A Comparative Study of Predictive Machine Learning Algorithms for COVID-19 Trends and Analysis

Kunjir, Ajinkya; Joshi, Dishant; Chadha, Ritika; Wadiwala, Tejas; Trikha, Vikas

doi:10.1109/smc42975.2020.9282953

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Despite the involvement of many excellent models, Chakraborty et al (33) stressed that predicting and forecasting COVID-19 is challenging primarily due to seven major factors, including limited availability of data and extreme sources of uncertainty resulting in no gold standard for accurately forecasting the pandemic data. DT observes an object's features and trains a model which is represented in the form of a binary tree to predict data in the future (25,28,29). Figure 3A shows that the prediction is made by taking the root node of the binary tree with a single input variable, splitting the dataset based on the variable, and its leaf nodes have resulted as the output variable (25,26).…”

Section: Methodsmentioning

confidence: 99%

A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting

Nazirun¹,

Omar²,

Selvaganeson³

et al. 2022

MJMHS

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Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19.

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Section: Methodsmentioning

confidence: 99%

A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting

Nazirun¹,

Omar²,

Selvaganeson³

et al. 2022

MJMHS

View full text Add to dashboard Cite

show abstract

“…The literature presents a plethora of successful applications ranging from simple Recursive Neural Networks (RNN) to more sophisticated applications involving Long-Short Term Neural Networks (LSTM) [11,12]. Other approaches rely, instead, on the use of Convolutional Neural Networks, which allow capturing local trends in the data, namely temporal locality when it comes to temporal series [13]. Epidemics find a natural representation in graph networks.…”

Section: Related Workmentioning

confidence: 99%

Informed Deep Learning for Epidemics Forecasting

et al. 2022

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The SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy.

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“…Most basic CNNs applications rely on the use of ordered time series as input and require the model to predict the next data points in the series; however, CNNs are known to be prone to overfitting historical data. Nevertheless, this can be sufficient in presence of poor contextual data to outperform LSTM or simpler algorithms such as decision trees (DT) [Kunjir et al, 2020]. Predictions can be improved by feeding the models with multiple correlated time series.…”

Section: Vanilla Deep Learningmentioning

confidence: 99%

“…RNNs. The approach was first presented in [Lea et al, 2016] and it is based on the deploy-ment of causal convolution for sequence-to-sequence learning tasks. In this method, the kernel is applied only over data relative to the present time step or previous input points in the sequence, avoiding information leakage from future to past.…”

Section: Vanilla Deep Learningmentioning

confidence: 99%

Deep Learning for Virus-Spreading Forecasting: a Brief Survey

Baldo¹,

Dall'Olio²,

Ceccarelli³

et al. 2021

Preprint

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The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes. In this paper, we will outline the main Deep Learning approaches aimed at predicting the spreading of a disease in space and time. The aim is to show the emerging trends in this area of research and provide a general perspective on the possible strategies to approach this problem. In doing so, we will mainly focus on two macro-categories: classical Deep Learning approaches and Hybrid models. Finally, we will discuss the main advantages and disadvantages of different models, and underline the most promising development directions to improve these approaches.

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A Comparative Study of Predictive Machine Learning Algorithms for COVID-19 Trends and Analysis

Cited by 8 publications

References 3 publications

A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting

A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting

Informed Deep Learning for Epidemics Forecasting

Deep Learning for Virus-Spreading Forecasting: a Brief Survey

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