The SARS-CoV-2 outburst in March 2020 has led to the lockdown of several countries across the world. Mobility restrictions have been constantly put into action and reversed to find the trade-off between minimizing the number of infections and death and mitigating the inevitable damage to the economy and the societal systems. These emergency measures lead to collateral effects that prove the need for robust and dynamic models for policymakers to make efficient and targeted decisions in short amounts of time. We desire to predict the impact that trips have on the spreading and provide insight into the motivation behind the observed trips to generate a suitable and unbiased response. For this reason, we aim at building a disaggregate model using the agent-based approach to provide insights and forecasts on transport demand and its epidemiological consequences. We will couple people’s daily activity schedules and infectious disease spreading. This addition is especially appealing since it includes the different behaviors, contact patterns, and population heterogeneity linked to the activities and their consequence of spatial movement, especially during travel. Consequently, we believe that this method will help to guide authorities to ultimately assess the effectiveness of different policy approaches based on socio-economic variables.