Ebola epidemiology roundup #10

This post covers material published roughly in the month of January 2015.

A. Charting the Epidemic

A1. Epidemic trajectory

Past/Present. The big news in January has been the apparent rapid decline in case numbers in Sierra Leone, following the equally big fall-off in Liberia in December.  Indeed, as of January 23rd, it is being reported that there were only five active, in-care cases of Ebola in Liberia.  Not the end, but maybe the beginning of the endgame? The situation in Guinea remains more murky.  Although there has been a clear decline in reported cases in Guinea throughout January, there are concerns that some areas are still refusing access to case finders and health educators, and indeed that things may be getting worse.  Thus falling reported numbers may not reflect falling actual numbers. Late edit: In the last week of January, case numbers in Guinea ticked up by 10, a 50% increase on the preceding week, and spread to new parts of the country.

While covering the epidemic trajectory, I would be remiss not to mention this WHO overview of the past year.  I won’t pretend to have read it all, but it looks like a strong resource for getting up to speed on the epidemic, if with the standard WHO angle on why things happened the way they did.

And finally, I wanted to take a step back to look at the impact of Ebola on non-human primates (NHP).  A story rolling along this week suggested that up to one-third of all chimpanzees and gorillas worldwide have been killed by Ebola since it was discovered in humans in 1976.  This number appears to have come originally from an unpublished study by Peter Walsh and colleagues. One angle on this was that a vaccine should also be provided to NHPs.  Of course, this is the population on which human vaccines are tested, so we should have a fairly good idea whether or not it will work for them (spoiler: the ones currently in development seem to work excellently on NHPs, maybe because at least one of them is based on a Chimp adenovirus? [this last is pure speculation on my part]).

Future. As uncertainty about the overall impact of this epidemic subsides, concern has turned to new areas.  At the heart of most concerns is that people will get complacent, allowing the epidemic to smoulder and then potentially re-ignite.  Along with the well-publicised fall in interest in the global (social) media interest in Ebola, and concomitant worries about the end of funding for disease control, there already appears to be declining interest and concern within Liberia (see also Containment, Schools below).  One marker for this is the ending of “risk allowances” to healthcare workers in Sierra Leone by the end of March.

Ultimately, much of the concern arises from the possibility that Ebola may become an endemic disease.  As Peter Piot highlighted (in the last link), this is unlikely in strict terms:

We (humans) are a very bad host from the virus’ point of view… A host that’s killed by a virus in a week or so is absolutely useless.

As a result, case numbers will either rise up (if we do nothing) or die down (as has happened in this outbreak) reasonably fast in each epidemic.  However, given ongoing human-bat contact, multiple new outbreaks remains a likely future scenario –  which looks a lot like an endemic situation to many.

Modelling. There isn’t a lot of modelling left to do in real-time on the epidemic curve as it heads slowly downwards.  Which is not to say that there isn’t plenty more modelling to be done.  Both of these points, and the opt-repeated one that modelling is hard, especially about the future, were raised and discussed at the Ebola Modelling Workshop in Atlanta last week.  Helpfully, all the slides from presentations at the meeting, along with a forthcoming curated summary of the discussions, are available on the workshop website.  A must-read for those – like me – who could use some collected wisdom on the topic.

A2. Epidemic parameters

MSF Sierra Leone, under the lead authorship of Silvia Dallatomasinas has published clinical epidemiology data on 489 cases seen in Kailahun between June and October (Dallatomasinas paper). A key finding was that one-third of cases came from outside Kailahun district – largely from Makeni in Bombali district, but also from as far afield as Freetown. For me this emphasizes the importance of mobility for transmission, especially early in the epidemic.

Another interesting insight into transmission dynamics comes from a study by Ousmane Faye and colleagues of transmission chains from March to August in Conakry (Faye paper). The authors show that the great majority of secondary infections were generated in the community – rather than at hospitals or funerals – with over 80% of infections being within families. As Christian Drosten notes in his commentary on the paper, since family sizes vary little by urbanicity, so the reproductive number was no higher in Conakry than in the countryside. Faye et al also show that higher viral loads are associated with greater transmission: hardly shocking, but useful data to accrue.

Concern appears to remain regarding whether ongoing infection could occur via sexual contact with survivors or through breastmilk. While no definitive transmissions have been documented, perceived sexual transmissions are being reported in SL, while concern is being aired that the requirement to abstain from sex for up to six months is not feasible for men.

The issue of potential asymptomatic infection is also rumbling along. Again there are no definitive serological studies of past infections to date, but some small studies suggest that close household contacts test positive without clear symptoms of Ebola. Additionally, one study at Kenema government hospital showed that 22% of suspected Lassa Fever cases presenting from 2001-14 tested IgG/IgM positive for Ebola. Which could beg the question: how specific Ebola tests are?

Another popular topic in the media – for Ebola, but also for almost any other infectious disease, is mutation (on which, please see this great general-audience overview of mutation and infectious disease, in the context of Ebola).  So I appreciated this measured article by Abayomi Olabode and colleagues on the Biological arXiv (so not yet peer-reviewed), which highlights that mutations over the past 40 years have led to no apparent shift in functional mechanism of infection. (Olabode paper). I’m sure that there will be lots more on this topic once we have more sequences from this epidemic, but an interesting first step.

And lastly, a quick dive into the zoonotic.  Raina Plowright and colleagues note that we believe Ebola (and several other viruses) reach humans via bats, but cross the species barrier only rarely, despite common human-bat interactions. (Plowright paper). Plowright’s paper focuses on Hendra virus, but posits two possible reasons for the rarity of zoonotic events: episodic shedding or transient epidemics (such as those typically seen for SIR-type infections in humans, e.g. measles).  I may be far behind the curve on the Ebola-bat literature, but if the world knows as little about Ebola’s cheiropteran lifecycle as I do, this seems to raise some interesting, testable hypotheses.

A3. Visualization

I know that this plot has been around for a bit – since well before Ebola became big news – but it’s a useful benchmark for comparison. Clearly we can quibble about whether 70% is the right CFR, but it gives you an idea of where Ebola sits in the pantheon (or rather the anti-pantheon) of diseases.  As you can see, diseases as virulent as Ebola tend not to be very contagious.  This is good for us, but also good for the disease – if you kill off all your hosts, there’s not much chance of continuing to exist as a species for very long, especially when you do it as fast as Ebola does.

And in case the various visualization sites I have been pointing you to aren’t quite enough, here’s another blog that focuses _only_ on visualizations for Ebola, by John Tigue.  Enjoy.

B. Stopping the Epidemic

B1. Containment

Movement: Speaking as we were of the importance of mobility for transmission, I should quickly update the tale of movement restrictions and associated intensified case-finding in Sierra Leone. At the beginning of January the isolation (quarantine?) of northern districts was extended to ensure that Ebola was under control there. This was followed by “Operation Western Area Surge”, a house-to-house search across several districts, which apparently found well over 200 additional cases. (On a side-note, there is a nice description of the operations of the SL national call centre during September’s “ose-to-ose” campaign in the MMWR). Late in January, the general decline in new cases led to the lifting of inter-district travel restrictions across SL on Thursday 22nd, and a subsequent stream of traffic leaving Freetown. Whether this will lead to an uptick in cases remains to be seen.

Case finding: As the epidemic ebbs, much attention is turning to case finding, the role of contact tracers and other focused prevention activities – in contrast to the earlier focus on broad campaigns. Kai Kupferschmidt has written a nice “day in the life” piece on contact tracers in Bong county, Liberia; while a CommCare app is being used in Guinea to allow real-time reporting and geotagging of potential contacts. Sam Crowe and colleagues have also outlined a system of community-based, event-based surveillance in Bo district, SL. (Crowe paper). The system works through local healthcare workers triggering enhanced monitoring if they hear about clusters of illness, death or traditional burials.

As has been noted, a truly rapid test would be of great benefit to case finders – ensuring false positives can be quickly reassured, and true positives quickly taken into care. Such tests would be even more useful if they could identify infections pre-symptoms: although the feasibility of this remains unclear. Nevertheless, rapid tests appear do appear to be in the pipeline; indeed a “lab-in-a-suitcase” is apparently undergoing testing in West Africa already, using Recombinase Polymerase Amplification (RPA) instead of the standard PCR approach. However, various sources have cautioned that while such an approach would be welcome, complex methods that work in the theory, fail in field-testing, and thus such a lab’s usefulness remains to be proven.

Another important aspect of case-finding is that many cases are found only through reports from community members.  In this context, a letter from Ruth Kutalek and collagues in the Lancet was educational. (Kutalek letter). The Liberian government, WHO and the World Bank had been considering paying $5 per case reported.  A rapid focus-group evaluation of this proposal both rejected the incentive scheme as being disruptive to the community, and highlighted that better case-reporting would require improvements throughout the care continuum that made entering into care a more feasible and acceptable proposition.  Which buttresses other reports highlighting the importance of listening to local communities, to maximize the chances of affecting behaviour change and ending the epidemic.

Isolation: While case finding may be the on-the-ground flavour of the month, there remains a great deal of academic interest in how best to control the epidemic in the healthcare or para-healthcare setting.

One important paper this month was published by Stefano Merler and colleagues (building from the work of Alex Vesgipnani’s lab at Northeastern University). (Merler paper). The team has build a realistic model of the population structure in Liberia at the individual level, and then calibrated epidemic parameters to observed outcomes up to August 2014. They use the model to show the likely impact of various interventions – safer burials, building of ETUs, etc. One key finding of theirs is that local transmission is key to disease propagation – although Gerardo Chowell and colleagues note that long-distance transmission has also been key (and also see Dallatomasinas’ paper above). Chowell and Hiroshi Nishiura also provide both an overview of network models and a plea for spatiotemporal data in reflecting on the publication of John Drake’s work (covered previously when on the arXiv) in PLoS Biology. (Drake paper).

On a side-note, Merler et al find a 40/30/10 split in secondary cases arising from hospitals/home/funerals; this is very different from the numbers seen in Conakry by Faye et al (see above). Whether this is due to different methodologies or truly different dynamics, this seems like a very interesting question that might be crucial to understanding how epidemic dynamics differ by country.

I also appreciated a blogpost by Silvia Munoz-Price, who highlighted the unusual situation in which clinical staff are seeking out Infection Control professionals, in contrast to their usual efforts to avoid all advice on the subject. She doesn’t provide any quick answers on how to sensitize HCWs in other settings – aside from breeding an HCW-preferring strain of C difficile – but the issue is one that has broad resonance for healthcare provision in the age of drug-resistant bugs.

Community-based care continues to be raised as an important part of the care continuum, given its cost and speed benefits over building Ebola Treatment Centres (ETC). Michael Washington and colleagues used a simple SEIR model to estimate the relative benefit of increased ETC and CCC (community care centres) in Montserrado and Lofa counties, Liberia, in September/October last year. (Washington paper). Their models suggested that both were useful individually, and even more so jointly, but that CCCs were likely to have a greater impact if one approach had to be prioritized. In this context, Sharon Salmon and colleagues’ letter highlighting that the Liberian government and NGOs trained up thousands of community volunteers for basic preventative care (and I believe Sierra Leone did similarly) is informative and likely an important component of control efforts.

And finally, a(nother) paper by Nishiura and Chowell compares/contrasts the dynamics of Ebola to that of Influenza, highlighting that the direct-contact mode of transmission of Ebola means that even though the R0 of these two diseases is roughly similar, the speed of epidemic growth is much slower (and the potential for control through social distancing much higher) for EVD. (Nishiura paper).

Safe burials: While safe burials may well have been an important factor in controlling this epidemic, they have not always been appreciated – especially when they were not conducted in a culturally sensitive, or just a sensitive, manner. In this context, it was interesting to me to read Carrie Nielsen and colleagues’ outline of how an SOP for safe and respectful burials was drawn up in Sierra Leone in October, as control efforts were rapidly expanded, and how efforts were then monitored and adjusted as needed. (Nielsen paper). In contrast, Liberia took a crematory approach to safe management of deceased bodies, and as this journalist’s report from Monrovia highlights, this has led to distrust, unhappiness and potentially future social upheaval as families are left without the ability to conduct key social/cultural practices with their deceased relatives. One of these countries looks like it has done a better job on this front.

B2. Treatment

Drugs: Trials of antiviral drugs as Ebola treatments continue in several parts of West Africa through MSF facilities. A trial of favipiravir in Guinea is being run by National Institute of Health and Medical Research (INSERM) – details on dosing being used are here; a trial of brincidofivir is being run by University of Oxford researchers at the MSF ETC in Paynesville, Monrovia (stop press: the brincidofivir trial appears to have been stopped; reasons for this are unclear, maybe due to the low number of patients enrolled). In line with past MSF comments, these are being run without a control group (one assumes using historical outcomes as controls). There will also be a trial of convalescent serum (i.e. part-blood transfusions from recovered patients) in Conakry starting soon (MSF overview here). On this last I would highlight a letter by Melanie Bannister-Tyrrell and colleagues highlighting the potential stigma for both donors and recipients in the context of passing blood between people. The importance of local knowledge, once more.

Also, I would be remiss not to highlight in light of my reporting last month on allegedly unethical testing of amiodarone as a treatment for Ebola at Emergency’s ETC in Freetown. Emergency have responded very firmly to such allegations in a blogpost. As well as contesting many of the facts in the case, the author(s) appear to believe that one reason for opposition to amiodarone’s use is its out-of-patent status and the consequent lack of profits available for its use. I’m not sure which side in this argument holds the greatest validity, but I wanted to make sure I was showing you all the arguments.

B3. Vaccines

Before diving into the practicalities, I noticed that Yazdan Yazdanpanah and colleagues (whom I last saw read while working on HIV cost-effectiveness a decade ago) have put together a brief overview of the intracellular lifecycle of EVD. (Yazdanpanah paper). A wonderful introduction for those of us with little knowledge of at the cellular level.

Now, on to those practicalities. When last we spoke, two vaccines (GSK/NIAID’s ChAd3 and NewLink/Merck/CanGov’s VSV) were in phase II trials, although the VSV trials had been put on hold due to some non-negligible joint pain. The ChAd3 team reported immunogenicity this past week in the NEJM.

The VSV trial resumed in the first week of January after the Christmas/holiday break, at much lower doses. A third product (led by Janssen, a Johnson & Johnson company) has also now begun trials too.

Of course, the real action will happen once trials reach phase III: the search for efficacy. Fortunately, a key challenge to proving the efficacy of any vaccine at the present time: the lack of new infections in the three countries. Clearly this is a good situation to be in from the perspective of the current outbreak; however without evidence for which vaccine(s) work now, planning for future outbreaks will be hampered.

In order to maximize the chances of seeing any true effect (i.e. maximize power) each of three teams putting together phase III trials (one per country) are considering new methods and evolving their plans very rapidly. The first trial will be in Liberiastarting in the first week of February. This will be run in conjunction with the NIH, and will involve three arms – quite probably two treatments (AdCh3 and VSV) and one control involving vaccination for something other than Ebola – with around 9,000 participants in each arm. It will be a classic Randomized Controlled Trial.

The second trial will be run by the CDC in Sierra Leone. The exact methodological details remain sketchy, but the population will be high-risk individuals involved in the Ebola response (ETC workers, burial teams, case finders) and the method will be a cluster randomized trial of some description (it was going to be a Stepped Wedge design; I gather this plan has been changed now). If there prove to be very few cases in Liberia, the SL and Liberia trials may get merged to improve power.

The third trial is decidedly innovative: it will involve “ring vaccination”. In this approach – previously used as a vaccination roll-out strategy, one vaccinates the contacts (approximately 50 local residents here) of each case found, but at different speeds: either immediately, or after 4-8 weeks. This allows a difference in effect to potentially be seen, without denying anyone a chance to get some kind of treatment. It also should help to control the epidemic – something that may be most urgent in Guinea.

And if you thought all that wasn’t complicated enough, CIDRAP at the University of Minnesota and the Wellcome Trust put together a report on potential roadblocks, Bruce Lee and colleagues at the International Vaccine Access Center at John’s Hopkins highlighted seven possible vaccine roll-out issues and two moderators at ProMed added a couple more of their own. Getting to vaccination is a long road that is being heroically shortened for this epidemic, but that doesn’t make it a short path.

B4. Social factors/impact

IMF debateLast month I noted the publication of a commentary by Alex Kentikelenis et al. placing some of the blame for the size of the West Africa outbreak on the IMF’s past policies.  The IMF and Chris Blattman responded on the Monkey Cage blog.  And then things took off. On the Monkey Cage, Adia Benton and Kim Yi Dionne highlighted some key readings on how the IMF affects social spending, Alex responded to Chris, and Chris responded again.  Other notable contributions to the debate included one by Morten Jerven and another by Ken Opalo.

The takeaways are many, and will vary depending on your subjective view of the situation.  As someone who sees everything in shades of grey (no, that link is not to anything EL James related), I can see how the IMF has historically limited the scope for social expenditure in countries it has aided, but can also see how this limitation may have had beneficial knock-on effects on the countries in question, and how existing national political structures might have limited such spending even in the absence of IMF guidelines/strictures. Bottom line: as several commenters have said, the level of evidence in this debate could probably be improved. If, you know, anyone has the spare time and money to get that done…

Education: One impact of this outbreak that I have not been discussing much is the closure of schools. Aside from loss of learning, there have also been concerns within the affected countries about possible increases in teenage pregnancy [refs] All three most-affected countries are now considering re-opening schools, as a very public and visible sign of confidence that the worst is over.

Liberia is planning to reopen in February – although there is concern about children coming to school when sick, potentially because they are not able to understand/communicate their risk, or keep a social distance from classmates (this echoes what I’ve heard about treating young children at ETCs). There are also plenty of logistical problems with enrolling more than six-months’ worth of new pupils. And then of course this has become a political issue, with Senator George Weah (yes, that George Weah) criticizing the move. Upshot: schools are now set to open mid-February.

The minister of Education in Sierra Leone said on Jan 9th that it wouldn’t reopen until the epidemic is over, however by the 21st they have now slated the restart to be in March. Guinea was planning to reopen as early as Feb 2nd, but that now appears likely likely to be pushed back too.

Agriculture: I have seen stories ranging from “everything’s falling apart” to “it’s not really that bad”. In this context, this piece by Lisa Hamilton in The Atlantic provides a nice overview of the current situation: not great, could get worse is the gist I took away.

And finally, couple of things I wanted to include, but haven’t got a clear category for:

  1. A brief insight into a huge future issue: what happens economically after the epidemic ends? It’s not a pretty question, but it is a necessary one.
  2. A symposium on how Ebola looks from the Political Science world.  There is more than one Health/PoliSci person in there worth reading.

C. Journalism

A couple of notable general-consumption longform pieces that caught my eye in the past month:

Finally, I have seen a few more first-person accounts that I would argue are worth a few minutes of your time:

If you have seen something I’ve missed (or links are broken), you can reach me @harlingg. And as ever, thank you to all those on whose intellectual shoulders I am standing.

There are nine previous posts in this series and a summary of data/research sources.


Ebola epidemiology roundup #9

This post covers roughly the month of December.

A. Charting the Epidemic

A1. Epidemic trajectory

Present. There is no doubt that the rapid expansion in case counts seen in September-November 2014 are now beginning to subside. This doesn’t necessarily mean a decline in case numbers – although this does still appear to be the case in Liberia – but does mean that growth is no longer exponential.  This is a good thing, even though there is a long way to go.  Whether this truly represents an improvement is less clear, although I have seen little suggestion that things are still as out of control as they were six weeks ago.

Future. The best estimates I’ve seen for final epidemic size (based on the admittedly imperfect data available), do seem to suggest that things are starting to come under control:

Tuite and Fisman, authors of the above figure, note that this downturn is a function of improvement in their model’s “control parameter”: i.e. the impact of some combination of human interventions appears to be being felt.

Modelling. There’s also an ongoing, rumbling debate about the role of modelling in the epidemic.  There continue to be concerns that projections were intentionally set high, in order to gain funds – either due to need or greed.  Others, for example Ian Mackay, have seen things differently, arguing that in a crisis, we make the best estimates we can and they necessarily come with uncertainty attached. In the end, I think it all comes down to how you view the unviewable – the motives of those making predictions.  And in this context, there were two excellent “explainer” pieces in Science and PNAS this month, laying out how modellers approach problems and how to read their results.  Andy Dobson laid out why this epidemic is hard to predict (no comparable case series for Ebola to compare it to) but that mathematical models present our only option for quantifying our uncertainty (Dobson commentary). Eric Lofgren and colleagues buttressed this argument with a step-by-step walk-through of how modellers think and why their efforts can provide useful structure in an uncertain situation (Lofgren commentary).

However, there is also an important paper out on the arXiv from Aaron King and colleagues, who highlight that fitting deterministic models has the potential to overestimate certainty in predictions (King paper).  By modelling our lack of knowledge arising from the poor quality of Ebola data using stochastic methods (they use a partially observed Markov process), the authors show how we can provide meaningful descriptions of the uncertainty present in model estimates. Which might help reduce the anger towards point predictions that prove incorrect.

A2. Epidemic parameters

At this point in the epidemic, many of Ebola’s natural history parameters are well described.1  However, the parameters that rely on the interaction of infection and response are not.  Primary amongst these is the case fatality rate (CFR), which continues to vary with time and space, and which is promoting a lively debate about what is the minimum level achievable – and how do we achieve it (see Treatment, below).  The WHO sitrep reports a CFR of around 60% at this point for hospitalized patients, but rates of around 35-50% seem to be more common in recent reports.  Indeed, Rachel Ansumana and colleagues at the Hastings ETC in Freetown have seen the mortality rate fall to 24% since early November (Asnumana letter).

The increasing number of cases recorded has also allowed for some interesting investigations of co-factors that reside within the human body.  Michael Lauck and colleagues showed that in a subsample of 49 patients from Gire et al.’s May/June Sierra Leone case series, the 13 that were infected with GB virus C (also known as Pegivirus, and previously as Hepatitis G) and that risk of death was almost halved, even after adjustment for age and sex, in those co-infected. (Lauck paper).  This is very preliminary data, but given previous evidence that GBV-C may be associated with better survival for HIV-infected persons, it’s a line of enquiry that may well be pursued in the future.

On another ongoing topic of interest – undetected Ebola, either at present or prior to this outbreak – I think that I have failed to previously flag a study by Randal Schoepp and colleagues from July 2014. (Schoepp paper).  Studying blood submitted to Kenema hospital between 2006 and 2008 to look for Lassa fever, the authors found that 8.6% of 253 patients tested positive for Ebola.  Since these individuals had a fever, this is a selected sample of the population, but suggests that a substantial proportion of infections previously thought to be other fevers in the area where Ebola emerged in 2014 may in fact have been Ebola.

Which links neatly to a study released this week by

Sam Scarpino and colleagues also used data from the Gire case series to look at clustering of social contacts. (Scarpino paper). The authors show that a best-fit network-structured state transition model requires far greater levels of triadic closure (i.e. the chance that two of my friends are friends with each other) than would be expected at random. This is hardly surprising, since we know such features are typical within close-knit groups such as those who attend funerals or care for one-another.  But a nice addition to the literature that highlights the importance of linking social factors to biological ones.

On the occupational health front, a paper from Peter Kilmarx and colleagues has quantified quite how hard the healthcare professional has been hit by Ebola, but also shows that infection rates probably peaked in August. (Kilmarx paper). The authors note that this fall-off in infection may reflect the closure of many healthcare settings, or improved preventative measures – an important question if efforts to re-open non-Ebola care settings are to proceed.

And finally, a worrying new development in recent weeks has been the emergence of post-recovery health problems for some patients. I wonder if anyone has the resources to track survivors to check on this over the next few months, or even to get at what aspect of the disease/treatment process has led to these conditions.

A3. Visualization

I know the past couple of posts have been light on visual representations, and I really can’t apologize enough about that.  This month, however, I can link you a Qlik Ebola widget that automatically builds from the data available on Datamarket to provide charts and dataviz on the outbreak. Lots to explore in there.  For example, here’s a figure comparing case and mortality rates at the sub-national level in each of the three most-affected countries.

I can also point you towards the sterling efforts of the Centre for the Mathematical Modelling of Infectious Diseases at LSHTM.  They are now putting out weekly sitreps with a wealth of data, and using that data to make epidemic need predictions (see Containment, below). Just to scratch the surface, here’s a figure of county-specific weekly case rates in Sierra Leone by type (suspected, probable, confirmed):

CMMID SL Cases Fig 31Dec2014

B. Stopping the Epidemic

B1. Containment

One topic that arose earlier in the year was the use of Community Containment Centres (CCC) to fill the gap when there was insufficient Ebola Treatment Centre/Unit (ETC, ETU) capacity available.  These CCCs would provide minimal care, but by keeping infectious individuals physically separate from their communities, might help reduce onward transmission.  Concern was raised that this amounted to sub-standard care, but in a limited resource situation in which building ETCs was going to take weeks, CCCs were built. (Notably, CCCs are classically public health orientated – benefiting the population, rather than the infected individual.) They have subsequently been assessed for safety, effectiveness, and operational feasibility (results not publicly available, so far as I know).  A recent modelling exercise by Adam Kucharski and colleagues showed that if individuals accessed CCCs within three days of becoming infectious, and if such facilities effectively isolated infectious patients, CCCs could shift Re below 1 (Kucharski paper). I’m not clear how necessary CCCs are now that ETC provision has been ramped up, but it’s good to know how good they need to be, in order to impact the epidemic.

And another ongoing issue: sexual transmission.  As a short report this month from Karen Rogstad and Anne Tunbridge shows, no case of sexual transmission has yet been seen – although virus may be present for several months post-infection in semen (Rogstad paper).  The authors highlight the need to study this in the future; another reason that following up recovered cases might be worthwhile, if/as/when we have the resources to do so.

Behaviour change. While I am quite sure that there is lots of social mobilization going on at the ground-level in SL, Liberia and Guinea, that isn’t getting much news coverage.  What is getting coverage, is bans on congregating at Christmas (in Sierra Leone and Liberia) and New Year (in Guinea, but not in Liberia).  Although allowances are apparently being made for religious gatherings – which may have led to an outbreak in Monrovia.

There also remains concern that burials are not being performed in a safe manner.  Quite possibly due to the perfectly valid fear that engaging with the Ebola care system is associated with death.  Although of course the alternative of not engaging with the system may be even more strongly associated with death. (Understanding counterfactuals is hard enough in a graduate school classroom, let alone in the midst of an epidemic.)  Sierra Leone has been conducting further lockdowns and house-to-house searches.

Whether this top-down approach will have the same impact as bottom-up change remains to be seen. The bottom-up approach is also coming in for some critical consideration this month too – Clare Chandler and colleagues at the Ebola Anthropology Response Platform highlighted the biomedical, exoticising and one-size-fits-all nature of current behaviour change messages for Ebola (Chandler comment).  The authors point to a more tailored messaging programme for best results.

B2. Treatment

One of the up-sides to outbreaks such as the current one is the spur it provides for “general-purpose” technologies that should carry over to many future epidemics and non-epidemics.  On this front, the more engineering-style Ebola hackathons have been very useful.  Recent examples include a double-layer PPE system involving magnets to remove the highest-risk outside layer and another PPE system with battery-powered cooling systems to allow for longer workperiods.  Hopefully, some of these designs will be cost- and build-feasible in the near future.

Another key aspect of treating the epidemic is identifying cases.  As of last week we have a faster test available, however as Laurie Garrett noted, a three-hour waiting time and the need for a lab make it a pretty poor step forwards.  The need for an accurate, rapid, field-ready test remains.

And yet another key factor is how we treat patients once they are in care.  A recent articles in the New York Times played up the level of debate amongst physicians about how aggressively to hydrate patients. My impression of the discussion of this article is that there is more heat than light to this, but there are certainly differing opinions over who/when to treat intravenously, with peripherally inserted central catheters or even intraosseous insertions.  Given the range of medical settings in place in Guinea/SL/Liberia, I expect to see continued variation in care and in opinion in the future.

Drugs. Turning to trials of novel treatments for Ebola, we have mixed news. As a backgrounder, I really liked this clear outline of the various Ebola lifecycle entry points for treatment, and available drugs, in an article by Dr Lai Kang-Yiu published late in November (Lai paper). I’m not a huge fan of the colour scheme, but this figure from the paper lays it all out for the more visual learners amongst us:

The idea of providing infected individuals with serum – roughly, the non-water part of blood – from recovered Ebola patients has been around for months, and in use in high-income settings.  A recent overview from Thomas Kriel highlighted the importance of virus-inactivating any blood collected (primarily for non-Ebola infections), as well as methods to negate the need for blood-matching.  In mid-December the first trial of convalescent serum in West Africa began in Liberia.

The not-so-good side of novel treatments is this story from a treatment centre in Freetown, where the antiarrhythmic amiodarone was being used without any formal testing process.  The (NHS) clinical team threatened to walk out of the Emergency-run facility , before agreement was reached to stop amiodarone’s use. I imagine that this is only one of many difficult processes that will arise in the roll-out of novel therapies.

I’ll also note this recent call for RCTs of proposed therapies in the NEJM.  Given the current fluctuations in mortality rates within and between hospitals, the case for randomized trials of some description may hold more water than it did when outcomes were pretty consistent. Especially in cases where effectiveness is not clear.

B3. Vaccines

No progress on in-country trials in West Africa, that I’ve heard of.  But there were a couple of vaccine stories in December.  First, the second Ebola vaccine in the pipeline (VSV-EBOV; originally Canadian, now NewLink/Merck) faced some hiccups during phase 1 (safety) trials, when some participants complained of joint pain and the trial was stopped early.  While some commentators saw this as a Very Bad Thing, the WHO claimed that such joint pain was not atypical for vaccines. I also noted with interest the final comment by Thomas Geisbert, who was involved in developing the VSV vaccine

[VSV-EBOV] is the best vaccine virus I’ve ever seen for the kind of viruses we work with. I don’t know why it’s so good…I’d never take [the GSK vaccine]. Not with the VSV. I’d take my chances with some joint pain.

Second, a phase 1b safety trial of a more general filovirus vaccine that has been running all year in Uganda was published showing effectiveness (in generating an immune response) and safety (Kibuuka paper). These findings have already been built into the related, but more advanced, Chimpanzee adenovirus ChAd3 that passed phase 1 testing in the US last month.  But researchers were happy to see that this family of vaccines had effect in African populations.  I’ll chalk this up as supporting evidence.

B4. Social factors/impact

There was some recent brouhaha over Alex Kentikelenis and several other Cambridge sociologists/epidemiologists’ letter to Lancet suggesting that the IMF’s policies in recent years had led to an underfunded healthcare system, and thus increased the impact of the current Ebola outbreak. (Kentikelenis letter).  The IMF responded to the say they didn’t see it that way, and had been proactive in providing funding once Ebola had emerged.  Chris Blattman, a Political Science professor at Columbia suggested the original authors misunderstood the IMF’s level of influence over health policy in the affected region. Of course, the impact of macroeconomic policy is very hard to evaluate – finding a realistic counterfactual for any action is close of impossible; so it all comes back down to how you read the tealeaves.  Or at least that’s how I see it…

The flip side of financial problems as a cause of the epidemic is financial problems as the fall-out.  In addition the massive macroeconomic impact of the epidemic, there are lower-level effects worth noting. Within the healthcare systems, clearly everything except Ebola treatment has been greatly scaled-back during the epidemic. This was highlighted in a recent paper by Håkon Bolkan and colleagues surveying changes in inpatient admissions and surgeries at 40 facilities across Sierra Leone in September and October  (Bolkan paper).  Not surprisingly, as Ebola rose, everything else fell precipitously:

On a side note, I really appreciated this paper as a nice, clean study that makes a simple point well.

The impact of Ebola on the healthcare system will have knock-on effects in years to come, especially for infectious diseases. (Although in some cases the effects are being felt already – see this recent report on the side-effects and complications being caused by antimalarials provided nationwide in Sierra Leone last month.) Effects on women are likely to be particularly severe, since they are both the most likely to be healthcare providers and consumers. Outside the healthcare sector, education has been put on hold for six months generally, agriculture and trading activity has been greatly affected, while NGO work in other sectors has also come to a halt. Food insecurity is not yet widespread, but may be considerable in rural subsistence settings.

C. Journalism

There are also a growing number of first-person narratives from foreign responders, worth reading to see what it is like working in ETCs or in other capacities on the front line.  I present these in no particular order (see also previous posts):

  • Paul Pronyk, infectious disease and public health physician working in Freetown.
  • John Wright, a British physician, was in Sierra Leone last month.
  • African platypus, an anonymous US nurse practitioner, was in Liberia in November/December.
  • Pieter Baker, a US epidemiologist working on surveillance in Sierra Leone.
  • Gillian McKay, a Canadian nurse and ETC PPE trainer in Sierra Leone.

I am only too aware that all of these are out-of-region persons who have arrived for this epidemic.  I would love to include more viewpoints from locals, but am not connected to them at present. I do see some one-off posts via American/European news sources, for example this report of a day in the life of a contact tracer in Monrovia. But more pointers very welcome.

The local person I read the most is Umaru Fofana, who writes for the BBC, Politico Sierra Leone and himself on Facebook.  But he is a journalist, rather than a practitioner. On that front, I’d also point you towards the range of other local journalism recently highlighted by Crawford Kilian.

D. Getting involved

  • For those of you with French language skills, there’s an Ebola MOOC from Unige (l’Université de Genève) and UNF3S (l’université numérique francophone des sciences du sport et de la santé), starting on January 12th.
  • If you are a modeller, you may be interested in the upcoming workshop on “Modeling the Spread and Control of Ebola in West Africa“, January 22-23 in Atlanta. They’re accepting posters too, but the deadline for that is January 8th.

If you have seen something I’ve missed (or links are broken), you can reach me @harlingg. And as ever, thank you to all those on whose intellectual shoulders I am standing.

There are eight previous posts in this series and a summary of data/research sources.

1 This is mostly for my reference, but there was a paper published this month highlighting that phylodynamic methods can estimate key epidemiological parameters using only the 74 initial outbreak cases, as opposed to the larger number needed for count-based analysis (Samuel Alizon et al, Virulence 2014).

Ebola epidemiology roundup #8

This post covers new materials made available between roughly 18 and 5 December. Travel commitments have stretched me a little thin, hence the belated posting and possible failure to catch all the new scientific articles this week.

A. Charting the Epidemic

The WHO has made some changes to how they present their data and situation reports.  Data are now updated daily on their Ebola data page, but the main SitRep is still put out every Wednesday.  I, and others, have been impressed by the addition of a new “map journal” style that links images and text together very neatly (built through ArcGIS’ ESRI Story map app, apparently).

One important note on data: as of mid-November the WHO is reporting two concurrent data series for each country.  One is the SitRep they have been providing all along; the other output from the “patient database” which should be closer to the source – i.e. what is maintained in hospitals, provincial ministries, etc. My understanding is that the “patient database” is built from a CDC-based contact tracing database, while the SitRep data is from the national MoH, but I’m not certain about this (anyone able to confirm/deny?).  Even without certainty though, more data sources at least allows for some triangulation, hopefully.

A final word on data quality.  Many people have been concerned for some time about underreporting.  Efforts to measure the undercount to date have been limited to a rough estimate made by Meltzer and colleagues based on bed capacity.  So a blog post by Les Roberts, who was working in Sierra Leone with the WHO in October/November, provides important data on this topic. He and colleagues conducted a survey of randomly selected villages, and compared reported case numbers in the national database to local assessments.  The result, a classic good news/bad news situation: bad news, it seems that only one-third of cases are making it to the government records; good news, this is roughly in line with what researchers had thought previously, so we may not need to radically re-assess the level of interventions needed to stamp this epidemic out.  As ever, though, it’s good to have some data on this.

And then one word on data access.  There has been plenty of concern about the lack of publicly-available individual-level data in this epidemic, from the WHO, CDC, national governments and others.  And quite justifiably so.  But Roberts, in a separate post, provides the best explanation of non-data-provision I have seen to date: the overburdening of on-the-ground responders means that data quality is imperfect, and the provision of data to the press/public/researchers without detailed explanation would lead to misleading and potentially harmful news stories, which would lead to increased burdens on said responders to manage the ensuing hue and cry.  So they don’t hand out the data.  Sad, but understandable.

A1. Epidemic trajectory

After reporting a jump of 1000 (largely historical) deaths in Liberia in the last week of November, the WHO has reported that this was due to a clerical error in Liberia, and removed the events. As a result, the epidemic continues at a steady but non-negligible rate in Guinea and Liberia, and continues to burn fiercely in western Sierra Leone.

There were a couple of important papers put out recently by Gerardo Chowell and colleagues highlighting the dynamics of the epidemic.  First, Chowell highlighted the dependance of predictions relating to final epidemic size on the modelling assumptions we use. (Chowell PLoS Currents Outbreaks paper). Specifically, the authors contrast exponential growth models to logarithmic growth models, showing that the latter are more useful for curve-fitting (or as Chowell says, “phenomenological”) models once some kind of control has been achieved in a given setting.  Exponential models are useful for (mass-action or random-mixing) models in situations where growth is unchecked, but not once growth rates change.  As the figure below highlights for Sierra Leone (green line, exponential; black line, logistic):


Second, Chowell highlights that the national cumulative incidence curves we have been seeing for Guinea, Liberia and SL can be broken down into county-level curves, each of which looks far closer to a logistic function than an exponential one.  The combination of multiple curves, each taking off and flattening off on different timelines has allowed the production of a exponential growth curve at the national level. (Chowell arXiv paper). Of course, we can take this several steps further – these county data are the combination of asynchronous village-level curves, and then household-level curves.  All this work highlights, for me, the importance of considering how structured (i.e. non-homogenously-mixed) contacts for Ebola are, and thus how important information on contacts is.

A2. Epidemic parameters

I haven’t seen any new individual-level data recently, but there are a few new pieces of data that might be of interest.

Given the news coverage of the man in India who arrived having recovered from Ebola, but whose semen tested positive for the virus, I will highlight this brief review on evidence for Ebola in semen by Ian Mackay and Katherine Arden.  To my knowledge, no-one has yet shown conclusively that transmission occurred through this channel, but we may not need an “abundance of caution” to believe that prevention efforts are worth expending on this topic.

I wanted to flag a couple of papers this week – one old, one new – considering how animal and human populations interact in the context of Ebola.  In the new article, Wondwossen Gebreyes and colleagues note the Global One Health paradigm, in which human and non-human health are intertwined via zoonotic infections, and the benefits that can arise from communication between public health and veterinary fields.  (Gebreyes paper). In the old article, Jean Paul Gonzalez and colleagues highlight the the possibility of non-pathogenic strains as an explanation for the 15 year gap in human Ebola epidemics in the Congolese basin up to 2000.  Of course this would also fit well with the “jump” of Ebola from central to west Africa in the current epidemic.  (Gonzalez paper). Asymptomatic/low symptom infection remains a topic of limited investigation, despite some evidence of its presence in various settings (see also this Les Roberts post, which notes anecdotal evidence for sub-clinical infection and the absence of whole-village outbreaks – although the latter may relate to the close-contact nature of infection). As Roberts says, maybe this is something we can study after the epidemic is under control.

And on the modelling front, I somehow missed this previously, but Gerardo Chowell and Hiroshi Nishiura published in early October a review of Ebola parameter estimates from previous epidemics and the early days of this one.  A tour-de-force that should be on the desk of all those building models and interventions in this epidemic.  For a briefer, and more biological, take on the same topic, please try this review by Marco Goeijenbier and colleagues.

B. Stopping the Epidemic

One overarching letter of note, following the recent claim by Declan Butler that “models overestimate the epidemic“, is the rebuttal by Caitlin Rivers and many others, that models are useful for far more than predicting case numbers – in particular, for predicting the impact of interventions, and that Butler’s “assertion that models of the Ebola epidemic have failed to
project its course misrepresents their aims”.  I couldn’t agree more.

B1. Containment.

Ebola can typically be passed on in three contexts: in the household, in healthcare settings and at funerals.  Given strong efforts to reduce risk in Ebola treatment centres/units and at funerals – both highly identifiable settings – the essence of prevention for Ebola at present lies in a single act: find infectious individuals living in the community.1 But this act is not simple. Finding infectious individuals requires communication channels for directing individuals to care, staff to bring potential cases into the healthcare system and rapid, accurate tests to determine who is in fact infected. On the first front, I note with concern the apparent lack of connection between UN-proposed social media SOPs and on-the-ground follow-up. On the second, this report of a day-in-the-life of a health surveillance team member in SL is very sobering. On the third, it seems that we are no closer to a test that will conclusively show positive within the first 2-3 days of infection, but there is good news in the form of a 15 minute rapid Ebola test for those who are beyond this “window period”, currently in trials in Guinea. Such a test should significantly reduce the need to quarantine suspected cases, and thus the fear of testing, and so potentially increase the ease of finding infectious persons.

A slightly different approach to avoiding onward transmission with communities is to have infectious individuals identify themselves and self-isolate.  This is an argument discussed in this Nature commentary by Christopher Whitty and colleagues, describing the building of community isolation centres for self-isolation by those who believe that they may be protected.  These would have minimal treatment capacity, but could help protect neighbours and family.  In the absence of sufficient hospital beds, or long distances to these hospitals, or community fears about hospitals as sources of infection, such community centres may have a vital role to play. (Whitty article)

B2. Treatment.

A couple of treatment topics this time around.  First, hydration.  It seems that much consensus exists that those patients who become massively dehydrated are at great risk of death.  While there are differing views on how aggressively to use IV fluids (see recent call for IV, nasogastic feeding tubes, but also concern for healthcare worker safety when using sharp objects around confused patients).  But the basic message is – hydrate early, hydrate often.

Second, as mentioned previously, there are several new treatments in the pipeline.  And there is a nice overview table in a recent roundup article on new treatment options in Science:

However, the article notes that scale-up of such treatment options is likely to be slow, and thus it is not clear how large role they will play in this epidemic.  When it comes to testing these drugs out – as with vaccines, see below – the big question at the moment appears to be an ethical one: can we randomize provision?  On this topic, the NYT has an in-depth debate on the ethical issues that acts as a solid primer.

Oh, and on a more nerdy epidemiologist note, Les Roberts highlighted an important point that made a lot of sense once I had read it: hospitals have been reporting very low fatality rates – sometimes 40% or less – and some have claimed this speaks to the importance of intensive care.  However, as Roberts notes, another good explanation for low CFR is survivor bias: when tests take days to come back, and entry into the hospital requires a positive test, and those who die from Ebola tend to do so relatively fast, those who are admitted are a highly non-random portion of all those infected.  Not happy news, but important things to bear in mind before trusting that current Standard of Care is likely to make a large dent in the epidemic directly (as opposed to indirectly by isolating infectious persons).

B3. Vaccines.

If the big issue for treatment testing is speed of scale-up, it looks like the question for vaccines is cold-chain.  While it is not clear yet quite how cold any vaccine will need to be, it is clear that maintaining low temperatures and transporting vaccine across West Africa is going to be a serious undertaking.

The good news this week was the announcement by Julie Ledgerwood and colleagues that phase I human safety trials for the most-advanced Ebola vaccine (ChAd3; NIAID/GSK) had shown that large doses of the vaccine were able to stimulate an immune response comparable to that seen in vaccinated non-human primates who had successfully fought off the virus. (Ledgerwood paper). The news was entirely positive, however, as noted by Helen Branswell since there were some moderate side-effects (largely fever) and the dose required was significant.  From my point of view, the fever side-effect seems particularly worrying, since vaccinated individuals would then have fever and antibodies – making a non-PCR test for the virus virtually useless.  The second-placed vaccine in the development footrace (rVSV-EBOV; Canadian government/NewLink) got a boost too, with the investment of $30-50 million by Merck.

Despite all these efforts, it is not clear that vaccines are going to play a big role in controlling this epidemic – as opposed to potentially avoiding the next one.  A modelling paper by David Fisman and Ashleigh Tuite this week notes that unless either vaccines reach millions by March 2015, or a vaccine is able to reduce the effective reproductive number well below one, then it won’t add much to the downward trend in the epidemic curve that they expect to see before June of next year. (Fisman paper).  This doesn’t make vaccine development pointless, but does highlight that it may be an investment for tomorrow, not for today.

B4. Social factors/impact.

The wide-ranging impact of Ebola is raised often in discussion, but I really liked this infographic from the MPH@GW program which highlighted quite how far the effects spread:

To which I add only a little more context for some of the figures given:

  1. Economy.  There was a new World Bank report out this week highlights both the direct costs of non-employment, and the indirect costs of lower investment both within the country and from abroad; as well as knock-on effects in neighbouring countries.  As the infographic notes, the high estimate for the overall two-year regional effect is $32.6 billion; the lower bound is a still-staggering $3.6 billion.
  2. Education. Given that no schools have been open in Guinea, SL or Liberia since the summer, there has been discussion about how best to provide education at home, potentially through technology.  While I wonder about its scalability, one programme of note is this crowdsourcing attempt to send loaded tablets to West Africa.  It’s funding until the end of December.  Do others know of other, more low-tech options?

While the impact of Ebola is clearly being felt in several social spheres, there are also efforts to understand why Ebola has been so able to spread in this outbreak.  Clearly mobility has played a large role here, and mobility has many potential causes that might be targeted to reduce infection. The Internal Displacement Monitoring Centre outlines five key displacement causes in the current outbreak: (i) fleeing the virus; (ii) fleeing quarantine; (iii) seeking health care; (iv) forced evictions/stigma; (v) violence/rights violations. I would argue, to grossly generalize, that we can see two broad categories of social determinants (which often overlap) in Distrust and Poverty.

  1. Distrust. As Helen Epstein highlights, this has been an epidemic of rumours (they so often are).  The lack of trust in authorities is deeply rooted, arising from the long history of neglect and abuse by the powerful: stretching from colonial slavery through Americo-Liberian class hegemony and the looming power of Firestone to more recent dictatorship and civil war.  Fear that the government is trying to infect, or use infection to control, you appears to run deep in these countries.
  2. Poverty. As Umaru Fofana highlights, while everyone can be infected, it is the poor who most often are, who are least able to manage their illness, and who are most affected by food prices, quarantines, etc.

The upshot of all this is a population that is hard to reach, hard to persuade to co-operate with public health officials or to access care, and thus hard to protect.  Hopefully the launch of the Ebola Response Anthropology Platform – a joint effort of LSHTM, the Institue for Development Studies and the University of Exeter – will go some way to increasing the quantity of local understanding that goes into programme design and execution. Hopefully.

C. Miscellanea

C1. Risk communication

A couple of quick notes here.  First, evidence appears to be coming in that the recent US quarantine debacle has led to a decline in volunteers to work in West Africa possibly due to the 21-day self-monitoring (or potentially quarantining) that is required on return to the US.  And second, this week I saw a wonderful take-down of the term “abundance of caution”.  Read the whole (short) article, but this was a great sentence from within:

[When] I hear “abundance of caution” being used in a sentence about Ebola, I translate it to “what I am suggesting makes no actual sense but demonstrates my extreme seriousness about fighting the very idea of Ebola”. 

C2. Journalism

I’ve linked to him several times above, but the series of blog posts by Les Roberts of Columbia University are worth reading from end to end.  Phenomenal insight into what’s going on on the ground; especially for those who geek out over epidemiology or who care about humanistic reads on the epidemic.  Hopefully the overlap of these two groups is significant. For a quicker read, this interview with a CDC epidemiologist who had worked in SL was also illuminating.

And while I’ve linked to it above too, Helen Epstein’s article in the New York Review of Books is an important read – as are so many of her pieces, whether you agree with their angle or not.  Her thesis here: historical politics is a key driver of the Liberian epidemic.

In addition, there have been a number of articles I have read as background to the epidemic and how it is changing.  In case you are in need of some bedtime reading.  I’ll note that they skew heavily towards Liberia; I’m not too sure why this is, but thought it worth highlighting.

D. Getting involved

This week, or rather next month, an opportunity to learn from several very big names in the field of Ebola science: LSHTM is running an online course for two weeks starting January 19th.  And it’s free.

As ever, if you have seen something I’ve missed (or links are broken), you can reach me @harlingg.  And as ever, thank you to all those on whose intellectual shoulders (and tweets) I am standing.

There are seven previous posts in this series and a summary of data/research sources.

1 This is not to say that there are not problems with numbers of treatment beds – there clearly are, especially in Sierra Leone – but a clear plan for expansion exists in this area and is being implemented; I’m not so convinced that a similar plan for case finding does. Of course, this is only my opinion.

Ebola epidemiology roundup #6

Another summary of breaking science surrounding Ebola, with a strong skew towards the epidemiological. Apologies for the delayed release; hope it’s useful to you.

A. Modelling the Epidemic

A1. Data quality

Things appear to be getting increasingly murky.  Case numbers shot up early in the week, when previously un-included events from earlier in the year were added to the totals in Liberia; and then dropped down at the end of the week as a number of suspected cases from Guinea were ruled out.   Upshot: building models from the cumulative case curve, or even the day-to-day new case numbers is going to be increasingly difficult as these figures cease to even reflect truly new cases in each reporting period.  And I will note that Hans Rosling is now in Monrovia working with the MoH, which may explain some of the changes in reporting.

A2. Epidemic trajectory

The reporting front. Following on from last week, there have been efforts to determine if the epidemic really is slowing in Liberia.  While numbers of patients at hospitals, particularly around Monrovia, are definitely down, there has been suggestions that this may be due to healthcare avoidance by relatives who don’t want their relatives cremated. Numbers elsewhere do not appear to be falling.  There is a nice overview of the past month’s epidemic (up to October 18th) in MMWR; it’s shocking to think how fast things have changed in just a few weeks.

The modelling front (theoretical aside).

As people have been worrying more about data quality, the use of models to predict case numbers is falling away.  So I’m going to use this section to provide a brief primer on mass-action mathematical models for Ebola, which may be handy for the section below on treatment.  A compartmental model is one in which each individual is in a single state at any given time, and can move between them according to set rules.  These rules can be simple (every period, you move on with a 10% probability), or complicated (every period, you move on if you meet someone infectious and neither of you has been vaccinated, with a probability of 7% if you are under 5 years old, and 3% otherwise).  As well as complex transition rules, you can also have simple or complex sets of compartments.  A simple version is the linear one SEIR. Here everyone is susceptible (S), exposed but not symptomatic (E), Infectious (I) or Recovered (R). With Ebola, one can have multiple infectious states, e.g. when at home (I), in hospital (H) or at a funeral (F).  This thought process led to Legrand et al‘s 2007 SEIHFR model, commonly used for modelling Ebola. A slight tweak on this in the past month was Camacho et al‘s model which keeps track of how individuals were infected. One can, however, get very complicated with these models, as seen in Pandey et al’s advanced-ninja model in Science this week.  Such models track everything with great care and allow you to see exactly who is where when.  Oh, and the mass-action part means we don’t restrict who can infect whom; we can change all that with agent-based models, which track every single person separately.  They usually require serious computers.

My takeaway here though, is that is important not to be seduced by complexity: as my math modelling teachers always said, don’t trust the findings if the model is detailed, trust them if the level of complexity is sufficient to get at the research question, but no more so. And with that, back to the research.

The one paper looking at the “natural history” of the epidemic this week is that of Jeffrey Shaman and colleagues at Columbia.  The authors use an SEIR model and incidence data up to 28 September to show similar estimates for R0 to other papers (1.89 in Guinea; 1.72 in Liberia; 1.45 in Sierra Leone).  They also provide additional estimates of the incubation, infectious and symptoms-to-death time periods. The key message I take from the paper is that their forecasts of cases for the 6 weeks from the end of data collection have consistently undershot for Liberia, but not for Sierra Leone and Guinea.  As the authors note, and in line with observations mentioned above, something different is going on in Liberia – either in terms of reporting or the epidemic trajectory.  The big question is then, what? (Shaman paper; Shaman website for live updates)

A3. Epidemic parameters, various

Stephen Goldstein provides a great summary of what we know about infection risk in asymptomatic, infected individuals.  Goldstein draws on research from the 1995 Kikwit outbreak showing no transmissions without physical contact during clinical illness (n=78) and from the Uganda 2000 outbreak showing barely detectable viral load in the first 2-3 days of symptoms, to suggest that risk of infection during incubation and early illness is probably magnitudes lower than later on.1

There were also a couple of important descriptive epidemiology papers this week.2  First, John Schieffelin and colleagues reviewed patient-level data from 106 Ebola-diagnosed patients Kenema hospital in Sierra Leone – an initial epicentre of the SL outbreak – from May and June 2014.  As highlighted in the press, key predictors of mortality included older age (over 45 was highest risk, but dose-response so that even young adults were at increased risk to those <21 – this last, not statistically significant, but a 20% absolute survival difference) and viral load at presentation (again, dose-response).  A prognostic score for fatality risk included these two factors, plus baseline temperature, diarrhoea, weakness, dizzyness, abdominal pain and  three blood tests (blood urea nitrogen; creatinine; aspartate aminotransferase [AST]). (Scheiffelin paper)

Second, Angela Rasmussen and colleagues presented evidence suggesting that genetic factors may play a part in causing divergent outcomes in patients: specifically, haemorrhage and thus risk of death from Ebola occurs in mice with certain genetic variations.  The authors suggest that finding similar variations in humans might be an important first step to reducing the mortality rate.  (Rasmussen paper)  I will admit that I am no expert in lab studies, and relied somewhat on articles such as this explain things to me.  But I did connect this with something I saw previously: work from the 2000 Uganda outbreak showing a connection between HLA-B alleles and Ebola fatality.

Alert: subjective read ahead. My synthesis of the evidence I’ve read over the past few weeks, suggests to me that those infected with Ebola may fall into three categories: (a) sub-clinical, infected but never infectious; (b) rarely-fatal, infected but on an early trajectory to serious but manageable illness, even in settings with limited or no healthcare; and (c) often-fatal, infected and with a very high risk of death if not treated very early in high-quality healthcare settings.  This doesn’t mean that genetics are destiny, but they may play a part in determining how serious illness is if someone is infected. I would also note that trajectories for children may be very different from adults, and would probably benefit from careful study as a separate population.   </opinionation>


B. Visualization

A quiet week here.  But I will note that the National Geospatial-Intelligence Agency has unclassified its data for West Africa to provide a mapping platform for response efforts.  Currently it looks like they have data for Guinea and Liberia, but not Sierra Leone. Maybe this will help someone?

And there is now a single source for data on all the historic Ebola outbreaks, often linking individual cases geospatially and temporally.  Gathered by Simon Hay and his research group at Oxford.  What a great resource.


C. Stopping the Epidemic


The above evidence on low blood-based viral load early in the disease process highlights the difficulty of effectively testing people at airports, or elsewhere.  PCR (polymerase-chain reaction) tests, the typical gold standard for diagnosis, requires virus to be in the blood, but early on Ebola hangs out in the organsRapid blood tests are being developed, but since they are either PCR or antibody tests (the latter being even slower to emerge, since they require the body to respond to the virus), even fast tests aren’t likely to be effective in catching people early in their infections.


Two papers have come from Alison Galvani‘s team at Yale (in addition to the one from last week). In the first paper, Dan Yamin and colleagues used data from the Liberian Ministry of Health and Social Work, including contact tracing data on 246 cases from August 2014.  They built an SEIR-type model with two infectious compartments (early and late) and allowed infectiousness (taken from 2000 outbreak data) and contact rates to vary daily for each individual.  This was thus an individual-agent model. Two key findings arise from this paper.  First, the authors highlight that survivors and non-survivors look very different in terms of how many secondary cases they generate (i.e. their own Re) as the viral load of non-survivors becomes much higher as their illness progresses.  As a result, a strategy which suceded in isolating 75% of those patients who subsequently die within 4 days from symptom onset is likely to eliminate the disease.  Alternatively, reducing all patients’ contact rate by 60% from the first day of symptoms would have the same impact. (Yamin paper)

The second paper, led by Abhishek Pandey and Katherine Atkins used a Legrand-style model but stratified each category by location (community, hospital, quarantine) and healthcare worker status (yes/no).  They, in line with most other modelling work to date, found that only a combination of treatment, quarantine and safer funeral practices would be sufficient to curtail the epidemic.(Pandey paper)

Eric Lofgren and colleagues at Virginia Tech highlight that simply ramping up hospital provision is unlikely to do more than slow down the epidemic.  Their paper considered differential effectiveness of specialized Ebola Treatment Centres (ETC), community-based centres and home-kit approaches, but even in optimistic scenarios other prevention interventions were needed to bring the epidemic under control. This finding is in line with previous papers showing that treatment alone is not going to stop the epidemic any time soon. (Lofgren paper on the arXiv)

Buttressing Lofgren and Pandey/Atkins’ findings, Anton Camacho and colleagues modelled case data from the original 1976 Ebola outbreak, using individual-level “line list” data.  They found that transmission mostly fell away prior to hospital closure (the hospital was the focal point for infection in this outbreak).  The authors point to behaviour change by residents as the most likely cause of epidemic die-out. (Camacho paper)

Which leaves us with an apparent divergence between the message that containment/treatment alone may or may not be sufficient to end the epidemic.  One way of reading the Yamin paper is that changes in funeral practices and reduced home-based care are baked into the modelled intervention – since contact rates are being severely curtailed.  I think my takeaways are: (i) there needs to be a significant reduction in contact rates to end the epidemic; (ii) this will require changes at every point in the Ebola illness process; and (iii) these changes aren’t impossible to envisage/carry out.

Social epidemiology.

PLoS has taken a second unusual step in responding to Ebola.  In addition to their rapid-turnaround PLoS Currents they have now decided to blog-publish pre-peer-review papers they think urgent and likely to be accepted, via their Speaking of Medicine blog.  The first paper up is on a topic of great interest to me: the social setting of rural Sierra Leone and how it feeds the epidemic. Paul Richards and colleagues draw on a mass of data collected over the past four years to show how trust (or lack thereof in national institutions), marital patterns, funeral practices and inter-village dependencies (for marriage, school, work, markets) help to propagate the epidemic.  The authors suggest the need to convey accurate information, make hospital attendance truly attractive and to leverage family/village structures to protect people from infection by involving them in decision-making for care practices. (Richards paper pre-acceptance).

Reading this article, especially the migration parts, reminded me of the Economist article last week highlighting the potential of cellphone data to help in understanding epidemic spread, and how it efforts are being stymied by a lack of experience in managing data sharing.  A sad read, and a journalistic follow-up to this earlier commentary on the subject.

A second, briefer, social determinants publication this week was this letter by Robert Synder and colleagues, highlighting the role of urban slums in propagating the epidemic, and how their residents are often highly mobile but not via formal channels.  Thus, the poorest are least able to avoid infection, and also likely to then pass their infection on. A reminder that social policies to alleviate urban poverty can be great health policies too.


D. Miscellanea

D1. International travel restrictions/Quarantine

Short and sweet here: a long but thoroughly argued piece by Judy Stone caught my eye this week.  She highlights, among other things, the inconsistency of quarantining returning HCW with no specific risk but not similar HCW who treated patients in the United States.  I imagine the argument would be that conditions are better here than there; but at some level we probably are going to have to trust HCW to know when they are/are not at risk of developing infection.  And given the benefits of early treatment, there’s a fair amount of self interest in turning yourself in if your temperature spikes. And as others have noted, quarantines in West Africa don’t appear to have worked very well.  While in a perfect world, quarantines will act as fire-breaks, as ever we have to consider their effectiveness (on-the-ground) not their efficacy (in-the-laboratory-in-our-heads), and balance this against the economic and human rights costs.

D2. Risk communication

I don’t know quite what to make of this one, but I want to put it out there.  Jody Lanard and Peter Sandman have a new blogpost positing that messaging around Ebola needs to be far more focused on the worst-case scenario. As with much of this epidemic, there is a balance between overreacting and underreacting; since we don’t know what is going to happen, we have to make educated guesses at the probability of various scenarios.  Lanard and Sandman argue that the downside of overreacting is less than the downside of underreacting.  I think they underestimate the impact of scaring the public; something about the intelligence of a mob?  There’s a debate to be had here, but I think that at present the product of the probability of an epidemic in the United States (which it appears we don’t have the data to estimate accurately, but it’s pretty darn low) and the potential damage caused by highlighting the impact of such an epidemic doesn’t make a convincing case for going negative on this one.

And to offset this perspective, I offer you a letter using the 2003 SARS outbreak to highlight that data presentation can be crucial for how information is understood.  The authors note that daily figures are easier to comprehend than cumulative numbers, conveying what is happening now, rather than everything that has happened to date – and thus allowing readers to update their understanding of the margin, often more important than the mean/total.  A quick point that made me think about how I display data…

D3. Journalism

  • I felt I needed to link to this New Yorker piece by Richard Preston, he of The Hot Zone.  I should note that many people find his tone unreasonably sensationalizing – especially since he purports to be providing non-fiction.  But he is part of the Ebola conversation, so a link is provided, along with one to one of his strongest critics.
  • And a shorter article by Helen Epstein, mostly on falling hospital numbers in Liberia.  While I don’t always agree with Epstein’s messages, her health journalism is amongst the very best out there.
  • And the Lancet held an Ebola twitter chat and then Storified the resulting discussion.  Very high-calibre respondents make this a great resource for getting up to speed on some of the key debates.

If you have seen something I’ve missed (or links are broken), you can reach me @harlingg.  And as ever, thank you to all those tweeting Ebola science out there, from whom I glean so much.

There are 5 previous posts in this series: #1, #2, #3, #4, #5 ; and a summary of data/research sources.

1 I will add a footnote here (for my own reference really) about the work of Daniel Bausch and colleagues, also in the 2000 outbreak, showing the ability to extract viral markers from many clinical specimens, but very few hospital surfaces.
2 I use the term “descriptive” as a differentiator from modelling papers, rather than to denigrate them in contrast to, say, “causal”.

Ebola roundup #2

This series is the second in a weekly round-up of stories I’ve seen relating to Ebola with a strong lean towards the scientific. This post is probably longer than last week’s, and certainly not as well organized.  Maybe wider-ranging?  Hopefully useful. Anyway, let’s dive in with a quick asterisk relating to:

  • Data quality. Those building models are starting to seriously worry about the data going into them (cf GIGO).  Up to mid-September, despite rapid growth in cases, contact tracing in Sierra Leone and Liberia has been pretty impressively strong: Caitlin Rivers at Virginia Polytechnic Institute and State University (i.e. Virginia Tech) showed that follow-up rates remain over 90% for known contacts.  However, it seems that the two WHO data drops this week have a far higher ratio of new cases to fatalities than was expected, which has led model updates (see below). What is unclear for now is whether the change is due to the epidemic outstripping our ability to follow it, or things actually changing in terms of infections or deaths.

With the above proviso, here’s what I’ve seen in Ebola science this week:

  • Epidemic size (update). Given the above concerns re varying data quality, estimates on epidemic size continue vary greatly.  No-one I have seen has any certainty regarding what the overall population at risk is, and thus when the epidemic might “top-out” and stop growing exponentially.  When contacts between individuals have community structure – i.e. villages, townships, any human setting really – we should expect to see growth spurts, leveling off and then renewed take-off (which would be one explanation for the recent jump in cases in Guinea).  The implication is that even when growth curves appear to be slowing, we shouldn’t assume that exponential growth is really tapering off.
  • Effective reproductive rate (update). The only scientific publication I saw this week on this topic was from Sherry Towers at Arizona State University, and colleagues, who have developed methods to deal with the irregular reporting of case/mortality data by WHO.  Their approach provides ‘local’ estimates of exponential growth in case numbers over small time periods, and then converts them through a standard SEIR mass-action model into an estimate for Rt. Using data up to September 8th, they generate country Rt estimates of 1.6-2.3 for Guinea, 1.5-2.1 for Liberia and 1.2-1.3 for Sierra Leone depending on assumptions regarding incubation and infectious periods. They note that these numbers are in line with the previous work by Althaus and by Fisman et al. that I reported last week. (Towers paper)
  • Intervention impact.  This is the stuff that I’ve been waiting to see come into the open.  If there’s one area in which mathematical models should have a massive comparative advantage in an outbreak, it’s predicting what impact different proposed interventions will have.  Of course, in order to model impacts one needs a solid base model of what is happening now, and that model needs to be “as simple as possible, but no simpler”.  Personally, I would love to be building geographically accurate models of contact networks in each country, but I would be pushing on a piece of string if I did so.  So instead I’m watching others building faster and better.  Anyway, digression aside, there’s a paper up on the arXiv as of Tuesday from a team at Virginia Tech led by Caitlin Rivers (see also above) and also including Eric Lofgren and Bryan Lewis, starting to dig into the question of ‘what might work’. In a model with three transmission hubs (homes, hospitals and funerals), they show that:
    • improved contact tracing shifts infections from homes and funerals to hospitals as cases are brought into care;
    • improved infection control within hospitals reduces healthcare infections; and
    • a pharmaceutical intervention has a greater proportional impact on home and funeral infections, but overall is little more effective than contact tracing.
Their conclusions are pretty bleak – none of the above will end the epidemic, although each can have an impact and save lives. The next step for me in such models will be to look at the available resources (financial, human, equipment) and determine what the highest-impact set of interventions looks like. (Rivers paper)


  •  Airborne transmission (update). Still lots of traffic on this.  Still everyone is saying that the selective pressure for such a mutation is limited and the chances are very small.  Some links to make that point.  Just keep moving along people, nothing to see here.
  • Human resources for Ebola. I am no health systems researcher, but I really liked this pair of posts by Shane Granger on healthcare worker impacts of Ebola (discussion blog; data blog).  Granger’s key message is the need not only for ‘loss of supply’ data, but also ‘unmet demand’ data; which seems essential to any effective response.  But as a newbie to the field, I really liked his overview of categories of healthcare as they relate to this outbreak:
    • Operationally critical job roles (OCJR). The linchpins who plan out everything else, with deep relevant knowledge.  E.g.s senior medical staff, virologists, nurse practice managers.
    • Critical job roles (CJR). Skilled individuals with expertise. E.g.s doctors, nurses, logistics specialists.  These are the positions I see advertised through US/European health networks (and on Facebook) every day.
    • Hard-to-fill (HtF).  Not high-skill, but high-demand/low-supply positions.  E.g. burial crews, ambulance crews, ward staff. Granger adds nuance by highlighting that this group may or may not overlap with Hard-to-replace (HtR): people in this category may die more often than the critical staff, leading to shortfalls, and also an ever-lower desire to take on such roles.
In related news, the recent murder of 8 people working to raise awareness of Ebola in Guinea highlights the dangers for those involved in outreach, and of denialism and fear that Ebola can engender, particularly in countries with a long history of violence and centralized power.


  • Historical Ebola material.  For those of you who would like some historical context, or just have a few more spare minutes, I would point you to some items that caught my eye this week.
    • The Kikwit outbreak – see above – seems like a really important case to study, since it was an urban epidemic.  I haven’t delved into the literature in depth, but there are papers out there considering lessons to be learned, e.g. Heymann et al. on international preparedness (Heymann was the WHO lead on SARS, now at LSHTM, Chatham House and PHE) and Hall et al. on medical preparedness.  I would love to see something on lessons for community control…
    • Dynamics of epidemics – within and between.  Every mass-action model I’ve seen used in this outbreak seems to be built from the model in Legrand et al.’s 2007 paper.  Built on the 1995 DRC and 2000 Uganda outbreaks, they find an R0 of 2.7 in both cases.  Good background reading.  For a different take, Thomas House has estimated the gap between outbreaks, and final case and fatality size, based on the past 24 outbreaks.  It mainly seems to highlight the heterogeneity of past outbreaks, but their frequent occurrence. (House paper)
    • What has Uganda done right?  Tara Smith is always worth reading, but she provides a really nice overview of how a nation with endemic zoonotic breakouts (Uganda) has set up structures to manage and minimize harm from them.  (Aetiology blog)

An aside on sourcing.  With apologies, I haven’t cited every claim mentioned in this post, since many are based my reading of twitter posts by people who have been somewhere between highly trustworthy and heroic to date.  Clearly all errors in this post are due to me, not them. A non-exhaustive list of people I place in this category, many of them also cited above:

In journalism, I can also recommend Cédric Moro (often in French) and Umaru Fofana (Sierra Leone) for detailed up-to-the-minute reports on what is happening in the three countries mainly affected.  I know there are many more out there, I just don’t have time to follow them all.

I’d be very interested in hearing about other people producing Ebola science – especially those tweeting results or discussion.  As ever, I’m @harlingg

Ebola roundup #1

I know Haemorrhagic Fever is hardly the wheelhouse of this blog, but infectious diseases come in many forms, and as several people have pointed out the current Ebola outbreak shows the inequalities and weaknesses of healthcare systems worldwide. Thus I’ll argue it fits somewhat within the scope of social determinants of health – especially as/when we finally get behavioural prevention measures. And in the end I’m a nerd for science and disease.

Anyway, the below is a selection of pieces of interest that passed through my twitter account in recent days.  The emphasis is on science, or articles highlighting science.  The quantity of good, useful research on this outbreak is frankly stunning – lead by the rise of (new to me) PLoS Currents: Outbreaks – seriously, check it out.  I know that I am both (a) missing lots of stuff and (b) going on forever, but this is just an attempt to gather my thoughts coherently.  Comments/additions/subtractions very welcome.

1. Modelling the epidemic

  • Reproductive rate (R0/Rt). There has been lots of work on this, unsurprisingly.  Numbers fluctuate a lot, but not surprisingly the rate is estimated as highest in Liberia and lowest in Guinea.  With all the rates between 1 and 2.5, which is promising for epidemic control at some point.  Nice examples include work by David Fisman at University of Toronto (Fisman article), Christian Althaus at University of Bern (Althaus article) and Hiroshi Nishiura at University of Tokyo (Nishiura article). I particularly like Fisman’s figure of the national epidemic curves highlighting possible flattening for Sierra Leone and Guinea in contrast to the ongoing rise for Liberia.  And Nishiura’s dynamic estimates of Rt, building on work by Laura White and Marcelo Pagano, whose HSPH course I was fortunate enough to take a few years ago. I would note that White and Pagano have a new method that is sensitive to spatial heterogeneity that might be very relevant to this outbreak.
  • Predicted epidemic size. It’s really hard to predict final epidemic size when the epidemic is growing exponentially, since we don’t know when the growth is going to slow down.  This has led to a very wide range of estimates for case and mortality numbers.  The latest figures I have seen are via Nishiura’s Eurosurveillance paper, which gives a range from 77,000 to 277,000 additional cases by the end of the year.  For me, the scary thing here is that the lower bound on this is still an order of magnitude higher than what we’ve seen to date.  Very sobering.  Oh, and another recent find (via Stéphane Helleringer) is this interactive, if short-term prediction model from Columbia University.  Test your hypotheses to your heart’s content. Edit: I should note that Fisman’s projections without intervention are ~25,000 cases by the end of the year (and ~140,000 by the end of the epidemic in about 18 months time).  So there’s still a lot of uncertainty on this.
  • Case fatality rate (CFR): initial reports on CFR were vague, then high (~90%) then low (~50%).  But as Maia Majumder at Healthmap neatly showed, once we allow for the lag between case reports and mortality, our current best guess is that the CFR is around 80-85%.  As Maia notes, this is in line with what Médecins Sans Frontières is reporting, and past Ebola outbreaks. (Majumder blog post.)
  • International mobility of animal Ebola. With earlier findings that the West Africa strain of Ebola is linked to those seen in the DRC in the past, there was a nice study led by David Pigott and Simon Hay in the Zoology department at University of Oxford, mapping the habitat of the bats and primates from whom Ebola appears to have reached humans in the past.  The upshot is that there may be 22 countries at risk of future outbreaks, although several of the ecological niches identified are non-contiguous, which much reduces the risk of spread (in particular, there is a large gap between the main historical risk area in Central Africa areas and some small areas on the East Africa coast; and several smaller gaps between the core and the West African forests of the current epidemic – which might explain why it’s taken 38 years for an outbreak to occur in Guinea/Sierra Leone/Liberia).  In any case, I’m a sucker for a good map…
  • International mobility of human Ebola. Obviously much of the world’s most immediate concern is whether the epidemic will reach them.  As many people have pointed out, in countries with strong health systems, even such introductions are unlikely to have anything more than minor impacts on health.  Marcelo Gomes, Allessandro Vespignani and colleagues at Northeastern and elsewhere – showed which countries are at highest risk of Ebola introductions in the near future. (Spoiler: Ghana, Nigeria, Gambia in West Africa; UK and US – but with lots of uncertainty – elsewhere.). My immediate reaction was to ask how much international travel is happening from Liberia, Sierra Leone and Guinea to anywhere else right now, and whether this model is using current rather than historical mobility data.  The Methods in the article aren’t entirely clear on this, but I’d guess they are not building in the fact that right now the only foriegn places you can reach from Freetown are Conakry, Monrovia and on a good day Dakar, Casablanca or Brussels. I stand to be corrected, but regardless it’s a nice article setting up a structure for future analysis. (Gomes article.) I would also note the ongoing work of Flowminder on mapping within-West Africa mobility using cellphone data.

2. Rebutting strange media claims

This category still contains science, but usually being used push back against scare stories.  It’s a pretty large category.  And of course much of material in category 1 is needed to fight misunderstandings, such as border closures, flight bans and fear of touching anyone who has lived in Africa in the past year.

  • Bushmeat. Source of scare: Newsweek.  Twitter hashtag: #newsweekfail. Response: A rebuttal highlighting racism implicit and explicit: Washington Post opinion piece on the Monkey Cage by Kim Yi Dionne (@dadakim) and Laura Seay (@texasinafrica).  See also this discussion on Scientific American and this podcast roundtable (long, behind paywall, but lots of good stuff in there) from Peter Tinti.  On an important side note, bushmeat is often the source of the first case in an Ebola outbreak, but as a recent paper in Science notes, there was almost certainly only one transmission in this entire outbreak from animal to human.  So, roughly 1 animal-human transmission to 5000 human-human ones.  It’s not the bats we should be focusing on right now.
  • Ebola as the Kardashian of diseases. Definition: Kardashian (n.) Famous for no good reason. A bit more detail: The idea that while Ebola is garnishing much coverage, it isn’t killing anything like as many people as diarrhoeal disease, HIV, Malaria, etc. Source of scare: Chris Blattman initially tweeted and blogged this.  It was then picked up by the Washington Post. Response: Initially, a tweet by the aforementioned Stéphane Helleringer showing the potential impact of Ebola if it simply stayed at August rates of infection within Liberia.  Subsequently expanded into articles in English (with the aforementioned Kim Yi Dionne) and in French in Libération (with Dionne and others). Unlike the other points in this section, the discussion was pretty amicable, with Blattman highlighting that his primary concern was around the stigmatizing impact of the only news out of Africa being about a scary infectious disease (cf Bushmeat, above). But the core messages from the responders appear to be: (i) “Africa is not a country”.  I.e. Ebola is serious in a subset of countries, so although across Africa HIV and Diarrhoea are worse, in Liberia they may not be: and (ii) “exponential growth”, so although at the time Ebola was small fry, if it reaches ~300,000 people (see above) and kills ~240,000 (see above), then we are starting to get into the same territory as some of the more pressing causes of death out there.
  • Airborne transmission. Source of scare: New York Times, perhaps set off by research in Scientific Reports from two years ago that showed in a lab the ability to pass Ebola from pigs to primates through the air.  Response: this is the most recent scare, and the science community is getting better at marshaling its forces, so there was a response on Time within the day based on NIAID (National Institute for Allergies and Infectious Diseases) director Anthony Fauci’s press-conference.  There were also blogs up rapidly from Healthmap (again) walking people through the lab trial, and viriologists such as Ian MacKay at ViriologyDownUnder pointing out that virus evolution is generated by selective environmental pressure, and there’s no clear reason why Ebola needs to go airborne to achieve some extremely nasty results.

You may notice one large section missing here – how do we stop the epidemic?  There have been some suggestions, such as: close off the slum with many infections in it (West Point Monrovia, two weeks ago); bring in military resources (MSF, USG, last week); or stop all inter-house movement for three days (Sierra Leone, next week).  But I haven’t seen any science on the topic, and relatively little considering how individual-level rather than top-down behaviour change might play a role.  I know that behaviour change interventions are being implemented by NGOs on the ground, but it is unclear how much these interventions are informed by the specific problems raised by Ebola or if they are informed by past research – not least because such low-level preventative work gets very limited coverage (welcome to the world of Public Health, where treatment >> prevention most of the time).  One thing that immediately pops up on the Google is this Ebola Resource document (via Comminit) from Health Communication Capacity Collaborative based out of Johns Hopkins.  Not immediately clear how solid this is, but at least it’s a start.

Anyway, that’s far more than enough for now. More to follow if I get organized enough next week.  And please do point me towards important articles or conversations: I’m @harlingg.