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  1. Article ; Online: Application of the ARIMA model on the COVID-2019 epidemic dataset.

    Benvenuto, Domenico / Giovanetti, Marta / Vassallo, Lazzaro / Angeletti, Silvia / Ciccozzi, Massimo

    Data in brief

    2020  Volume 29, Page(s) 105340

    Abstract: ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ... Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
    Keywords covid19
    Language English
    Publishing date 2020-02-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2786545-9
    ISSN 2352-3409 ; 2352-3409
    ISSN (online) 2352-3409
    ISSN 2352-3409
    DOI 10.1016/j.dib.2020.105340
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Application of the ARIMA model on the COVID-2019 epidemic dataset

    Benvenuto, Domenico / Giovanetti, Marta / Vassallo, Lazzaro / Angeletti, Silvia / Ciccozzi, Massimo

    Data Brief

    Abstract: ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ... Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #2363
    Database COVID19

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  3. Article ; Online: Application of the ARIMA model on the COVID-2019 epidemic dataset

    Benvenuto, Domenico / Giovanetti, Marta / Vassallo, Lazzaro / Angeletti, Silvia / Ciccozzi, Massimo

    Data in Brief, 29:105340

    2020  

    Abstract: ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ... Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
    Keywords COVID-19 ; COVID-2019 epidemic ; Forecast ; ARIMA model ; Infection control ; covid19
    Language English
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: Application of the ARIMA model on the COVID-2019 epidemic dataset

    Domenico Benvenuto / Marta Giovanetti / Lazzaro Vassallo / Silvia Angeletti / Massimo Ciccozzi

    Data in Brief, Vol 29, Iss , Pp - (2020)

    2020  

    Abstract: ... in real time. Keywords: COVID-2019 epidemic, ARIMA model, Forecast, Infection control ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time. Keywords: COVID-2019 epidemic, ARIMA model, Forecast, Infection control
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
    Language English
    Publishing date 2020-04-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  5. Article: Application of the ARIMA model on the COVID-2019 epidemic dataset

    Benvenuto, Domenico / Giovanetti, Marta / Vassallo, Lazzaro / Angeletti, Silvia / Ciccozzi, Massimo

    Data in Brief. 2020 Apr., v. 29

    2020  

    Abstract: ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ... Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
    Keywords COVID-19 infection ; data collection ; econometric models ; evolution ; prediction
    Language English
    Dates of publication 2020-04
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 2786545-9
    ISSN 2352-3409
    ISSN 2352-3409
    DOI 10.1016/j.dib.2020.105340
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states

    Panda, Mrutyunjaya

    medRxiv

    Abstract: The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society ... to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic ... forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA ...

    Abstract The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society. The whole world is working relentlessly to find some solutions to fight against this deadly virus to reduce the number of deaths. Strategic planning with predictive modelling and short term forecasting for analyzing the situations based on the worldwide available data allow us to realize the future exponential behaviour of the COVID-19 disease. Time series forecasting plays a vital role in developing an efficient forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA (Auto regression integrated moving average) and Holt-Winters time series exponential smoothing are used to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic. The modelling and forecasting are done with the publicly available dataset from Kaggle as a perspective to India and its five states such as Odisha, Delhi, Maharashtra, Karnataka, Andhra Pradesh and West Bengal. The model is assessed with correlogram, ADF test, AIC and MAPE to understand the accuracy of the proposed forecasting model.
    Keywords covid19
    Language English
    Publishing date 2020-07-16
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.07.14.20153908
    Database COVID19

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  7. Article ; Online: Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states

    Panda, M.

    Abstract: The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society ... to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic ... forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA ...

    Abstract The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society. The whole world is working relentlessly to find some solutions to fight against this deadly virus to reduce the number of deaths. Strategic planning with predictive modelling and short term forecasting for analyzing the situations based on the worldwide available data allow us to realize the future exponential behaviour of the COVID-19 disease. Time series forecasting plays a vital role in developing an efficient forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA (Auto regression integrated moving average) and Holt-Winters time series exponential smoothing are used to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic. The modelling and forecasting are done with the publicly available dataset from Kaggle as a perspective to India and its five states such as Odisha, Delhi, Maharashtra, Karnataka, Andhra Pradesh and West Bengal. The model is assessed with correlogram, ADF test, AIC and MAPE to understand the accuracy of the proposed forecasting model.
    Keywords covid19
    Publisher MedRxiv; WHO
    Document type Article ; Online
    DOI 10.1101/2020.07.14.20153908
    Database COVID19

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  8. Article ; Online: Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods

    Christopher J. Lynch / Ross Gore

    Data in Brief, Vol 35, Iss , Pp 106759- (2021)

    2021  

    Abstract: The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance ... in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's ... Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data ...

    Abstract The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: (1) a Naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics are created with each forecast to evaluate the forecast with respect to existing historical data. These error metrics are aggregated to provide a means for assessing which combination of forecast method, forecast length, and lookback length are best fits, based on lowest aggregated error at each geographic level.The data set is comprised of an R-Project file, four R source code files, all 1,329,404 generated short-range forecasts, MdAE and MdAPE error metric data for each forecast, copies of the input files, and the generated comparison tables. All code and data files are provided to provide transparency and facilitate replicability and reproducibility. This package opens directly in RStudio through the R Project file. The R Project file removes the need to set path locations for the folders contained within the data set to simplify setup requirements. This data set provides two avenues for reproducing results: 1) Use the provided code to generate the forecasts from scratch and then run the analyses; or 2) Load the saved forecast data and run the analyses on the stored data. Code annotations provide the instructions needed to accomplish both routes.This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting.
    Keywords Coronavirus COVID-19 ; Infectious diseases ; Epidemic modeling ; ARIMA(p,d,q) model ; ARMA model ; Holt-winters exponential smoothing model ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
    Subject code 330
    Language English
    Publishing date 2021-04-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  9. Article: Application of the ARIMA model on the COVID-2019 epidemic dataset

    Data in Brief, 29:105340

    2020  

    Abstract: ... Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019 ... We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins ... Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies ...

    Abstract Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
    Keywords COVID-19 ; COVID-2019 epidemic ; ARIMA model ; Forecast ; Infection control
    Language English
    Document type Article
    Database Repository for Life Sciences

    Full text online

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