A novel dynamic model describing the spread of virus DOI Open Access

Veli Shakhmurov,

Muhammet Kurulay,

Aida Sahmurova

et al.

Published: Aug. 14, 2023

This study proposes a nonlinear mathematical model of virus transmission based on the SEIR model. In this study, interaction between viruses and immune cells is investigated using phase-space analysis Specifically, it focused dynamics stability behavior spread in population its with human systems cells. The endemic equilibrium points are found local all equilibria related obtained. Further, global either, at disease-free equilibria, or discussed by constructing Lyapunov function which shows validity concern exists. Finally, simulated solution achieved relationship highlighted.

Language: Английский

Stochastic disease spreading and containment policies under state-dependent probabilities DOI Creative Commons
Davide La Torre, Simone Marsiglio, Franklin Mendivil

et al.

Economic Theory, Journal Year: 2023, Volume and Issue: 77(1-2), P. 127 - 168

Published: April 12, 2023

We analyze the role of disease containment policy in form treatment a stochastic economic-epidemiological framework which probability occurrence random shocks is state-dependent, namely it related to level prevalence. Random are associated with diffusion new strain affects both number infectives and growth rate infection, such realization may be either increasing or decreasing infectives. determine optimal steady state framework, characterized by an invariant measure supported on strictly positive prevalence levels, suggesting that complete eradication never possible long run outcome where instead endemicity will prevail. Our results show that: (i) independently features state-dependent probabilities, allows shift leftward support measure; (ii) probabilities affect shape spread distribution over its support, allowing for alternatively highly concentrated low levels more out larger range (possibly higher) levels.

Language: Английский

Citations

9
Machine Learning for Infectious Disease Risk Prediction: A Survey DOI Creative Commons
Mutong Liu, Yang Liu, Jiming Liu

et al.

ACM Computing Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Infectious diseases place a heavy burden on public health worldwide. In this paper, we systematically investigate how machine learning (ML) can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious risks. First, introduce the background motivation for using ML risk prediction. Next, describe development application of various models prediction, categorizing them according to models’ alignment with vital concerns specific two distinct phases propagation: (1) pandemic epidemic (the P-E phaseS) (2) endemic elimination E-E phaseS), each presenting its own set critical questions. Subsequently, discuss challenges encountered when dealing model inputs, designing task-oriented objectives, conducting performance evaluations. We conclude discussion open questions future directions.

Language: Английский

Citations

0
Hybrid Modeling Approaches for Predicting COVID-19 Mortality: A Comparative Study Across USA, France, and India DOI Creative Commons

Bandu Uppalaiah,

Mallikarjuna Reddy Doodipala, Rajalakshmi Krishnamurthi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105092 - 105092

Published: April 1, 2025

Language: Английский

Citations

0
Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction DOI Creative Commons
Wenhui Ke, Yimin Lu

Mathematics, Journal Year: 2024, Volume and Issue: 12(3), P. 493 - 493

Published: Feb. 4, 2024

Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle accurately forecast number cases. To address this problem, a hybrid framework is proposed in study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, CNN-LSTM-ATT network model. This framework, named EEMD-FE-CNN-LSTM-ATT, applied predict COVID-19 study focuses on dataset from United States as research subject validate feasibility framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models all evaluation metrics, demonstrating its efficacy handling epidemic series. Furthermore, generalizability validated datasets France Russia. offers approach for predicting pandemic, providing important technical support future infectious forecasting.

Language: Английский

Citations

2
COVID-19 in Italy: Is the Mortality Analysis a Way to Estimate How the Epidemic Lasts? DOI Creative Commons
Pietro Marco Boselli, José M. Soriano

Biology, Journal Year: 2023, Volume and Issue: 12(4), P. 584 - 584

Published: April 11, 2023

When an epidemic breaks out, many health, economic, social, and political problems arise that require a prompt effective solution. It would be useful to obtain all information about the virus, including epidemiological ones, as soon possible. In previous study of our group, analysis positive-alive was proposed estimate duration. stated every ends when number (=infected-healed-dead) glides toward zero. fact, if with contagion everyone can enter phenomenon, only by healing or dying they get out it. this work, different biomathematical model is proposed. A necessary condition for resolved mortality reaches asymptotic value, from there, remains stable. At time, must also close This seems allow us interpret entire development highlight its phases. more appropriate than one, especially spread infection so rapid increase in live positives staggering.

Language: Английский

Citations

3
Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa DOI Creative Commons
Kedir Hussein Abegaz, İlker Etikan

Diagnostics, Journal Year: 2022, Volume and Issue: 12(11), P. 2861 - 2861

Published: Nov. 18, 2022

East Africa was not exempt from the devastating effects of COVID-19, which led to nearly complete cessation social and economic activities worldwide. The objective this study predict mortality due COVID-19 using an artificial intelligence-driven ensemble model in Africa. dataset, spans two years, divided into training verification datasets. To mortality, three steps were conducted, included a sensitivity analysis, modelling four single AI-driven models, development models. Four dominant input variables selected conduct Hence, coefficients determination ANFIS, FFNN, SVM, MLR 0.9273, 0.8586, 0.8490, 0.7956, respectively. non-linear approaches performed better than linear approaches, ANFIS best-performing approach that boosted predicting performance This fact revealed promising capability models for daily other parts globe.

Language: Английский

Citations

4
Disease outbreak prediction using natural language processing: a review DOI
Avneet Singh Gautam, Zahid Raza

Knowledge and Information Systems, Journal Year: 2024, Volume and Issue: 66(11), P. 6561 - 6595

Published: Aug. 6, 2024

Language: Английский

Citations

0
Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI DOI Creative Commons

Myung-joo Park,

Hyo-Sik Yang

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7205 - 7205

Published: Nov. 11, 2024

This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability performance of these models in predicting electricity consumption patterns, which a critical component implementing effective demand response (DR) strategies. study provides comprehensive analysis predictive accuracy, computational efficiency, scalability each algorithm dataset real-time collected from AMI systems over designated period. Through extensive experiments, we demonstrate that has distinct strengths weaknesses depending characteristics dataset. Specifically, SVM exhibited superior handling nonlinear patterns high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential modeling complex temporal dependencies but was sensitive to hyperparameter settings required substantial amount training research offers practical guidelines selecting optimal model based data application needs, contributing development more efficient dynamic energy management findings highlight importance integrating advanced techniques into smart grid enhance reliability responsiveness DR programs. lays solid foundation future real-world applications support stability.

Language: Английский

Citations

0
Water Transmission Increases the Intensity of COVID-19 Outbreaks DOI Creative Commons
Jianping Huang,

Xinbo Lian,

Yingjie Zhao

et al.

Frontiers in Public Health, Journal Year: 2022, Volume and Issue: 10

Published: May 25, 2022

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for acceleration in is still unknown. We describe statistical characteristics infected patients first case to June 2021, and trace causes two complete way, combined with data on natural disasters, environmental pollution population movements etc. found that water-to-human transmission accelerates COVID-19 spreading. The rate 382% higher than human-to-human during 2020 summer India. When syndrome 2 (SARS-CoV-2) enters human body directly through water-oral pathway, virus particles nitrogen salt water accelerate viral infection mutation rates gastrointestinal tract. Based results attribution analysis, without current effective interventions, could have experienced third monsoon season this year, which would increased severity disaster led South Asian economic crisis.

Language: Английский

Citations

2
Three Waves of COVID-19 in India-An Autoregression Model DOI Open Access
R. Gupta,

N. Nethravathi,

Kokila Ramesh

et al.

Published: June 15, 2023

COVID-19, the infectious disease caused by most recently discovered coronavirus is related to upper respiratory tract family of disorders.It triggers asthma, severe diseases, cause lung infection and bronchiolitis infections.Though severity these infections are getting obsolete but may remain in mild forms waves our lives.A thorough study about its spread across globe, prediction understanding transmission patterns, through various statistical models might be one effective ways provide an insight aspects suggest prevention strategies.In light this, Auto Regression (AR) developed for confirmed cases with 5 days lag, 6 different states India.The data has been trained from July 2020 2023 taking into account three impactful corona waves.August 2022 used testing & validating models.Based on population size total number Indian have classified categories: Most affected, moderately affected least states.Two selected each categories purpose research here.Auto all 3 waves.Finally, fourth wave done month using third AR models.The results varies state model.

Language: Английский

Citations

0