Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan, Zeynep

doi:10.1016/j.scitotenv.2020.138817

Cited by 519 publications

(503 citation statements)

References 39 publications

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“…This is clearly evident that COVID-19 among Libyan correspondence to a higher age. In fact, most deaths in infected individuals occurred in Italy, Spain and France in elderly people suffering from severe conditions, particularly in the early phases of epidemics [28]. However, a recent study carried out on the Libyan population demography shows that over 65% of the Libyan population is less than 60 years old [29].…”

Section: Discussionmentioning

confidence: 99%

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

Daw

El-Bouzedi

Ahmed

2020

Preprint

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Background: COVID-19 is a global pandemic that affectedaffects all aspects of life. Studies on understanding the geographical and epidemiological characteristics become particularly important in controlling the spread of the pandemic. Such studies are lacking in Northern African countries, particularly in Libya, which is considered being, the second largest country in Africa, with the longest coast facing Europe. The objectives of this study are to; determine the epidemiological parameters and spatiotemporal patterns of COVID-19 in this large country and outline the needed strategies to contain the spread and the consequences of the pandemic. Methods: This comprehensive study included all the confirmed cases of COVID-19 since its emergence in Libya, from early April 2020 until July 31th31. The epidemiological characteristics of COVID-19 were analyzed, and the spatial dynamic trends were explored. A region Region-based counts of weekly reported cases were used to characterize and quantify the spatial dynamics of COVID-19.Results: A total of 3695 of confirmed cases of OVID-19 were collected,; 2515 (68.1%) were males , and 1180 (31.9%) were females with a male-to-female (M:F) ratio of 2.1:1. Aged between 2 -and 78 years old. Older age patients infected with COVID-19 are at higher severity and mortality. A broad geographic variability and spatiotemporal spread variation of the COVID-19 pandemic in Libya was observed. Indicating. This indicates a more significant increase ofin COVID-19 from the middle of July, particularly in the West and Southern regions, although it was consistently observed in the Meddle and Southern regions.Conclusion: Evaluating the epidemiological data and the spatiotemporal dynamic trends of COVID-19 at early stages are particularly important in understanding the pandemic spread. These parameters are essential in designing effective prevention and control programs aimed at reducing the impact of the COVID- 19 pandemic, particularly in countries with limited resources.

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Section: Discussionmentioning

confidence: 99%

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

Daw

El-Bouzedi

Ahmed

2020

Preprint

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“…Although such mathematical models are useful in epidemic analysis, they are based on coarse policies that are subject to bias [28]. Therefore researchers have subsequently proposed alternate forecasting models involving machine learning algorithms like LSTM, SVR, ARIMA, and few others for forecasting COVID-19 cases in different countries [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Fig2: Total Confirmed Cases Of Covid-19 Worldwide From Janmentioning

confidence: 99%

Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India

Swaraj

Kaur

Verma

et al. 2020

Preprint

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Background: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.Methods: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data.Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.Result:The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.Conclusion: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

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“…Ceylan 6 developed an Auto-Regressive Integrated Moving Average (ARIMA) model to predict the epidemiological trend of COVID-19. They took into account the cases reported in Italy, Spain, and France.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, some studies have proposed mathematical and machine learning models to predict the trend of the growth curve in the number of confirmed cases and deaths due to COVID-19. Works like [3][4][5][6][7] are examples of research that uses machine learning (ML) models along these lines. From the mentioned works, due to the highly complex nature of the COVID-19 disease and variation in its behavior from nation-to-nation, we note that two questions are central for the correctness models accuracy: a) the geographic context of the data, i.e.…”

Section: Introductionmentioning

confidence: 99%

The quest for better machine learning models to forecast COVID-19-related infections: A case study in the state of Pará-Brazil

Torres¹,

Soares

Ohashi³

et al. 2020

Preprint

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COVID-19 disease has become an unprecedented public health crisis. Although a relatively small percentage of people require intensive care, due to the high degree of contagion of the disease, the public system quickly collapses. Due to the highly complex nature of this disease and variation in its behavior depending on the characteristics of each geographic region, in this work, we analyze data from the Amazon region in Brazil (Pará). We applied several machine learning models to forecast the contagious curve to up 10 days. The Linear SVM and Multilayer Perceptron presented the best overall performances. Until the discovery of a vaccine, every effort is needed to understand and anticipate this disease.

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Estimation of COVID-19 prevalence in Italy, Spain, and France

Cited by 519 publications

References 39 publications

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India

The quest for better machine learning models to forecast COVID-19-related infections: A case study in the state of Pará-Brazil

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