Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 1.999
Filtrar
1.
Recurso na Internet em Espanhol | LIS - Localizador de Informação em Saúde | ID: lis-47876

RESUMO

“TODA LA DATA EN UN SITIO, TODA LA DATA EN UN ARCHIVO”. Recopilación de la data relativa a la pandemia a nivel mundial y con especial énfasis en Venezuela, de libre acceso y gratuita, con actualizaciones diarias, en formato de progresión estadística y data simple descargable, y en formato de visualizaciones gráficas de apoyo.


Assuntos
Infecções por Coronavirus , Pandemias/estatística & dados numéricos , Previsões/métodos
2.
J Biol Dyn ; 14(1): 730-747, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32875961

RESUMO

In this study, we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lockdown restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing and possible medication scenarios. The pre-lock down value of R 0 is estimated to be 1.07 and the post lock down value is estimated to be 1.86. Using this analysis, we project the epidemic curve. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid-September, 2020, with the maximum projected active cases being close to 700, 000. In a realistic, best case scenario, we project that the epidemic peaks in early to mid-July, 2020, with the maximum active cases being around 120, 000. We note that social distancing measures and medication will help flatten the curve; however, without the reintroduction of further lock down, it would be very difficult to make R 0 < 1 .


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Surtos de Doenças , Pneumonia Viral/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , Bioestatística , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Surtos de Doenças/prevenção & controle , Surtos de Doenças/estatística & dados numéricos , Epidemias , Previsões/métodos , Humanos , Conceitos Matemáticos , Modelos Biológicos , Paquistão/epidemiologia , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Quarentena/estatística & dados numéricos
3.
Artigo em Inglês | MEDLINE | ID: mdl-32992643

RESUMO

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.


Assuntos
Infecções por Coronavirus/epidemiologia , Ciência de Dados , Saúde Global/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Previsões/métodos , Humanos , Pandemias
4.
BMC Infect Dis ; 20(1): 710, 2020 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-32993524

RESUMO

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , China/epidemiologia , Infecções por Coronavirus/virologia , Previsões/métodos , Humanos , Irã (Geográfico)/epidemiologia , Itália/epidemiologia , Pandemias , Pneumonia Viral/virologia , Prognóstico , República da Coreia/epidemiologia
5.
PLoS One ; 15(9): e0238214, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32946442

RESUMO

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.


Assuntos
Betacoronavirus , Infecções por Coronavirus/transmissão , Modelos Teóricos , Morbidade/tendências , Pneumonia Viral/transmissão , Brasil/epidemiologia , Análise por Conglomerados , Infecções por Coronavirus/epidemiologia , Surtos de Doenças/estatística & dados numéricos , Previsões/métodos , Humanos , Pandemias , Pneumonia Viral/epidemiologia , Fatores Socioeconômicos
6.
Western Pac Surveill Response J ; 11(1): 13-21, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32963887

RESUMO

Objective: This study aims to enhance the capacity of dengue prediction by investigating the relationship of dengue incidence with climate and environmental factors in the Mekong Delta region (MDR) of Viet Nam by using remote sensing data. Methods: To produce monthly data sets for each province, we extracted and aggregated precipitation data from the Global Satellite Mapping of Precipitation project and land surface temperatures and normalized difference vegetation indexes from the Moderate Resolution Imaging Spectroradiometer satellite observations. Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. Results: The final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. As a result, the obtained model presents a good fit at a regional level with the correlation value of 0.65 between predicted and reported dengue cases; nevertheless, its performance declines at the subregional scale. Conclusion: We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. Results indicated that this approach could be an effective method to predict regional dengue incidence and its trends.


Assuntos
Dengue/epidemiologia , Previsões/métodos , Humanos , Incidência , Modelos Estatísticos , Tecnologia de Sensoriamento Remoto , Vietnã/epidemiologia
7.
J Theor Biol ; 507: 110461, 2020 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-32866493

RESUMO

The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.


Assuntos
Busca de Comunicante/métodos , Infecções por Coronavirus/epidemiologia , Previsões/métodos , Modelos Teóricos , Pneumonia Viral/epidemiologia , Controle de Doenças Transmissíveis , Simulação por Computador , Infecções por Coronavirus/transmissão , Características da Família , Humanos , Michigan , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/transmissão , Quarentena , Instituições Acadêmicas , Local de Trabalho
8.
Caracas; Observatorio Nacional de Ciencia, Tecnología e Innovación; 15 ago. 2020. 11-25 p. ilus, tab.(Observador del Conocimiento. Revista Especializada en Gestión Social del Conocimiento, 5, 3).
Monografia em Espanhol | LILACS, LIVECS | ID: biblio-1119237

RESUMO

El objetivo principal de este trabajo es emplear modelos ARIMA para la estimación de nuevos contagios usando datos públicos disponibles para Venezuela y la región suramericana, actualmente foco principal de un segundo brote de la COVID-19. Se realiza la predicción a 30 días del número de casos de Covid-19 en países suramericanos usando los datos públicos disponibles. Se emplearon modelos ARIMA para estimar el impacto de nuevos contagios en las dinámicas de infección para Suramérica. Desde la aparición del primer caso de la nueva neumonía Covid-19 en China, esta enfermedad se ha convertido en un problema de salud pública global y representa un gran reto el control de la infección para los países de Suramérica. Al 24 de junio de 2020 un total de 1.866.090 casos han sido detectados en la región y en el caso particular de Venezuela un total de 4.365 casos. El rápido incremento en el número de casos y la alta tasa de contagios asociado con el virus han llevado al desarrollo de distintas aproximaciones matemáticas, tales como: modelos SIR, SEIR, redes neuronales y regresiones lineales que permitan predecir la probable evolución de la epidemia. Los modelos ARIMA han sido empleados con éxito en otras infecciones como influenza, malaria, SARS, entre otras. Los resultados de las estimaciones realizadas empleando estos modelos muestran que aún en la región hacen falta mayores esfuerzos que conlleven al control de la epidemia(AU)


The main objective of this work is to use ARIMA models for the estimation of new contagions using public data available for Venezuela and the South American region, currently the main focus of a second COVID19 outbreak. A 30-day prediction is made for the num-ber of Covid-19 cases in South American countries using available public data. ARIMA models were used to estimate the impact of new contagions on infection dynamics for South America Since the appearance of the first case of the new Covid-19 pneumonia in China, which has become a global public health problem and the great challenge that the infection has represented for the countries of South America to June 24, 2020, a total of 1,866,090 cases have been detected and in the particular case of Venezuela a total of 4,365 cases have been detected for the same date. The rapid increase in the number of cases and the high rate of contagion associated with the virus have led to the development of different mathematical approaches, such as: SIR, SEIR models, neural networks and linear regressions that allow predicting the probable evolution of the epidemic. The ARIMA model has been successfully used in other infections such as influenza, malaria, SARS, among others. In the following work, the 30 - day prediction of the number of Covid-19 cases in South American countries is made using public data available. The results of the estimates made using these models show that even in the region, greater efforts are needed to control the epidemic(AU)


Assuntos
Humanos , Modelos Lineares , Infecções por Coronavirus , Síndrome Respiratória Aguda Grave , Pandemias , Previsões/métodos
9.
PLoS One ; 15(7): e0236386, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32735581

RESUMO

This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Previsões/métodos , Modelos Teóricos , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Doenças Assintomáticas , China/epidemiologia , Infecções Comunitárias Adquiridas , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/virologia , Infecção Hospitalar , Confiabilidade dos Dados , Europa (Continente)/epidemiologia , Hospitalização , Humanos , Pandemias , Pneumonia Viral/mortalidade , Pneumonia Viral/virologia , Estados Unidos/epidemiologia
10.
MEDICC Rev ; 22(3): 32-39, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32812897

RESUMO

INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fi ght it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal. OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confi rmed cases and deaths in Cuba, as well as total number of cases. METHODS An inferential, predictive study was conducted using lo-gistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain-countries that have passed the initial peak of infection rates-were studied, and it was inferred from the results of these countries that their models were ap-plicable to Cuba. This hypothesis was tested by applying goodness-of-fi t and signifi cance tests on its parameters.RESULTS Both models showed good fi t, low mean square errors, and all parameters were highly signifi cant. CONCLUSIONS The validity of models was confi rmed based on logis-tic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba. KEYWORDS COVID-19, SARS-CoV-2, logistic models, pandemic, mortality, Cuba.


Assuntos
Infecções por Coronavirus/epidemiologia , Previsões/métodos , Modelos Logísticos , Pneumonia Viral/epidemiologia , Betacoronavirus , Cuba/epidemiologia , Humanos , Itália/epidemiologia , Pandemias , Espanha/epidemiologia
11.
PLoS One ; 15(8): e0229367, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790672

RESUMO

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Previsões/métodos , Algoritmos , Teorema de Bayes , Feminino , Humanos , Aprendizado de Máquina , Imageamento de Micro-Ondas , Modelos Teóricos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Máquina de Vetores de Suporte
12.
PLoS One ; 15(8): e0236278, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32841247

RESUMO

Rabies is a lethal viral disease and dogs are the major disease reservoir in the Philippines. Spatio-temporal variations in environmental factors are known to affect disease dynamics. Some rabies-affected countries considered investigating the role of weather components in driving rabies cases and it has helped them to strategize their control efforts. In this study, cointegration analysis was conducted between the monthly reported rabies cases and the weather components, such as temperature and precipitation, to verify the effect of weather components on rabies incidence in Davao City, Philippines. With the Engle-Granger cointegration tests, we found that rabies cases are cointegrated into each of the weather components. It was further validated, using the Granger causality test, that each weather component predicts the rabies cases and not vice versa. Moreover, we performed the Johansen cointegration test to show that the weather components simultaneously affect the number of rabies cases, which allowed us to estimate a vector-error correction model for rabies incidence as a function of temperature and precipitation. Our analyses showed that canine rabies in Davao City was weather-sensitive, which implies that rabies incidence could be projected using established long-run relationship among reported rabies cases, temperature, and precipitation. This study also provides empirical evidence that can guide local health officials in formulating preventive strategies for rabies control and eradication based on weather patterns.


Assuntos
Reservatórios de Doenças/virologia , Cães/virologia , Monitorização de Parâmetros Ecológicos/estatística & dados numéricos , Raiva/epidemiologia , Tempo (Meteorologia) , Animais , Causalidade , Cidades/estatística & dados numéricos , Conjuntos de Dados como Assunto , Previsões/métodos , Humanos , Incidência , Modelos Estatísticos , Filipinas/epidemiologia , Raiva/prevenção & controle , Raiva/virologia , Vírus da Raiva , Análise Espaço-Temporal
13.
BMC Infect Dis ; 20(1): 571, 2020 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-32758162

RESUMO

BACKGROUND: The incidence of cryptococcal meningitis (CM) has gradually increased in recent years. Cerebrospinal fluid (CSF) cytology and cell count are very important for CM on etiology diagnosis and assessment of disease status and therapeutic response. However, the clinical significance of CSF white cell count (WCC) in CM patients is not fully understood. Using longitudinal data of CSF WCC and its relationship with clinical outcomes in CM patients, we aimed to elucidate the clinical significance of this test. METHODS: We retrospectively analyzed the medical records of 150 CM patients admitted to our hospital between January 2008 and December 2018. RESULTS: CM patients with lower baseline CSF WCC, CSF protein concentration or CD4/CD8 ratio, and those with altered mentation or HIV coinfection were more likely to have poor clinical outcome (P<0.05). CM patients with triple therapy during the induction period presented with a better clinical outcome (P<0.05). Baseline CSF WCC had a moderate positive correlation with peripheral CD4+ T lymphocyte count (r = 0.738, P < 0.001) and CD4+ T lymphocyte percentage (r = 0.616, P < 0.001). The best cut-off value to predict a poor clinical outcome was 40 cells/µL during baseline CSF WCC. The predictive model incorporating longitudinal data of CSF WCC had better sensitivity, specificity, and accuracy than a model incorporating only baseline CSF WCC data. CONCLUSIONS: Our results indicated that baseline CSF WCC and changes in CSF WCC over time could be used to assess the prognosis of CM patients.


Assuntos
Relação CD4-CD8/métodos , Cryptococcus neoformans , Meningite Criptocócica/líquido cefalorraquidiano , Meningite Criptocócica/diagnóstico , Adulto , Antirretrovirais/uso terapêutico , Antifúngicos/uso terapêutico , China , Confiabilidade dos Dados , Feminino , Previsões/métodos , HIV , Infecções por HIV/tratamento farmacológico , Infecções por HIV/virologia , Humanos , Estudos Longitudinais , Masculino , Meningite Criptocócica/tratamento farmacológico , Meningite Criptocócica/microbiologia , Pessoa de Meia-Idade , Prognóstico , Estudos Retrospectivos , Sensibilidade e Especificidade , Resultado do Tratamento
14.
Braz J Microbiol ; 51(3): 1109-1115, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32809115

RESUMO

COVID-19 has killed more than 500,000 people worldwide and more than 60,000 in Brazil. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns, and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population, and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities' efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per million - the average level in which some countries start to relax the stay-at-home measures - was estimated. If the daily new deaths curve was bending today (i.e., about 1250 deaths per day), the predicted date would be on July 5. Forecast predictions allowed the estimation of overall death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve may correspond to additional 1685 deaths at the end of COVID-19 outbreak in Brazil (R2 = 0.9890). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision-making to properly fight COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Previsões/métodos , Pneumonia Viral/epidemiologia , Algoritmos , Brasil/epidemiologia , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/prevenção & controle , Detergentes/provisão & distribução , Educação/estatística & dados numéricos , Humanos , Análise dos Mínimos Quadrados , Dinâmica não Linear , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Pneumonia Viral/prevenção & controle , Política , Densidade Demográfica , Pobreza , Fatores Socioeconômicos , Estatística como Assunto , Fatores de Tempo , Abastecimento de Água/normas
15.
Sci Rep ; 10(1): 14042, 2020 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-32820210

RESUMO

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/sangue , Modelos Estatísticos , Pneumonia Viral/sangue , Idoso , Bilirrubina/sangue , Infecções por Coronavirus/terapia , Infecções por Coronavirus/virologia , Confiabilidade dos Dados , Estudos de Viabilidade , Feminino , Produtos de Degradação da Fibrina e do Fibrinogênio/análise , Previsões/métodos , Humanos , Leucócitos , Aprendizado de Máquina , Masculino , Mioglobina/sangue , Pandemias , Pneumonia Viral/terapia , Pneumonia Viral/virologia , Prognóstico , Protrombina/análise , Receptores de Interleucina-2/sangue , Estudos Retrospectivos , Sensibilidade e Especificidade , Resultado do Tratamento , Ureia/sangue
16.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32780189

RESUMO

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Assuntos
Infecções por Coronavirus/mortalidade , Previsões/métodos , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Ocupação de Leitos , Betacoronavirus , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Modelos Estatísticos , Mortalidade/tendências , New York/epidemiologia , Saúde Pública
18.
Math Biosci ; 328: 108431, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32738248

RESUMO

The novel coronavirus (COVID-19) pandemic is causing devastating demographic, social, and economic damage globally. Understanding current patterns of the pandemic spread and forecasting its long-term trajectory is essential in guiding policies aimed at curtailing the pandemic. This is particularly important in regions with weak economies and fragile health care systems such as West Africa. We formulate and use a deterministic compartmental model to (i) assess the current patterns of COVID-19 spread in West Africa, (ii) evaluate the impact of currently implemented control measures, and (iii) predict the future course of the pandemic with and without currently implemented and additional control measures in West Africa. An analytical expression for the threshold level of control measures (involving a reduction in the effective contact rate) required to curtail the pandemic is computed. Considering currently applied health control measures, numerical simulations of the model using baseline parameter values estimated from West African COVID-19 data project a 67% reduction in the daily number of cases when the epidemic attains its peak. More reduction in the number of cases will be achieved if additional public health control measures that result in a reduction in the effective contact rate are implemented. We found out that disease elimination is difficult when more asymptomatic individuals contribute in transmission or are not identified and isolated in a timely manner. However, maintaining a baseline level of asymptomatic isolation and a low transmission rate will lead to a significant reduction in the number of daily cases when the pandemic peaks. For example, at the baseline level of asymptomatic isolation, at least a 46% reduction in the transmission rate is required for disease elimination. Additionally, disease elimination is possible if asymptomatic individuals are identified and isolated within 5 days (after the incubation period). Combining two or more measures is better for disease control, e.g., if asymptomatic cases are contact traced or identified and isolated in less than 8 days, only about 29% reduction in the disease transmission rate is required for disease elimination. Furthermore, we showed that the currently implemented measures triggered a 33% reduction in the time-dependent effective reproduction number between February 28 and June 26, 2020. We conclude that curtailing the COVID-19 pandemic burden significantly in West Africa requires more control measures than those that have already been implemented, as well as more mass testing and contact tracing in order to identify and isolate asymptomatic individuals early.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , África Ocidental/epidemiologia , Número Básico de Reprodução , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/estatística & dados numéricos , Simulação por Computador , Busca de Comunicante , Infecções por Coronavirus/epidemiologia , Previsões/métodos , Humanos , Conceitos Matemáticos , Modelos Biológicos , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Saúde Pública
19.
PLoS One ; 15(8): e0237419, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32780765

RESUMO

BACKGROUND: Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. SETTING: One healthcare system in Ohio and Florida. PARTICIPANTS: All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. RESULTS: 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. LIMITATION: Retrospective cohort design. CONCLUSION: Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/fisiopatologia , Previsões/métodos , Hospitalização/tendências , Modelos Estatísticos , Pneumonia Viral/fisiopatologia , Adulto , Idoso , Tomada de Decisão Clínica , Infecções por Coronavirus/virologia , Progressão da Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Nomogramas , Pandemias , Pneumonia Viral/virologia , Prognóstico , Estudos Retrospectivos , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Fatores de Risco
20.
PLoS One ; 15(8): e0237165, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32764785

RESUMO

This study's objective was to estimate the temporal trends of leprosy according to sex and age groups, as well as to estimate and predict the progression of the disease in a hyperendemic city located in the northeast of Brazil. This ecological time-series study was conducted in Imperatriz, Maranhão, Brazil. Leprosy cases diagnosed between 2006 and 2016 were included. Detection rates stratified by sex and age groups were estimated. The study of temporal trends was accomplished using the Seasonal-Trend Decomposition method and temporal modeling of detection rates using linear seasonal autoregressive integrated moving average model according to Box and Jenkins method. Trend forecasts were performed for the 2017-2020 period. A total of 3,212 cases of leprosy were identified, the average incidence among men aged between 30 and 59 years old was 201.55/100,000 inhabitants and among women in the same age group was 135.28/100,000 inhabitants. Detection rates in total and by sex presented a downward trend, though rates stratified according to sex and age presented a growing trend among men aged less than 15 years old and among women aged 60 years old or over. The final models selected in the time-series analysis show the forecasts of total detection rates and rates for men and women presented a downward trend for the 2017-2020 period. Even though the forecasts show a downward trend in Imperatriz, the city is unlikely to meet a significant decrease of the disease burden by 2020.


Assuntos
Doenças Endêmicas/estatística & dados numéricos , Previsões/métodos , Hanseníase/epidemiologia , Adolescente , Adulto , Fatores Etários , Brasil/epidemiologia , Cidades/estatística & dados numéricos , Feminino , Humanos , Incidência , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Fatores Sexuais , Fatores de Tempo , Adulto Jovem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA