Sharif’s research combines predictive modeling and novel data sources to improve the detection and mitigation of avian influenza virus (AIV) outbreaks, enhancing surveillance systems with real-time data integration. Sharifs AIV decision support system helps impacted parties analyze risk and make informed decisions, playing a vital role in managing AIV and other infectious diseases for better food security, animal health, and public safety.
Managing avian influenza virus (AIV) is a significant and costly challenge for the Canadian poultry industry and worldwide. As global food demand drives increased poultry production, the risk of viral outbreaks also rises due to high production densities and close proximity between operations. For producers, an outbreak of AIV is devastating both economically and psychologically, with thousands of birds culled to control the spread. Nationally, the impacts on the food chain can be substantial and there is increasing concern about the spread of AIV to humans. Effectively mitigating the risk of AIV is essential to protect animal welfare, economic stability, and, increasingly, human health. Surveillance systems play a key role in controlling the spread of pathogens by monitoring outbreaks and collecting real-time data. To be effective, these systems must not only rely on up-to-date information but also generate new data that can inform decisions for epidemiologists, poultry producers, and animal health professionals. Enhancing current surveillance systems requires a deeper understanding of AIV’s complex transmission pathways, alongside advancements in predictive modeling to better detect and diagnose emerging outbreaks.
This interdisciplinary Food from Thought project lead by Dr. Shayan Sharif builds upon previous research to improve the ability to predict the risk of highly pathogenic AIV outbreaks and detect them early on. The project combines several years of research to extend current predictive approaches and generate frameworks on the transmission pathways of AIV. Most recently, Sharif’s team has combined this predictive modelling with novel data sources and data integration techniques, creating a decision support system (DSS) to be used by stakeholders to detect and diagnosis AIV outbreaks in real time. The prototype DSS integrates data from several sources including X (Twitter), weather records, poultry density data, feed and food trade, and migratory birds’ distribution information, as well as historical data from successful modelling case studies. Adapted to the Canadian context, Sharif’s decision support system predicts risk, enhances data analysis, and enables stakeholders to access timely high-quality data for evaluating risk factors.
The project demonstrates the capacity for new data technologies to rapidly predict and detect AIV. The team generated several valuable surveillance tools, combining innovative data sources with new modelling frameworks to create a viable AIV decision support system. In one study, the team constructed and successfully tested a modelling framework to uncover and even predict AIV patterns. The team also created a second modelling framework, in which they showed that outbreaks could be rapidly detected from social media data, namely Twitter (now X) data. The team demonstrated that most outbreaks could be detected from triaged Twitter data, pointing to the potential use of social media data to augment the speed and accuracy of current surveillance systems, both for AIV and other infectious or zoonotic diseases. Finally, the team developed a decision support system, incorporating components from earlier predictive models with a user-interface (dashboard) to provide answers to decision-makers regarding the risk of disease at different scales.
Sharif’s research significantly enhances the understanding of AIV transmission and improves upon current fragmented systems for monitoring and predicting future outbreaks. The project’s impact is twofold: it generates valuable new insights into transmission patterns and behaviors, while also granting access to more timely and reliable data through the validated decision support system. This project highlights the importance of collaborative efforts at multiple levels to effectively address AIV in Canada and beyond. The predictive modeling and decision support systems developed by Sharif will be crucial for enhancing surveillance and control efforts among epidemiologists, health and government bodies, and poultry industry stakeholders. Sharif’s work harnesses the power of data science to tackle complex challenges related to food security, human health, and animal health, cementing the team’s position as global leaders in the fight against AIV and emerging diseases.
Astill, J., Dara, R. A., Fraser, E. D. G., & Sharif, S. (2018). Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus. Frontiers in Veterinary Science, 5, 263. https://doi.org/10.3389/fvets.2018.00263
Raj, S., Alizadeh, M., Shoojadoost, B., Hodgins, D., Nagy, É., Mubareka, S., Karimi, K., Behboudi, S., & Sharif, S. (2023a). Determining the Protective Efficacy of Toll-Like Receptor Ligands to Minimize H9N2 Avian Influenza Virus Transmission in Chickens. Viruses, 15(1), 238. https://doi.org/10.3390/v15010238
Raj, S., Alizadeh, M., Shoojadoost, B., Hodgins, D., Nagy, É., Mubareka, S., Karimi, K., Behboudi, S., & Sharif, S. (2023b). Determining the Protective Efficacy of Toll-Like Receptor Ligands to Minimize H9N2 Avian Influenza Virus Transmission in Chickens. Viruses, 15(1), 238. https://doi.org/10.3390/v15010238
Raj, S., Matsuyama-Kato, A., Alizadeh, M., Boodhoo, N., Nagy, E., Mubareka, S., Karimi, K., Behboudi, S., & Sharif, S. (2023). Treatment with Toll-like Receptor (TLR) Ligands 3 and 21 Prevents Fecal Contact Transmission of Low Pathogenic H9N2 Avian Influenza Virus (AIV) in Chickens. Viruses, 15(4), 977. https://doi.org/10.3390/v15040977
Yousefinaghani, S., Dara, R., Poljak, Z., Bernardo, T. M., & Sharif, S. (2019). The Assessment of Twitter’s Potential for Outbreak Detection: Avian Influenza Case Study. Scientific Reports, 9(1), 18147. https://doi.org/10.1038/s41598-019-54388-4
Dr. Zvonimir Poljak, Dr. Lauren Grant, Dr. Sakib Shahriar, Dr. Nitish Boodhoo, Dr. Emily Maier, Dr. Jane Parmley, Dr. Rozita Dara
Marzieh Soltani, Sugandha Raj, Mina Khoshbazm, Erica Johncox
CARE-AI U of G, One Health Institute U of G, The Ontario Agri-Food Innovation Alliance Tier I Research Program, Egg Farmers of Ontario, NSERC Alliance, Canadian Food Inspection Agency, Environment and Climate Change Canada, Feather Board Command Centre, Food and Agriculture Organization
