Based on data and information collected from India’s city-scale epidemic simulator which is supported by SAP Labs India, scientists from the Indian Institute of Science (IISc), Bengaluru, and the Tata Institute of Fundamental Research (TIFR), have recommended that local trains resume operations with a 30% capacity as the country begins the process of reviving economic activity which has been severely hit by the lockdown restrictions made due to the COVID-19 virus.
The resumption in local train operations will begin slowly—just around 20% to 30% of use initially—before being re-examined in 3 to 4 weeks as new data is collected. Additionally, a specified report with different suggestions should be there in 1 or 2 weeks, according to Sandeep Juneja, Professor and Dean of the School of Technology and Computer Science at TIFR.
On Monday, June 1st, the Indian government started the three-phase plan to lift the nationwide lockdown—allowing more states to open up, as well as allowing hospitality, retail sectors and places of worship to open from June 8.
As the country slowly transitions to more-relaxed lockdown measures, having an extensive understanding of how the COVID-19 virus might spread and estimating its future course is key to driving policy decisions for logistics, health, demand & supply circumstances.
According to Sindhu Gangadharan, managing director of SAP Labs India:
“It is essential to know that we look at such intercessions to reduce the spread of coronavirus & support the government authorities to take the required and right steps.”
The IISc in Bengaluru and the TIFR in Mumbai, started utilizing the comprehensive city-scale epidemic simulator in May with technological support from SAP Labs India to assess the impact of restrictions imposed explicitly on some spaces and to enable the government to make informed decisions in implementing the nationwide lockdown. According to Gautam Menon, a professor of physics and biology at Ashoka University, the simulator is a high-quality study that implements a detailed agent-based model approach to an Indian city for the first time.
Further, Rajesh Sundaresan, a professor of electrical and electronic engineering at IISc, said:
“An agent-based model gives us an ability to study targeted interventions like closing schools and colleges alone, home quarantining, social distancing of the elderly, implementing odd-even strategies at workplaces, etc.”
India’s SAP Labs-supported epidemic simulator models a city with agents (i.e. individuals or groups) distributed across municipal wards, considers public spaces where interactions are likely to occur and accounts for the variations in demographics as it “seeds” the virus infections in the city-scale replica to see how the virus is transmitted based interactions among citizens.
The simulations compared the impact on infections and death rates of a scenario where lockdown is implemented for an indefinite period of time versus less stringent interventions—such as a return to normal activity after April 20, 2020, after May 3, 2020, and partial restrictions for a period after these dates.
The results of the epidemic simulator showed that coronavirus-related deaths would be reduced by 98% in Mumbai and 99.9% in Bengaluru with an indefinite lockdown period. While results on less stringent interventions showed that by July 1, 2020 death rates in Mumbai would be reduced by up to 82%, while in Bengaluru it would be reduced by up to 99.5%.
Moreover, the epidemic simulator has been forming sets of data to come up with practices and information over various locations to predict the number of beds or ICUs needed in hospitals, as well as draw up a plan on tackling a surge in COVID-19 active cases with the country’s existing health infrastructure.