Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Heliyon, Год журнала: 2021, Номер 7(10), С. e08143 - e08143

Опубликована: Окт. 1, 2021

COVID-19 has produced a global pandemic affecting all over of the world. Prediction rate spread and modeling its course have critical impact on both health system policy makers. Indeed, making depends judgments formed by prediction models to propose new strategies measure efficiency imposed policies. Based nonlinear complex nature this disorder difficulties in estimation virus transmission features using traditional epidemic models, artificial intelligence methods been applied for spread. importance machine deep learning approaches spreading trend, present study, we review studies which used these predict number cases COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network multilayer perceptron are among mostly regard. We compared performance several Root means squared error (RMSE), mean absolute (MAE), R

Язык: Английский

---

Temporal deep learning architecture for prediction of COVID-19 cases in India

Expert Systems with Applications, Год журнала: 2022, Номер 195, С. 116611 - 116611

Опубликована: Фев. 5, 2022

Язык: Английский

---

Wiener and Lévy processes to prevent disease outbreaks: Predictable vs stochastic analysis

Partial Differential Equations in Applied Mathematics, Год журнала: 2024, Номер 10, С. 100712 - 100712

Опубликована: Май 13, 2024

The study considers stochastic behavior along with modeling, mathematical analysis, theory development, and numerical simulation of the COVID-19 virus. To evaluate current trends make future projections regarding basic reproduction number infection case, we have taken modified five-compartment SEIRD model. Since R0 is not sufficient to predict outbreak, applied Itô differential equations (SDEs) Weiner process Lévy jump investigate nature disease outbreak. an infectious caused by severe acute respiratory syndrome coronavirus 2 "(SARS-CoV-2)", that has common symptoms including fever, cough, difficulty breathing, illustrated boundedness positivity solutions investigated stability endemic disease-free equilibrium points. findings show dynamics epidemics are influenced contact patterns. For all models, threshold quantity, calculated which key reason prove global local analysis Using least squares method for data fitting, performed a case on in Italy this article. A sensitivity part our investigation find important factors. This paper investigates epidemic model (SDE) using jumps processes. Local setting examined analysis. view analytical study, multitude results achieved. comprehend virus contagious order prevent similar outbreaks future.

Язык: Английский

---

Artificial intelligence computing analysis of fractional order COVID-19 epidemic model

AIP Advances, Год журнала: 2023, Номер 13(8)

Опубликована: Авг. 1, 2023

Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances machine learning. Machine learning is sphere artificial where machines are responsible for doing daily chores believed be intelligent than humans. Furthermore, significant behavioral, social, physical, biological engineering, biomathematical sciences, disciplines. Fractional-order modeling real-world problem powerful tool understanding the dynamics problem. In study, an investigation into fractional-order epidemic model novel coronavirus (COVID-19) presented using computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs exploited predict transmission COVID-19 disease by taking dataset from fractional numerical method based on Grünwald–Letnikov backward finite difference. datasets mathematical Wuhan Karachi metropolitan cities trained with biased unbiased input target values. proposed technique (BRBFNs) implemented estimate integer spread dynamics. Its reliability, effectiveness, validation verified consistently achieved accuracy metrics that depend error histograms, regression studies, mean squared error.

Язык: Английский

---

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 4, 2024

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and an economic downturn. The number verified coronavirus cases is increasing daily, putting whole human race at danger strain on medical experts to eradicate disease as rapidly possible. As consequence, it vital predict upcoming positive patients in order plan actions future. Furthermore, discovered all across globe that asymptomatic play significant part disease's transmission. This prompted us incorporate similar examples accurately forecast trends. A typical strategy for analysing rate pandemic infection use time-series forecasting technique. would assist developing better decision support systems. To anticipate COVID-19 active few countries, we recommended hybrid model utilizing fuzzy time series (FTS) mixed with non-linear growth model. case outbreak evaluated Italy, Brazil, India, Germany, Pakistan, Myanmar through June 5, 2020 phase-1, January 15, 2022 phase-2, forecasts next 26 14 days respectively. proposed framework fitting effect outperforms individual logistic techniques, R-scores 0.9992 phase-1 0.9784 phase-2. provided this article may be utilised comprehend country's epidemic pattern government effective interventions.

Язык: Английский

---

Does Air Quality Really Impact COVID-19 Clinical Severity: Coupling NASA Satellite Datasets with Geometric Deep Learning

Опубликована: Авг. 13, 2021

Given that persons with a prior history of respiratory diseases tend to demonstrate more severe illness from COVID-19 and, hence, are at higher risk serious symptoms, ambient air quality data NASA's satellite observations might provide critical insight into which geographical areas may exhibit numbers hospitalizations due COVID-19, how the expected severity and associated survival rates vary across space in future, most importantly given this information, health professionals can distribute vaccines efficient, timely, fair manner.

Язык: Английский

---

GIS-based AHP analysis to recognize the COVID-19 concern zone in India

GeoJournal, Год журнала: 2022, Номер 88(1), С. 451 - 463

Опубликована: Март 6, 2022

Язык: Английский

---

To Use Face Masks or Not After COVID-19 Vaccination? An Impact Analysis Using Mathematical Modeling

Frontiers in Applied Mathematics and Statistics, Год журнала: 2022, Номер 8

Опубликована: Апрель 27, 2022

The question of whether to drop or continue wearing face masks especially after being vaccinated among the public is controversial. This sourced from efficacy levels COVID-19 vaccines developed, approved, and in use. We develop a deterministic mathematical model that factors combination vaccination program as intervention strategies curb spread epidemic. use specifically assess potential impact masks, by individuals combating further contraction infections. Validation achieved performing its goodness fit Republic South Africa's reported positive cases data using Maximum Likelihood Estimation algorithm implemented fitR package. first consider scenario where uptake extremely low. Second, we people who are relatively high. Third, on an upward trajectory. Findings one two, respectively, indicate highly surging number infections low recorded For three, it shows increased extent at increasing vaccine mask average protection results accelerated decrease However, alone also reduction peak though delay clearing.

Язык: Английский

---

Analysis of COVID-19 Death Cases Using Machine Learning

SN Computer Science, Год журнала: 2023, Номер 4(4)

Опубликована: Май 17, 2023

Язык: Английский

---

Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19

Mathematics, Год журнала: 2023, Номер 11(4), С. 1051 - 1051

Опубликована: Фев. 19, 2023

The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As case study, the total in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning control. In this paper, proposed model making time-series forecast daily cases, recovered countrywide dataset two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used assess model’s performance: mean absolute error (MAE), root squared (RMSE), R2, percentage (MAPE). results generated TDGRU are compared actual numbers as well predictions conventional techniques, such autoregressive integrated moving average (ARIMA), machine learning linear regression (LR), time series-based deep method long short-term memory (LSTM). experiment on different horizons show that outperforms other forecasting methods deliver correct lower prediction errors. Furthermore, since based relatively simpler architecture than LSTM, comparison LSTM-based models, it features significantly reduced parameters, shorter training period, storage need, more straightforward hardware implementation.

Язык: Английский

---