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Projects/Data & AI
BioSentinel AI

BioSentinel AI

Imagine a SG digital platform empowering One Health Agencies to effectively prevent diseases and pandemics, safeguard our food security, and robustly protect our delicate environmental ecosystems. We have build a prototype, leveraging on (1) Modern Data Management and Engineering platforms to better organised diverse data sources that One Health Agencies relies on; (2) AI-powered Visualisation Tools to better map out the complex connections and intricate co-relations of the dataset; (3) a high-performing Agentic-AI system (based on a multi-agent architecture where a lead agent, the Orchestrator, directs other agents to solve tasks) to autonomously plans, tracks progress, and re-plans to recover from errors, while directing specialized agents to perform tasks (e.g. writing and executing Python code)

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BioSentinel AI

Imagine a SG digital platform that enables One Health Agencies prevent diseases, secure our food supply, and protect the environment

Team Members

NameDivision
Team LeadLiew Hui MingGovTech HQ
Product ManagerAily ChengGDT (NParks)
Data & AI, Business Intelligence AnalystKarthigesan PerumalluGovTech HQ
Data & AI, Business Intelligence AnalystRhys GohGDT (MOM)
Cloud EngineerMuthu RanganathanGDT (NParks)
GIS SpecialistRoy NewGDP (SLA)

Problem Statement:

Digitalisation of Biosurveillance - Strengthening early detection and intervention against potential outbreaks of zoonotic diseases for SG

What was done:

Built and developed a prototype based on the following hypothesis:

  1. Decoding data through Modern Data Engineering & Agentic AI

  2. Understanding complexity through simplicity – Visualisation

  3. Facilitating collaboration & sharing through digital platform

The prototype demonstrated its ability to move away from the current modus operandi, and challenges faced by the bioinformatician and scientists, in having:

  • To manually search for information animal disease outbreak and the information could be from multiple sources (e.g online database (WAHIS), Scientific mentions report via Tweets, etc)
  • To write customised scripts to extract the information, where possible
  • To present this from Excel sheets, with graphs and tables, Word, PowerPoint

Potential Scalability:

  • To every division /department who is responsible in the entire value-chain-process of Bio-surveillance in environmental scanning capabilities, allowing better control over the detection of various virus strains, matching accuracy, and wider range of data sources
  • To integrate that with the NParks Maven II Animal & Veterinary Service (M2 AVS) Biosurveillance module and NParks Data Platform, where Geospatial and textual analytics capabilities could be further enhanced through building data pipelines with different sources, e.g. M2 AVS (lab test results, import/export records, licences/permits), Quarantine information, Pet licensing records, and Vet clinic records
  • To leverage on WOG Vista Visualisation Platform as a common platform with One Health Agencies