Modeling of Physical Activity Behavioral Interventions Relying on MPC Strategy

Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.

Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments*

The 2020 CARES Act directed large cash payments to households. We analyze households' spending responses using data from a Fintech nonprofit, exploring heterogeneity by income, recent income declines, and liquidity as well as linked survey responses about economic expectations. Households respond rapidly to payments, with spending increasing by about $0.14 per dollar during the first week and plateauing around $0.25-$0.30 over 3 months. In contrast to previous stimulus programs, we see little response of durables spending. Households with lower incomes, greater income declines, and less liquidity display stronger responses whereas households that expect employment losses and benefit cuts display weaker responses.

On stability of nonzero set-point for nonlinear impulsive control systems

The interest in non-linear impulsive systems (NIS) has been growing due to their impact on application problems such as disease treatments (diabetes, HIV, influenza, COVID-19, among many others), where the control action (drug administration) is given by short-duration pulses followed by time periods of null values. Within this framework, the concept of equilibrium needs to be extended (redefined) to allow the system to keep orbiting (between two consecutive pulses) in some state-space sets out of the origin, according to the usual objectives of most real applications. Although such sets can be characterized by means of a discrete-time system obtained by sampling the NIS at the impulsive times, no agreement has been reached on their asymptotic stability (AS) under optimizing control strategies. This paper studies the asymptotic stability of control equilibrium orbits for NIS, based on the underlying discrete-time system, in order to establish the conditions under which the AS for the latter leads to the AS for the former. Furthermore, based on the latter AS characterization, an impulsive Model Predictive Control (i-MPC) that feasibly stabilizes the non-linear impulsive system is presented. The biomedical problems of intravenous bolus administration of Lithium and antiretrovirals administration for HIV treatments are considered as simulation examples to demonstrate the controller performance

Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario.

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.