Today’s media are breathlessly reporting that the WHO is predicting 5,000 – 10,000 new cases of Ebola virus disease per week by the beginning of December. There is no written documentation on this, but I did find this study in the New England Journal of Medicine (NEJM) from the WHO’s rapid response unit which suggested 20,000 cumulative cases by 2nd November, which would be 10,000 more cases than we are seeing now (roughly) in just two weeks, so 5,000 per week in November. Given the doubling times in that study were estimated to be approximately one month, that does suggest a rough number of 10,000 cases per week by December (if anything that number is probably slightly optimistic). If so we can expect to see 40,000 cumulative cases by the start of December (20,000 to 2nd November, then 20,000 more in November), and 80,000 by the end of the year. Assuming the same doubling, we will see another 20,000 a week in January, which takes us to a rough 150,000 by the beginning of February, assuming that there is no successful intervention by then.
The case fatality rate is now estimated to be about 70%, so those time frames would give respectively (and approximately); 30,000 deaths by the start of December; 55,000 by the end of the year; and about 100,000 by February. Those are large numbers, but on a national basis what does that mean? In this post I want to look at the implications of these numbers under three different scenarios, but first let’s just look at the number of deaths by the end of the year, and do some rough calculations of the implications.
First, let’s look at just Liberia. The NEJM article puts about 50% of all cases in Liberia, so if we follow that proportion forward, we can expect about 27,000 deaths by the end of the year, and 40,000 cases. It’s not necessarily wise to assume that proportion is static, since the disease appears to have taken hold in the capital of Liberia and Liberia seems to be the worst affected, so the disease may spread faster there or may burn out sooner; but for lack of better evidence let’s just go with that proportion. Liberia, according to Wikipedia, has a population of 4 million, and its capital Monrovia has a population of about 1 million. At first blush, 27,000 deaths is not a lot of people in a country of 4 million … but in 2005-2010 Liberia’s mortality rate was 12 per 1000, for a total of 48,000 deaths in 2014 (my estimate). The 30,000 extra cases of Ebola in Liberia that will occur by the end of the year will cause 21,000 deaths, 50% of its annual total. In just 2.5 months the disease will kill as many people as usually die in 6 months. That’s a traumatic increase in mortality, such as usually happens only in times of natural disaster and war.
In addition, however, controlling the epidemic requires isolation and monitoring of an enormous number of people. Consider this report of an outbreak of Marburg disease in Uganda in September. The disease – which is very similar to Ebola – was identified in a single person in a small town in Uganda, and killed the index case after 17 days. Contact tracing was carried out, and the WHO reports that
As of today, a total of 146 contacts have been identified and are being monitored for signs and symptoms compatible with MVD. Eleven of the contacts developed signs and symptoms compatible with Marburg virus disease.
In order to properly contain this disease, the doctors had to track down 146 people, and of those 11 developed signs and symptoms (fortunately in this case none of them were positive). In the Liberian context this would mean that for every case, more than 100 people need to be traced and 11 isolated as suspected Ebola. Even if we assume that people are starting to catch on to the risks, and so are having less contacts and less need for isolation, we can probably still safely assume that to properly control the disease we need to isolate 4-10 people and trace 100 or so. For 30,000 new cases from now till the end of the year that will mean isolating 120-300,000 individuals, for a period of as long as 21 days. The top end of that figure is about 8% of the population of the country.
Finally, the toll on health care workers of the first 10,000 cases has been genuinely shocking. The latest WHO situation report tells us that 201 health care workers in Liberia have caught the disease, and 95 have died. Assuming that rate persists, adding 30,000 more cases will lead to the death of 300 more health care workers. Wikipedia, again, tells us that Liberia had 5000 full- or part-time health care workers in 2006, of whom 51 were doctors. By December Liberia will have lost almost 20% of its entire health workforce, leading to huge setbacks for health in one of only seven countries in Africa to have met its Millenium Development Goal 4 (under-5 mortality) targets.
So let’s bear those basic figures in mind. 40,000 cases= 28,000 deaths, 120-300,000 isolated individuals, 1.2 – 3.0 million individuals being monitored for signs and symptoms, 20% of the health workforce dead. Also, a very large number of foreign health workers coming in to help, and entire new hospitals being constructed in a country with no suitable infrastructure. Now let’s consider three different scenarios, based around the UN’s 70-70-60 goal, which is to be able to isolate 70% of cases and bury 70% of bodies safely, within 60 days. The low basic reproductive number of Ebola – below 2.0 in most cases – means that preventing 70% of secondary cases should be sufficient to kill the epidemic (just!) So let’s assume that if this goal is reached and maintained, the epidemic will plateau and then begin to decline, in about the same time that it took to escalate. For simplicity we’ll count that time period in terms of numbers of cases – so after the disease peaks, we will assume as many new cases occur as the disease fades away as occurred in its growth. This is not unreasonable – most epidemic patterns don’t crash, but tend to go through a decline that looks roughly symmetric to the increase. This may not apply with a disease as fatal as Ebola, but no one will know till we come out the other side, so let’s assume it will behave as most other epidemic patterns do. This means that if we have x cases by new year, and the UN goal is attained at new year, we should expect to see a further x cases before the disease is gone.
The best case scenario: Liberia meets the UN goal on time
If the sudden inrush of aid workers and soldiers enables Liberia to meet the UN goal on time, we will reach 70-70 in 60 days from now, i.e. by mid-December. That means there will have been 60,000 cases by the time the epidemic begins to decline, or maybe 100,000 by its end. This means 70,000 deaths, 300-700,000 isolated individuals, and pretty much everyone in the country being monitored. About 50% of the workforce will be dead. If we assume the decline takes a few months, say until March, we can guess that we will see nearly two years’ mortality in just 6 months. Between 10-25% of the population will have been isolated for about one month during this period, unable to work or care for others. The goal of safely burying 70% of the dead means 50,000 bodies will need to be buried by specialist teams. The difficulty of their work can be seen in this excellent brief report from the NY Times, but I think it’s obvious that burying 50,000 bodies is going to have to be done in a very different way to this. I wonder if there is even a protocol for mass burial of highly-infectious bodies?
This is the best case scenario. On the basis of the numbers alone it is clearly a catastrophe for Liberia, but it isn’t enough to bring the country to its knees (at least with outside help). Less than 2.5% of the country is dead, and although the economic effects are alarming and the long-term destruction of the health system will set the country back years from its health goals, it doesn’t appear to be a recipe for total collapse (at least on paper). There is hope here, and if the containment efforts are good so that the epidemic crashes really fast, then we can expect it to have a much less significant effect on the health workforce.
The realistic scenario: Attaining 70-70-60 a month late
Suppose instead that the UN goal is missed by a month, taking us to mid-January. That will correspond with about (very roughly) 100,000 new cases by the time the epidemic peaks, or 200,000 by the time it finishes in probably March or April next year. From our calculations, this means 140,000 deaths, 600,000 – 1.5 million isolated individuals, and the remainder being monitored. The entire health workforce will die in this scenario, and about 100,000 bodies will need to be buried safely. Only 54% of the Liberian population are working age, or about 2 million; it’s quite possible that a large part of the adult workforce will be in isolation for more than 3 months, with a large part of the rest involved in basic Ebola-combat activities (burying bodies, contact tracing, logistics). The death toll is equivalent to three years’ mortality in 6 months. What this would mean for the agriculture sector I cannot guess, but it doesn’t seem good. At this level of disease spread, I think we are looking at a society on the verge of collapse, where trade-offs have to be made between isolation/contact tracing on the one hand, and maintaining basic functions of civil society on the other. If the UN goal is missed by a month, alternatives will need to be found to isolation systems, and a huge increase in available health workers will be needed.
The worst case scenario: Failure to contain the epidemic by February
Failure to contain the epidemic by February means 150,000 cumulative cases by February, and probably at least 300,000 (maybe more) over the next few months, with no sign of a slowdown. Every month we will see another doubling of the rate (40,000 per week in March, and so on). Just taking the minimum value here of 300,000 cases, there are 210,000 deaths, 840,000 – 2 million individuals in isolation, and the entire health workforce decimated. In this scenario most of the adult working-age population is isolated, and the entire economy has shut down. Without a huge influx of foreign aid – in food, water, field hospitals, and probably thousands of medical staff – the disease will break out of any containment system that might be left in place, and the only limit on its spread will be its own voracity. This suggests to me that we have until January to get an effective containment system in place, or Liberia as a country will cease to exist in any functional sense. We should assume, furthermore, that in the general breakdown of the social order that will ensue many people will leave the country, and the risk of the epidemic spreading to Nigeria will be very great.
Caveats and limitations
These figures are all rough guesses based on huge assumptions. The number of people who need to be isolated will not scale linearly with disease spread, for example, because one individual will begin to have multiple case connections, and as the disease spreads and social contact reduces, the number of people a new case will have actually touched or been near will decline rapidly. So my estimates of effects on the working age population are inflated, and these are the key cause of social breakdown, I think. Without the effect of isolation and disease containment efforts, even 300,000 cases and 210,000 deaths is not a society-ending event in a country of 4 million people, though nobody wants to think about how horrible that will be. My assumption that the downward side of the epidemic will cause as many cases as the upward side is based on the assumption that the basic reproduction number will be reduced only just below 1 by the 70-70-60 plan; this means each existing case gets a chance to cause another, but if the epidemic is contained more effectively once the plan is in place that assumption could be an over-estimate. Also there are geographical limits on the spread of the disease (especially once things get desperate and all travel within the country is shut down); this will mean that the disease rapidly burns through its available cases and dies out before it can spread fully. And finally, I don’t know what the time trend in deaths of health workers is, but I suspect these deaths were mostly in the early stage of the disease before the word was out, and that deaths are now declining rapidly towards zero. Given all these constraints, I think that an aggressive plan enacted now, aiming to achieve the 70-70-60 goal, and followed through aggressively thereafter, will probably stop the disease somewhere before the numbers provided in my best case scenario. This will still cause a years’ worth of mortality in a couple of months, take up to 10% of the working age population out of work for isolation, and kill up to 20% of the Liberian health workforce. It won’t cause a national collapse, but it is a catastrophe easily as bad as a tsunami or some other huge natural disaster.
What should this mean for the future of health planning in Africa?
We often talk about “fragile health systems” and “extreme poverty” in Africa, but in the rich nations we’re used to thinking of health system failure as poorly-managed diseases and unpleasant medical experiences, but it’s worth remembering that at the extremes of medicine there are disasters: car accidents, pandemic influenza, and incredibly horrible diseases like Ebola. In the best of times in Africa, “fragile health systems” means excess deaths due to preventable infant, maternal, HIV- and malaria-related mortality. But in the worst of times it means huge waves of mortality due to natural disasters, war or epidemics. This Ebola outbreak is showing the rich world what “fragile health systems” really means, and also showing us that we are not able to completely disconnect ourselves from these failures. We can’t expect to isolate ourselves from emerging infectious diseases forever, except perhaps at the cost of our humanity, so instead of trying to isolate ourselves we should try to seriously tackle the fundamental problems affecting health systems in Africa and some parts of South Asia. This is not like a military intervention where the best of intentions can bring about the worst of results; we know what works and we simply need to find the political will to make it happen. Once this disease is back in its box, and all three affected countries are able to contemplate a return to normality, we in the rich world should make a serious, final effort to fix global poverty and most especially to end the grotesque inequality in health systems around the world. It’s almost certainly not going to happen, but we have to recognize that any country with a fragile health system is one weird event away from a terrible humanitarian catastrophe, and we need to start thinking about how to stop this from happening again. That means we have to act to help those countries to genuinely strengthen their health systems, and achieve the kind of economic state that is able to sustain them. This may mean we fat, rich westerners pay a little more for our chocolate, coffee and clothes, but it’s a price I hope we are all going to be a little more willing to pay now that the threat of dying horribly in our own body fluids has begun to make itself felt.
This situation should also serve as a warning about the dangers of ignoring very rare but high-risk events. Ebola has been known for 40 years, and this is the first time it has ever escaped containment. Work on a vaccine has been delayed or ignored partly, I think, because the risk of this disease escaping its bounds is so low that people considered it negligible. I hope my calculations show that the cost of this disease is only negligible provided it never happens, and that once it does happen all our risk assessments look incredibly stupid. We need a new way of assessing risk which puts a serious value on low-probability events. In the era of climate change the implications of this are obvious. At the tail end of some of the global climate models there are some extreme, civilization-ending events that have been largely overlooked by policy-makers because they are so unlikely to happen. Hopefully this Ebola outbreak will convince the world that it is time we started looking more at the tails of our probability distributions, and not at the comforting bulges down near the low-cost events.