Webinar The Mathematics of COVID-19

Date: 11/11/2021

Schedule: 09h às 17h45min Local: Via Zoom

Presentation: Since the appearance of the new SARS-CoV-2 coronavirus in late 2019, a huge number of articles presenting “predictive” mathematical models have started to be published by researchers around the world. Articles by mathematicians, statisticians, physicists, engineers and economists proposing models that could guide the then astonished decision makers in public health. To get an idea of the “flood” of models that have appeared since then, just look in databases such as PubMed, which throughout its history registered 37790 articles on Mathematical Epidemiology. Of these 2115, or 5.6% referred to COVID-19. In broader databases, such as Google Scholar, 867000 articles refer to Mathematical Epidemiology, of which 35200, or 4% related to COVID-19. These numbers give an idea of the impact that COVID-19 has had on the community of mathematical epidemiologists and many other specialties who, for the most part, had never been interested in mathematical models of communicable diseases and who took (or tried to take advantage of) the opportunity to “appear” on the world stage at a time when all the concern of the academic (and political) world was voting for the new pandemic. In Brazil, this scenario was no different. As soon as the first cases emerged, dozens of groups with and without previous experience began to propose mathematical models, which most often tried to predict the course of the pandemic, most of which were of no practical use in guiding public health professionals. This Webinar brings together a small fraction of Brazilian researchers and two foreigners who will present their contributions in terms of mathematical models on the dynamics of COVID-19. The target audience of this meeting is extremely comprehensive and should reflect the interest that the subject has aroused in practically all specialties that, directly or indirectly, deal with quantitative models of dynamic phenomena of the most varied natures. We hope it will be profitable.

Eduardo Massad

CRONOGRAMA

Webinar The Mathematics of COVID-19

Hora Apresentador

9:00 – 9:15 Abertura (César Camacho e Eduardo Massad) FGV EMAp

9:15 – 9:50 Maira Aguiar Ikerbasque Researcher, Mathematical and Theoretical Biology. (MTB) Group

Leader.BCAM - Basque Center for Applied Mathematics, Spain

10:00 – 10:20 Moacyr Alvin Horta Silva FGV EMAp

10:20 – 10:40 Daniel Vilella PROCC-FIOCRUZ

10:40 – 11:00 Intervalo

11:00 – 11:20 Cláudia Sagastizábel Instituto de Matemática e Estatística, Universidade Estadual de Campinas

11:20 – 12:00 Maria Soledad Aronna FGV EMAp

12:00 – 14:00 Almoço

14:00 – 14:40 Abba Gumel Foundation Professor, Barrett Honors Faculty

Arizona State University, School of Mathematical and Statistical Sciences, USA

14:40 – 15:00 Flavio Codeço Coelho FGV EMAp

15:00 – 15:20 Eduardo Massad FGV EMAp

15:20 – 15:40 Intervalo

15:40 – 16:00 Max de Souza Instituto de Matemática e Estatística, Universidade Federal Fluminense

16:00 – 16:20 Luiz Max Carvalho FGV EMAp

16:20 – 16:40 Sergio Oliva Instituto de Matemática e Estatística, Universidade de São Paulo

16:40 – 17:00 Alberto Paccanaro FGV EMAp

17:00 – 17:20 Vinicius Albani Instituto de Matemática e Estatística, Universidade Federal de Santa Catarina

17:20 – 17:40 Américo Cunha Departamento de Matemática Aplicada, Universidade Federal do Rio de Janeiro

17:40 – 17:45 Encerramento

Palestrante 1: Maíra Aguiar The role of mild and asymptomatic infections on COVID-19 vaccines performance: a modeling study With approximately 96 COVID-19 vaccines at various stages of clinical development, there are currently four vaccines authorized for emergency use in Europe. Different vaccine efficacies are reported, with remarkable effectiveness against severe disease. However, the so called sterilizing immunity, occurring when vaccinated individuals cannot transmit the virus, is yet to be confirmed. With an uneven roll out of vaccination, we investigate, using mathematical models, the impact of asymptomatic/mild COVID-19 infections on vaccine performance. In this talk, results obtained for two vaccination models, the vaccine V1 protecting against severe disease, and the vaccine V2, protecting against disease and infection, are compared to a model without vaccination, evaluating the reduction of hospitalizations in a population. The different COVID-19 vaccines currently in use have features placing them closer to one or the other of these two extreme cases, V1 and V2, and insights on the importance of asymptomatic infection in a vaccinated population are of a major importance for the future planning of vaccination programme. Our study shows that vaccines protecting against severe disease but failing to block transmission might not be able to reduce significantly the severe disease burden under low or intermediate vaccine coverage. While in the case of asymptomatic or mild disease cases accounting for most of the transmission, the non-sterilizing vaccines are eventually increasing the number of overall infections in a population. Here, the effects of COVID-19 vaccination in different epidemiological scenarios of coverage and efficacy are evaluated, giving insights on how to best combine their use and optimize the reduction of hospitalizations.

Palestrante 2: Moacyr Alvin Horta Silva Título: "Identificabilidade dos parâmetros em modelos compartimentais para Covid-19" Resumo: Discutiremos a questão da identificabilidade dos parâmetros de um modelo compartimental simples para a Covid-19. Além da identificabilidade estrutural, será abordada a questão da identificabilidade que é possível ser alcançada na prática. Veremos como a falta de identificabilidade prática pode afetar as previsões do modelo. Minicurrículo: Doutor em Matemática/Computação Gráfica (2004) pela Associação Instituto Nacional de Matemática Pura e Aplicada – IMPA.

Tem atuado em pesquisa na área de geometria diferencial discreta e sua relação com equações diferenciais parciais. Suas áreas de interesse incluem também modelos matemáticos em epidemiologia, teoria dos jogos e modelos baseados em agentes.

Palestrante 3: Daniel Villela Título: Como os modelos matemáticos nos ajudam a avaliar a efetividade da vacinação contra COVID-19 Resumo: As dificuldades no controle da pandemia de COVID-19 ocorreram por diversos fatores, entre eles o número grande de suscetíveis e as limitações nos métodos de supressão de transmissão. Com a vacinação, passou a haver um importante meio de evitar casos graves e óbitos por COVID-19. Será apresentado como os modelos matemáticos e estatísticos podem ajudar a comparar cenários epidemiológicos com vacinação e outras possíveis medidas de controle. Será apresentada também a avaliação de efetividade da vacinação contra COVID-19 e as implicações das estimativas atuais. MiniBio: Daniel Antunes Maciel Villela é pesquisador da Fundação Oswaldo Cruz e atualmente coordenador do Programa de Computação Científica (PROCC/FIOCRUZ). Doutor pela Universidade de Columbia, tem experiência em modelagem matemática da dinâmica de transmissão de doenças infecciosas, com interesse em métodos quantitativos em Epidemiologia e Ecologia de vetores de importância para a Saúde Pública e atualmente realiza análises populacionais da dinâmica de COVID-19. Atua na Pós-Graduação (mestrado e doutorado) na Fundação Oswaldo Cruz e atualmente é Coordenador Adjunto do Programa de Epidemiologia em Saúde Pública (ENSP/FIOCRUZ).

Palestrante 4: Claudia Alejandra

Title: RobotDance

Robot Dance is an optimization platform based on a spatio-temporal epidemiological

model that splits the population into groups representing the mobility as links in a

complex network.

The limit in ICU capacity, a constraint in the optimization problem, is handled in the form

of probabilistic constraints.

The tool can anticipate the geographical evolution of the disease and evaluate the

potential impact of containment and prevention strategies.

Given appropriate mobility and epidemiological input data, Robot Dance can determine

different target policies and mitigation protocols that make efficient use of the ICU beds.

The proposed framework is highly flexible and adaptable to different available data sets

and control strategies.

Authors: Paulo J. S. Silva 1 , Tiago Pereira 2,3 , Claudia Sagastizábal 1 , Luis Nonato 2 ,

Marcelo Cordova 4 , Claudio J. Struchiner 5

1. Instituto de Matemática, Estatıstica e Computação Cientıfica, Universidade de

Campinas, São Paulo, Brazil

2. Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, São

Paulo, Brazil

3. Department of Mathematics, Imperial College London, London, UK

4. Departamento de Engenharia Elétrica, Universidade Federal de Santa Catarina,

Florianópolis, Brazil.

5. Fundação Getúlio Vargas, Rio de Janeiro, Brazil

Speaker: Claudia Sagastizábal

Claudia Sagastizábal is an applied mathematician specialized in optimization, both its theory and its numerical aspects.

Her research interests lie primarily in the area of nonsmooth optimization, stochastic programming, and variational analysis, with an emphasis on applications in the energy sector. She is known for her contributions in convex optimization and energy management, and for her co-authorship of the book Numerical Optimization: Theoretical and Practical Aspects.

In parallel with her academic activities, Claudia holds or has held consulting R&D appointments for companies such as EdF, Gaz de France-Suez, Engie, and Renault in France; Robert Bosch in Germany; and Petrobras, Bovespa and Eletrobras in Brazil.

Claudia is a researcher at the University of Campinas in Brazil and principal investigator at CEMEAI. She is currently Area Editor of JOTA and EJCO,Associate Editor of SIOPT, and, since 2015, Editor-in-Chief of the journal Set-Valued and Variational Analysis.

my web-page? https://www.ime.unicamp.br/~sagastiz

Palestrante 5: Maria Soledad Aronna

"A model for COVID-19 with isolation, quarantine and testing as control measures"

We propose a compartmental model for the dynamics of COVID-19. We take into

account the presence of asymptomatic infections and the main non-pharmaceutical

measures that have been adopted to contain the epidemic, namely social distancing,

isolation of a portion of the population, quarantine for confirmed cases and testing.

We obtain an explicit expression for the basic reproduction number in terms of the

parameters of the disease and of the control policies. In this way we can quantify the

effect that isolation and testing have in the evolution of the epidemic.

We additionally fit the model to the date of Rio de Janeiro to estimate the

underreported rate during the first wave of the epidemic.

Maria Soledad Aronna is an Associate Professor at the FGV EMAp, Rio de Janeiro. She

obtained her Ph.D. in 2011 from Ecole Polytechnique (France). She held postdoctoral

positions in Università di Padova (Italy), Imperial College of London (England) and IMPA

(Brazil). She holds a Fellowship from the Von Humboldt Foundation (Germany) since

2018. Experienced in Control Theory, with emphasis on Optimal Control, Stability of

Control Systems and Mathematical Biology, with focus in Epidemio

Palestrante 6: Abba Gumel

Title: Mathematics of Vaccination Against the COVID-19 Pandemic Abstract: The coronavirus that emerged out of Wuhan city in 2019 became the greatest public health challenge faced by mankind since the H1N1 influenza pandemic of 1918/1919. Control and mitigation efforts against the pandemic, which has (as of September 2021) caused over 230 million confirmed cases and over 4.6 million deaths globally, were largely based on the use of nonpharmaceutical interventions (NPIs), such as quarantine, isolation, social-distancing, face mask usage, community lockdowns etc., until a few safe and effective vaccines were given emergency use authorization (EUA) by the United States Food and Drug Administration (FDA). This presentation is based on the use of mathematical modeling and analysis, coupled with data analytics, to assess

the population-level impact of the widespread vaccination (using any of the three FDA-EUA vaccines) on curtailing the spread of COVID-19 in the United States. Conditions for achieving vaccine-derived herd immunity (needed for elimination of the disease) in the presence and absence of variant of concern will be derived. The combined impact of the vaccination program and other NPIs, notably the use of face masks, will also be assessed. By the way, how long is the presentation? As for biodata, I am really not good talking about myself (I prefer and enjoy talking about others...especially those I greatly like, such as Claudio and you). In the intro, you cou just say ``Abba is a good friend of ours, and one of his major life-long dreams is to visit the majestic Maracana stadium in Rio".... you can add that you and Claudio would make the dream come true after the unwelcome Lady Corona has left the scene.

Palestrante 7: Flavio Codeço

Título: COVID-19 endgame: Stochastic considerations about local elimination of

Transmission.

Abstract: In 2021 and 2022, many countries are facing the challenge of eliminating local

transmission of the SARS-CoV2 virus.

However, many factors can affect the probability of elimination including the

geographical heterogeneity of incidence and immunization.

in this talk, I discuss the challenges of developing a modelling approach to such

elimination scenarios including their intrinsic stochasticity.

Bio: Flávio is associate professor of Mathematical Epidemiology at the School of

Applied Mathematics of Getulio Vargas Foundation. He is also Scientific Collaborator of

the Institute for Global Health of the University of Geneva, where he conducts research

In partnership with the World Health Organization.

Palestrante 8: Eduardo Massad

Title: Time-Dependent Vaccine Efficacy Estimation Quantified by a Mathematical

Model

Abstract

A vaccination trial consists in choosing a voluntary cohort of susceptible individuals who receive either a vaccine or a placebo shot. This is normally done in the middle of an outbreak of a disease to which the vaccine is supposed to prevent. When the incidence of the infection is constant in time, calculation of vaccine efficacy is straightforward. When the incidence is time-dependent, however, some refinements are necessary and this is purpose of this paper.

Eduardo Massad: Medical Doctor, Professor of Medical Informatics and Tropical

Medicine, B.Sc. Physics, B.Sc.(Honors) Psychology, FIMA, Chartered Mathematician

(IMA-UK), Chartered Scientist (SciCoun-UK), FRSM.; Full Professor of Medical

Informatics, University of Sao Paulo; Head of the Department of Legal Medcine,

University of Sao Paulo; Honorary Professor of Infectious Diseases, London School of

Hygiene and Tropical Medicine; Courage Fund Visiting Professor of Medicine, National

University of Singapore; Visiting Professor of Statistics and Psychology, University of

Derby, UK; Fellow of the Institute of Mathematics and Its Applications (FIMA); Fellow of

The Royal Society of Medicine (FRSM)

Palestrante 9: Max de Souza

The outbreak of COVID-19 has led to a spectacular resurgence of interest in compartmental epidemic models, but with specificities related to the detection and isolation of infected individuals. We analyze the identifiability and observability of the well-known SIR epidemic model with an additional compartment Q of the sub-population of infected individuals that are placed in quarantine (SIQR model), considering that the flow of individuals placed in quarantine and the size of the quarantine population are known at any time. Then, we focus on the problem of identification of the model parameters, with the synthesis of an observer. This is joint work with F. Hamelin, A Iggidr, A. Rapaport and G. Sallet. Profile: MOS got a BSc and an MSc in Mathematics from PUC-Rio and and a PhD in Applied Mathematics from the University of Cambridge (1998). MOS has been actively

working in several areas of Mathematical Biology with a focus in evolution, epidemiology and ecology of vectors. He is currently a full professor at UFF and serves on a number of editorial boards, including the Journal of Mathematical Biology and the Bulletin of Mathematical Biology.

Palestrante 10: Luiz Max Carvalho

Título: "A brief and biased review of COVID-19 modelling in Brazil"

Resumo: "The ongoing pandemic has sparked a lot of interest from scientists outside the

usual realm of Epidemiology and Mathematical Epidemiology. In this talk I will review a

few of the COVID-19 modelling efforts lead by Brazilian teams, discuss their main

approaches and attempt to draw general conclusions about pandemic modelling in

hopes to learn something for the next Great Plague."

Luiz Max Carvalho holds an undergraduate degree (hons.) in Microbiology and

Immunology from the Federal University of Rio de Janeiro (2012) and a PhD in

Evolutionary Biology from the University of Edinburgh (2018).

His interests gravitate towards Bayesian statistics in biosciences.

Palestrante 11: Sergio Oliva Title: Human mobility as an instrument for dynamical analysis of COVID-19 pandemics

in Brazil Abstract: The COVID-19 pandemic has become a challenge for several areas of science

and proven to be a major burden in the population, causing deaths and economic impacts. In the beginning, without vaccines or proven drugs, the disease spread quickly and challenged society and government. The control was based mainly on non-pharmaceutical methods seeking either to reduce theodds of contact with an infected individual causing an infection, such as mask-use and handwashing, or toavoid the contact between an infected and susceptible individual, such as social distancing, lockdown, etc..Our research interest is the spatial dynamics of this disease, using anonymized mobility data in Brazil from cell phones, we explore some aspects of the epidemic.

Summary Sergio Muniz Oliva Filho

My academic trajectory begins in 1986 with a degree in Applied Mathematics at IME-USP. In 1989 I also obtained a master's degree in Applied Mathematics at IME and in 1993 I completed a PhD at Georgia Institute of Technology. During my studies, I got a permanent position in 1987 at the Department of Applied Mathematics at IME-USP. The core of my academic formation is mathematics, and more specifically the differential equations area, dynamic studies. A taste for the search for stable patterns in dynamical systems, the appearance of more chaotic patterns that, in my area, are characterized by non-constant dynamic objects that attract all the configurations of a system. In this way, I contributed in areas such as technological innovation, inclusion of people at social risk, inclusion of people with disabilities, environmental management and recycling, among others. This contribution was not always in scientific production, but it did eventually transforminto a contribution to the science / university / society interface.From a scientific point of view, in the last few years, I dedicated myself to work, from my initial training, in the areas of mathematical modeling, biomathematics and, more recently, the study of infectious diseases. I have a special interest in the spatial spread of diseases, the human mobility interface, data adjustment and propagation mechanisms.

Palestrante 12: Alberto Paccanaro

Title: Machine Learning and Network Medicine approaches for Drug Repositioning for

COVID-19

Abstract:

The development timeline for treatments against emergent viral diseases can be

significantly reduced by re-using drugs already available on the market – a concept

known as drug repositioning. In this talk, I will present two complementary machine

learning approaches for drug repositioning that target SARS-CoV-2 and its cellular

processes in the host, respectively. Our first approach consists of a non-negative matrix

factorisation algorithm to rank broad-spectrum antivirals. Our second approach, rooted

in ideas from network medicine, uses graph kernels to rank drugs according to the

perturbation that they induce on a subnetwork of the human interactome that is crucial

for SARS-CoV-2 infection/replication. Our experiments show that our top predicted

broad-spectrum antivirals include drugs already indicated for compassionate use in

COVID-19 patients worldwide; and that the ranking obtained by our perturbation

analysis approach aligns with independent experimental data. I will also introduce

CoREX, a freely available online tool that we developed at FGV EMAp that enables

scientists to reason and formulate hypothesis about drug repurposing in the context of

biological networks and pharmacological information. While we have developed and

tested all these methodologies for COVID-19, our approaches are disease-agnostic and

can be applied to any viral disease.

Short Bio of Alberto Paccanaro:

I am Full Professor at the School of Applied Mathematics (EMAp) at FGV in Rio de

Janeiro, which I joined in 2020. I obtained my PhD in Computer Science in 2002 from

University of Toronto, specializing in Machine Learning under the supervision of

Geoffrey Hinton. Between 2002 and 2006 I was a postdoc in Computational Biology, first

in Mansoor Saqi’s lab at Queen Mary University of London, and then in Mark Gerstein’s

lab at Yale University. I became a PI in 2006, obtaining a Lecturer position at Royal

Holloway University of London, where I started my lab (www.paccanarolab.org). In 2014

I became Full Professor of Machine Learning and Computational Biology and Director of

the Centre for Systems and Synthetic Biology, in the same University. I am visiting

professor at the Catholic University of Asuncion (Paraguay) where I lead an outpost of

my lab. I have also been visiting professor/fellow at Cornell, Yale and the University of

Venice. I am responsible for several international collaborations in the field of Machine

Learning applied to Biology and Medicine. I co-direct research grants together with

academics at Yale University, Cornell University, Liverpool School of Tropical Medicine

and the Catholic University of Asuncion. Several of my Machine Learning algorithms

have been published in Journals such as Nature, Nature Methods, Nature

Communications, Nature Machine Intelligence, Cell, PNAS

Palestrante 13: Vinicius Albani Title: COVID-19: Modeling, Calibration and some Applications to Underreporting Estimation and Vaccination Strategies Abstract: We shall revise some recent results obtained using a susceptible-exposed-infected-removed-type (SEIR-type) model. We illustrate how some generalizations in the model allows appropriate data fit and accurate short-term predictions. The calibration procedure, based on Tikhonov-type regularization, is also presented. The proposed model is then used to generate scenarios to evaluate the impact of vaccination delay and disease underreporting or underestimation in disease spread control. About the author: Vinicius Albani is an Assistant Professor at the Federal University of Santa Catarina, Brazil. He was a visiting professor at the Faculty of Mathematics of the University of Vienna, Austria and a postdoctoral researcher at IMPA. He obtained his PhD from IMPA, Rio de Janeiro in 2012, his M. Sc. from IMPA in 2008, and his B. Sc. from

the Federal University of Rio de Janeiro in 2006. His main research interests are Model Calibration Techniques and Mathematical Modeling.

Palestrante 14: Américo Cunha Data-driven discovery of mechanist evolution equations for an epidemic Abstract: Infectious diseases are a historic reality, with violent epidemics affecting people's lives from time to time. In an epidemic, so that public managers and health professionals can better respond to the demands of the affected population, it is necessary to obtain a detailed understanding of the underlying mechanism of spread of the infectious disease. Mathematical models are a fundamental tool in this context, as they are able to provide rational explanations for the spread of the disease and, consequently, predict the intensity of its progress and test the effectiveness of different control strategies. In this presentation we will expose a novel strategy to discover informative mechanist evolution equations for epidemic phenomena directly from data, by means of a machine learning framework that employs sparse regression. Short CV: Americo Cunha is an Assistant Professor at the Department of Applied Mathematics at the Rio de Janeiro State University - UERJ, Associate Editor of the Journal of Vibration Engineering & Technologies (JVET) edited by Springer-Nature, and Secretary of the Nonlinear and Chaotic Phenomena Committee at Brazilian Society of Mechanical Sciences and Engineering (ABCM). He holds a bachelor's, master's and doctorate degree in Mechanical Engineering from PUC-Rio, where he also earned a bachelor's degree in Applied Mathematics. He also holds a second doctoral degree from the Université Paris-Est in France. His research involves several fronts in the area of nonlinear dynamics, with a recent interest in modeling and simulating epidemic phenomena.