World leaders have taken various approaches to contain the Covid-19 virus.
While catastrophic levels of infection ravaged European countries such as Italy and Spain, Germany took early preventative measures, resulting in fewer mortalities. And now a new modelling study reveals how the increase in Covid-19 cases dropped off, thanks to a series of three physical distancing interventions. There was a two-week delay after each intervention, but the scientists conducting the study note that it was only after the third intervention, i.e. the contact ban, that numbers dropped significantly.
Contact ban crucial to lower case numbers
In trying to control the outbreak, testing began on 19 January, soon after the Covid-19 outbreak hit Germany. The country has one of the world’s strongest healthcare systems, with a high number of intensive care beds. At one point, the country was even able to take in a number of critically ill Italian patients.
To estimate the impact of the different levels of physical distancing on the spread of the virus, a modelling study was carried out by Jonas Dehning and colleagues. The team, whose results were published in the journal Science, combined a Susceptible-Infected-Recovered (SIR) transmission model with Bayesian parameter inference (a technique used in mathematical statistics). According to the Institute for Disease Modeling (IDM), the SIR model is a generic epidemiological model that provides a simplified way of looking at the transmission of an infectious disease, such as Covid-19, through individuals. The model specifically looks at individuals passing through the following states: susceptible, infectious, and recovered.
Using these methods, the team analysed the three interventions, the first of which began on 7 March and implemented the cancellation of large public events. The third intervention was a contact ban, enforced later in March, and was carried out over three weeks. The peak of the outbreak in the country, Business Insider reports, took place between 26 March and 3 April, during which it maintained a lower death rate than Spain and Italy.
It was the contact ban, the team’s data shows, that resulted in a significant decline in new cases on a daily basis. The team used data on Covid-19 deaths until 21 April, and their report shows evidence of three "change points" each of which was detectable two weeks after an intervention.
Interestingly, their estimates suggest that delaying physical distancing by as little as five days can have tragic consequences on case numbers. Their results also show that lifting restrictions too much, and too early, could leave policymakers and others “effectively blind” to a grave situation for almost two weeks. According to their findings, decision-makers should consider lifting restrictions only when the number of active cases is so low, that a two-week increase will not pose a serious threat to the country’s healthcare system.
As of 18 May, Germany has just over 176 000 confirmed cases and over 7 900 confirmed deaths, the latest stats by Johns Hopkins Coronavirus Resource Centre indicate. Due to the stable number of daily infections, and Germany's flattened curve, they are now slowly loosening the lockdown and "reopening", the New York Times reports.
Modelling: key to decision making
Mathematical modelling is a crucial activity and a key driver of policy decision making. Scientists around the world have been using these tools to help understand the spread of the new coronavirus. Earlier this month, Health24 reported on mathematical modelling by experts from the University of Johannesburg (UJ), which showed how physical distancing as a line of prevention works in the spread of the virus.
The latest confirmed cases in South Africa stand at 15 515, with a total of 264 confirmed deaths.
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