ABSTRACT
In this paper, an analysis of changes in dynamic process models described by variables that represent social behavior from the point of view of people's mobility and of economic indices in the framework of the COVID19 pandemic is presented. Here, the mobility described by Google and Apple is used as a proxy for the social behavior to correlate it with the dynamic evolution of daily COVID19 infections. In addition, indices related from the global economy are used as a proxy of the socio-economic process, where two of ascending evolution (MSFT Microsoft and NASDAQ, Inc.) and another with smooth evolution (WTI oil gallon price) are analyzed. The evolution of such proxies are related to the daily COVID19 cases. In the latter case, it is difficult to detect a territorial region of influence given the number of origins of influences that the selected indices have, but the impact of the first peak in China and the subsequent evolution in the world can be studied, especially in our country and in the Netherlands. The main findings include that the underlying model for social behavior has changed in different stages, depending on the months of the year and that after mid-2021 an unstable equilibrium is on the track, with the addition of the new possibilities provided by the vaccination process and the rules of social coexistence. It is concluded that it is necessary to analyze which decision should be taken at the social level of public policy and which personal decisions for each individual. © 2021 IEEE.
ABSTRACT
Mathematical models are a powerful tool to study and predict he dynamic behaviour of processes and systems, physical and biological, as well as to assist in decision making, and to design control systems. In the case of the coronavirus pandemic, COVID-19, its dynamic behaviour is generally in line with traditional models proposed, such as the Susceptible-Infected-Recovered (SIR) or the (SEIR), that includes the Exposed, which are useful tools to estimate the spread of the virus, the number of infected, the recovered individuals, and amount of deaths, as well as finding the outbreak start, the rise time, the peak time and overshoot, and fading stage. In COVID-19, the knowledge of the maximum peak and its delay time are important to prepare the healthcare system capacity, and therefore have enough intensive care units (ICUs) with automatic ventilators. In this work, a simple but robust control strategy for sequencing social distancing and confinement is proposed. The main control objective is to control the COVID-19 outbreak to avoid the collapse of the healthcare system and saturation of ICUs capacity, generating a control action sequence of social distancing and confinement such as the number of new cases requiring ICU is below a threshold set-point. An On-Off control action is analysed, and a Proportional-Integral-Derivative (PID) controller is proposed to generate a public policy (a sequence of decisions) applied once a week or every fortnight. Simulation results showing the practical feasibility and performance of the approach are given, and somehow supporting and validating strategies carried out by many healthcare teams from many countries. © 2021 IEEE.
ABSTRACT
This work is a contribution to the call to The IFAC-CSS Corona Control Community Project in February 2020. Scientific evidence to the effectiveness of the three main non-pharmacological tools to mitigate a pandemic is given. A control system to control the Covid-19 outbreak caused by the Sars-CoV2 virus, called Coronavirus, is proposed, for preventing collapse of the health systems and saturation of Intensive Therapy Units (ITUs) capacity. The proposed system is based on the three existing non-pharmacological tools for the mitigation of epidemics and pandemics: social distancing, confinement and testing with isolation of a population in which there is community circulation of the virus. Both in the analysis and in the design of the control system, the mathematical model SEIRD (Susceptible - Exposed - Infected - Recovered - Deceased) is used, which describes the dynamics of a pandemic, adjusted in this work to the behavior in space and time of the Sars-CoV2. In this work the incidence and impact of testing with isolation (or testing-quarantining) is incorporated in the model. The proposed control system uses, as a feedback signal, the demanded quantity of critical beds and ITUs, which is compared with the available beds capacity to generate the error signal as input to a PID controller. As control actions, five Phases of Social Distancing and Confinement (SD&C) are proposed, which must be applied by the public authority. The control system thus generates a SD&C decision sequence or policy, which can be applied once a week or every fortnight. Simulation results showing the practical feasibility and good performance of the proposed control system are given, preventing collapse of Healthcare Capacity based on Social Distancing, Confinement and Testing-quarantining as control actions. ©2020 IEEE