Researchers developed COVIBOT, a smart chatbot to spread awareness and assist during the COVID-19 pandemic in Saudi Arabia. The chatbot uses Azure Cognitive Services and is available in both English and Arabic. COVIBOT's use cases were tested and validated using a scenario-based approach.
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.