Global Infectious Disease Early Warning Models: An Updated Review and Lessons from the COVID-19 Pandemic DOI Creative Commons
Weihua Hu, Huimin Sun, Yongyue Wei

et al.

Infectious Disease Modelling, Journal Year: 2024, Volume and Issue: 10(2), P. 410 - 422

Published: Dec. 3, 2024

An early warning model for infectious diseases is a crucial tool timely monitoring, prevention, and control of disease outbreaks. The integration diverse multi-source data using big artificial intelligence techniques has emerged as key approach in advancing these models. This paper presents comprehensive review widely utilized models around the globe. Unlike previous studies, this encompasses newly developed approaches such combined Hawkes after COVID-19 pandemic, providing thorough evaluation their current application status development prospects first time. These not only rely on conventional surveillance but also incorporate information from various sources. We aim to provide valuable insights enhancing global systems, well informing future research field, by summarizing underlying modeling concepts, algorithms, scenarios each model.

Language: Английский

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges DOI Creative Commons

Yang Ye,

Abhishek Pandey,

Carolyn E. Bawden

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 10, 2025

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for modeling. While fusion AI and traditional approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview emerging integrated applied across spectrum infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our highlights practical value models, including advances in disease forecasting, model parameterization, calibration. However, key research gaps remain. These include need better incorporation realistic decision-making considerations, expanded exploration diverse datasets, further investigation into biological socio-behavioral mechanisms. Addressing these will unlock synergistic modeling to enhance understanding dynamics support more effective public health planning response. Artificial has improve diseases by incorporating data sources complex interactions. Here, authors conduct use summarise methodological advancements identify gaps.

Language: Английский

Citations

2
A constrained optimisation framework for parameter identification of the SIRD model DOI Creative Commons
Andrés Miniguano–Trujillo, John W. Pearson, Benjamin D. Goddard

et al.

Mathematical Biosciences, Journal Year: 2025, Volume and Issue: unknown, P. 109379 - 109379

Published: Jan. 1, 2025

Language: Английский

Citations

1
Pattern formation of network epidemic model and its application in oral medicine DOI

Linhe Zhu,

Yue Li,

He Le

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: 264, P. 108688 - 108688

Published: March 6, 2025

Language: Английский

Citations

0
Characterization of loading, relaxation, and recovery behaviors of high‐density polyethylene using a three‐branch spring‐dashpot model DOI
Furui Shi, P.‐Y. Ben Jar

Polymer Engineering and Science, Journal Year: 2024, Volume and Issue: 64(10), P. 4920 - 4934

Published: Aug. 1, 2024

Abstract This paper presents an analysis of the stress evolution high‐density polyethylene (HDPE) at loading, relaxation, and recovery stages in a multi‐relaxation‐recovery (RR) test. The is based on three‐branch spring‐dashpot model that uses Eyring's law to govern viscous behavior. comprises two branches represent short‐ long‐term time‐dependent responses deformation, quasi‐static branch time‐independent response. A fast numerical framework genetic algorithms was developed determine values for parameters so difference between simulation experimental data could be less than 0.08 MPa. Using this approach, were determined as functions deformation time can simulate response RR also generated 10 sets parameter examine their consistency. study concludes serve suitable tool analyzing mechanical properties HDPE, potentially used characterize among PEs performance. Highlights Developed computer programs automatically. Explained unusual drop during after unloading. Evaluated statistical range good fitting.

Language: Английский

Citations

1
Global Infectious Disease Early Warning Models: An Updated Review and Lessons from the COVID-19 Pandemic DOI Creative Commons
Weihua Hu, Huimin Sun, Yongyue Wei

et al.

Infectious Disease Modelling, Journal Year: 2024, Volume and Issue: 10(2), P. 410 - 422

Published: Dec. 3, 2024

An early warning model for infectious diseases is a crucial tool timely monitoring, prevention, and control of disease outbreaks. The integration diverse multi-source data using big artificial intelligence techniques has emerged as key approach in advancing these models. This paper presents comprehensive review widely utilized models around the globe. Unlike previous studies, this encompasses newly developed approaches such combined Hawkes after COVID-19 pandemic, providing thorough evaluation their current application status development prospects first time. These not only rely on conventional surveillance but also incorporate information from various sources. We aim to provide valuable insights enhancing global systems, well informing future research field, by summarizing underlying modeling concepts, algorithms, scenarios each model.

Language: Английский

Citations

0