2020.04.14

Facebook has recently begun to collaborate with NTHU and Harvard University in using big data to counter the spread of coronavirus in Taiwan. This international study is being led by Assistant Professor Holly Chang of the Institute of Bioinformatics and Structural Biology at NTHU. The preliminary results indicate that the risk of local transmission is higher than long-distance transmission between counties and cities. Chang’s advice for the upcoming holiday is to stay at home and to avoid crowds.

 

Based on the results of another study she has recently conducted using mathematical modeling to assess the effectiveness of wearing face masks, Chang strongly supports the Central Epidemic Command Center’s (CECC) decision to set up a system for distributing masks, since it prevents the hoarding of an item essential for limiting the spread of the epidemic. She also suggests that giving priority to such high-risk groups as seniors over 70 years old and those with chronic diseases would make the policy even more effective.

 

Using Facebook’s big data on the movement of people

 

At the end of January Facebook—with some 250 million active users, the world's largest social media platform—announced that it would begin providing big data for a joint study by NTHU and Harvard University's School of Public Health on the spread of coronavirus. The data includes estimates of the number of people moving between sectors 4.5 square kilometers in size.

 

Taipei City—Taiwan’s coronavirus hotspot

 

Based on Facebook’s data on people’s movements, the research found that the five cities in Taiwan where the probability of contracting coronavirus is the highest are Taipei, New Taipei City, Kaohsiung City, Keelung City, and Hsinchu City, respectively. The study also found that the five municipalities most at risk from infections brought in from other areas of Taiwan are Taipei City, Hsinchu City, Chiayi City, New Taipei City, and Hsinchu County, respectively.

 

Chang explained that the large number of people commuting to Greater Taipei and Greater Hsinchu on weekdays puts these areas at higher risk of outside infections. What’s surprising is that Chiayi City—the 18th largest municipality in Taiwan, with a population of only 260,000—is ranked so high; Chang speculates that this might be due to the relatively small population of Chiayi City, which makes it more susceptible to outside influence.

 

High-risk crowding

 

Chang's research team also found that local movement has a stronger correlation with the transmission of the coronavirus than does long-distance movement. This runs counter to the widespread perception that distance traveled is the most important risk factor; what matters most is actually the number of people contacted and the length of time one is in close contact with them. Thus visiting crowded places near one’s home may be even riskier than traveling to popular tourist attractions. In Chang’s view, the best way to reduce the risk of infection is to simply stay at home as much as possible.

 

For Chang, one rather concerning finding of the study was that, despite the repeated warnings issued by the CECC since the outbreak of the epidemic, domestic travel in Taiwan has continued nearly unabated, pointing out that over the past two months, the number of trips between Taipei and Yilan, Taipei City and New Taipei City, and Changhua and Taipei has not decreased, not even on Valentine's Day or on the long weekend at the end of February. The team’s findings have been provided to the CECC for use in formulating future policies and control measures.

 

The team has also used mathematical modeling to simulate the effect of wearing a mask on the infection rate. According to Chang, there is a clear correlation between the widespread and correct use of masks and lower infection rates. Thus millions of masks are being produced and distributed daily in Taiwan, and this is one of the reasons the nation has been able to ride out the pandemic relatively unscathed.

 

Priority distribution of masks

 

A study by Chang and Colin Worby, a computational biologist at the Broad Institute jointly established by the Massachusetts Institute of Technology (MIT) and Harvard University, shows that the proper use of masks by the majority of people reduces both the infection rate and the mortality rate of covid-19.

 

They also found that when masks are in short supply, giving priority to such high-risk groups as seniors over 70 years old and those with chronic diseases helps to reduce the overall infection rate. Chang said that at the beginning of the epidemic the CECC commandeered the mask factories in Taiwan and set up a mask distribution system to prevent panic buying and hoarding—two key measures which have helped the nation stay ahead of the curve.

Dr. Holly Chang of the Institute of Bioinformatics and structural Biology, NTHU.