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.
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 Professor Hernando Ombao is leading the Biostatistics Group to develop statistical models for projecting hospitalization surges during the COVID-19 pandemic. The group uses techniques like time series analysis and stationary subspace analysis to understand complex biological processes. The models aim to provide public health officials with accurate hospitalization estimates under varying scenarios. Why it matters: This research contributes to preparedness and resource allocation in healthcare systems during public health crises, with potential applications beyond COVID-19.
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.
KAUST's Laboratory of Stem Cells and Diseases, led by Assistant Professor Antonio Adamo, uses induced pluripotent stem cells (iPSCs) to model diseases like diabetes. The lab employs a reprogramming technique to revert patient fibroblasts into iPSCs, enabling the study of disease progression in vitro. Adamo's research focuses on enzymes and disregulated transcriptional/epigenetic mechanisms to understand disease onset. Why it matters: This research contributes to regenerative medicine and offers insights into metabolic diseases relevant to the GCC region.
KAUST's Rapid Research Response Team (R3T), including Professor Samir Hamdan, is working to understand and counteract the spread of COVID-19. The team assembled a complete homemade, one-step RT-PCR test, comparable to commercial kits, with a patent-free manufacturing recipe. KAUST R3T is also researching faster, more accurate point-of-care tests, including a CRISPR-based molecular test. Why it matters: This research provides accessible testing solutions and contributes to more effective and rapid detection methods for combating viral spread in the region and globally.
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.