societies can create a “new normal” and operate again if they use extensive measures and big data analytics to track the spread of the disease
A simulator to predict the impact of policies on the spread of COVID-19 has been created by Professor Atakan Varol, Director of the Institute of Smart Systems and Artificial Intelligence (ISSAI) at Nazarbayev University and a group of researchers. It’s free for countries to adapt and use.
The simulator was created using real data such as population density and healthcare capacity of each region of the country of Kazakhstan. In order to model the spread of the virus, they used the experience of China, the Diamond Princess cruise ship, Lombardy in Italy, and Kazakhstan’s transport networks including air, railway, and highway connections.
They ran four simulation scenarios:
1. Continued strict quarantine regime in the country – similar to UK’s own response of only letting individuals go outside for certain reasons. This led to the lowest number of deaths but meant the epidemic continued until autumn 2020. This is despite Kazakhstan implementing stricter measures earlier than other countries and having a much lower infected and mortality rate than other countries. This scenario could also lead to economic contraction, inflation, psychological issues, increased marital violence, and health problems due to sedentary lifestyle.
2. Strict quarantine measures completely lifted on April 14th 2020 allowing society to return to normal, with increased hand washing and some social distancing. The outcome of this was a drastic increase in infections and deaths. This suggests our lives cannot return to normal too soon unless a cure or vaccine is found.
3. Lifting measures on April 14th 2020 after making preparations such as increasing hospital capacity, ventilators, and mandatory mask-wearing in public places. The results were better than Scenario 2 but still not enough to fully control the epidemic.
4. The final scenario was a “new normal” of less strict measures with widespread tracking of society using smartphone location data. This would increase the quarantine rate of infected individuals by tracking who they have been in contact with. In this scenario, the epidemic did not exponentially rise and the number of new infections and deaths plateaued. This suggests societies can create a “new normal” and operate again if they use extensive measures and big data analytics to track the spread of the disease.
By using the simulator to estimate the impact and future course of the disease based on different policy measures, it can allow for better planning to prevent spread of the virus.
The researchers have shared the source code so the simulator can be used and adapted by anyone. Since spread of the disease is modelled using the transportation network of the country, this would need to be adapted for a different country. The real value of the simulator is that it enables you to model what results different measures could have, such as the effects of limiting travel and quarantining a region.
If you would like more information on this project, information on using the simulator, or to speak with Professor Varol, please contact Kyle Grizzell from BlueSky PR on 07904706136 or at firstname.lastname@example.org
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