Prediction of COVID-19 Active Cases Using Polynomial Regression and ARIMA Models

Neily, Neji; Ammar, Boulbaba Ben; Kammoun, Habib M.

doi:10.1007/978-3-030-96308-8_125

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…This is achieved by fitting a mathematical model to the time series data and using this model to make predictions. ARIMA models have been used to predict a wide range of phenomena, such as stock prices, weather patterns, and disease outbreaks [36].…”

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

“…The I component involves differencing the time series to make it stationary, which means that its mean and variance remain constant over time. The M A component involves modeling the errors or residuals of the time series as a linear combination of past errors or residuals [36].…”

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

“…The parameter d represents the number of times the time series needs to be differentiated to make it stationary. The parameter q represents the number of moving average terms, which are used to model the dependency of the current value of the time series on the errors or residuals of its past values [36].…”

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

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Enhancing malaria prediction in Amazonas, Brazil: A comparative analysis of deep learning models based on clustering

Barboza,

Monteiro,

Dourado

et al. 2023

Preprint

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Backgound: Malaria is a disease transmitted by the female Anopheles mosquito and can be treatable. However, in 2019 there were over 247 million instances of malaria reported, along with 619,000 fatalities. More than 42 million Brazilians are at danger of developing malaria, with 99 percent of cases occurring in or around the Amazon forest (WHO, 2020). In Brazil, malaria is still a significant public health issue, despite the decline in occurrences and deaths. Methods: Accurate spatiotemporal forecasting of malaria transmission could help to better allocate resources to help fight the disease. In order to estimate malaria cases in the Amazonas state, we compare and evaluate deep learning, machine learning, and statistical models in this work. We used k-means clustering to group municipalities using a Brazilian dataset of around 6 million records (January 2003 to December 2019) to test if the performance of the models may be improved when grouping municipalities with statistically similar incidence rates of malaria. Results: The results indicate that the ARIMA model achieved better performance, but, the other models obtained similar values. The division of municipalities by clusters reinforced the application of models for municipalities with similar statistical values. Conclusions: As for geographic distribution, the results are similar. However, evaluating municipalities by grouping K-means, based on statistical values, is an alternative for decision-makers, as it will be necessary to monitor only 5 large groups instead of 9 health regions. The behavior of the models comparing the dataset with all types of malaria and Malaria Vivax was similar since the amount of data available for the models to perform the learning is quite close.

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Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

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Enhancing malaria prediction in Amazonas, Brazil: A comparative analysis of deep learning models based on clustering

Barboza,

Monteiro,

Dourado

et al. 2023

Preprint

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“…A type of linear regression known as polynomial regression evaluates the connection between two variables using polynomials of the nth degree [20], which we set as the 8th degree for our prediction. Polynomial regression can be illustrated with the help of this particular instance of multiple linear regression.…”

Section: Machine Learning Models For Forecasting Prediction 101 Polyn...mentioning

confidence: 99%

A Forecasting Prognosis of the Monkeypox Outbreak Based on a Comprehensive Statistical and Regression Analysis

Yasmin

Hassan²,

Zaman³

et al. 2022

Computation

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The uncommon illness known as monkeypox is brought on by the monkeypox virus. The Orthopoxvirus genus belongs to the family Poxviridae, which also contains the monkeypox virus. The variola virus, which causes smallpox; the vaccinia virus, which is used in the smallpox vaccine; and the cowpox virus are all members of the Orthopoxvirus genus. There is no relationship between chickenpox and monkeypox. After two outbreaks of a disorder resembling pox, monkeypox was first discovered in colonies of monkeys kept for research in 1958. The illness, also known as “monkeypox”, still has no known cause. However, non-human primates and African rodents can spread the disease to humans (such as monkeys). In 1970, a human was exposed to monkeypox for the first time. Several additional nations in central and western Africa currently have documented cases of monkeypox. Before the 2022 outbreak, almost all instances of monkeypox in people outside of Africa were connected to either imported animals or foreign travel to nations where the illness frequently occurs. In this work, the most recent monkeypox dataset was evaluated and the significant instances were visualized. Additionally, nine different forecasting models were also used, and the prophet model emerged as the most reliable one when compared with all nine models with an MSE value of 41,922.55, an R2 score of 0.49, a MAPE value of 16.82, an MAE value of 146.29, and an RMSE value of 204.75, which could be considerable assistance to clinicians treating monkeypox patients and government agencies monitoring the origination and current state of the disease.

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Enhancing Pain Level Assessment in Post-Surgery Patients Using Artificial Intelligence Algorithms

Othman,

Yumuk,

Copot

et al. 2024

2024 European Control Conference (ECC)

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Prediction of COVID-19 Active Cases Using Polynomial Regression and ARIMA Models

Cited by 3 publications

References 6 publications

Enhancing malaria prediction in Amazonas, Brazil: A comparative analysis of deep learning models based on clustering

Enhancing malaria prediction in Amazonas, Brazil: A comparative analysis of deep learning models based on clustering

A Forecasting Prognosis of the Monkeypox Outbreak Based on a Comprehensive Statistical and Regression Analysis

Enhancing Pain Level Assessment in Post-Surgery Patients Using Artificial Intelligence Algorithms

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