Effectiveness of decisions to control the pandemic: The value of Big Data

Anette Hosoi, Ali Jadbabaie and Juncal Arbelaiz

The Rafael del Pino Foundation organised, on 29 June 2020, the live dialogue through www.frdelpino.es entitled "Effectiveness of decisions to control the pandemic: The value of Big Data" in which Anette Hosoi, Ali Jadbabaie and Juncal Arbelaiz participated.

Anette Hosoi is associate dean of the MIT School of Engineering and Neil and Jane Pappalardo Professor of Mechanical Engineering. She is dedicated to unlocking the full potential of students through educational initiatives and strategic planning that advance the school's role as a world leader in engineering research and education. Hosoi holds a bachelor's degree from Princeton University and master's and doctoral degrees in physics from the University of Chicago. Her research interests include fluid dynamics, unconventional robotics, and biologically inspired design.In 2012, Hosoi was named a fellow of the American Physical Society and in 2018, honoured with the Stanley Corrsin Award. She is also a recipient of the Ruth and Joel Spira Distinguished Teaching Award, the Bose Junior Award for Education, the Bose Award for Excellence in Teaching, and the Den Hartog Distinguished Educator Award from the Department of Mechanical Engineering.

Ali Jadbabaie is JR East Professor in the Department of Civil and Environmental Engineering, and the Associate Director of the Institute for Data, Systems and Society (IDSS). He is also Director of the Sociotechnical Systems Research Centre and Principal Investigator of the Laboratory for Information and Decision Systems (LIDS). He has been a member of the ECO community since 2016. He is known for his work in the areas of network science, network economics, optimisation for machine learning, control theory and multi-agent coordination.

Juncal Arbelaiz obtained her bachelor's degree (2014) and master's degree (2016) in Industrial Technologies Engineering from the University of Navarra. After graduating at the top of her class, she obtained the Kutxa Banking Foundation's End of Degree Award, the Extraordinary End of Degree Award from the University of Navarra and the National End of Degree Award granted by the Spanish Government. His interest in the use of advanced analytical and computational techniques in the field of operations research led him to start in September 2016 his PhD in Applied Mathematics at the Massachusetts Institute of Technology (M.I.T.), in Cambridge, USA. His PhD thesis focuses on the design of algorithms for control, optimisation and decision making in complex, autonomous and large-scale dynamic systems and operations.


On 29 June 2020, the Rafael del Pino Foundation organised the dialogue "Decision Effectiveness to Control the Pandemic: The Value of Big Data", featuring Anette Hosoi, Associate Dean of the MIT School of Engineering, and Ali Jadbabaie, JR East Professor in the Department of Civil and Environmental Engineering and Associate Director of the Institute for Data, Systems and Society (IDSS), also at MIT.

Anette Hosoi explained that the IDSS is a system that uses data and big data analysis systems to tackle problems that affect society. At IDSS, they are well equipped to provide a solution to the coronavirus because they are used to interacting to see how physical laws fit with behaviour. Secondly, there is the data part. A number of institutions, such as John Hopkins University or the New York Times, made a lot of data publicly available. With that data they could start to think about possible solutions, look at the crisis from different facets, predict when the next wave is going to come, how it fits in with economic impacts, how it affects issues of justice and equity, what the basic physical mechanisms of transmission are. With this information it is possible to design policies at university, city and state level.

Ali Jadbabaie said how interesting it was to see that with the coronavirus they were working in a different way than academics usually do, normally they think, they think, they spend years writing an academic paper. Now they had to react immediately, because the meetings to study the data were daily. This is not an academic exercise, but it is going to have consequences in terms of policy. The IDSS is designed to achieve these objectives.

Anette Hosoi said that the results can be found on the IDSS website. They have not yet gone through the academic review process because there has not been time. The idea is for these ideas to be looked at by the rest of the community so that solutions can be found.

Ali Jadbabaie noted that the coronavirus crisis has been an exercise for IDSS students and PhD students in the Social Engineering programme. The coronavirus is a multifaceted problem, involving data, biological processes, transmission mechanisms and social aspects.

Anette Hosoi stressed that they got to work quickly because coronavirus is a peremptory problem, where COVID itself places limitations. Fortunately, the data was available. Overcoming the hurdle of getting it was essential.

Ali Jadbabaie said that many of the participants in the work have been studying networks and the spread of epidemics for a long time. Others in the group have studied very similar problems. We know the epidemic models. What has been a challenge is that, because of the exponential growth nature of the problem, the trajectories of epidemic spread are very sensitive to the budgets we use. So the estimates have been very different. The problem is that there are too many moving parts, too many sensitivities. One of the possible approaches to the problem is a mechanistic model. Basically, they try to explain the spread of the virus in terms of the equations using the information collected. The other approach is to look at it as a curve-fitting exercise. What we want to do is to choose a basis function to get the parameters to fit. Both approaches exist with their advantages and disadvantages. We have tried to establish the link between the two perspectives.

Anette Hosoi added that, as we are facing a global crisis, all researchers must get down to work together. This is not the time to be territorial. There is something special about the IDSS group, which is the breadth of vision and knowledge of the group as a whole. The spread of disease is a fluid problem because they are aerosols or droplets. So you have to understand the physical process of spread. This is an example of the importance of having interdisciplinary teams. She is an engineer with expertise in fluid mechanics, a necessary knowledge to solve the problem. At the IDSS, they are used to working in an interdisciplinary way.

As a result, Ali Jadbabaie explains, they understand what is happening and how it is happening, although there are a number of other long-term issues that also need to be studied. One of the key ingredients in his work is the ability to be very demanding with policy evaluations and counterfactual hypotheses. The idea is that often what we want to do is to have a control group and a treatment group, but it is not always possible to do that because we don't have a parallel universe. But different countries have made different choices. You had to take advantage of that heterogeneity and see if you could create, for example, a synthetic version of the United States and see what would have happened if you had applied, for example, the Indian policy. With that, you can see how many lives would have been saved if they had been applied earlier.

Anette Hosoi stressed that in this way, the importance of having interdisciplinary teams is understood, because experts in epidemiology are not experts in synthetic controls. We have been fortunate to be able to divide things up, we have not had to go straight from policy to the impact of the virus. Mobility can be measured and linked to the spread of the virus. This allows us to do this robust analysis, looking at this synthetic control data, looking at, for example, Google data. The other part is how policies affect mortality. We can help when we say what is the level of mobility we should achieve or what are the right policies for this or that culture.

Ali Jadbabaie pointed out that in all of this, the social, the economic and the cultural are very important, even the political systems. The US has a very decentralised political system, with its health systems, its cultures, its norms. South Korea was more prepared because it has overcome previous crises, such as SARS, and was able to adjust policies quickly. There, moreover, it has been possible to impose social distancing in a strict way.

For Anette Hosoi, the most exciting outcome of the work is a set of rules of thumb. This is a very complex system, and the ability to reduce it to guidelines that we can explain to a policy decision-maker is amazing. Imagine how a disease spreads in a population. When a person is infected they are asymptomatic, they have no symptoms. The problem is that he or she is infecting others, but the others don't know it. In an ideal world you want to limit their numbers. That has a tremendous impact on the spread of the virus. You can do that by limiting the number of people who are infectious, with masks, social distancing, confinement, but you can also speed up the identification of these people so that they don't infect anyone. Infected people can recover, they can be symptomatic and can be isolated. We can speed this up a bit by doing health reports. If tests can be done, these people can be found and asked to be confined. It is important for the spread of the coronavirus because most people are asymptomatic. This can be turned into policy decisions.

Ali Jadbabaie distinguished two types of approaches. One is to create a mechanistic model and transform it into a differential equation. Having data from the different parts of this model makes it possible to predict what will happen next. The other is to do an exercise based on a lot of data and a curve model to fit it. If the approach is not structural, if it does not emanate from the real dynamics of the process, it is not possible to understand the effect of public interventions, because if those interventions change, a parameter of the model may be affected. Secondly, all these models try to be simplistic. They show us exponential growth and then there is a degradation. In the data, the degradation is not as rapid as in the simplistic models. It is due to the heterogeneity of the population. Those who have more contacts tend to get infected earlier. That is why the first figures overestimated infections and deaths. Now the spread is slower because it reaches people with fewer contacts.

Masks are another example that Anette Hoisi likes. If you have an infected person and they are breathing or talking, you can work out the prevalence of the virus around that person relative to another person one metre away. You can calculate how many particles that other person is going to ingest and the probability of infection. Once you can quantify the mechanisms of spread, you can calculate the impact of a mask. It is a very robust solution to prevent contagion, because it is sufficient that in each interaction only one person wears the mask.

Ali Jadbabaie warned that although we understand the mechanism of propagation, there is such a level of uncertainty in relation to the data that the confidence intervals are large. With higher granularity data that we could integrate into the model, the intervals would be smaller. For example, transport data, or determining who has symptoms. We also need to better understand the network of contacts, because network models require knowledge of the structure of the network. This type of modelling makes it possible to determine the resources needed.

For Anette Hoisi, it is important to know how people interact because all of this will impact on the strength of the connection, of those nodes. Another very useful thing would be to have data on whether the main mode of transmission is through aerosols or droplets. Understanding that will tell us how best to apply resources, whether it is better to ventilate a place or to keep distance between people.

The Rafael del Pino Foundation is not responsible for the comments, opinions or statements made by the people who participate in its activities and which are expressed as a result of their inalienable right to freedom of expression and under their sole responsibility. The contents included in the summary of this conference, written for the Rafael del Pino Foundation by Professor Emilio González, are the result of the debates held at the meeting held for this purpose at the Foundation and are the responsibility of the authors.

The Rafael del Pino Foundation is not responsible for any comments, opinions or statements made by third parties. In this respect, the FRP is not obliged to monitor the views expressed by such third parties who participate in its activities and which are expressed as a result of their inalienable right to freedom of expression and under their own responsibility. The contents included in the summary of this conference, written for the Rafael del Pino Foundation by Professor Emilio J. González, are the result of the discussions that took place during the conference organised for this purpose at the Foundation and are the sole responsibility of its authors.