Nowcasting influenza from wastewater with machine learning
A machine-learning study, in collaboration with the Luxembourg Institute of Science and Technology, that estimates influenza cases from viral concentrations in wastewater, turning environmental signals into an early epidemic indicator.
The problem
Traditional influenza surveillance lags behind real transmission. The COVID-19 pandemic showed that wastewater carries an early epidemiological signal. The question was whether that signal could reliably estimate influenza cases in Luxembourg.
Approach
I reviewed existing statistical methods, weighed their trade-offs, and benchmarked a wide field of predictive models against real data (training on 80% and validating on 20%) to find which best recovered case counts from viral load.
- Reviewed and compared candidate statistical methods
- Benchmarked 12 models (LOESS, KNN, Random Forest, Prophet, GAM, SVM, and more)
- 80/20 train-validation split, evaluated by MAE
- LOESS performed best, closely followed by KNN
Data & method
Weekly samples were collected over four years (March 2020-March 2024) from four wastewater treatment plants covering southern Luxembourg, analyzed via droplet-digital PCR. Influenza A and B viral concentrations were combined and modelled against reported cases.
- dd-PCR viral concentration measurements
- Influenza A + B loads combined
- A Statistical Analysis Plan guiding the pipeline
Outcome
The work fed into the VIRALERT research programme and associated scientific publications, establishing that machine-learning models can recover epidemic trends from wastewater data.
Public-health value
Wastewater-based nowcasting offers a low-cost, population-level early-warning signal for influenza, complementing clinical surveillance and giving health authorities more lead time to respond.
Lessons learned
Rigorous benchmarking beats intuition: the simplest models (LOESS, KNN) outperformed more complex ones on this signal.
A clear Statistical Analysis Plan up front makes results defensible and reproducible.
Environmental data can be a powerful, underused source of public-health intelligence.