Imagine a smoke detector, but for diseases in a city's water system. Instead of waiting for people to get sick and visit a doctor, this system scans wastewater to find traces of germs and antibiotic resistance. It's like finding a needle in a haystack using a high-tech digital magnet to warn health officials before a full-blown crisis hits.
Public health officials in resource-poor regions lack real-time data on disease outbreaks and antibiotic resistance. Current clinical reporting is often too slow to prevent widespread infection.
A mobile wastewater surveillance system and semi-automated bioinformatics tools. These include a visual dashboard for reporting genomic trends in human pathogens.
If you are a water utility provider dealing with unpredictable contamination in remote areas — this project developed a mobile wastewater surveillance system that allows for timely detection and reporting of outbreaks. This enables faster response times and better protection of drinking-water supplies.
If you are a diagnostics company dealing with the need for specialized tools in resource-poor regions — this project developed semi-automated bioinformatics tools for processing genomic epidemiology data. This provides a blueprint for creating streamlined, low-infrastructure sequencing workflows.
If you are a risk management firm dealing with the lack of real-time data on antimicrobial resistance — this project developed a visual dashboard for genomic data. This allows for data-driven evidence to be conveyed to decision-makers for more efficient public health policies.
Based on available project data, no specific pricing or cost structures for the system are provided.
The project focuses on creating a sustainable model for sub-Saharan conditions, including mobile surveillance systems and semi-automated data processing to handle large genomic data sets.
Based on available project data, specific licensing terms are not mentioned, though the project emphasizes developing standards for sharing genomic data across borders.
It integrates by transferring generated data to key health system actors via a visual dashboard to support public health interventions.
The project period is from 2023-07-01 to 2026-06-30.
The consortium is heavily academic and research-oriented, consisting of 4 universities and 5 research organizations across 8 countries. There is a 0% industry ratio, meaning the current output is focused on scientific validation and capacity building rather than immediate commercial productization. The presence of partners in both Europe and sub-Saharan Africa (e.g., BF, CD, TZ) indicates a strong focus on cross-border implementation and field testing.
Lunds Universitet, Sweden
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