Big data could be key to tracking Covid-19 and easing lockdown, simulator suggests
Scientists in Kazakhstan have created a simulator to predict the impact of policies on the spread of Covid-19.
The simulator was developed by researchers at Nazarbayev University using real data based on experiences in China, the Diamond Princess cruise ship, Lombardy in Italy, and Kazakhstan’s own transport networks, in order to model the spread of the virus.
The team developed the simulator to estimate the impact and future course of Covid-19 based on different policy measures, potentially allowing for countries to plan better and prevent further spread of the disease.
So far, the researchers have run four simulation scenarios based on; strict quarantine measures; lifting lockdowns with certain measures in place such as social distancing; and lifting lockdowns but using big data analytics to track the rate of new infections.
The team found that a simulation of continued strict quarantine measures led to the lowest number of deaths, but that the virus would continue until at least Autumn 2020. This scenario also included a range of other population issues including poor mental health, economic uncertainty, increased marital violence and health problems related to sedentary lifestyles.
In a simulation assessing what would happen if strict quarantine measures were lifted on April 14th 2020, the team found that even with increased hand washing and some social distancing, death rates and infections dramatically increased. The situation was similar even if countries had made better preparations such as increasing hospital capacity, ventilators, and mandatory mask-wearing in public places.
The last scenario looked at using smartphone location data to track the spread of the virus alongside lifting lockdowns under less strict measures. The researchers found that this scenario did not lead to an exponential rise of infections, and that new infections and deaths plateaued. The data suggests that countries could possibly operate again under “normal” circumstances if big data analytics tracked the spread of the disease.
The researchers have shared the source code so the simulator can be used and adapted by anyone.