Santiago Rosa

Understanding pedestrian dynamics is critical for mitigating crowd-related risks and improving public safety. In this work, we propose a novel data-driven mesoscopic modeling framework that combines the kinetic theory of active particles (KTAPs) with data assimilation techniques, thereby offering a data-driven learning approach applicable to collective systems with learning and decision-making behavior, in line with recent KTAP developments in this area. The framework uses an ensemble Kalman filter to sequentially estimate the time-dependent spatial distribution of pedestrians and model parameters by fusing observations with the mesoscopic forward model state. Through a series of twin experiments, we show that the panic factor—a key behavioral parameter—is identifiable within this framework. We also evaluate the robustness of the approach by assimilating synthetic observations generated by an agent-based model (ABM). This setup introduces structural model error because the ABM is governed by microscopic rules that differ fundamentally from the mesoscopic kinetic equations. Despite this discrepancy, the ensemble Kalman filter, through the observation-based innovation term, successfully drives the kinetic model to track the observed pedestrian density while simultaneously recovering the panic factor. In this framework, observations act as a physical constraint on the evolution of the kinetic model. Crowd-dynamics models, including ABMs and kinetic models, often rely on phenomenological terms to describe social interactions, with parameters that are highly uncertain. Our findings indicate that in such systems, free-running simulations inevitably may diverge from the true state, whereas an online data-driven approach effectively constrains the system’s trajectory to its underlying dynamical manifold.

The COVID-19 pandemic, with its multiple outbreaks, has posed significant challenges for governments worldwide. Much of the epidemiological modeling relied on pre-pandemic contact information of the population to model the virus transmission between population age groups. However, said interactions underwent drastic changes due to governmental health measures, referred to as non-pharmaceutical interventions. These interventions, from social distancing to complete lockdowns, aimed to reduce transmission of the virus. This work proposes taking into account the impact of non-pharmaceutical measures upon social interactions among different age groups by estimating the time dependence of these interactions in real time based on epidemiological data. This is achieved by using a time-dependent transmission matrix of the disease between different population age groups. This transmission matrix is estimated using an ensemble-based data assimilation system applied to a meta-population model and time series data of age-dependent accumulated cases and deaths. We conducted a set of idealized twin experiments to explore the performance of different ways in which social interactions can be parametrized through the transmission matrix of the meta-population model. These experiments show that, in an age-compartmental model, all the independent parameters of the transmission matrix cannot be unequivocally estimated, i.e., they are not all identifiable. Nevertheless, the time-dependent transmission matrix can be estimated under certain parameterizations. These estimated parameters lead to an increase in forecast accuracy within age-group compartments compared to a single-compartmental model assimilating observations of age-dependent accumulated cases and deaths in Argentina. Furthermore, they give reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission are crucial for an accurate prediction and diagnosis of healthcare demand.

The COVID-19 pandemic and its multiple outbreaks have challenged governments around the world. Much of the epidemiological modeling was based on pre-pandemic contact information of the population, which changed drastically due to governmental health measures, so called non-pharmaceutical interventions made to reduce transmission of the virus, like social distancing and complete lockdown. In this work, we evaluate an ensemble-based data assimilation framework applied to a meta-population model to infer the transmission of the disease between different population agegroups. We perform a set of idealized twin-experiments to investigate the performance of different possible parameterizations of the transmission matrix. These experiments show that it is not possible to unambiguously estimate all the independent parameters of the transmission matrix. However, under certain parameterizations, the transmission matrix in an age-compartmental model can be estimated. These estimated parameters lead to an increase of forecast accuracy in agegroups compartments assimilating age-dependent accumulated cases and deaths observed in Argentina compared to a single-compartment model, and reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission may be important for an accurate prediction and diagnosis of health care demand.