International cooperation is a key ingredient

Ultimately, the success of the global effort to use AI techniques to address the COVID-19 pandemic hinges upon sufficient access to data. Machine Learning, and Deep Learning in particular, requires notoriously large amounts of data and computing power in order to develop and train new algorithms and neural network architectures.

Few of the systems we reviewed in this research are yet to have the operational maturity needed to combat the virus at this stage. However, they have moved the needle in the right direction and have much to teach us. In order to operationalize these efforts, we must work together to define a road map and a funnel for AI applications in order to understand how this technology can help today, and as the pandemic evolves. Even more, international cooperation based on multidisciplinary AI research and open science can help to prepare the regions of the world which have not yet experienced widespread outbreaks, and those where the majority of vulnerable populations live in.

It is our hope that this mapping exercise provides the community of practice with relevant information that they can use in their own research to turn the tide and get us closer to defeating this ‘invisible enemy.’

Download our paper “Mapping the landscape of artificial intelligence applications against COVID-19.”

Projecting the trajectory of the COVID-19 pandemic: A review of available tools

By now, most of us have become used to the frightening daily drip of numbers about the COVID-19 pandemic. The number of new cases and deaths, together with cumulative figures, are reported by the media all over the world. Many are familiar with the Johns Hopkins University map and dashboard which is frequently updated. The European Center for Disease Prevention and Control maintains a similar tool. The JHU tool offers numbers at the subnational level for some countries, which is useful as the spread of the disease varies greatly within country. An Excel sheet extracted from the ECDC data with the daily number of new reported cases and deaths by country – very useful for any cross-country comparison or analysis - can be downloaded in two clicks from the Our World in Data platform (daily data tables). Both tools, together with the raw data from Johns Hopkins, and a large array of other COVID-19 relevant indicators and data sets can be accessed through the COVID-19 page from the World Bank Data Group. You can also learn more about the World Bank COVID-19 response here.

Current reported cases and death numbers are tricky to interpret

As we look at those numbers, we should remember that the current number of reported cases and deaths are indicators which are difficult to interpret. The current number of reported cases is problematic because, in most settings, it relies on testing individuals who have symptoms and present themselves to health facilities which have testing capacity: it misses people who have symptoms but do not come to health facilities where tests are done, and more importantly it misses people who are asymptomatic. A few countries have deployed testing to a much larger segment of the population, but, currently, testing rates vary widely across countries (see figure 3 from this blog post by Dawoon Chung and Hoon Sahib Soh): from 26,772 per million people in Iceland to 9.5 per million in Pakistan (with 6,148 in South Korea, 3,499 in Italy and 314 in the US for example, data as of 3/20/2020). This calls for increased testing across the world, if possible, in representative samples of the population, to better track and understand the epidemic. Given that testing varies widely across country and is not random, reported deaths might end up being a better indicator of the epidemic’s progress than confirmed cases. The problem with deaths as an indicator is the lag between the onset of symptoms and death: on average, the number of deaths informs on the number of infections about 20 days ago, but meanwhile the number of infections is likely to have grown rapidly. This Khan academy video makes this point very intuitively. Also, using deaths might be more problematic in developing countries where many COVID-19 deaths might not be diagnosed or reported as such because of the lack of testing.

Projections are needed to inform policy

If current reported cases and deaths numbers are difficult to interpret, how can countries, especially in the developing world, prepare to confront the pandemic? Ideally, one would like to be able to predict the course of the pandemic over time. And we would also like to know how different sets of containment measures are likely to affect the scale and trajectory of the disease to be able to plan them ahead, trigger them as early as possible to contain the outbreak in the country before it is generalized, and modulate them as efficiently as possible given the disruptions and economic costs imposed by physical/social distancing.

At this point, a disclaimer is called for: I am an applied microeconomist with a focus on health, but I have not been trained as an epidemiologist and for most of my career I have been very reluctant to use, let alone offer, predictions or projections. But over the last few weeks, as I have interacted with teams in developing countries trying to understand the challenges that they are facing, I have tried to figure out how best to put these tools to work.

The basic tool is the Susceptible-Infected-Recovered (SIR) model. This post is not the place to describe it. To learn more about this type of models I suggest having a look at a set of slides and videos on Paolo Surico and Andrea Galotti’s COVID-19 page or for a more formal description at Andrew Atkeson’s NBER working paper.