Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach

Kumar, Pavan; Kalita, Himangshu; Patairiya, Shashikanta; Yd, Sharma; Nanda, Chintan; Rani, Meenu; Rahmani, Jamal; Bhagavathula, Akshaya Srikanth

doi:10.1101/2020.03.30.20046227

Cited by 75 publications

(56 citation statements)

References 11 publications

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“…And the final epidemic size could be between 194,000 and 206,000 cases. 13 A similar downward trend is obtained by fitting a specific ARIMA model for the single regions. Specifically, Emilia Romagna requires 42 days to come closer to zero local new cases (Figure 3), Lombardy needs 32 days (Figure 4), Tuscany requires 56 days ( Figure 6), and Veneto needs 28 days to significatively flatten the COVID-2019 curve (Figure 7), at least.…”

Section: Data Descriptionsupporting

confidence: 66%

“…14 12 I mean the inflection point of the cumulative number of COVID-2019 confirmed cases. 13 The epidemic final size is obtained by summing the original values until April 4, 2020, and the forecast values for the period after April 4, 2020, minus and plus the mean standard deviation calculated for forecast values. 14 The only exception is Lombardy and Veneto, that have two (lag 4 and 11) and one (lag 3) significant spikes, respectively.…”

Section: Data Descriptionmentioning

confidence: 99%

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An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

Perone

2020

Preprint

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Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point and final size. Highlights:❖ ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖ These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖ The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖ Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

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Section: Data Descriptionsupporting

confidence: 66%

Section: Data Descriptionmentioning

confidence: 99%

An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

Perone

2020

Preprint

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“…The complete paucity in India was observed between 1year and 9months, 10 months, 8 months, and 5 months from slower to faster declining rates. During the year 1918 Spanish deadly flu pandemic claimed more 50 million lives [12,17,18] and take for its complete paucity during the next two years of its timeline. The paucity of COVID-19 cases depends upon government and local people support to strictly follow the WHO guideline and awareness.…”

Section: Discussionmentioning

confidence: 99%

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation

Rodríguez‐Morales

Singh

et al. 2020

Preprint

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Background: The highly contagious Co rona vi rus d isease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). Various nation’s administration including the India government called for the regional and local lockdown. We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. Method: The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. Results: The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. Conclusion: The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. Keywords: SARS-CoV-2; Lockdown; GDP; Nobel-Corona; Confirmatory data model

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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 Jan mentioning

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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Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach

Cited by 75 publications

References 11 publications

An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation

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

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