Ebola: a primer.

Reddit IAmA on Ebola and the DRC outbreak!

I’ll be hosting an ‘ask me anything’ session on Reddit’s IAmA subreddit, answering user questions, on

21 May 2018, 13:15 (Eastern)/17:15 GMT/19:15 CEST

To be kept up to date and make sure you get the thread link about 30 minutes before the event starts, RSVP for the event on Facebook!


As I’m writing this, the beginnings of what could well be a major outbreak are raging in Bikoro territory, Equateur province, in the northeast of the Democratic Republic of the Congo (DRC). Recent news indicate that Mbandaka, the capital of Equateur and home to a busy port and a million people, has now reported cases as of 17 May. The death toll has reached 25 as of the time of writing, and it’s anyone’s guess how bad it’ll get – having learned from the unexpectedly extensive devastation of the West African Zaire ebolavirus outbreak (2013-16), everybody is preparing for the worst case scenario. Me and ebolaviruses have a long relationship, going back over a decade – I sometimes tend to wistfully remark that I know more about virion protein (VP) 24 of the Zaire ebolavirus (EBOV) than I know about some of my own family members. The reverse of the medal is that reading some of the nonsense in the press is borderline physically painful. I’ve assembled these resources for interested laypeople – especially journalists intending to comment on the Bikoro outbreak, in hopes that it will somewhat reduce misunderstandings.

Some taxonomy pedantry for starters

To start with, a point of pedantry: there are multiple ebolaviruses, so technically, ‘Ebola virus’ is a misnomer. Viral taxonomy is a complex thing, governed largely by the International Committee on the Taxonomy of Viruses (ICTV). The latter has preliminarily determined the taxonomy of filoviruses to look as follows:1

  • Family Filoviridae
  • Genus Ebolavirus
    • Species Bundibugyo ebolavirus (BDBV)
    • Species Reston ebolavirus (RESV or RESTV)
    • Species Sudan ebolavirus (SUDV)
    • Species Taï Forest ebolavirus, formerly Côte d’Ivoire ebolavirus (TAFV)
    • Species Zaire ebolavirus (EBOV or ZEBOV)
  • Genus Marburgvirus
    • Species Marburg marburgvirus (MARV)

By far the most important of these are EBOV and SUDV. These have been responsible for almost all major outbreaks – TAFV had only one single human case (CFR:2 1/0, 0%), RESV killed a lot of monkeys3 but a number of humans, despite seroconverting,4 did not fall ill. SUDV is generally regarded as somewhat more benign than EBOV, with a CFR around 50% (range 41-65%, discounting the 2011 Luweero case, where the single patient died). EBOV is the type species of ebolavirus, and it commonly has mortalities up to 93%. It is almost definite that the current outbreak in the DRC is an EBOV outbreak.

Viral species are further subdivided into strains. This is important for ebolaviruses, EBOV in particular, because there seems to be an emerging divergence. Typically, ebolavirus outbreaks claim up to 3-400 lives at most, tend to be over in 3-4 months and are fairly localised. Because non-RESV ebolaviruses, at least in humans, need contact with bodily fluids, long chains of transmission are rare. The 2013-16 West African outbreak, however, seems to have upended this hypothesis. That outbreak lasted almost twelve times the average for all known outbreaks until then, and claimed more lives than all known ebolavirus outbreaks (since the index outbreak in Yambuku, DRC, in 1976) put together. Why this was the case is a bit of a mystery, but there is now an understanding that EBOV strains that are more similar to the Mayinga (EBOV/May) strain isolated in 1976 are different from strains more similar to the Makona strain (EBOV/Mak), which was the prevalent strain in the West African outbreak.

Background and history

Courtesy of SIB/ViralZone.

Ebolaviruses belong to the family of filoviridae, so named for their threadlike appearance – they are among some of the longest viruses, reaching a length of up to 14,000nm and a width of approximately 80nm. The genome of ebolaviruses is relatively simple, approximately 19,000 base pairs long, and stored as a single-strand negative sense RNA, making ebolaviruses, and all other filoviridae, (–)ssRNA or Baltimore V viruses. This is significant as negative-sense single-strand RNA viruses need to be translated into positive-sense RNA by RNA polymerase, and therefore aren’t directly infectious.

Ebolaviruses, and other filoviruses, are probably pretty old – in fact, the study by Taylor et al. (2014) has found genetic fossils5 of EBOV-like VP35 in the same location of several cricetid rodents’ (voles and hamsters) genomes, suggesting that ebolaviruses have diverged from marburgviruses around the time the common ancestor of hamsters and voles lived, sometime around the miocene (16-23 million years ago).6

The Yambuku mission hospital’s record for Mr Mabalo, a school teacher, who was the first recorded human case of Ebola Virus Disease, dated 26 August 1976. Mr Mabalo would eventually succumb to EVD on 06 September.
Photo courtesy of Guido van der Groen/ITM Antwerp.

We also know that EBOV only relatively recently diverged from other ebolaviruses (sometime in the last century), but the first acknowledged outbreak of an ebolavirus took place in 1976 in Yambuku, in what was then Zaïre and is today the DR Congo. The story of this outbreak is extensively told in a retrospective article by Joel Breman and a number of others who have been present at the initial outbreak, written four decades later. Arguably, we saw the emergence of a physiologically and epidemiologically different strain of EBOV during the West African EBOV epidemic, too – at least in the wild, EBOV/Mak behaved very differently from EBOV/May: characterised by long chains of transmission, a somewhat lower CFR7 and a much longer epidemic duration with a significantly larger number of cases – indeed, the 2013-16 outbreak claimed more lives than every single known filoviral outbreak since the first recorded filoviral epidemic, the 1967 Marburg outbreak, put together. Recent evidence seems to suggest that infection with EBOV/Mak does seem to exhibit some significant differences from the previously known strains that are clinically different to the point that they might explain the difference between the 2013-2016 West African outbreak and previous epidemics, which typically were regionally limited, originated in central Africa (Sudan and Zaire) rather than the coastal states of the Gulf of Guinea and lasted a few months with no more than 3-400 cases.8

Worth reading:

  • Two of the protagonists of the 1976 Yambuku outbreaks have written amazing autobiographies that are worth reading. No Time to Lose, by Peter Piot, is a fascinating book, although most of it – like Peter Piot’s career – is devoted to STDs, especially the fight against AIDS. His colleague and countryman, Guido van der Groen, has also written an engaging and well-written memoir, On the Trail of Ebola.
  • Murphy, F.A. (2016): Historical perspective: what constitutes discovery (of a new virus)? In: Kielian, M. et al. (eds)., Advances in Virus Research95:197-220. – What’s it like to discover a virus? Fred Murphy, whose transfer electron micrograph graces the header of this blog  post and has become inextricably associated with ebolaviruses, was working as CDC’s chief viropathologist in 1976, and if not a father of EBOV’s discovery, he is at the very least its godfather. His experiences with Ebola specifically are summarised in section 5.8 of the chapter.
  • Tropical medicine professor and ID physician David Brett-Major‘s book, A Year of Ebola, is an up-close-and-personal account of a year of the 2013-2016 West African outbreak, and the challenges that rendering assistance in the chaos of such an outbreak. For those unfamiliar with what a major, multi-party public health intervention involves, this book is a must-read.
  • A good and somewhat lighthearted starter is my interview with Devon from the Bugs, Blood and Bones podcast: part 1 | part 2. This discusses many of the principal points you should know about ebolaviruses, especially the reason we can’t simply eliminate ebolaviruses as easily as, say, smallpox.

The (proteomic) nature of the beast

In its octameric ‘ring’ or ‘crown’ configuration, VP40 is a regulator of RNA viral transcription. Author’s figure.

Ebolaviruses are remarkably simple for all the destruction they’re capable of. To understand the issues that curing ebolavirus infections raises, it’s important to understand how the virus itself is constructed and how it operates on a molecular level. The ebolavirus genome encodes seven proteins: a nucleoprotein (NP), a RNA polymerase (L), the glycoprotein GP, and four viral proteins (VPs): VP24, VP30, VP35 and VP40 (sometimes referred to as the matrix protein). For this reason, some of Ebola’s viral proteins ‘moonlight’ – that is, they fulfill multiple functions, depending on their polymerisation state.

  • The overall structure of the virion is given by the ebola matrix protein or VP40. As a hexamer looking a bit like the S-shaped Tetris piece,9 it’s responsible for the structure of the virion, while as a crown-shaped octamer wrapped around the RNA, it regulates RNA transcription. The matrix protein’s main purpose, other than serving as a physical outer shell, is to connect the nucleocapsid with the target cell’s membrane, allowing penetration. VP40 also gives ebolaviruses the characteristic structure. For this reason, and the fact that it also coordinates some aspects of the viral lifecycle – in particular virion assembly and ‘budding’, that is, egress from infected cells –, it’s being considered as a therapeutic target.10
    As a hexamer, VP40 is the primary matrix protein of filoviral virions.
  • The RNA is surrounded by a dynamic nucleocapsid, made up of VP35, VP30 and VP24. The purpose of this is to store and, at the necessary time, deliver, the genetic payload. The nucleoprotein NP is wrapped around the RNA genome.
  • VP24 is also used to disrupt the innate immune system, specifically the STAT1 signalling pathway. Normally, in response to viral infections, interferons phosphorylate the STAT1 protein, which then binds to karyopherin alpha (KPNA). Karyopherin alpha is an ‘importin’, a shuttle protein. Once STAT1 is bound to KPNA, it is ferried to the nucleus, and stimulates gene transcription. VP24 selectively tricks this: it binds competitively to KPNA, so that STAT1 cannot bind to it. In a sense, VP24 is hijacking the cell’s internal shuttle system, preventing an adequate immune response but maintaining the ability to use the system for its own purposes.
  • L, or RNA-dependent RNA polymerase, is required because ebolaviruses are negative-sense single strand RNA viruses, and thus a complementary, positive sense strand needs to be generated for transcription.
  • GP, the ebolavirus glycoprotein, is perhaps the most essential part of the internal machinery of an ebolavirus. GP is responsible for infecting new cells, and for a cytopathogenic effect on endothelial cells – in other words, GP damages the cells that line blood vessels in particular and has been observed to cause endothelial cell loss. This in turn results in the haemorrhagic symptoms that characterise EVD’s haemorrhagic stage.11

Ebola virus disease (EVD) and pathophysiology

Human and primate ebolavirus infection (regardless of species or strain) causes Ebola Virus Disease (EVD), sometimes referred to as Ebola haemorrhagic fever (EHF). EVD is more accurate as the well-known haemorrhagic manifestations are far from ubiquitous (about half the cases at best).12

Illustration courtesy of T.W. Geisbert and H. Feldmann.

EVD begins with nonspecific signs – like a bad flu: after an incubation time of about 4 days to two weeks, fatigue, fever, loss of appetite and muscle aches set in, along with vomiting, diarrhoea and more vomiting. Despite its apparent simplicity, ebolaviruses carry out a complex and multifactorial propgramme of destruction:

  1. Prodromic stage: In the early, prodromic stage, the viral protein VP24 inhibits interferon type I and II signalling, effectively cutting the communication lines of the immune system and allowing the virus to proliferate in peace. During this time, the patient may be asymptomatic or have nonspecific symptoms like headaches, fatigue and a mild
  2. Early disseminating stage: Ebolaviruses preferentially attack certain white blood cells that allow it to spread through the lymphatic system, in particular dendritic cells, macrophages and monocytes, and later on spread prolifically through liver cells and the adrenal gland, causing liver damage (leading to clotting issues and the diagnostically significant elevated transaminase levels). The death of the infected monocytes (called a cytopathic or cytopathogenic effect) causes immunosuppression through low lymphocyte counts and releases pro-inflammatory molecules, in particular TFN-alpha, and the interleukins IL-6 and IL-8, creating a state somewhat reminiscent of sepsis. GP also assists in inhibiting neutrophils, white blood cells crucial for immune reactions, from activating.
  3. Vascular endothelial damage: Glycoprotein (GP) in vascular endothelial cells (the cells lining the walls of blood vessels) destroys the integrity of blood vessels around three to four days after infection, leading to bleeding.
  4. Liver injury and DIC: GP, when expressed in the liver, causes liver damage, and also suppresses the production of integrins. Integrins are transmembrane proteins that allow cells to attach to the various molecules outside the cell, which is crucial for clotting. Together, these lead to a paradoxical state called disseminated intravascular coagulation (DIC): small blood clots form in the capillaries all over the body, leading to ischemia (lack of blood supply) and organ failure, while at the same time using up all the clotting factors and platelets. This is responsible for the later haemorrhagic manifestations.
  5. At this stage, patients that do not recover succumb to the combined effects of multi-organ failure, volume loss from diarrhoea and massive haemorrhage.

Together, these have a damaging effect on vascular endothelial cells, the cells lining the walls of blood vessels, leading to internal bleeding and the haemorrhagic manifestations.

Eventually, the haemorrhagic (bleeding) symptoms – bleeding under the skin, uncontrollable bleeding from blood draws, bleeding into the sclerae (the whites of the eyes), blood in vomit and faeces – may begin, largely because damage to the liver and depletion of clotting factors.

Death usually occurs 8-14 days from onset of symptoms. Contrary to popular perception, death is actually not caused by bleeding out – the blood loss is quite simply not enough to be fatal, even in the haemorrhagic cases. Rather, ebolaviruses turn the body’s own inflammatory cascades on overdrive, causing a state that’s somewhat similar to septic shock. Survivors begin to feel better around 10-14 days after first symptoms, but recovery is slow and can take months.

Worth reading:

  • Geisbert, T.W. and Feldmann, H. (2011): Ebola haemorrhagic fever. Lancet 377:849-62. – a great summary, while intended for professional audiences, it is probably the most comprehensive article on what we know about ebolaviruses. Nb. that it was written before the 2013-16 West African outbreak.
  • Munoz-Fontela, C. and McElroy, A.K. (2017): Ebola virus disease in humans: pathophysiology and immunity. In: Mühlberger E. et al. (eds.), Marburg- and Ebolaviruses. Springer, 2017. – This is a rather pricey book, and aimed at public health experts, but is probably the best summary of post-West African outbreak scholarship on all things ebola- and marburgviruses. For those writing for a professional audience or desiring a more comprehensive understanding of the underlying biology, it’s a must-have. Disclaimer: many of the chapter authors and editors are friends and/or valued colleagues.

Ecology and reservoir hosts

Finding the reservoir host of ebolaviruses and Marburg marburgvirus has consumed an incredible amount of scientific effort during the 1980s and 1990s, with relatively little to show for it. It was clear from the very beginning that ebolaviruses are zoonotic – that is, there’s a reservoir host, an animal in which the virus can persist and multiply without causing disease. This explains why it sometimes appears as if ebolaviruses (and Marburg) came out of nothing, wreaked havoc, then disappeared as fast as they appeared. Using RT-PCR and qRT-PCR, it’s now clear that that the reservoir hosts are bats, and a number of species, in particular certain fruit bats. Bats have a complex interferon (IFN) system, much more complex than the human or NHP13 IFN system. This seems to give them an ability to manage the infection in their bodies (see the Kühl and Pöhlmann paper below).There’s a global increase of bat-borne pathogens causing outbreak – these are almost all viral (the related henipaviruses Hendra virus in Australia and Nipah virus in Malaysia/Bangladesh, the coronaviruses MERS-CoV and SARS-CoV, rabies, etc.). As humanity, in need of arable land across the world to feed the exploding population and mineral resources like diamonds and coltan, encroaches upon traditional habitats of Chiropteran species, especially the caves and jungles where they roost, interactions between bats and humans will become more and more frequent, raising the risk of infections. Clearly a strategy to manage ebolaviruses must also be able to manage the ecological problem of habitat loss.

Worth reading:

  • Kühl, A. and Pöhlmann, S. (2012): How Ebola Virus Counters the Interferon System. Zoon Pub Health 59:116-131. – great paper, but tough to digest for non-technical audiences. For those who prefer a slightly more relaxed version, see the next link.
  • Fagre, A. (2016): Why don’t bats get Ebola? Scientific American Guest Blog, July 18, 2016. – same topic as above, just for more popular audiences.
  • On ecology, the chapter Ecology of Filoviruses in Mühlberger et al. (eds.), op cit, is worth reading.
  • For understanding zoonotic diseases, Spillover by David Quammen (2013) is an excellent read. Ebola: The Natural and Human History of a Deadly Virus, written in 2014, updates his chapter on ebolaviruses – largely EBOV – for an audience hungry for information after the 2013 West African outbreak. – Quammen has a great style and writes well, without Preston’s sensationalism. If this is your first foray into writing about, or trying to understand, filoviral zoonoses, both books are very much worth reading. The added value of whatever was added to the Ebola chapter in Spillover in Ebola: The Natural and Human History is, to me at least, dubious. It is, however, a much shorter read for those pressed for time.

Treatment and prophylaxis

So far, no particular agent has proved to be conclusively effective against EBOV infection after symptoms have emerged, and treatment is mainly symptomatic. It is haunting that the state of the art in treating filoviral haemorrhagic fever 2018 is not much different from the approach Margaretha Isaäcson and her team used on the three Marburg cases – Cases 1 and 2, Australian hitchhikers, and Case 3, a nurse who took care of both Cases 1 and 2 – in 1975:

At this stage, it became clear that there would be no specific treatment that could be relied upon to attack and kill the virus responsible for this infection. The girls’ only chance of survival would, therefore, depend on meticulous, ongoing monitoring of various organ functions and managing clinical problems in anticipation or as they presented themselves. This approach required a large team in support of the core formed by the clinicians responsible for the daily evaluation, treatment and general management of the patients.
– from the notes of Margaretha Isaäcson, 26 February 1975

A model Ebola Treatment Centre. The three wards each segregate low-likelihood, high-likelihood and confirmed cases. The orange building in the lower right corner is a field morgue. Double fencing allows patients’ families to communicate with their loved ones from a safe distance, without needing to breach isolation. Illustration courtesy of MSF.

Treatment is focused on volume and electrolyte replacement (intravenously or using oral rehydration salts aka ORSs), pain management and blood transfusions to combat blood loss. To manage disseminated intravascular coagulation and the ensuing coagulopathy, heparin and clotting factors have both been used, with mixed success. Intensive care can greatly increase survival chances, but in low resource settings this remains a challenge. The West African outbreak has demonstrated the utility and sustainability of three-segment (four, if you count the morgue) Ebola Treatment Centres (ETCs, see image) as an easy and inexpensive way to reduce nosocomial spread (spread within a healthcare facility). The model ETC design, which separates confirmed, low-probability and high-probability cases, reduces the risk to lower probability cases by separating them from higher-probability or confirmed cases. One of the painful lessons of the 1976 Yambuku outbreak was that reuse of medical equipment, in particular of hypodermic needles and syringes, can greatly contribute to the spread of ebolaviruses, and this makes overcoming the logistic challenges of dealing with an ebolavirus outbreak in an isolated and ill-accessible location all the more acute.

There are no specific treatment options for EVD that have stood the test of time and rigorous trials. A few of the most often discussed specific treatment options are outlined below:

  • Convalescent plasma has for a long time been the best hope against filoviral infections, but is not always accessible and has its own risks, such as residual viral loads. It also doesn’t keep too well (like liquid plasma, it must be kept between +2ºC and +6ºC). It is taken from survivors of the infection using plasmapheresis, a process quite similar to haemodialysis except in this case, the dialysate is retained. This contains antibodies that the patient developed following his infection. Convalescent plasma also contains a range of other antibodies, and these can cause various immune reactions – importantly, convalescent plasma must come from healthy individuals (‘donor qualified’, i.e. adequate haemoglobin levels and free from bloodborne pathogens) that are compatible with the recipient’s blood type. In regions where ebolaviruses are endemic, this is one of the easiest treatment options to implement, but the efficacy of convalescent plasma may be hampered by epitopic dissimilarity (that is, if the strain the donor recovered from and the strain the recipient is suffering from are too dissimilar, the antibodies won’t work). The WHO has worked out a detailed guideline on using convalescent plasma, which also highlights one of its greatest drawbacks: it works best for patients with early stage disease.
  • ZMapp is a biological drug, specifically a monoclonal antibody. Monoclonal antibodies are artificially created equivalents of the antibodies in convalescent plasma. The great benefit of ZMapp over convalescent plasma is that it only contains antibodies specifically against EBOV, and as such the risk of immune reactions is negligible. ZMapp’s efficacy is quite controversial, as due to the scarcity and cost of the drug, the number of patients treated was too low to really be able to draw conclusions from.
  • Brincidofovir is a broad spectrum antiviral against DNA viruses, such as cytomegalovirus, smallpox and herpes simplex. For some reason, its lipid moiety appears to have shown some efficacy against EBOV, even though EBOV is not a DNA virus but a (-)ssRNA (negative single sense RNA, Baltimore Group V) virus. However, a very small (n=4) Phase II trial in Liberia was prematurely cancelled, and all enrolled subjects died of EVD, after the manufacturer decided to stop pursuing EVD as a target for brincidofovir.
  • Favipiravir is also a broad spectrum antiviral, with specific activity against RNA viruses, initially developed against influenzaviruses. The JIKI trial was conducted in Gueckedou, the ground zero of the 2013-2016 outbreak, in September 2014, and has indicated some efficacy for patients with less severe disease (low to medium viral loads). Controversially, because the criteria weren’t met for a proper randomised clinical trial in late 2014, the JIKI trial was historically controlled, and this has drawn extensive professional criticism.

There are a range of ebolavirus vaccines, most specifically targeting EBOV. The two currently available vaccines are rVSV-ZEBOV and the cAd3-ZEBOV vaccine (colloquially referred to as the NIAID vaccine).

  • rVSV-ZEBOV is a somewhat quirky viral vaccine. It is intended to create antibodies to GP, the virion glycoprotein of EBOV. Normally, vaccines contain an adjuvant and an antigen, such as a viral protein (e.g. the HPV vaccine contains the protein shell, called the L1 major capsid protein, of various HPV strains). The immune system then recognises this as foreign and generates antibodies against them. rVSV-ZEBOV works a little different – it actually contains a live virus, VSV (vesicular stomatitis virus or Indiana vesiculovirus, a distant relative of rabies), which is harmless in humans but causes a disease very similar to foot and mouth disease in cattle and horses. This recombinant (hence r) VSV expresses small amounts of GP, to which the body then generates antibodies. In a ring vaccination trial called Ebola ça Suffit-GRV Trial, 7,284 participants were recruited in Guinea and a parallel trial with the rVSV-ZEBOV vaccine was carried out in Sierra Leone by the CDC (the STRIVE VSV-EBOV trial). The trial faced complex ethical dilemmas. Placebo control would clearly not be ethically (or politically) acceptable, so instead the trial participants were randomised into two cohorts, some of whom received the vaccine after a three week delay. However, due to encouraging early results, the control arm was effectively dispensed with and everybody was vaccinated. The National Academies of Sciences, Engineering and Medicine published an report in which they assessed the trials, and found that much like in the case of favipiravir, it’s hard to do assess a life-saving treatment in the middle of a lethal epidemic. The WHO has announced that it will use the rVSV-ZEBOV vaccine to ring vaccinate contacts of known, laboratory confirmed cases, from 21 May onwards, and has a stock of 7,000 doses of the vaccine in cold storage in Kinshasa. Ring vaccination has been used successfully in the eradication of smallpox, and there is ample evidence to its efficacy and the ability to control further spread, provided contact tracing is successful.
  • cAd3-ZEBOV aka the NIAID/GSK vaccine is a similarly structured vaccine, but derived from a chimpanzee adenovirus, ChAd3. Like the rVSV-ZEBOV vaccine, the cAd3-ZEBOV vaccine expresses glycoproteins from EBOV and, depending on configuration, SUDV.14 This vaccine is considered less ‘ready for use’, and while it’s been found safe, it is not clear what efficacy it will ultimately have.

Worth reading:

  • On Ebola treatment centres, Chowell, G. and Viboud, C. (2015): Controlling Ebola: key role of Ebola treatment centres. Lancet Inf Dis 15(2):139-141. – a good outline of the cheap yet surprisingly effective three-stage treatment centre model.
  • Medecins Sans Frontieres, who have pioneered the three-stage treatment centre structure, have a great interactive guide to a treatment centre that reflects the idea of segregation by infection probability quite well.
  • David Kroll’s article in Forbes asks the question on everyone’s mind: how will we know if the Ebola drugs used during the West African outbreak have indeed worked?Most patients received multiple different treatments, and the sample size was quite small – most of the patients in Africa have only received the usual symptomatic treatment. Clearly, there’s a huge ethical issue, and one of health equity, involved here: many drugs, high costs, many patients, and a willingness to give patients every possible chance at survival. The moral imperatives and the practicalities of the situation make it hard for researchers to gauge efficacy of individual treatments.
  • Adebamowo, C. et al. (2014). Randomised controlled trials for Ebola: practical and ethical issues. Lancet 384:1423-1424. – when it comes to clinical trials for diseases with high mortality, complex ethical issues arise. This makes research and the traditional methods of evaluating treatments difficult. Randomised controlled trials, the gold standard when it comes to assessing the efficacy of medical interventions, are difficult to conduct in the middle of a devastating epidemic, and raises complex ethical issues.
  • National Academies of Sciences, Engineering and Medicine (2017). Integrating Clinical Research into Epidemic Response: The Ebola Experience. The National Academies Press, Washington, DC. – this is probably the best overview of the current state of the art when it comes to vaccines for EBOV after the West African outbreak. Chapter 4 is a must-read for vaccines, and chapter 3 for clinical treatments. Furthermore, Chapter 2 is a great in-depth exploration of the Scylla and Charybdis of doing high-quality, evidence-based clinical research in the middle of an epidemic with a high-mortality viral disease.

Keeping up to date & other stuff to read

The situation is currently quite rapidly evolving, and information flow is often quite restricted due to unreliable communication links. Perhaps the best source of information about what’s going on at the time is ProMED-mail, run by ISID. I also tweet pretty prolifically about the emerging crisis and other public health issues (you can find me at @chrisvcsefalvay), and of course you can find all my blog posts and public appearances that involve filoviruses on this page. I’m also always happy to answer questions, here in the comments thread or using the contact form (if you’re writing for a publication, please use the contact form).

I hope this primer to ebolaviruses was helpful, and if you intend to write about the subject, you now feel better informed. Please feel free to raise any questions that you think remain open in the comment thread below!

References   [ + ]

1. See ICTV page on filoviral taxonomy.
2. Case-fatality rate, i.e. the number of cases versus the number of deaths. Typically given as case/fatality, percentage – e.g. 10/3 (30%) means 10 cases, 3 died, 30% CFR.
3. This is the outbreak dramatised in Preston’s Hot Zone.
4. Seroconversion refers to developing antibodies against a pathogen. It does not mean actually becoming sick as well, just that the body has encountered the pathogen and has responded to it.
5. A fossil gene is what happens when a virus does not infect or kill the host, but rather incorporates bits and pieces of the viral genome into its own.
6. Taylor, D.J. et al. (2014). Evidence that ebolaviruses and cuevaviruses have been diverging from marburgviruses since the MiocenePeerJ 2 Sep 2014, 2:e556.
7. Case-fatality ratio or case-fatality rate, which is a misnomer, since it’s neither a rate nor a ratio in the epidemiological sense. Normally given as a percentage, it is defined as \frac{C_d}{\Sigma C}, where C_d describes all deceased cases and \Sigma C is defined as the total of all cases that meet the inclusion criteria.
8. Versteeg, K. and Geisbert, T.W. (2017). Infection with the Makona variant results in a delayed and distinct host immune response compared to previous Ebola virus variantsScientific Reports 7:9730.
9. Officially, a ‘mirrored Z free tetromino‘. Except, of course, it’s a hexomino.
10. Madara, J.J., Harty, R.N. et al. (2015). The multifunctional Ebola virus VP40 matrix protein is a promising therapeutic targetFuture Virol (10)5: 537-546.
11. Yang, Z.Y., Nabel, G.J. et al. (2000). Identification of the Ebola virus glycoprotein as the main viral determinant of vascular cell cytotoxicity and injury. Nature Med 6(8):886-9.
12. The descriptions of ebolaviruses or even Marburg turning patients into bags of goo or exploding with blood, largely inspired by Preston’s Hot Zone, are wildly inaccurate. Still, it’s one nasty disease.
13. Non-human primate.
14. The vaccine is intended to express glycoproteins from both when in production use. The current Phase II UK trials, conducted by Oxford University’s Jenner Institute, are done with a variant expressing only EBOV GP.

Herd immunity: how it really works

There are few concepts as trivial yet as widely misunderstood as herd immunity. In a sense, it’s not all that surprising, because frankly, there’s something almost magical about it – herd immunity means that in a population, some people who are not or cannot be immunized continue to reap the benefit of immunization. On its own, this may even be counter-intuitive. And so, unsurprisingly, like many evidently true concepts, herd immunity has its malcontents – going so far as to condemn the very idea as a ‘CDC lie’ – never mind that the concept was first used in 1923, well before the CDC was established.1

Now, let’s ignore for a moment what Dr Humphries, a nephrologist-turned-homeopath with a penchant for being economical with the truth when not outright lying, has to say – not because she’s a quack but because she has not the most basic idea of epidemiology. Instead, let’s look at this alleged ‘myth’ to begin with.

Herd immunity: the cold, hard maths

Our current understanding of herd immunity is actually a result of increasing understanding of population dynamics in epidemiology, towards the second half of the 20th century. There are, on the whole, two ways to explain it. Both are actually the same thing, and one can be derived from the other.

The simple explanation: effective R_0 depletion

The simple explanation rests on a simplification that makes it possible to describe herd immunity in terms that are intelligible at the level of high school maths. In epidemiology, R_0 (pron. ‘arr-nought‘, like a pirate), describes the basic reproduction rate of an infectious disease.2 To put it at its most simplistic: R_0 is the number of cases produced by each case. The illustration on the side shows the index case (IDX) and the first two generations of an infection with R_0 = 3.

Now, R_0 is a theoretical variable. It is usually observationally estimated, and don’t take measures intended to reduce it into account. And that’s where it gets interesting.

Consider the following scenario, where a third of the population is vaccinated, denoted by dark black circles around the nodes representing them. One would expect that of the 13 persons, a third, i.e. about. 4 , would remain disease-free. But in fact, over half of the people will remain disease-free, including three who are not vaccinated. This is because the person in the previous generation did not pass on the pathogen to them. In other words, preventing spread, e.g. by vaccination or by quarantine, can affect and reduce R_0. Thus in this case, the effective R_0 was closer to 1.66 than 3 – almost halving the R_0 by vaccinating only a third of the population.

We also know that for infections where the patient either dies or recovers, the infection has a simple ecology: every case must be ‘replaced’. In other words, if the effective R_0 falls below 1, the infection will eventually peter out. This happens quite often when everyone in a population is dead or immune after an infection has burned through it (more about that later).

Thus, the infection will be sustainable if and only if

R_{0} \geq 1

Under the assumption of a 100% efficient vaccine, the threshold value \bar{p_V} after which the infection will no longer be able to sustain itself is calculated as

\bar{p_V} = 1 - \frac{1}{R_0}

Adjusting for vaccine efficacy, E_V, which is usually less than 100%, we get

\bar{p_V} = \frac{1-\frac{1}{R_0}}{E_V} = \frac{R_0 - 1}{R_0 E_V}

For a worked example, let’s consider measles. Measles has an R_0 around 15 (although a much higher value has been observed in the past, up to 30, in some communities), and the measles vaccine is about 96% effective. What percentage of the population needs to be vaccinated? Let’s consider \bar{p_V}, the minimum or threshold value above which herd immunity is effective:

\bar{p_V} = \frac{R_0 - 1}{R_0 E_V} = \frac{15-1}{15 \cdot 0.96} = \frac{14}{14.4} \approx 97.22\%

The more complex explanation: \mathcal{SIR} models

Note: this is somewhat complex maths and is generally not recommended unless you’re a masochist and/or comfortable with calculus and differential equations. It does give you a more nuanced picture of matters, but is not necessary to understand the whole of the argumentation. So feel free to skip it.

The slightly more complex explanation relies on a three-compartment model, in which the population is allotted into one of three compartments: \mathcal{S}usceptible, \mathcal{I}nfectious and \mathcal{R}ecovered. This model makes certain assumptions, such as that persons are infectious from the moment they’re exposed and that once they recover, they’re immune. There are various twists on the idea of a multicompartment model that takes into account the fact that this is not true for every disease, but the overall idea is the same.3 In general, multicompartment models begin with everybody susceptible, and a seed population of infectious subjects. Vaccination in such models is usually accounted for by treating them as ‘recovered’, and thus immune, from t = 0 onwards.

Given an invariant population (i.e. it is assumed that no births, deaths or migration occurs), the population can be described as consisting of the sum of the mutually exclusive compartments: P = \mathcal{S}(t) + \mathcal{I}(t) + \mathcal{R}(t). For the same reason, the total change is invariant over time, i.e.

\frac{d \mathcal{S}}{d t} + \frac{d \mathcal{I}}{d t} + \frac{d \mathcal{R}}{d t} = 0

Under this assumption of a closed system, we can relate the volumes of each of the compartment to the transition probabilities \beta (from \mathcal{S} to \mathcal{I}) and \gamma (from \mathcal{I} to \mathcal{R}), so that:

\frac{d \mathcal{S}}{d t} = - \frac{\beta \mathcal{I} \mathcal{S}}{P}

\frac{d \mathcal{I}}{d t} = \frac{\beta \mathcal{I} \mathcal{S}}{P} - \gamma \mathcal{I}

\frac{d \mathcal{R}}{d t} = \gamma \mathcal{I}

Incidentally, in case you were wondering how this connects to the previous explanation: R_0 = \frac{\beta}{\gamma}.

Now, let us consider the end of the infection. If \mathcal{S} is reduced sufficiently, the disease will cease to be viable. This does not need every individual to be recovered or immune, however, as is evident from dividing the first by the third differential equation and integrating and substituting R_0, which yields

\displaystyle \mathcal{S}(t) = \mathcal{S}(0) e^{\frac{-R_0 (\mathcal{R}(t)-\mathcal{R}(0))}{P}}

Substituting this in, the limit of \mathcal{R}, as t approaches infinity, is

\displaystyle \lim_{t\to\infty}\mathcal{R}(t) = P - \lim_{t\to\infty}\mathcal{S}(t) = P - \mathcal{S}(0) e^{\frac{-R_0 (\mathcal{R}(t)-\mathcal{R}(0))}{P}}

From the latter, it is evident that

\displaystyle \lim_{t\to\infty}\mathcal{S}(t) \neq 0 \mid \mathcal{S}(0) \neq 0

In other words, once the infection has burned out, there will still be some individuals who are not immune, not immunised and not vaccinated. These are the individuals protected by herd immunity. This is a pretty elegant explanation for why herd immunity happens and how it works. There are three points to take away from this.

First, herd immunity is not unique to vaccination. The above finding in relation to the nonzero limit of \lim_{t\to\infty}\mathcal{S}(t) holds as long as \mathcal{S}(0) \neq 0, but regardless of what \mathcal{R}(0) is. In other words, herd immunity is not something artificial.

Two, for any i \in \mathcal{S} (that is, any susceptible person) at time t, the probability of which compartment he will be in at t+1 depends on whom he encounters. That, statistically, depends on the relative sizes of the compartments. In this model, the assumption is that the sample i will encounter will reflect the relative proportions of the individual compartments’ sizes. Thus if i meets n people at time t, each compartment will be proportionally represented, i.e. for any compartment \mathcal{C}, the proportion will be \frac{\mathcal{C}(t)}{P-1} for all \mathcal{C} \neq \mathcal{S}, for which the proportion will be \frac{\mathcal{S}(t) - 1}{P - 1}, since one cannot meet oneself. Given that the transition probability \beta_{i}(t) is assumed to equal the probability of meeting at least one element of \mathcal{I}, the following can be said. i‘s risk of infection depends on the relationship of n and \mathcal{I}(t), so that i is likely to get infected if

\displaystyle n \frac{\mathcal{I}(t)}{P-1} \geq 1

This elucidates two risk factors clearly, and the way to reduce them: reduce interactions (quarantine/self-quarantine), thereby reducing n, and reduce the proportion of infectious cases (\frac{\mathcal{I}(t)}{P-1}). The latter is where herd immunity from immunisation comes in. Recall that for a constant n, i‘s risk of infection at t rises as \mathcal{I}(t) rises.4 Recall also that while susceptible cases can turn into infectious cases, recovered (or vaccinated) cases cannot. And so, as \mathcal{R}(0) converges to P-1,5 i‘s risk of infection at any time t, denoted by \beta_{i}(t), falls. In other words,

\displaystyle \lim_{\mathcal{R}(0) \to P-1} \beta_{i}(t) = 0

Or to put it simply: the more are vaccinated at the start, the lower the probability, all things being equal, to meet someone who can pass on the infection.6

A final point to note is that this is primarily a model of statistical dynamics, and deals with average probabilities. It does not – it cannot – take account of facts like that some some susceptible people are just darn unlucky, and bump into a flock of unvaccinated, shiny-eyed snowflakes. Equally, in some places, susceptible people and infected people congregate, creating a viral breeding ground, also known as a Waldorf school. There are agent based models, which are basically attempts at brute force hacking reality, that can take account of such disparities. The takeaway is that herd immunity does not mean no susceptible individual will get infected. What it does mean is that their probability of getting infected is going to be significantly lower, for two reasons. First given a constant number of encounters (n), the likelihood of one of them being with an infectious individual is going to be much lower. More importantly, however, because of herd immunity, the disease is going to be able to persist in the population for a far shorter time – eventually it will burn through the small number of ‘accessible’ susceptible persons. Since the cumulative risk \beta_{i}^T for i \in \mathcal{S} for an infection that dies out after time T is defined as

\beta_i^T = \displaystyle \int\limits_0^T \beta_{i}(t) \, \mathrm{d}t

– the sooner the infection dies out, the smaller the likelihood that i will be infected. With that mathematical basis, let’s tackle a few of the myths about herd immunity.

Myth #1: herd immunity only works with naturally acquired immunity

This argument goes roughly along the following lines: herd immunity does exist, but it only exists if and where the immunity is acquired the ‘natural’ way, i.e. by surviving the disease. Case in point:

The $64,000 question, of course, is what the difference is between the residual immunity from a vaccine and the residual immunity from having survived the illness. A vaccine effectively ‘simulates’ the illness without actually causing the pathological symptoms. From the perspective of the immune system, it is largely irrelevant whether it has been exposed to an actual virus that can damage the body, or merely a capsid protein that is entirely harmless but will nonetheless elicit the same immune reaction. That should suffice to bust this myth, but it’s worth considering immunity quantitatively for a moment. As we have seen above, the source of immunity doesn’t matter. In fact, it doesn’t even have to be immunity: culling every animal except one in a herd is an entirely good way to reduce disease transmission. So is sealing oneself away from the rest of society and spending the evenings telling sexually explicit stories, as the heroes and heroines of Boccaccio’s Decameron have done, since we know that

\displaystyle n \frac{\mathcal{I}(t)}{P-1} \geq 1

Boccaccio’s crowd of assorted perverts knew nothing of all this, of course, but they did know that if they reduced n, the number of contacts with possibly infected persons, their chances of surviving the plague would increase. As it indeed did. Score one for medieval perverts. The bottom line is that it is entirely immaterial how immunity was obtained.

Myth #2: Herd immunity is a concept deriving from animals. It doesn’t work on humans.

This is one of the more outlandish claims, but shockingly, it actually has a tiny kernel of truth.

Now, much of the above is a veritable storehouse of insanity, but the point it makes in the beginning has some truth to it. In human populations, herd immunity sometimes behaves anomalously, because humans are not homogenously distributed. This is true a fortiori for humans who decide not to vaccinate, who – for better or worse – tend to flock in small groups. The term of venery for a bunch of anti-vaxxers is, in case you were wondering, a ‘plague’.7

Herd immunity was, in fact, observed in a range of species. Humans are different as we can knowingly and consciously decide to create herd immunity in our population and protect our fellow men, women and children, the last of whom are particularly susceptible to infectious diseases, from some of the worst killers.

Myth #3: If herd immunity can be obtained through natural immunity, surely we don’t need vaccines.

This argument has recently been peddled by the illustrious Kelly Brogan MD, who bills herself as a ‘holistic psychiatrist’ who threw away her script pad, which means she tends exclusively to the worried well and those with mild mental health issues where medication does not play as decisive a role as it does in, say, schizophrenia, severe PTSD, crippling anxiety disorders or complex neuropsychiatric post-insult phenomena.8 Here’s her foray into epidemiology, something she vaguely remembers studying in her first year of med school.

In this, Dr Brogan has successfully found almost a century old evidence for what everybody knew, namely that herd immunity can be naturally obtained. To anyone who has read the maths part above, this should evoke a sensation of ‘DUH!’. The problem is twofold. One, the ‘actual virus’ has an unsavoury fatality rate of 0.1%, not including the horribly tragic, heartbreaking late consequence of measles known as SSPE.9 Two, and perhaps more important: you don’t get lifelong, natural immunity if you die. This may have somehow escaped Dr Brogan’s attention, but part of the point of herd immunity is to protect those who would not survive, or would suffer serious sequelae, if they contracted the infection. What we don’t know, of course, how many of that 68% suffered permanent injuries, and how many are not included because they died. What we do know is that all 68% probably had a miserable time. Anyone who thinks measles is so fantastic should start by contracting it themselves.

Myth #4: Herd immunity means 95% need to be vaccinated to prevent a disease.

This one comes courtesy of Sarah aka the Healthy Home Economist,10, who, to what I presume must be the chagrin of her alma mater, states she has a Master’s from UPenn. Suspiciously enough, she does not state what in. I am somehow pretty sure it’s not public health.

The tedious conspiracy theory aside, it is quite evident just how little she understands of herd immunity. No – herd immunity is not based upon11 the idea that 95% must be vaccinated, and it is most definitely not based on the idea that 100% must be vaccinated. Indeed, the whole bloody point of herd immunity is that you do not need to vaccinate 100% to protect 100%. In fact, given the R_0 and vaccine efficacy E_V, we can predict the threshold vaccination rate for herd immunity quite simply, as demonstrated earlier: the threshold value, \bar{p_V}, can be calculated as

\bar{p_V} = \frac{R_0 - 1}{R_0 E_V}

As an illustration, the herd immunity threshold \bar{p_V} for mumps, with an efficacy of 88%12 and an R_0 of around 5.5, is \approx 92.98\%, while for Ebola, which has a very low R_0 around 2.0, herd immunity sets in once about 50% are immune.13

And those ‘conventional health authorities’? That’s what we call health authorities whose ideas work.

Myth #5: If vaccines work, why do we need herd immunity?

This argument is delightfully idiotic, because it, too, ignores the fundamental underlying facts of herd immunity. Quite apart from the fact that some people cannot receive some or all vaccines and other people can receive vaccines but may not generate sufficient antibody titres to have effective immunity, sometimes vaccines simply fail. Few things are 100%, and while vaccines are designed to be resilient, they can degrade due to inappropriate storage or fail to elicit a sufficient response for some other reason. Unlike wearing deodorant (or ‘deoderant’, as spelling-challenged anti-vaxxers would say), infections can sometimes be imagined as a chain of transmission. This is a frequently used model to explain the consequences of not vaccinating on others.

In this illustration, an index patient (IDX) is infected and comes in contact with G1, who in turn comes into contact with G2 who in turn comes into contact with G3. In the first case, G1, G2 and G3 are all vaccinated. The vaccine may have a small failure rate – 2% in this case – but by the time we get to G3, his chances of contracting the infection are 1:125,000 or 0.0008%. In the second case, G2 is unvaccinated – if G1’s vaccine fails, G2 is almost guaranteed to also fall ill. By not vaccinating, his own risk has increased 50-fold, from 0.04% to 2%. But that’s not all – due to G2’s failure to vaccinate, G3 will also be affected – instead of the lottery odds of 1:125,000, his risk has also risen 50-fold, to 1:2,500. And this 50-fold increase of risk will carry down the chain of potential transmission due to G2’s failure to vaccinate. No matter how well vaccines work, there’s always a small residual risk of failure, just as there is a residual risk of failure with everything. But it takes not vaccinating to make that risk hike up 50-fold. Makes that deodorant (‘deoderant’?) analogy sound rather silly, right?


Admittedly, the mathematical basis of herd immunity is complex. And the idea itself is somewhat counterintuitive. None of these are fit excuses for spreading lies and misinformation about herd immunity.

I have not engaged with the blatantly insane arguments (NWO, Zionists, Masonic conspiracies, Georgia Guidestones), nor with the blatantly untrue ones (doctors and public health officers are evil and guided just by money as they cash in on the suffering of innocent children). I was too busy booking my next flight paid for by Big Pharma.14 Envy is a powerful force, and it’s a good way to motivate people to suspect and hate people who sacrificed their 20s and 30s to work healing others and are eventually finally getting paid in their 40s. But it’s the myths that sway the well-meaning and uncommitted, and I firmly believe it’s part of our job as public health experts to counter them with truth.15

In every social structure, part of co-existence is assuming responsibility not just for oneself but for those who are affected by our decisions. Herd immunity is one of those instances where it’s no longer about just ourselves. Many have taken the language of herd immunity to suggest that it is some sort of favour or sacrifice done for the communal good, when it is in in fact the very opposite – it is preventing (inadvertent but often unavoidable) harm to others from ourselves.

And when the stakes are this high, when it’s about life and death of millions who for whatever reason cannot be vaccinated or cannot form an immune response, getting the facts right is paramount. I hope this has helped you, and if you know someone who would benefit from it, please do pass it on to them.

References   [ + ]

1. Topley, W. W. C. and Wilson, G. S. (1923). The spread of bacterial infection; the problem of herd immunity. J Hyg 21:243-249. The CDC was founded 23 years later, in 1946.
2. Why R_0? Because it is unrelated to \mathcal{R}, the quantity denoting recovered cases in \mathcal{S(E)IR} models – which is entirely unrelated. To emphasize the distinction, I will use mathcal fonts for the compartments in compartment models.
3. I hope to write about SIS, SEIR and vital dynamic models in the near future, but for this argument, it really doesn’t matter.
4. Technically, as \frac{\mathcal{I}(t)}{P - 1} rises, but since the model presupposes that P is constant, it doesn’t matter.
5. Since otherwise \mathcal{R} = P and \mathcal{S} = 0, and the whole model is moot, as noted above.
6. Note that this does not, unlike the R_0 explanation, presuppose any degree of vaccine efficacy. An inefficiently vaccinated person is simply in \mathcal{S} rather than \mathcal{R}.
7. Initially, ‘a culture’ was proposed, but looking at the average opponent of vaccination, it was clear this could not possibly work.
8. In other words, if you have actual mental health issues, try an actual psychiatrist who follows evidence-based protocols.
9. Subacute sclerosing panencephalitis is a long-term delayed consequence of measles infection, manifesting as a disseminated encephalitis that is invariably fatal. There are no adjectives that do the horror that is SSPE justice, so here’s a good summary paper on it.
10. As a rule, I don’t link to blogs and websites disseminating harmful information that endangers public health.
11. Correct term: ‘on’
12. As per the CDC.
13. Efficacy E_V is presumed to be 100% where immunity is not acquired via vaccination but by survival.
14. Anyone in public health is happy to tell you those things don’t merely no longer exist, they never even existed in our field.
15. And, if need be, maths.

MedDRA + VAERS: A marriage made in hell?

This post is a Golden DDoS Award winner

So far, this blog was DDoS’d only three times within 24 hours of its publication. That deserves a prize.

Quick: what do a broken femur, Henoch-Schönlein purpura, fainting, an expired vaccine and a healthy childbirth have in common? If your answer was “they’re all valid MedDRA codes”, you’re doing pretty well. If you, from that, deduced that they all can be logged on VAERS as adverse effects of vaccination, you’re exceeding expectations. And if you also realise that the idea that Jane got an expired HPV vaccine, and as a consequence broke her femur, developed Henoch-Schönlein purpura, and suddenly gave birth to a healthy baby boy is completely idiotic and yet can be logged on VAERS, you’re getting where I’m going.

MedDRA is a medical nomenclature specifically developed for the purposes of pharmacovigilance. The idea is, actually, not dreadful – there are some things in a usual medical nomenclature like ICD-10 that are not appropriate for a nomenclature used for pharmacovigilance reporting (V97.33: sucked into jet engine comes to my mind), and then there are things that are specific to pharmacovigilance, such as “oh shoot, that was not supposed to go up his bum!” (MedDRA 10013659: vaccine administered at inappropriate site), “we overdosed little Johnny on the flu vaccine!” (MedDRA 10000381: drug overdose, accidental) and other joys that generally do only happen in the context of pharmacovigilance. So far, so good.

At the same time, MedDRA is non-hierarchical, at least on the coding level. Thus, while the ICD code V97.33 tells you that you’re dealing with an external cause of mortality and morbidity (V and Y codes), specifically air and space transport (V95-97), more specifically ‘other’ specific air transport accidents, specifically getting sucked into a jet engine (V97.33), there’s no way to extract from MedDRA 10000381 what the hell we’re dealing with. Not only do we not know if it’s a test result, a procedure, a test or a disease, we are hopelessly lost as to figuring out what larger categories it belongs to. To make matters worse, MedDRA is proprietary – which in and of itself is offensive to the extreme to the idea of open research on VAERS and other public databases: a public database should not rely on proprietary encoding! -, and it lacks the inherent logic of ICD-10. Consider the encoding of the clinical diagnosis of unilateral headache in both:

SOC: System Organ Class, HLGT: High Level Group Term, HLT: High Level Term, PT: Preferred Term, LLT: Lower Level Term

Attempting to encode the same concept in MedDRA and ICD-10, note that the final code in MedDRA (10067040) does not allow for the predecessors to be reverse engineered, unless a look-up table is used – which is proprietary. ICD-10, at the same time, uses a structure that encodes the entire ‘ancestry’ of an entity in the final code. Not only does this allow for intermediate codes, e.g. G44 for an ‘other’ headache syndrome until it is closer defined, it also allows for structured analysis of the final code and even without a look-up table (which is public, as is the whole ICD-10), the level of kinship between two IDC-10 codes can be ascertained with ease.

We know that an ICD code beginning with F will be something psychiatric and G will be neurological, and from that alone we can get some easy analytical approaches (a popular one is looking at billed codes and drilling down by hierarchical level of ICD-10 codes, something in which the ICD-10 is vastly superior to its predecessor). MedDRA, alas, does not help us such.

Garbage in, garbage out

OK, so we’ve got a nomenclature where the codes for needlestick injury, death, pneumonia, congenital myopathy and a CBC look all the same. That’s already bad enough. It gets worse when you can enter any and all of these into the one single field. Meet VAERS.

The idea of VAERS is to allow physicians, non-physicians and ‘members of the public’ to report incidents. These are then coded by the CDC and depending on seriousness, they may or may not be investigated (all reports that are regarded as ‘serious’ are investigated, according to the CDC). The problem is that this approach is susceptible to three particular vulnerabilities:

  • The single field problem: VAERS has a single field for ‘symptoms’. Everything’s a symptom. This includes pre-existing conditions, new onset conditions, vaccination errors, lab tests (not merely results, just the tests themselves!), interventions (without specifying if they’re before or after the vaccine), and so on. There is also no way to filter out factors that definitely have nothing to do with the vaccine, such as a pre-existing birth defect. The History/Allergies field is not coded.
  • The coding problem: what gets coded and what does not is sometimes imperfect. This being a human process, it’s impossible to expect perfection, but the ramifications to this to certain methods of analysis are immense. For instance. if there are 100 cases of uncontrollable vomiting, that may be a signal. But if half of those are coded as ‘gastrointestinal disorder’ (also an existing code), you have two values of 50, neither of which may end up being a signal.
  • The issue of multiple coding: because MedDRA is non-hierarchical, it is not possible to normalise at a higher level (say, with ICD-10 codes, at chapter or block level), and it is not clear if two codes are hierarchically related. In ICD-10, if a record contains I07 (rheumatic tricuspid valve disease) and I07.2 (tricuspid stenosis with tricuspid insufficiency), one can decide to retain the more specific or the less specific entry, depending on intended purpose of the analysis.

In the following, I will demonstrate each of these based on randomly selected reports from VAERS.

The Single Field Problem (SFP)

The core of the SFP is that there is only one codeable field, ‘symptoms’.

VAERS ID 375693-1 involves a report, in which the patient claims she developed, between the first and second round of Gardasil,

severe stomach pain, cramping, and burning that lasted weeks. Muscle aches and overall feeling of not being well. In August 2009 patient had flu like symptoms, anxiety, depression, fatigue, ulcers, acne, overall feeling of illness or impending death.

Below is the patient’s symptom transposition into MedDRA entities (under Symptoms):

Event details for VAERS report 375693-1

The above example shows the mixture of symptoms, diagnostic procedures and diagnostic entities that are coded in the ‘Symptoms’ field. The principal problem with this is that when considering mass correlations (all drugs vs all symptoms, for instance), this system would treat a blood test just as much as a contributor to a safety signal as anxiety or myalgia, which might be true issues, or depression, which is a true diagnosis. Unfiltered, this makes VAERS effectively useless for market basket analysis based (cooccurrence frequency) analyses.

Consider for instance, that PRR is calculated as

PRR_{V,R} = \frac{\Sigma (R \mid V) \/ \Sigma (V)}{\Sigma (R \mid \neg V) \/ \Sigma (\neg V)} = \frac{\Sigma (R \mid V)}{\Sigma (V)} \cdot \frac{\Sigma (\neg V)}{\Sigma (R \mid \neg V)}

where V denotes the vaccine of interest, R denotes the reaction of interest, and the \Sigma operator denotes the sum of rows or columns that fulfill the requisite criteria (a more detailed, matrix-based version of this equation is presented here). But if \{R\}, the set of all R, contains not merely diagnoses but also various ‘non-diagnoses’, the PRR calculation will be distorted. For constant V and an unduly large R, the values computationally obtained from the VAERS data that ought to be \Sigma(R \mid V) and \Sigma(R \mid \neg V) will both be inaccurately inflated. This will yield inaccurate final results.

Just how bad IS this problem? About 30% bad, if not more. A manual tagging of the top 1,000 symptoms (by N, i.e. by the number of occurrences) was used as an estimate for how many of the diagnostic entities do not disclose an actual problem with the vaccine.

In this exercise, each of the top 1,000 diagnostic codes (by N, i.e. by occurrence) were categorised into a number of categories, which in turn were divided into includable (yellow) and non-includable (blue) categories. An includable category reveals a relevant (=adverse) test result, a symptom, a diagnosis or a particular issue, while non-includables pertain to procedures, diagnostics without results, negative test results and administration, storage & handling defects.

According to the survey of the top 1,000 codes, only a little more than 70% of the codes themselves disclose a relevant issue with the vaccine. In other words, almost a third of disclosed symptoms must be pruned, and these cannot be categorically pruned because unlike ICD-10, MedDRA does not disclose hierarchies based on which such pruning would be possible. As far as the use of MedDRA goes, this alone should be a complete disaster.

Again, for effect: a third of the codes do not disclose an actual side effect of the medication. These are not separate or identifiable in any way other than manually classifying them and seeing whether they disclose an actual side effect or just an ancillary issue. Pharmacovigilance relies on accurate source data, and VAERS is not set up, with its current use of MedDRA, to deliver that.

The coding problem

Once a VAERS report is received, it is MedDRA coded at the CDC. Now, no manual coding is perfect, but that’s not the point here. The problem is that a MedDRA code does not, in and of itself,  indicate the level of detail it holds. For instance, 10025169 and 10021881 look all alike, where in fact the first is a lowest-level entity (an LLT – Lower-Level Term – in MedDRA lingo) representing Lyme disease, while the former is the top-level class (SOC – System Organ Class) corresponding to infectious diseases. What this means is that once we see a MedDRA coded entity as its code, we don’t know what level of specificity we are dealing with.

The problem gets worse with named entities. You see, MedDRA has a ‘leaf’ structure: every branch must terminate in one or more (usually one) LLT. Often enough, LLTs have the same name as their parent PT, so you get PT Lyme disease and LLT Lyme disease. Not that it terrifically matters for most applications, but when you see only the verbose output, as is the case in VAERS, you don’t know if this is a PT, an LLT, or, God forbid, a higher level concept with a similar name.

Finally, to put the cherry on top of the cake, where a PT is also the LLT, they have the same code. So for Lyme disease, the PT and LLT both have the code 10025169. I’m sure this seemed like a good idea at the time.

The issue of multiple coding

As this has been touched upon previously, because MedDRA lacks an inherent hierarchy, a code cannot be converted into its next upper level without using a lookup table, whereas with, say, ICD-10, one can simply normalise to the chapter and block (the ‘part left of the dot’). More problematically, however, the same code may be a PT or an LLT, as is the case for Lyme disease (10025169).

Let’s look at this formally. Let the operator \in^* denote membership under the transitive closure of the set membership relation, so that

  1. if x \in A, then x \in^* A,
  2. if x \in A and A \subseteq B, then x \in^* B.

and so on, recursively, ad infinitum. Let furthermore \in^*_{m} denote the depth of recursion, so that

  1. for x \in A:  x \in^*_{0} A,
  2. for x \in A \mid A \subseteq B:  x \in^*_{1} B,

and, once again, so on, recursively, ad infinitum.

Then let a coding scheme \{S_{1...n}\} exhibit the Definite Degree of Transitiveness (DDoT) property iff (if and only if) for any S_m \mid m \leq n, there exists exactly one p for which it is true that S_m \in^*_{p} S.

Or, in other words, two codes S_q, S_r \mid q, r \leq n, may not be representable identically if p_q \neq p_r. Less formally: two codes on different levels may not be identical. This is clearly violated in MedDRA, as the example below shows.

Violating the Definite Degree of Transitiveness (DDoT) property: PT Botulism and LLT Botulism have different p numbers, i.e. different levels, but have nonetheless the same code. This makes the entity 10006041 indeterminate – is it the PT or the LLT for botulism? This is a result of the ‘leaf node constraint’ of MedDRA’s design, but a bug by design is still a bug, not a feature.

Bonus: the ethical problem

To me as a public health researcher, there is a huge ethical problem with the use of MedDRA in VAERS. I believe very strongly in open data and in the openness of biomedical information. I’m not alone: for better or worse, the wealth – terabytes upon terabytes – of biomedical data, genetics, X-ray crystallography, models, sequences  prove that if I’m a dreamer, I’m not the only one.

Which is why it’s little short of an insult to the public that a pharmacovigilance system is using a proprietary encoding model.

Downloads from VAERS, of course, provide the verbose names of the conditions or symptoms, but not what hierarchical level they are, nor what structure they are on. For that, unless you are a regulatory authority or a ‘non-profit’ or ‘non-commercial’ (which would already exclude a blogger who unlike me has ads on their blog to pay for hosting, or indeed most individual researchers, who by their nature could not provide the documentation to prove they aren’t making any money), you have to shell out some serious money.

MedDRA is one expensive toy.

Worse, the ‘non-profit’ definition does not include a non-profit research institution or an individual non-profit researcher, or any of the research bodies that are not medical libraries or affiliated with educational institutions but are funded by third party non-profit funding:

This just keeps getting worse. Where would a non-profit, non-patient care provider, non-educational, grant-funded research institution go?

There is something rotten with the use of MedDRA, and it’s not just how unsuitable it is for the purpose, it is also the sheer obscenity of a public database of grave public interest being tied to a (vastly unsuitable and flawed, as I hope it has been demonstrated above) nomenclature.

Is VAERS lost?

Resolving the MedDRA issue

Unlike quite a few people in the field, I don’t think VAERS is hopelessly lost. There’s, in fact, great potential in it. But the way it integrates with MedDRA has to be changed. This is both a moral point – a point of commitment to opening up government information – and one of facilitating research.

There are two alternatives at this point for the CDC.

  1. MedDRA has to open up at least the 17% of codes, complete with hierarchy, that are used within VAERS. These should be accessible, complete with the hierarchy, within VAERS, including the CDC WONDER interface.
  2. The CDC has to switch to a more suitable system. ICD-10 alone is not necessarily the best solution, and there are few alternatives, which puts MedDRA into a monopoly position that it seems to mercilessly exploit at the time. This can – and should – change.

Moving past the Single Field Problem

MedDRA apart, it is crucial for VAERS to resolve the Single Field Problem. It is clear that from the issues presented in the first paragraph – a broken femur, Henoch-Schönlein purpura, fainting, an expired vaccine and a healthy childbirth – that there is a range of issues that need to be logged. A good structure would be

  1. pre-existing conditions and risk factors,
  2. symptoms that arose within 6 hours of administration,
  3. symptoms that arose within 48 hours of administration,
  4. symptoms that arose later than 48 hours of administration,
  5. non-symptoms,
  6. clinical tests without results,
  7. clinical tests segmented by positive and negative results, and
  8. ancillary circumstances, esp. circumstances pertaining to vaccination errors such as wrong vaccine administered, expired vaccine, etc.

The use of this segmentation would be able to differentiate not only time of occurrence, but also allow for adequate filtering to identify the correct denominators for the PRR.

A future with (for?) MedDRA

As said, I am not necessarily hostile to MedDRA, even if the closet libertarian in me bristles at the fact that MedDRA is mercilessly exploiting what is an effective monopoly position. But MedDRA can be better, and needs to be better – if not for its own economic interests, then for the interests of those it serves. There are three particular suggestions MedDRA needs to seriously consider.

  1. MedDRA’s entity structure is valuable – arguably, it’s the value in the entire project. If coding can be structured to reflect its internal hierarchy, MedDRA becomes parseable without a LUT,1 and kinship structures become parseable without the extra step of a LUT.
  2. MedDRA needs to open up, especially to researchers not falling within its narrowly defined confines of access. Especially given the inherent public nature of its use – PhV and regulation are quintessentially public functions, and this needs an open system.
  3. MedDRA’s entity structure’s biggest strength is that it comprises a range of different things, from administrative errors through physical injuries to test results and the simple fact of tests.


VAERS is a valuable system with a range of flaws. All of them are avoidable and correctable – but would require the requisite level of will and commitment – both on CDC’s side and that of MedDRA. For any progress in this field, it is imperative that the CDC understand that a public resource maintained in the public interest cannot be driven by a proprietary nomenclature, least of all one that is priced out of the range of the average interested individual: and if they cannot be served, does the entire system even fulfill its governmental function of being of the people and for the people? It is ultimately CDC’s asset, and it has a unique chance to leverage its position to ensure that at least as far as the 17% of MedDRA codes go that are used in VAERS, these are released openly.

In the end, however sophisticated our dissimilarity metrics, when 30% of all entities are non-symptoms and we need to manually prune the key terms to avoid denominator bloat due to non-symptom entities, such as diagnostic tests without results or clearly unconnected causes of morbidity and mortality like motor vehicle accidents, dissimilarity based approaches will suffer from serious flaws. In the absence of detailed administration and symptom tracking at an individual or institutional level, dissimilarity metrics are the cheapest and most feasible ways of creating value out of post marketing passive reports. If VAERS is to be a useful research tool, as I firmly believe it was intended to be, it must evolve to that capability for all.

References   [ + ]

1. Look-up table

SafeGram: visualising drug safety

Update: an RMarkdown notebook explaining the whole process is available here.

Visualising vaccine safety is hard. Doing so from passive (or, as we say it in Britain, ‘spontaneous’!) pharmacovigilance (PhV) sources is even harder. Unlike in active or trial pharmacovigilance, where you are essentially dividing the number of incidents by the person-time or the number of patients in the cohort overall, in passive PhV, only incidents are reported. This makes it quite difficult to figure out their prevalence overall, but fortunately, we have some metrics we can use to better understand the issues with a particular medication or vaccine. The proportional reporting ratio (PRR) is a metric that can operate entirely on spontaneous reporting, and reflect how frequent a particular symptom is for a particular treatment versus all other treatments.

Defining PRR

For convenience’s sake, I will use the subscript * operator to mean a row or column sum of a matrix, so that

N_{i,*} = \displaystyle \sum_{j=1}^{n} N_{i,j}


N_{*,j} = \displaystyle \sum_{i=1}^{m} N_{i,j}

and furthermore, I will use the exclusion operator * \neg to mean all entities except the right hand value. So e.g.

N_{i, * \neg k} = \displaystyle \sum_{j=1, j \neq k}^m N_{i,j}

Conventionally, the PRR is often defined to with reference to a 2×2 contingency table that cross-tabulates treatments (m axis) with adverse effects (n axis):

Adverse effect of interest
All other adverse effects
(\neg i)
Treatment of interest
a = D_{i,j} b = D_{i, * \neg j} a + b = D_{i, *} = \displaystyle \sum_{j = 1}^{n} D_{i, j}
All other treatments
(\neg j)
c = D_{* \neg i, j} d = D_{* \neg i, * \neg j} c + d = D_{* \neg i, *} = \displaystyle \sum_{k=1, k \neq i}^{m} \sum_{l = 1}^{n} D_{k, l}


With reference to the contingency table, the PRR is usually defined as

\frac{a / (a+b)}{c / (c+d)} = \frac{a}{a + b} \cdot \frac{c + d}{c}

However, let’s formally define it over any matrix D.

Definition 1. PRR. Let D be an m \times n matrix that represents the frequency with which each of the m adverse effects occur for each of the n drugs, so that D_{i,j} (i \in m, j \in n) represents the number of times the adverse effect j has occurred with the treatment i.

For convenience’s sake, let D_{*,j} denote \sum_{i=1}^{m} D_{i,j}, let D_{i,*} denote \sum_{j=1}^{n} D_{i,j}, and let D_{*,*} denote \sum_{i=1}^{m} \sum_{j=1}^{n} D_{i,j}. Furthermore, let D_{* \neg i, j} denote \sum_{k \neq i}^{m} D_{k,j} and D_{i, * \neg j} denote \sum_{k \neq j}^{n} D_{i, k}.

Then, PRR can be calculated for each combination D_{i,j} by the following formula:

PRR_{i,j} = \frac{D_{i,j} / D_{i,*}}{D_{* \neg i, j} / D_{* \neg i, *}} = \frac{D_{i,j}}{D_{i,*}} \cdot \frac{D_{*\neg i, *}}{D_{*\neg i, j}}

Expanding this, we get

PRR_{i,j} = \frac{D_{i,j}}{\displaystyle\sum_{q=1}^n D_{i,q}} \cdot \frac{\displaystyle\sum_{r=1, r\ne i}^{m} \displaystyle\sum_{s=1}^{n} D_{r,s}}{\displaystyle\sum_{t=1, t\ne i}^{m} D_{t,j}}

Which looks and sounds awfully convoluted until we start to think of it as a relatively simple query operation: calculate the sum of each row, then calculate the quotient of the ADR of interest associated with the treatment of interest divided by all uses of the treatment of interest on one hand and the ADR of interest associated with all other drugs (j \mid \neg i or c) divided by all ADRs associated with all treatments other than the treatment of interest. Easy peasy!

Beyond PRR

However, the PRR only tells part of the story. It does show whether a particular symptom is disproportionately often reported – but does it show whether that particular symptom is frequent at all? Evans (1998) suggested using a combination of an N-minimum, a PRR value and a chi-square value to identify a signal.1 In order to represent the overall safety profile of a drug, it’s important to show not only the PRR but also the overall incidence of each risk. The design of the SafeGram is to show exactly that, for every known occurred side effect. To show a better estimate, instead of plotting indiviual points (there are several hundreds, or even thousands, of different side effects), the kernel density is plotted.

This SafeGram shows four vaccines – meningococcal, oral and injectable polio and smallpox -, and their safety record based on VAERS data between 2006 and 2016.

The reason why SafeGrams are so intuitive is because they convey two important facts at once. First, the PRR cut-off (set to 3.00 in this case) conclusively excludes statistically insignificant increases of risk.2 Of course, anything above that is not necessarily dangerous or proof of a safety signal. Rather, it allows the clinician to reason about the side effect profile of the particular medication.

  • The meningococcal vaccine (left upper corner) had several side effects that occurred frequently (hence the tall, ‘flame-like’ appearance). However, these were largely side effects that were shared among other vaccines (hence the low PRR). This is the epitome of a safe vaccine, with few surprises likely.
  • The injectable polio vaccine (IPV) has a similar profile, although the wide disseminated ‘margin’ (blue) indicates that ht has a wider range of side effects compared to the meningococcal vaccine, even though virtually all of these were side effects shared among other vaccines to the same extent.
  • The oral polio vaccine (OPV, left bottom corner) shows a flattened pattern typical for vaccines that have a number of ‘peculiar’ side effects. While the disproportionately frequently reported instances are relatively infrequent, the ‘tail-like’ appearance of the OPV SafeGram is a cause for concern. The difference between meningococcal and IPV on one hand and OPV on the other is explained largely by the fact that OPV was a ‘live’ vaccine, and in small susceptible groups (hence the low numbers), they could provoke adverse effects.
  • The smallpox vaccine, another live vaccine, was known to have a range of adverse effects, with a significant part of the population (about 20%) having at least one contraindication. The large area covered indicates that there is a rather astonishing diversity of side effects, and many of these – about half of the orange kernel – lies above the significance boundary of 3.00. The large area covered by the kernel density estimate and the reach into the right upper corner indicates a very probable safety signal worth examining.


A SafeGram for each vaccine shows the two-dimensional density distribution of two things – the frequency and the proportional reporting rate of each vaccine (or drug or device or whatever it is applied to). When considering the safety of a particular product, the most important question is whether a particular adverse effect is serious – a product with a low chance of an irreversible severe side effect is riskier than one with a high probability of a relatively harmless side effect, such as localized soreness after injection. But the relative severity of a side effect is hard to quantify, and a better proxy for that is to assume that in general, most severe side effects will be unique to a particular vaccine. So for instance while injection site reactions and mild pyrexia following inoculation are common to all vaccines and hence the relative reporting rates are relatively low, reflecting roughly the number of inoculations administered, serious adverse effects tend to be more particular to the vaccine (e.g. the association of influenza vaccines with Guillain-Barré syndrome in certain years means that GBS has an elevated PRR, despite the low number of occurrences, for the flu vaccines). Discarding vaccines with a very low number of administered cases, the SafeGram remains robust to differences between the number of vaccines administered. Fig. 1 above shows a number of typical patterns. In general, anything to the left of the vertical significance line can be safely ignored, as they are generally effects shared between most other vaccines in general and exhibit no specific risk signal for the particular vaccine. On the other hand, occurrences to the right of the vertical significance line may – but don’t necessarily do – indicate a safety signal. Of particular concern are right upper quadrant signals – these are frequent and at the same time peculiar to a particular vaccine, suggesting that it is not part of the typical post-inoculation syndrome (fever, fatigue, malaise) arising from immune activation but rather a specific issue created by the antigen or the adjuvant. In rare cases, there is a lower right corner ‘stripe’, such as for the OPV, where a wide range of unique but relatively infrequent effects are produced. These, too, might indicate the need for closer scrutiny. It is crucial to note that merely having a density of signals in the statistically significant range does not automatically mean that there is a PhV concern, but rather that such a concern cannot be excluded. Setting the PRR significance limit is somewhat arbitrary, but Evans et al. (2001) have found a PRR of 2, more than 3 cases over a two year period and a chi-square statistic of 4 or above to be suggestive of a safety signal. Following this lead, the original SafeGram code looks at a PRR of 3.0 and above, and disregards cases with an overall frequency of 3Y, where Y denotes the number of years considered.


The SafeGram inherently tries to make the best out of imperfect data. Acknowledging that passive reporting data is subject to imperfections, some caveats need to be kept in mind.

  • The algorithm assigns equal weight to every ‘symptom’ reported. VAERS uses an unfiltered version of MedDRA, a coding system for regulatory activities, and this includes a shocking array of codes that do not suggest any pathology. For instance, the VAERS implementation of MedDRA contains 530 codes for normal non-pathological states (e.g. “abdomen scan normal”), and almost 18,000 (!) events involve at least one of these ‘everything is fine!’ markers. This may be clinically useful because they may assist in differential diagnosis and excluding other causes of symptoms, but since they’re not treated separately from actually pathological symptoms, they corrupt the data to a minor but not insignificant extent. The only solution is manual filtering, and with tens of thousands of MedDRA codes, one would not necessarily be inclined to do so. The consequence is that some symptoms aren’t symptoms at all – they’re the exact opposite. This is not a problem for the PRR because it compares a symptom among those taking a particular medication against the same symptom among those who are not.
  • A lot of VAERS reports are, of course, low quality reports, and there is no way for the SafeGram to differentiate. This is a persistent problem with all passive reporting systems.
  • The SafeGram gives an overall picture of a particular drug’s or vaccine’s safety. It does not differentiate between the relative severity of a particular symptom.
  • As usual, correlation does not equal causation. As such, none of this proves the actual risk or danger of a vaccine, but rather the correlation or, in other words, potential safety signals that are worth examining.
Grouped by pathogen, the safety of a range of vaccines was examined by estimating the density of adverse event occurrence versus adverse event PRR. Note that adverse events reported in VAERS do not show or prove causation, only correlation. This shows that for the overwhelming majority of vaccines, most AEFI reports are below the PRR required to be considered a true safety signal.

SafeGrams are a great way to show the safety of vaccines, and to identify which vaccines have frequently occurring and significantly distinct (high-PRR) AEFIs that may be potential signals. It is important to note that for most common vaccines, including controversial ones like HPV, the centre of the density kernel estimate are below the margin of the PRR signal limit. The SafeGram is a useful and visually appealing proof of the safety of vaccines that can get actionable intelligence out of VAERS passive reporting evidence that is often disregarded as useless.

References   [ + ]

1. Evans, S. J. W. et al. (1998). Proportional reporting ratios: the uses of epidemiological methods for signal generation. Pharmacoepidemiol Drug Saf, 7(Suppl 2), 102.
2. According to Evans et al., the correct figure for PRR exclusion is 2.00, but they also use N-restriction and a minimum chi-square of 4.0.