Spread &amp; Peak Prediction of Covid-19 using ANN and Regression (Workshop Paper)

Prakash, Anupam; Sharma, Piyush Kumar; Sinha, Indrajeet Kumar; Singh, Upendra

doi:10.1109/bigmm50055.2020.00062

Cited by 12 publications

(11 citation statements)

References 21 publications

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“…Various studies related to virus transmission and its influence factors have been carried out to predict (a) the spread of the virus [29,30,31,32,33]; (b) the person suspected of being infected [34]; (c) new infection areas [35]; (d) the likelihood of the second and third waves of the epidemic [36]; (e) COVID-19 contamination scenario based on people movement [37]; and (f) the increased number of cases [38]. COVID-19 task-force stakeholders can use these epidemiological predictions to prepare the necessary measures and policies.…”

Section: Healthcarementioning

confidence: 99%

“…This technique predicts future events by analyzing past trends, assuming that future trends will be similar to historical trends. A study on COVID-19 has applied a regression model to predict infected cases and compared the model with another prediction model of ANN [31]. ANN is also used to indicate the spread and the peak number of COVID-19 cases.…”

Section: Regression and Time Series Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Riswantini

Nugraheni

Munandar

et al. 2021

Preprint

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COVID-19 has induced many problems in various sectors of life for humanity around the world. After one year of pandemic, many studies have been carried out in exposing various technology innovations and applications to combat the coronavirus that has killed more people than most. The pandemic has accelerated the use of Big Data technology to mitigate the threats of COVID-19. This survey aims to explore the Big Data research for COVID-19. We collected and analyzed the relevant academic articles to identify how Big Data technology can cover the challenges faced in overcoming the pandemic. In determining the research areas addressed by the past studies, we highlight the technology contributions to five major areas of healthcare, social life, government policy, business and management, and the environment. We discuss how analytical techniques of machine learning, deep learning, statistics, and mathematics can solve pandemic issues. The Big Data research for COVID-19 used a wide variety of data sources available publicly or privately. At the end of the discussion, we present the data source used in the past studies encompassing government official data, institutional service data, IoT generated data, online media data, and open data. We hope that this survey will clarify the role of Big Data technology in enhancing the research for COVID-19.

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Section: Healthcarementioning

confidence: 99%

Section: Regression and Time Series Forecastingmentioning

confidence: 99%

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Riswantini

Nugraheni

Munandar

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The same authors used the logistic model to forecast daily predictions and the epidemic size for China, South Korea, and the rest of the World [12]. Prakash [26] used ANN and regression to model the COVID-19 pandemic in India and other countries like The USA, Italy and Spain. The authors observed that the results of polynomial regression follow the ground truths in India and the USA but not in Italy and Spain.…”

Section: Introductionmentioning

confidence: 99%

“…Prakash [26] used ANN and regression to model the COVID-19 pandemic in India and other countries like The USA, Italy and Spain. The authors observed that the results of polynomial regression follow the ground truths in India and the USA but not in Italy and Spain.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

Lounis

Malavika

2021

Preprint

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The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

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“…[ [17] considered a new model algorithm, which replaced the regression [18] with the regression to improve the performance of the approach. Thus, [2] proposed another predictive models where some potential predictor variables are considered.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID 19 and Its Economic Impact in Africa

Ht¹

2021

Preprint

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This paper presents a two-way factor design incorporating both spatial and temporal variation in the prediction of COVID 19 in Africa. In line with this, the impact of COVID-19on the GDP in Africa is well scrutinized. In contrast to the existing works [1–3], this work also extends the two-factor design into the one-way factor design through incorporating covariates into spatial effects. The data rely on the spatial and temporal obtained from WHO datasets [4, 5]. The one-factor design with more covariates is taken into consideration to identify the major potential predictor variables responsible for the deaths and confirmed cases due to COVID 19 in Africa. The MANCOVA considered population density, temperature, humidity; perception, and wind are all considered as co-variates. Simulations show that the two-way analysis of variance has shown that there is a statistically significant difference between the spatial (Fcal= 8.2704, Pvalue= 3.099&lowast;10−6)and temporal (Fcal= 48.7964, Pvalue= 9.147&lowast;10−16) effects. South Africa and Nigeria are highly influencing due to the pandemic where their GDP also relatively mostly declined. A significant economic change is observed before the pandemic and after the outbreak of the pandemic(tcal= 2.9548, Pvalue= 0.01805). COVID 19 negatively influenced the economy of1 most of the African countries. The population density, temperature, and wind are found to be statistically significantly associated with COVID 19 cases and deaths.

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Spread & Peak Prediction of Covid-19 using ANN and Regression (Workshop Paper)

Cited by 12 publications

References 21 publications

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

Prediction of COVID 19 and Its Economic Impact in Africa

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