When an Ebola outbreak was declared in the Democratic Republic of Congo this spring, there were all kinds of predictions about how the epidemic would play out.
At first the outbreak was confined to a remote rural area, so the hope was it could be easily contained. There simply weren’t a lot of people who could have come in contact with the infected individuals.
Then a case was identified in Mbandaka, a city of more than 1 million, raising concerns that this outbreak could echo the 2014 outbreak, where a single case in Guinea, a country in West Africa, triggered an epidemic in which more than 28,500 people were infected with the virus and 11,325 died.
But now it looks as if the outbreak is slowing down. And one mathematical epidemiologist is predicting it’ll all be over soon.
Christian Althaus, head of the Immuno-Epidemiology Research Group at the University of Bern, predicts that the number of Ebola cases will level off by the end of June — resulting in no more than 67 infections. As of June 6, there have been a reported 58 cases — including 27 deaths — in the outbreak in DRC, which started in early April.
To make this prediction, Althaus created a mathematical model for how easily Ebola was spreading in Congo. Models are a common tool epidemiologists use to get a rough idea of how epidemics will develop.
Specifically, he used case numbers shared by the World Health Organization in twice-a-week updates to calculate the average number of people infected by one person carrying Ebola.
At the start of the Congolese outbreak, Althaus calculated that the transmission or “reproduction number” was 3.2 — meaning, on average, an infected individual spread the virus to three others. That explains the graph’s rapid incline. But weeks later, that reproduction number has fallen well below 1, creating the graph’s sudden plateau. When the reproduction number drops below 1, an outbreak starts to slow down because some infected people don’t spread the virus to anyone else.
Note that the solid line represents the expected number of cases of Ebola that will arise. The two dotted lines signify the model’s broader estimate of potential Ebola cases that may arise. If the model holds true, the final number of cases will likely be somewhere within the bounds of these dotted lines.
“Given the current public health response and awareness, I’m quite confident that the final size of the outbreak will remain within the model projections,” Althaus wrote in an email to NPR.
Althaus believes the drop in cases has been caused by the rapid disease response efforts in the country.
Since early May, when the government of Congo declared there was an outbreak, officials have jumped to quell Ebola’s spread. These efforts include quickly isolating infected individuals and tracking those they came in contact with. There’s also been the mass distribution and administration of a newly created vaccine that wasn’t available during the 2014 epidemic.
As Jason Beaubien reported in May:
Four thousand doses of an experimental Ebola vaccine — which has to be stored at minus 60 degrees Celsius — have shipped to the DRC. Plans are being developed to try to vaccinate hundreds if not thousands in areas near where Ebola cases have been found.
Doctors Without Borders is setting up isolation wards and Ebola treatment centers both near the epicenter of the outbreak and in the port city of Mbandaka.
The Red Cross is recruiting local volunteers to collect and safely bury the dead.
The Red Cross is also setting up sanitation teams to disinfect houses, clinics and other places that may have been exposed to the virus.
Bryan Lewis, a computational epidemiologist at Virginia Tech, thinks Althaus’ prediction is sound — and not surprising. Like Althaus, Lewis applies techniques from math, computer science and other specialties to create models and predictions about the spread of diseases.
“It seemed like this outbreak generated sufficient alarm and the DRC is well familiar with Ebola outbreaks,” Lewis wrote in an email to NPR. This is the ninth Ebola outbreak the country has experienced in the last four decades.
But he admits there’s always a chance things could go awry.
“Predictions about infectious disease are very challenging since humans are the primary drivers of infection, and we are so unpredictable,” Lewis writes. “Ebola in particular is very random; one ‘unlikely’ event can change the course of an outbreak [and] spike cases.”
Althaus agrees: Computational models can’t predict everything.
For example, an infected person could travel to a previously uninfected area, or dozens of people could attend the funeral of someone who died of Ebola and touched the body (Ebola is still highly transmittable after a patient’s death). Both are unlikely events given the awareness and disease response efforts of the DRC, the two researchers say — but there are no guarantees in the world of epidemics.
Like Althaus, though, Lewis is hopeful, “As a forecaster of infectious disease, I’d say we aren’t as good as weather forecasters, but even if forecasts on the numbers are wrong, we are often correct about the gross prediction.”
In other words, he, too, believes that the outbreak will likely die out soon.