The paper introduces Guided Deep List, a tool for automating the generation of epidemiological line lists from open source reports. The tool uses distributed vector representations and dependency parsing to extract tabular data on disease outbreaks. It was evaluated on MERS outbreak data in Saudi Arabia, demonstrating improved accuracy over baseline methods and enabling epidemiological inferences.
This study investigates the correlation between Google Trends data for COVID-19 symptoms and the actual number of COVID-19 cases in Saudi Arabia between March and October 2020. The researchers found that searches for "cough" and "sore throat" were most frequent, while "loss of smell", "loss of taste", and "diarrhea" showed the highest correlation with confirmed cases. The study concludes that Google searches can serve as a supplementary surveillance tool for monitoring the spread of COVID-19 in Saudi Arabia. Why it matters: The research demonstrates the potential of using readily available digital data to augment traditional surveillance methods for public health monitoring in the region.
A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.
A KAUST research team is using cellphone mobility data, Google searches, and social media to model and predict COVID-19 spread. The models aim to forecast cases in the coming weeks and inform resource allocation, including hospital beds and medical staff. The team is using aggregated and anonymized data from cellphone companies to respect people's privacy. Why it matters: Integrating real-time digital data with epidemiological modeling can improve the speed and effectiveness of public health responses in the region and globally.
KAUST researchers developed a new model integrating SIR compartment modeling in time and a point process modeling approach in space-time, also considering age-specific contact patterns. They used a two-step framework to model infectious locations over time for different age groups. The model demonstrated improved predictive accuracy in simulations and a COVID-19 case study in Cali, Colombia, compared to existing models. Why it matters: This model can assist decision-makers in identifying high-risk locations and vulnerable populations for better disease control strategies in the region and globally.
KAUST Associate Professor Peiying Hong delivered a lecture on using wastewater testing to detect outbreaks earlier. The lecture explains how wastewater testing could lead to faster detection and more effective response to future pandemics. The research was presented at King Abdullah University of Science and Technology. Why it matters: Wastewater epidemiology can provide early warnings for emerging pathogens and improve public health preparedness in the region.
MBZUAI hosted an AI Talks session featuring Dr. Mohammad Yaqub discussing AI's role in fighting COVID-19 and predicting future pandemics. AI can detect outbreaks by mining news and social media for unusual patterns, as demonstrated by companies flagging pneumonia cases in Wuhan before the official announcement. AI-empowered drug repurposing identified Baricitinib as a potential COVID-19 treatment and can predict virus mutations. Why it matters: This highlights the potential of AI to enhance pandemic preparedness and response in the region, particularly through institutions like MBZUAI.
Dr. Paula Moraga, an Assistant Professor at KAUST, has been awarded the 2023 Letten Prize for her work on disease surveillance systems. The prize recognizes researchers under 45 for contributions to health, development, environment, and equality. Moraga's research enables early epidemic detection, and she was selected from 164 applicants. Why it matters: This award highlights KAUST's contributions to public health research and underscores the importance of AI and data science in addressing global health challenges.