Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Leon, Mazharul Islam; Iqbal, Ifraham; Azim, Sayed Mehedi; Mamun, Khondaker A.

doi:10.1109/icict4sd50815.2021.9396820

Cited by 10 publications

(1 citation statement)

References 38 publications

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“…The authors Leon et al [20] also evaluated multiple machine learning models to find the best model for predicting COVID-19 infections and deaths in Bangladesh. The authors compared Linear Regression, Polynomial Regression (PR), Support Vector Regression, Auto Regressive Model, Moving Average Model, Holt's Winter Additive model, Auto Regressive Integrated Moving Average Model, and Facebook Prophet's Model (FP).…”

Section: Forecasting Of Covid-19 Cases Using Machine Learningmentioning

confidence: 99%

Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE

Sankalpa,

Dhou,

Pasquier

et al. 2024

Applied Sciences

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Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.

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Section: Forecasting Of Covid-19 Cases Using Machine Learningmentioning

confidence: 99%