Using machine learning to drive social learning in a Covid-19 Agent-Based Model

P.W.M. Augustijn*, R. Aguilar Bolivar, S. Abdulkareem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

24 Downloads (Pure)

Abstract

Disease transmission and governmental interventions influence the spread of Covid-19. Models can be essential tools to optimise these governmental interventions. This requires the exploration of various ways to implement government agent behaviour. In Agent-Based Models (ABMs), government agent behaviour can be rule-based or data-driven, and the agent can be an isolated learner (using only its own data) or a social learner. We explore the creation of a data-driven social approach in which behaviour is based on a Machine Learning (ML) algorithm, and the government considers data from other European countries as input for their decision-making. Governmental actions start with risk perception, based on several parameters, e.g. the number of disease cases, deaths, and hospitalisation rate. The interventions are measured via the stringency index, measuring the simultaneous number of interventions (working from home, wearing a facemask, closing schools, etc.) taken. We test four machine learning algorithms (Bayesian Network (BN), c4.5, Naïve Bayes (NB) and Random Forest (RF)), using a 5-class and a 3-class classification of the stringency level. The algorithms are trained on disease data from many European countries. The best-performing algorithms were c4.5 and RF. The next step is to implement these algorithms into the ABM and evaluate the outcomes compared to the original model.
Original languageEnglish
Title of host publication26th AGILE Conference on Geographic Information Science
Subtitle of host publicationSpatial data for design
EditorsP. van Oosterom, H. Ploeger, A. Mansourian, S. Scheider, R. Lemmens, B. van Loenen
PublisherCopernicus
Pages1-4
Number of pages4
Volume4
DOIs
Publication statusPublished - 6 Jun 2023
Event26th AGILE Conference on Geographic Information Science, AGILE 2023: Spatial data for design - Delft, Netherlands
Duration: 13 Jun 202316 Jun 2023
Conference number: 26
https://agile-online.org/conference-2023

Publication series

NameAGILE: GIScience Series
PublisherCopernicus

Conference

Conference26th AGILE Conference on Geographic Information Science, AGILE 2023
Abbreviated titleAGILE 2023
Country/TerritoryNetherlands
CityDelft
Period13/06/2316/06/23
Internet address

Fingerprint

Dive into the research topics of 'Using machine learning to drive social learning in a Covid-19 Agent-Based Model'. Together they form a unique fingerprint.

Cite this