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.
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 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.
Professor Eran Segal presented The Human Phenotype Project, a longitudinal cohort study with over 10,000 participants. The project aims to identify molecular markers and develop prediction models for disease using deep profiling techniques including medical history, lifestyle, blood tests, and microbiome analysis. The study provides insights into drivers of obesity, diabetes, and heart disease, identifying novel markers at the microbiome, metabolite, and immune system level. Why it matters: Such large-scale phenotyping initiatives could inform personalized medicine approaches relevant to the Middle East's specific health challenges.
Eran Segal from Weizmann Institute of Science presented The Human Phenotype Project, a large-scale prospective cohort with over 10,000 participants. The project aims to identify novel molecular markers and develop prediction models for disease onset using deep profiling. The profiling includes medical history, lifestyle, blood tests, and molecular profiling of the transcriptome, genetics, microbiome, metabolome and immune system. Why it matters: Such projects demonstrate the growing focus on personalized medicine in the region, utilizing advanced AI and machine learning techniques for disease prevention and treatment.
KAUST Associate Professor Xiangliang Zhang is using machine learning to analyze social media posts on Twitter related to COVID-19. Her team at KAUST's Computational Bioscience Research Center is analyzing sentiment in tweets using hashtags like #coronavirus and #covid19. Zhang aims to use this data to help predict localized outbreaks and provide an early warning system for governments and organizations. Why it matters: This research demonstrates the potential of AI-powered sentiment analysis to support public health efforts and inform decision-making during pandemics in the Middle East and globally.