How did human mobility affect COVID-19 case growth? medrxivpreprint Sydney_Uni COVID19 SARSCoV2 HumanMobility Mobility Roads
By Dr. Priyom Bose, Ph.D.Feb 6 2023Reviewed by Aimee Molineux The coronavirus disease 2019 pandemic, caused by severe acute respiratory syndrome coronavirus-2 , has caused a significant amount of illness, mortality, and hospitalization globally. This ongoing pandemic has severely impacted the healthcare sectors, along with the global economy, which saw the greatest recession since the 1930s.
The classical Susceptible-Infected-Recovery model was modified with fuzzy parameters, such as recovery rate, infection rate, and death rate due to SARS-CoV-2 infection. Although classical statistical models could not incorporate important determining factors, machine learning models provided an effective alternative to understanding complicated datasets. The artificial neural network -based model was developed to predict the SARS-CoV-2 transmission pattern.
The current study included SARS-CoV-2 infection data for one hundred different suburbs of the Greater Sydney area of New South Wales, Australia. Two distinct periods were selected, i.e., during Delta variant circulation and during Omicron variant circulation, to determine infection statistics of the suburbs.
In contrast, during the Omicron phase, no lockdowns were implemented. The only mandatory requirements were social distancing, wearing face masks, and business capacity capping. Double vaccination coverage increased from 77% to almost 79%. Unsurprisingly, people’s mobility within and across the suburbs during this phase was significantly more than in the Delta phase.
In the case of the Omicron variant, the regression model and the neighborhood measure needed to provide more insights because the R-square value was almost the same as the delta variant. In addition, the neighborhood measure revealed a negative impact on infection counts counter-intuitively.
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