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Mining and Summarization of Early Warning Pandemic Signals Interest1 #701140 Mining and Summarization of Early Warning Pandemic Signals for vector-borne diseases (Lyme and Chikungunya, etc.). | - This project aims to develop algorithms and tools to automatically detect early warning signals (EWS) for a pandemic in multiple available data sources like internet activity (e.g., Twitter, Facebook, etick.ca). Social media data and web-scraping are especially effective to detect and understand public sentiment for some infectious diseases (ID).We will investigate the design algorithms to detect signals from social media texts, e.g., detection of pandemic-related entities and events, tracing opinions about a particular event across multiple sources, and offering clues of contrastive viewpoint.
- Co-Project Investigators: Gias Uddin (University of Calgary), Bouchra Nasri (Université de Montréal), Jude Dzevela Kong (York University), and Mark Lewis (University of Alberta)
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+Citations (1) - CitationsAdd new citationList by: CiterankMapLink[1] An early warning indicator trained on stochastic disease-spreading models with different noises
Author: Amit K. Chakraborty, Shan Gao, Reza Miry, Pouria Ramazi, Russell Greiner, Mark A. Lewis, Hao Wang Publication date: 9 August 2024 Publication info: J. R. Soc. Interface.212024019920240199, August 2024, Volume 21, Issue 217 Cited by: David Price 11:02 PM 8 December 2024 GMT Citerank: (5) 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 70113403 Early Warning Systems of Infectious DiseasesAnalysis of early warning signals is a crucial component of a coordinated response to emerging infectious diseases (EIDs). The goal of OMNI-RÉUNIs’ projects is to provide both an analysis of these signals and of the possibility of disease establishment, and collectively these could be used to inform public health regarding the level of disease threat.859FDEF6, 701222OMNI – Publications144B5ACA0, 704045Covid-19859FDEF6 URL: DOI: https://doi.org/10.1098/rsif.2024.0199
| Excerpt / Summary [Journal of the Royal Society Interface, 9 August 2024]
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator’s effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts. |
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