Social distancing is effective, according to the maths

A mathematical modelling expert from the Oxford University says we can be sure that social distancing does work.

Junior Research Fellow Dr Robin Thompson, has found that social distancing substantially reduces the ‘pass-on rate’ of COVID-19.

Amongst the uncertainty surrounding the virus, Dr Thompson claims this is something we can be certain of.

His mathematical modelling which is based on current measure to combat COVID-19 through reducing person-to-person contact presents numbers of infected could be reduced by ‘almost 90%’.

Without the social distancing policy in place, one infected person could start chains of transmission with 1,093 cases over a 6-week period.

With a social distancing policy in place, the same sufferer could start chains of transmission with 127 cases.  

The reasoning behind the modelling is based on one infected person passing the virus on to three others.

Then, the three newly infected will pass the virus on to another three each, and so on.

With social distancing, there removes ‘any question of people wondering whether they or a particular individual has the infection.’

Dr Thompson on Covid-19 says:

“It’s very hard to self-determine whether or not we’re infected.”

“Even if we are carrying the virus, we may have no or very few symptoms.”

“We could therefore be spreading the virus without even knowing it.”

“We all need to go shopping sometimes. However, by following social distancing, we can all make a difference.”

“It’s all of our responsibility to avoid going on a hike where lots of people group together; social distancing works.”

“For SARS, social distancing measures were not required because infectious individuals displayed clear symptoms.”

“As a result, the outbreak could be controlled by finding and isolating symptomatic cases.”

“That appears not to be the situation for Covid-19, due to the wide spectrum of symptoms.”

Back in January, Dr Thompson and Professor Christi Donnelly conducted a lecture on Real-time analysis in understanding the Novel Coronavirus.

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