Introduction

Teodoro Alamo
Tuesday, July 14, 13.00-13.08

Teodoro Alamo is full professor in the Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Spain. He has co-authored a review paper on Data-Driven Methodologies to Control COVID-19 (https://arxiv.org/abs/2006.01731) and is coordinating CONCO-TEAM, which joins the effort of different universities to combat the pandemic. He is also an active collaborator in the IFAC/IEEE Corona Control Community Website (https://covid.ifac-control.org/).

Abstract: In his short introduction he presents the session and briefly analyzes the interplay between Data Science, Epidemiology and Control Theory in the combat against COVID-19.

 

 

 

Data Science to fight against COVID-19

Nuria Oliver
Tuesday, July 14, 13.08-13.25

Nuria Oliver, PhD, Data Scientist, Commissioner for AI and Covid-19 at the Valencian Government, Spain and co-founder of ELLIS Alicante (The European Laboratory of Learning and Intelligent Systems). She has designed contact-tracing methodologies, analyzed the role of mobile phones in the pandemic and created a large-scale citizen's COVID-19 survey: https://covid19impactsurvey.org/.

Abstract: In my talk, I will describe the work that we have done within the Commission on AI and COVID-19 for the President of the Valencian Region. As commissioner, I have led a multi-disciplinary team of 20+ scientists who have volunteered since March 2020. We have been working on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation and individual models); (3) predictive models; (4) citizen surveys (https://covid19impactsurvey.org/). I will describe the results that we have produced in each of these areas and will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey), the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level).

Real-time Global Disease Forecasting for COVID-19

Sara Del Valle
Tuesday, July 14, 13.25-13.43

Sara Del Valle, PhD, is a Scientist and Deputy Group Leader at Los Alamos National Laboratory (LANL), where she works on the development of mathematical, statistical, and computational models for infectious diseases. In response to the COVID-19 pandemic, she has been working with a team of LANL scientists on developing a real-time global forecasting model for COVID-19 (https://covid-19.bsvgateway.org/). This model is part of the Center for Disease Control and Prevention (CDC)'s COVID-19 Forecasting effort (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html) as well as other forecasting efforts such as FiveThirtyEight (https://projects.fivethirtyeight.com/covid-forecasts/).

Abstract: In this talk, I will describe some of the mathematical models we have developed in response to COVID-19. Specifically, I will describe our global forecasting model (https://covid-19.bsvgateway.org/) and discuss some of the challenges and opportunities for predictive modeling.

Control Strategies for COVID-19/Social Distancing Policies in Brazil

Julio E. Normey-Rico
Tuesday, July 14, 13:43-14.00

Julio E. Normey-Rico, PhD, Full Professor at the Dept. of Automation and Systems Engineering, Federal University of Santa Catarina (UFSC), Brazil. He and his research team (GPER/UFSC) are collaborating with researchers from the University of Brasilia (UnB), developing models for the pandemic in Brazil and synthesizing model predictive controllers to determine when to implement social isolation/lock-down measures.

Abstract: This talk presents our investigation related to the use of Model Predictive Control (MPC) strategies to determine the time and duration of social distancing policies to be applied in order to mitigate the spread of COVID-19 pandemic and
avoid the saturation of the health system. We use Brazilian data in the period from March to May of 2020 and consider that the available data regarding the number of infected individuals and deaths suffers from sub-notification due to the absence of mass tests and the relevant presence of the asymptomatic individuals. Thus, we obtain prediction models considering data uncertainties for control design. Moreover, we also use in our model an additional dynamic state variable that mimics the response of the population to the social distancing policies as determined by the government, which also affects the SARS-CoV-2 virus rate of transmission. We present several simulation results in order to illustrate how such optimal control policy would operate and when the peak of infections could occur in the different scenarios.