An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil

Mathematics, Journal Year: 2023, Volume and Issue: 11(14), P. 3069 - 3069

Published: July 12, 2023

This comprehensive overview focuses on the issues presented by pandemic due to COVID-19, understanding its spread and wide-ranging effects of government-imposed restrictions. The examines utility autoregressive integrated moving average (ARIMA) models, which are often overlooked in forecasting perceived limitations handling complex dynamic scenarios. Our work applies ARIMA models a case study using data from Recife, capital Pernambuco, Brazil, collected between March September 2020. research provides insights into implications adaptability predictive methods context global pandemic. findings highlight models’ strength generating accurate short-term forecasts, crucial for an immediate response slow down disease’s rapid spread. Accurate timely predictions serve as basis evidence-based public health strategies interventions, greatly assisting management. model selection involves automated process optimizing parameters autocorrelation partial plots, well various precise measures. performance chosen is confirmed when comparing forecasts with real reported after forecast period. successfully both recovered COVID-19 cases across preventive plan phases Recife. However, model’s observed extend future. By end period, error substantially increased, it failed detect stabilization deceleration cases. highlights challenges associated such under-reporting recording delays. Despite these limitations, emphasizes potential while emphasizing need further enhance long-term predictions.

Language: Английский

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Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects

Engineering Structures, Journal Year: 2023, Volume and Issue: 285, P. 116063 - 116063

Published: April 7, 2023

Language: Английский

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Innovative applications of artificial intelligence during the COVID-19 pandemic

Infectious Medicine, Journal Year: 2024, Volume and Issue: 3(1), P. 100095 - 100095

Published: Feb. 21, 2024

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of management and response. In the present review, we discuss possibilities AI technology in addressing global posed by pandemic. First, outline multiple impacts current on public health, economy, society. Next, focus innovative applications advanced areas such as prediction, detection, control, drug discovery treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, omics data to forecast disease spread patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems support risk assessment, decision-making, social sensing, thereby improving epidemic control health policies. Furthermore, high-throughput virtual screening enables accelerate identification therapeutic candidates opportunities repurposing. Finally, future research directions combating COVID-19, emphasizing importance interdisciplinary collaboration. Though promising, barriers related model generalization, quality, infrastructure readiness, ethical risks must be addressed fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise stakeholders is imperative developing robust, responsible, human-centered solutions against emergencies.

Language: Английский

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Changes in Gross Primary Productivity: A past and future approach to climate, land use and wildfires in the western Amazon, Brazil

Environmental Development, Journal Year: 2025, Volume and Issue: unknown, P. 101150 - 101150

Published: Jan. 1, 2025

Language: Английский

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Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolitan cities using deep learning models

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 1, 2025

The COVID-19 outbreak has significantly impacted human lifestyles and life patterns. Therefore, data related to social may tell us the increase or decrease in number of confirmed cases. However, although cases is affected by life, it difficult find studies that attempt predict using various lifestyle data. This paper attempted an exploratory analysis see if could be predicted more accurately including We included taking public transportation, watching a movie at cinema, accommodation motel Finally, 'lifestyle addition' set was constructed added past search term frequency deep learning algorithms used are neural networks (DNNs) recurrent (RNNs). Performance differences across sets between models were tested statistically significant. Among metropolitan cities South Korea, Seoul (9.6 million) with largest population Busan (3.4 second had lowest error rate set. When predicting set, Seoul, reduced 20.1%, Busan, graph actual almost identical. Through this study, we able identify three notable results contribute patients infected epidemic future.

Language: Английский

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Leveraging Ensemble and Hybrid Forecasting Tools to Increase Accuracy: Turkey COVID-19 Case Study

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Feb. 7, 2025

Language: Английский

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Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation

Sustainability, Journal Year: 2023, Volume and Issue: 15(5), P. 4618 - 4618

Published: March 4, 2023

Electricity plays a vital role in the economic development and welfare of countries. Examining electricity situation defining scenarios for developing power plant infrastructure will help countries avoid misguided policies that incur high costs reduce people’s welfare. In present research, three from 2021–2040 have been defined Iran’s status. The first scenario continues current trend forecasts population, consumption, carbon dioxide emissions plants with ARIMA single triple exponential smoothing time series algorithms. As part second scenario, only non-hydro renewable resources be used to increase supply. By ensuring existence potential, annual growth patterns defined, taking into account generation achieved by successful nations. third involves integrating operating gas turbines combined cycles exchange buyback contracts. Economically, this calculates return on investment through an arrangement various contracts seller company fuel savings buyer.

Language: Английский

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Application of ARIMA model in forecasting remittance inflows: evidence from Yemen

International Journal of Economic Policy Studies, Journal Year: 2024, Volume and Issue: 18(1), P. 283 - 303

Published: Jan. 16, 2024

Language: Английский

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MFTM-Informer: A multi-step prediction model based on multivariate fuzzy trend matching and Informer

Information Sciences, Journal Year: 2024, Volume and Issue: 681, P. 121268 - 121268

Published: July 26, 2024

Language: Английский

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Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 257, P. 125054 - 125054

Published: Aug. 14, 2024

Language: Английский

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