Computer scientists tracking the deadly coronavirus epidemic have been working diligently to predict the virus’s next moves. The novel virus, which causes a respiratory illness dubbed COVID-19, has taken the lives of more than 2,100 people. It first emerged in December in the Chinese city of Wuhan, and has since infected more than 75,000 people, mostly in China. The numbers of new cases have begun to drop in China, but concern is growing over expanding outbreaks of COVID-19 in Singapore, Japan, South Korea, Hong Kong, and Thailand.
Alessandro Vespignani, a computer scientist at Northeastern University in Boston who has developed predictive models of the epidemic, spoke with IEEE Spectrum about computational efforts to thwart a global pandemic. His team has developed a tool, called EpiRisk, that estimates the probability that infected individuals will spread the disease to other areas of the world via travel. The tool also tracks the effectiveness of travel bans.
IEEE Spectrum : What’s the status of the COVID-19 epidemic now?
Alessandro Vespignani: In the last few days, we have seen some good signals in terms of reduced case reports from the ground in China. The fear now is the possibility of chains of transmission in other countries near China that we are not detecting. Places such as Hong Kong have instituted strong interventions like restricting transportation and closing schools. But other regions are not doing that. If these countries start seeing cases that were not imported from China, that could signal that the epidemic is spreading in other places. And it’s possible that these regions could become new epicenters.
Spectrum : We’re almost two months into the COVID-19 epidemic—what are disease modelers focusing on now?
Vespignani: Everything. We need an all-hands-on-deck approach. We need to understand what’s happening in China and the effect of the interventions there. We need to understand if there are signals that the epidemic is spreading elsewhere. We need to understand if there are new epicenters. There is no piece of this puzzle that is not important at this stage. That’s why we’re all stretched at the moment.
Vespignani: Our modeling approach is to use all the possible data sources. At the moment, we’re focusing on the surveillance data coming from China and nearby countries. Social media and news sources are also on the table. First, we model the epidemic to achieve situational awareness, particularly outside China. Since we know how many people are traveling from regions hit by the epidemic in China, it’s possible to then infer the size of the epidemic elsewhere from the cases of COVID-19 detected internationally. We’re able to make these predictions ahead of on-the-ground reporting systems, since those often rely on official confirmation. Then we have the models look at how interventions like travel restrictions affect the transmission of the disease. Around Wuhan, airports are shut down, long-range transportation is shut down, schools are closed. We’re trying to understand the likelihood that these measures can contain the epidemic in China, and the likelihood of seeing cases outside China.