A machine-learning model developed by researchers at MIT analyzes and compares how the number of COVID-19 infections across 70 countries in Europe, North America, South America, and Asia differed with how effectively the nations’ governments maintained their quarantine measures. This diagnostic tool is highly accessible and trained on all publicly available COVID-19 data sets, so it could help policymakers inform better quarantine measures across the globe.
The model is based on a traditional SIR model, an epidemiological model used to predict disease spread based on the number of people who are considered “susceptible,” “infectious,” or “recovered” It was enhanced with a neural network then trained on international COVID-19 data to identify patterns in infections and recovery. To determine ‘quarantine strength,’ the algorithm calculated the number of infected individuals who are not transmitting COVID-19 to others by following the quarantine measures in place in their region. As new data is published, the model can show how that area’s quarantine strength changes and evolves over time as safety regulations change.
The MIT team focused on the United States and used their model to calculate how effectively a state has enforced its safety measures and limited the spread of the disease. In spring and early summer, parts of the southern and central United States began reopening businesses and relaxing strict quarantine measures, which led to a sharp increase in COVID-19 cases in those regions. The model calculated that if these states did not re-open so early on or re-opened with strictly enforced safety measures like wearing masks and social distancing, 40% of COVID-19 infections could have been avoided. In Texas and Florida, specifically, maintaining stricter quarantine and stay-at-home measures would have avoided as many as 100,000 infections.
The research paper’s lead author, Raj Dandekar, a graduate student in MIT’s Department of Civil and Environmental Engineering, emphasizes that “If you look at these numbers, simple actions on an individual level can lead to huge reductions in the number of infections and can massively influence the global statistics of this pandemic.” As the number of COVID-19 cases across the United States rise and cities like Los Angeles run out of available ICU beds, this machine-learning model could be vital in informing what level of quarantine measures to put in place. Co-author Christopher Rackauckas, an applied mathematics professor at MIT, says “What I think we have learned quantitatively is, jumping around from hyper-quarantine to no quarantine and back to hyper-quarantine definitely doesn’t work. Instead, good consistent application of policy would have been a much more effective tool.”
This novel machine-learning model can help policymakers determine the best course of action for quarantine measures in different countries, illuminate patterns of COVID-19 spread across different demographics, such as socioeconomic level and race, and save millions of lives.