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Updated: May 29, 2025
High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR
Published on: November 28, 2016
Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of
Z Movahedi Nia1, L Seyyed-Kalantari2, M Goitom3
1Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; Laboratory for Industrial and Applied Mathematics (LIAM), York University, Toronto, Canada.
Background:
Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies.
Methodology:
Six different datasets were collected for different provinces of Canada: number of influenza cases, number of COVID-19 cases, Google Trends, Reddit posts, satellite air quality data, and weather data. Moreover, five different data sources were collected for southern African countries whose COVID-19 number of cases were significantly correlated with each other: number of COVID-19 infections, Google Trends, Wiki Trends, Google News, and satellite air quality data. For each infectious disease, i.e. COVID-19 and Influenza for Canada and COVID-19 for southern African countries, data was processed, scaled, and fed into the deep learning model which included four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a linear Neural Network (NN). Hyperparameters were optimized to provide an accurate 56-day-ahead prediction of the number of cases.
Result:
The accuracy of our models in real-time surveillance, prediction, forecasting, and early warning of respiratory diseases are evaluated against state-of-the-art models, through Root Mean Square Error (RMSE), coefficient of determination (R2-score), and correlation coefficient. Our model improves R2-score, RMSE, and correlation by up to 55.98 %, 39.71 %, and 44.47 % for 56 days-ahead COVID-19 prediction in Ontario, 34.87 %, 25.52 %, 50.91 % for 8 weeks-ahead influenza prediction in Quebec, and 51.04 %, 32.04 %, and 28.74 % for 56 days-ahead COVID-19 prediction in South Africa, respectively.
Conclusion:
This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.
