Work

Explainable Machine Learning Models for Predicting COVID-19 Cases in Catalonia Based on Wastewater Monitoring Data

Year: 2023

Type: book-chapter

Abstract: Surveillance of infectious diseases that can spread quickly among the population has become a critical goal nowadays due to the dramatic effect of diseases like SARS-CoV-2. One promising method to be able to monitor the spread of such diseases among the population of a city is the analysis of biological compounds in the sewage network of different cities. In this paper, we summarize the results of training a prediction model for SARS-CoV-2 cases based on historical biological data collected from different wastewater treatment plants (WWTP) located in different parts of Catalunya. We consider different approaches for the prediction problem and develop models based on extreme gradient boosting. Finally, we evaluate the quality of the results of our model and study the relevance of the different variables of the model using SHapley Additive exPlanations (SHAP) analysis.

Source: Frontiers in artificial intelligence and applications

DOI: 10.3233/faia230691

Authors: Marc Ribalta, Josep Pueyo-Ros, Lluis Corominas, Ramón Béjar, Carles Mateu, Edgar Rubión

Institutions: Universitat de Lleida, EURECAT Centre Tecnològic de Catalunya, Catalan Institute for Water Research, Universitat de Girona

Language: English

Topic: SARS-CoV-2 detection and testing

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDGs: Clean water and sanitation, Good health and well-being

Open Access status: hybrid

Funders: Centro para el Desarrollo Tecnológico Industrial, Agència de Gestió d'Ajuts Universitaris i de Recerca, European Regional Development Fund, Agencia Estatal de Investigación