Antiviral public transport infrastructure – preparing for future pandemics

Antiviral public transport infrastructure – preparing for future pandemics

 

AVPT

 

 

UP FAMNIT

 

Rektorjev sklad UP

 

 

1.07 - Računalniško intenzivne metode in aplikacije

 

1.10.2022 - 30.9.2024

 

Recent global events, such as the coronavirus pandemic, have highlighted the vulnerability of our society in case of a worldwide pandemic. It is clear that next to its direct effects, SARS-CoV-2 also caused a substantial protracted economic crisis, which entails more and more long-term adverse effects, such as high inflation and collapsing supply chains in many different areas. In these situations, diffusion models are often used to simulate the spread of diseases through the population, which helps policymakers make the right decisions and minimize the number of infected people. However, using these immediate restrictive provisions is not a long-term solution, and without changing our daily infrastructure, e.g. public transport, policymakers cannot adapt to the fast-changing environment we experience nowadays. Moreover, even if SARS-CoV-2 disappears in the following years and we will be able to solve the arising economic problems, the chance of a possible more severe future pandemic is higher than ever due to globalization, uncontrolled deforestation and global warming. Therefore, it is essential to learn from today’s events and consider possible preventive scenarios to minimize the adverse effects of a possible future pandemic. Public transport is one of the most important parts of our human infrastructure, which supports many pillars of the global economy. It is thus crucial to plan a robust and sustainable system even in case of a pandemic.

This project aims to provide an innovative solution that supports the decision-making process in public transport planning taking into account the possible epidemiological risks and keeping the efficiency of the original system. We integrate the toolset of epidemiological modeling – more precisely, compartmental models, temporal networks, agent-based models and optimization – into an intelligent system that can support the decision-making process of public transport companies in case of a global pandemic. The project aims to introduce, simulate and solve a real-world use case from an actual city as well as to show the broad usage of the models using randomly generated and simulated environments. In our real-world use case, we explore the possible epidemiological scenarios and the effect of different intervention strategies using public transport infrastructure as an example.

 

Oddelek za informacijske znanosti in tehnologije