Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana
Abstract
In this study, five distinct COVID-19 models developed in different countries, each designed to reflect the prevailing epidemiological condition at the time of formulation, are examined. The models are reformulated while still maintaining their original structure, using their common transmissions from one compartment to the other. Modified Patankar-Runge-Kutta (MPRK) methods are then applied to approximate the solutions of the resulting system of nonlinear ordinary differential equations (ODEs) representing each model to produce unconditionally positive approximations and to preserve the conservative part of the ODEs. In particular, we incorporate the numerical solution into a cost function to improve the estimates for the non-autonomous model hyperparameters. In a first step we obtain piecewise constant parameters that fit real data. Later we perform a WENO reconstruction in a post-process to approximate the true time-dependent coefficients inside the ODEs. As a proof-of-concept, we apply our approach to improve the parameters of a paper concerned with modeling COVID-19 in Ghana, where we can make 5-day predictions within a 10% error range.
Versions
Related
- Fuzzy-guided hyperparameter optimization for efficient CNN-based plant disease classification
- Multi-temporal UAV multispectral phenotyping and machine learning for modeling cotton defoliation dynamics across diverse accessions
- Physical characterization and hybrid modeling of erosion wear in industrial waste-filled glass/epoxy composites using machine learning and computational fluid dynamics
- Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences
- Enhancement of gut-microbiome-based classification of prodromal Parkinson's Disease cases by hyperparameter tuning, integration of multimodal data, and meta-analysis.
BibTeX
@misc{izgin2026using,
title = {{Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana}},
author = {Thomas Izgin and Andreas Meister and Isaac Azure},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.02650},
eprint = {2606.02650},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.02650},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana
AU - Thomas Izgin
AU - Andreas Meister
AU - Isaac Azure
PY - 2026
DA - 2026/06/01
PB - arXiv
DO - 10.48550/arxiv.2606.02650
UR - https://arxiv.org/abs/2606.02650
AB - In this study, five distinct COVID-19 models developed in different countries, each designed to reflect the prevailing epidemiological condition at the time of formulation, are examined. The models are reformulated while still maintaining their original structure, using their common transmissions from one compartment to the other. Modified Patankar-Runge-Kutta (MPRK) methods are then applied to approximate the solutions of the resulting system of nonlinear ordinary differential equations (ODEs) representing each model to produce unconditionally positive approximations and to preserve the conservative part of the ODEs. In particular, we incorporate the numerical solution into a cost function to improve the estimates for the non-autonomous model hyperparameters. In a first step we obtain piecewise constant parameters that fit real data. Later we perform a WENO reconstruction in a post-process to approximate the true time-dependent coefficients inside the ODEs. As a proof-of-concept, we apply our approach to improve the parameters of a paper concerned with modeling COVID-19 in Ghana, where we can make 5-day predictions within a 10% error range.
ER -