A model-driven software development platform for Climate-Sensitive Infectious Disease Modelling
- Funded by Wellcome Trust
- Total publications:0 publications
Grant number: 226107/Z/22/Z
Grant search
Key facts
Disease
Disease XStart & end year
20232028Known Financial Commitments (USD)
$331,118.97Funder
Wellcome TrustPrincipal Investigator
Prof Marios FokaefsResearch Location
CanadaLead Research Institution
York University (Canada)Research Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
N/A
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The COVID-19 pandemic demonstrated the value of epidemiological models in battling against the disease. However, modelling is not a trivial task. It requires time, effort and continuous maintenance to address the evolution of the disease and of the countermeasures. On one hand, this requires a systematic and robust development process to ensure the effectiveness and the quality of the produced models. On the other hand, it also implies the need for a change management process that will handle the maintenance and the evolution of the models. Furthermore, infectious diseases have to be studied in conjunction with other affecting parameters, include climate and sociodemographics. Therefore, modellers need to be able to consider multidimensional and hybrid models to better study the phenomenon. In this project, we propose the application of software engineering and model-driven engineering principles to aid the design, development and simulation of climate-sensitive infectious disease models. More specifically, we propose a integrated development platform that will support (a) the definition and design of models, (b) the simulation of scenarios based on these models, (c) the automatic validation and verification of models and code generation, (d) the control of model versions, and (e) the merging of models from different domains.