Naina Mishra
Tribune News Service
Chandigarh, September 18
The UT is likely to witness 14,376 cases of Covid-19 by October 13. The projection has been made by the Ministry of Health and Family Welfare.
The ministry has also estimated the requirement of intensive care infrastructure, as per which the city would need 624 oxygen-supported beds, 208 ICU beds and 104 ventilators by mid-October.
According to the data available with the Centre, the city had 820 oxygen-supported beds, 93 ICU beds and 46 ventilators on September 13.
Arun Gupta, Principal Secretary, Home, UT, said, “The ministry has made some estimates based on mathematical modelling. However, the actual trend prevailing today is slightly different. If it is taken into consideration, the infrastructure required in October will be much lower as compared to what has been projected by the Centre.”
“The available infrastructure with the ministry is also slightly under-reported as Chandigarh has 160 ICU beds in dedicated Covid hospitals and 95 ventilators,” said Gupta. The Union ministry has projected 14,376 cases by October 13, taking into consideration the doubling rate of 29 days as on September 13 when there were 7,480 Covid-19 cases in the city.
Of the cases projected for October 13, the ministry has estimated 4,200 active cases. Around 15 per cent (624) of them may require oxygen-supported beds, five per cent (208) may need ICU beds and 50 per cent of ICU patients (104) may require ventilators.
Gupta added, “If this modelling of required infrastructure is applied to the current situation, then of 3,200 active cases, around 480 (15 per cent) should have been requiring oxygen and 160 (5 per cent) ICU. However, this does not hold true as on date. Patients requiring oxygen in the city is only five per cent and ICU beds only two per cent.”
The ministry had also said the estimates were derived on the basis of mathematical modelling and do not take into account the effort being made by states and union territories.
The states and UTs may modify the estimates by factoring in variables such as positivity rate, extent and level of testing and hospitalisation.
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