissíveis Emergentes/tratamento farmacológico , Doenças Transmissíveis Emergentes/prevenção & controle , Métodos Epidemiológicos , Análise Custo-Benefício , Surtos de Doenças/prevenção & controle , Surtos de Doenças/estatística & dados numéricos , Previsões/métodos , Humanos , Formulação de Políticas , Padrões de Referência
18.
Medicine (Baltimore) ; 100(23): e26246, 2021 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-34115013

RESUMO

ABSTRACT: Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.


Assuntos
Previsões/métodos , Aprendizado de Máquina/normas , Pneumonia Associada à Ventilação Mecânica/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Boston , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Unidades de Terapia Intensiva/organização & administração , Unidades de Terapia Intensiva/estatística & dados numéricos , Aprendizado de Máquina/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Respiração Artificial/efeitos adversos , Estudos Retrospectivos , Sensibilidade e Especificidade
19.
J Med Internet Res ; 23(7): e28615, 2021 07 14.
Artigo em Inglês | MEDLINE | ID: mdl-34081612

RESUMO

BACKGROUND: The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic. OBJECTIVE: This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge. METHODS: Followers of the three main emergency physician professional organizations were identified using Twitter's application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization. RESULTS: A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of "covid," "coronavirus," or "pandemic" in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states. CONCLUSIONS: COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.


Assuntos
COVID-19/epidemiologia , Comunicação , Medicina de Emergência , Previsões/métodos , Hospitalização/estatística & dados numéricos , Hospitalização/tendências , Médicos , Mídias Sociais/estatística & dados numéricos , COVID-19/diagnóstico , Vacinas contra COVID-19/administração & dosagem , Humanos , Análise de Classes Latentes , Estudos Longitudinais , Pandemias , Estudos Retrospectivos , SARS-CoV-2 , Vacinação/estatística & dados numéricos
20.
PLoS One ; 16(6): e0253367, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34138956

RESUMO

The COVID-19 has caused the deadliest pandemic around the globe, emerged from the city of Wuhan, China by the end of 2019 and affected all continents of the world, with severe health implications and as well as financial-damage. Pakistan is also amongst the top badly effected countries in terms of casualties and financial loss due to COVID-19. By 20th March, 2021, Pakistan reported 623,135 total confirmed cases and 13,799 deaths. A state space model called 'Bayesian Dynamic Linear Model' (BDLM) was used for the forecast of daily new infections, deaths and recover cases regarding COVID-19. For the estimation of states of the models and forecasting new observations, the recursive Kalman filter was used. Twenty days ahead forecast show that the maximum number of new infections are 4,031 per day with 95% prediction interval (3,319-4,743). Death forecast shows that the maximum number of the deaths with 95% prediction interval are 81 and (67-93), respectively. Maximum daily recoveries are 3,464 with 95% prediction interval (2,887-5,423) in the next 20 days. The average number of new infections, deaths and recover cases are 3,282, 52 and 1,840, respectively, in the upcoming 20 days. As the data generation processes based on the latest data has been identified, therefore it can be updated with the availability of new data to provide latest forecast.


Assuntos
Teorema de Bayes , COVID-19/diagnóstico , Previsões/métodos , Modelos Lineares , SARS-CoV-2/isolamento & purificação , Algoritmos , COVID-19/epidemiologia , COVID-19/virologia , Humanos , Paquistão/epidemiologia , Pandemias/prevenção & controle , SARS-CoV-2/fisiologia
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