Anaesthesia Critical Care & Pain Medicine, Journal Year: 2023, Volume and Issue: 43(1), P. 101329 - 101329
Published: Nov. 20, 2023
ACS Infectious Diseases, Journal Year: 2024, Volume and Issue: 10(3), P. 808 - 826
Published: Feb. 28, 2024
Recent pandemics, including the COVID-19 outbreak, have brought up growing concerns about transmission of zoonotic diseases from animals to humans. This highlights requirement for a novel approach discern and address escalating health threats. The One Health paradigm has been developed as responsive strategy confront forthcoming outbreaks through early warning, highlighting interconnectedness humans, animals, their environment. system employs several innovative methods such use advanced technology, global collaboration, data-driven decision-making come with an extraordinary solution improving worldwide disease responses. Review deliberates environmental, animal, human factors that influence risk, analyzes challenges advantages inherent in using surveillance system, demonstrates how these can be empowered by Big Data Artificial Intelligence. Holistic Surveillance Framework presented herein holds potential revolutionize our capacity monitor, understand, mitigate impact infectious on populations.
Language: Английский
Citations
23
Molecular Biomedicine, Journal Year: 2025, Volume and Issue: 6(1)
Published: Jan. 3, 2025
Abstract Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates multidimensional role AI in pandemic, which arises as a global health crisis, and its preparedness responses, ranging from enhanced epidemiological modelling to acceleration vaccine development. The confluence technologies guided us new era data-driven decision-making, revolutionizing our ability anticipate, mitigate, treat infectious illnesses. begins by discussing impact on emerging countries worldwide, elaborating critical significance modelling, bringing enabling forecasting, mitigation response pandemic. In epidemiology, AI-driven models like SIR (Susceptible-Infectious-Recovered) SIS (Susceptible-Infectious-Susceptible) are applied predict spread disease, preventing outbreaks optimising distribution. also demonstrates how Machine Learning (ML) algorithms predictive analytics improve knowledge disease propagation patterns. collaborative aspect discovery clinical trials various vaccines is emphasised, focusing constructing AI-powered surveillance networks. Conclusively, presents comprehensive assessment impacts builds AI-enabled dynamic collaborating ML Deep (DL) techniques, develops implements trials. focuses screening, contact tracing monitoring virus-causing It advocates for sustained research, real-world implications, ethical application strategic integration strengthen collective face alleviate effects issues.
Language: Английский
Citations
9
Gene, Journal Year: 2024, Volume and Issue: 905, P. 148174 - 148174
Published: Jan. 18, 2024
Language: Английский
Citations
2
Mathematics, Journal Year: 2023, Volume and Issue: 11(10), P. 2330 - 2330
Published: May 16, 2023
Globally, the COVID-19 pandemic’s development has presented significant societal and economic challenges. The carriers of transmission have also been identified as asymptomatic infected people. Yet, most epidemic models do not consider their impact when accounting for disease’s indirect transmission. This study suggested investigated a mathematical model replicating spread coronavirus disease among A was conducted on every aspect system’s solution. equilibrium points basic reproduction number were computed. endemic point disease-free had both undergone local stability analyses. geometric technique used to look into global dynamics point, whereas Castillo-Chavez theorem point. transcritical bifurcation at discovered exist. system parameters changed using number’s sensitivity technique. Ultimately, numerical simulation apply population Iraq in order validate findings define factors that regulate illness breakout.
Language: Английский
Citations
3
PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0307092 - e0307092
Published: Aug. 23, 2024
Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) have been generally used in analyzing epidemiological data forecasting the trajectory of transmission infectious diseases COVID-19. Experience shows that accurately COVID-19 curve is a big challenge for researchers field modeling because multiple unquantified factors can affect transmission. In past years, we new model, l-i to analyze trend United States. Unlike conventional model delayed use or partially approximation temporal homogeneity, takes into account chronological order infected individuals both latent ( l ) period i period, thus improves accuracy diseases, especially during periods rapid rise fall number infections. This paper describes (1) how (a mechanistic model) combined with fitting methods simulate predict transmission, (2) social interventions variants significantly change trends by changing rate coefficient β n , fraction susceptible people S / N ), reinfection rate, (3) why difficult, (4) what are strategies improve forecast outcome (5) some successful examples obtained.
Language: Английский
Citations
0
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 11, 2024
ABSTRACT Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with transmissions between hosts. However, no computational simulators are available assist the characterization of dynamics. This limits our ability interpret epidemiological predictions incorporating and validate inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for simulation infection dynamics across population, tissue, cellular levels. Apollo is scalable hundreds millions genomes can handle complex demographic population genetic models. replicate real evolution; accurately recapturing observed sequences from an HIV cohort derived initial population-genetic configurations. For practical applications, using Apollo-simulated transmission networks, validated uncovered limitations widely used tool.
Language: Английский
Citations
0
Anaesthesia Critical Care & Pain Medicine, Journal Year: 2022, Volume and Issue: 41(2), P. 101056 - 101056
Published: April 1, 2022
Citations
1
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: Aug. 8, 2023
Abstract Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) have been generally used in analyzing epidemiological data forecasting the trajectory of transmission infectious diseases COVID-19. Experience shows that accurately COVID-19 curve is a big challenge to researchers field modeling. Multiple factors (such social distancing, vaccinations, public health interventions, new variants) can affect transmission. In past years, we model, l-i analyze trend United States. The letters l i are two parameters model representing average time length latent period period. takes into account temporal heterogeneity infected individuals thus improves accuracy diseases. This paper describes how these multiple mentioned above could significantly change trends, why difficult, what strategies improve forecast outcome, some successful examples obtained.
Language: Английский
Citations
0
Anaesthesia Critical Care & Pain Medicine, Journal Year: 2023, Volume and Issue: 43(1), P. 101329 - 101329
Published: Nov. 20, 2023
Citations
0