How Maths can help curb Ebola epidemic

Swiss researchers have calculated new benchmark figures to precisely describe the Ebola epidemic in West Africa from a mathematical perspective.

Their results may help health authorities to contain the epidemic.

"Our computer programme is ready. If we are given access to current Ebola sequences, we will be able to gain a detailed insight into the spread of the epidemic literally overnight," researchers noted.

In the current Ebola epidemic, several estimates based on official data of recorded cases of illness were used to derive these figures.

Using a statistical computer programme, a team led by Tanja Stadler, professor of computational evolution at ETH Zurich in Basel has now calculated these parameters based on the gene sequence of the virus in various patient samples.

"A major benefit of our method is that we can use it to calculate unreported cases and therefore the true scale of the epidemic," asserted Stadler.

The virus sequences were obtained from blood samples taken from patients in Sierra Leone in the first few weeks after the epidemic migrated to the country from neighbouring Guinea in May and June 2014.

Using the data made available to them, the ETH researchers were able to calculate an unreported case rate of 30 percent (patients of which blood samples were not taken).

"However, this applies only to the situation analysed in Sierra Leone in May and June. We do not have any blood samples since June at all," Stadler claimed.

The researchers were also able to calculate the incubation period for Ebola (five days) and the infectious time.

Patients can pass on the virus from 1.2 to 7 days after becoming infected.

To obtain these values, the researchers created a phylogenetic tree based on the gene sequences of the virus samples.

"The Ebola virus changes in the body of the patient from day to day, meaning that the virus sequence varies slightly from patient to patient," Stadler explained.

With the knowledge of the different sequences, the researchers were able to determine at what point in the past infection events happened between patients.

From this, they were able to calculate the epidemiological parameters.

"These epidemiological values are important in developing strategies to contain the epidemic and evaluate the effectiveness of these measures. Imposing a curfew is one such measure," researchers pointed out.