Ebola epidemiology roundup #7

Here’s another synthesis of Ebola science news from the couple of weeks.  If you’re interested in slightly older material, there’s lots on past papers and news in the six preceding posts – links at the bottom of this post.

A. Charting the Epidemic

I’m reorganizing this section going forwards, to drop the part on ‘data quality’ – it’s getting boring to say “it’s not good, and probably getting worse” – and the modelling part on epidemic trajectory – a corollary of the data quality.  One thing I will note here though, is how easy it is to misunderstand the role of “curve fitting” early in an epidemic.  There was a news piece in Nature last week entitled “Models overestimate Ebola cases“.  I imagine that this was written to be provocative, but as many commenters noted on twitter (sorry, I don’t have specific tweets to hand), this is more a statement of fact than of surprise.  These efforts aim to estimate disease parameters, and to give some idea about a worst-case scenario.  They can never predict when the exponential rise in cases is going to occur, and aren’t aiming to do so.  Some commenters on the Nature post brought up the George Box quote: “all models are wrong, but some are useful“.  But I would argue that in this case the models were right – they got us to things like the true R0 or CFR – but people were looking to them to provide answers to questions that were not being asked, and could not be answered, by these tools.

A1. Epidemic trajectory

As a new cluster of cases emerges in Mali, the epidemic situation in Liberia may be diverging somewhat from Sierra Leone and Guinea.  The initial reports of empty ETC beds have continued, at least in some parts of Liberia, and consideration is now turning from stopping runaway growth to putting out flare-ups as they arise. The news reports are now bolstered by reports from the field in both Lofa and Montserrado counties that reported cases, hospital admissions and positive tests are all on a downward trajectory.  However, this doesn’t rule out citizens deciding to distance themselves from treatment units, and the decline appears to have stalled this week.  I think we need more information, preferably from those on the ground, before we can reach conclusions on where Liberia’s epidemic is going.

In Sierra Leone, in contrast, reported cases have been at or near record levels in the past two weeks.  While control measures appear to have had an impact in the north-east (Kenema and Kailahun) where Ebola first reached SL, it remains considerable in and around Freetown, and has made serious inroads into the previously (officially) unaffected Koinadugu. Cases in Guinea are not rising rapidly, but incident numbers continue at approximately the same level as in recent weeks.

I would also highlight that the WHO SitRep page is much improved, having shifted from pdfs to a webpage for each report, making it considerably easier to read and digest the information they provide.

Finally, way back in September we were discussing how serious Ebola was, compared to other causes of mortality.  Despite a Vox graphic highlighting the very small impact on the continent as a whole, I recently saw an image outlining the relative impact of Ebola on monthly mortality in the three most-affected nations. The takeaway: at this point, it’s as/more serious as malaria in all three countries, and as HIV in Sierra Leone and Liberia.

A2. Epidemic parameters

There was a small-ish set (80 patients with symptoms, 37 confirmed with Ebola) of clinical data reported by Elhadj Bah and colleagues from Conakry last week (Bah paper).  The data provided on disease timings were in line with previous reports, although the mortality rate of 43% (a rate the authors see as still too high) was impressively low, possibly a result of aggressive hydration policies.

Aside from the Conakry data, the only new thoughts I’ve seen on epidemiological parameters recently came from a paper by Joshua Weitz and Jonathan Dushoff on the arXiv.  The authors highlight that since there are multiple possible channels for infection, incidence case data is insufficient to allow the identification of the relative importance of post-death infection (or any other single channel).  However they show that in the data for this year’s outbreak is inconsistent with a model that doesn’t include such a source of infections (Weitz paper).1

I would also note that Maria Kiskowski‘s paper on network-structured epidemic spread, that I mentioned a few weeks ago, is now published in PLoS Currents Outbreaks.  Go read it is its updated, formatted form.

A3. Virus biology

After the early concern about mutation of the Ebola virus for airborne transmissibility (spoiler alert: very unlikely), things went quiet for a while on the biological front.  However, with the ramp up in searches for treatments and vaccines, there has been increasing interest in viral genetics and pathways. This is not an area I know much about; but I should do.  So the material in this section may be a little simplistic for some of you, or a little more comprehensible than usual for others.  Corrections and clarifications are welcome.

Individual disease progression: Helen Branswell provides a tour-de-force overview of how the Ebola progresses through its human host.  She highlights the importance of dose, age and genetic susceptibility in terms of clinical outcomes, but also how Ebola disables, misleads and co-opts our innate immune system.  For a slightly more technical explanation of the same, Rachel Cotton posted a nice overview of Ebola’s immunopathology on PLoS Student Blog in June. And for something more detailed, here’s an extract and link to a more academic paper on how Ebola affects organs once lodged therein.

Population genetics: There has been discussion in recent weeks about the absence of enough genetic samples to allow good analysis of how Ebola is changing over time. While data is now being brought together in central locations (e.g. UCSC’s Ebola Portal, described here), a debate is beginning to emerge over how fast the disease is evolving.  Gire and colleagues had previously suggested rapid evolution.  Using the same data, two papers this week come to very different conclusions.  Marta Luksza and colleagues suggest that 3 distinct clades were present in the 78 genomes Gire et al used, and that the most recently arising clade has a higher reproductive number, potentially leading to it dominating the others within a few months (Luksza paper). 

B. Visualization

I seem to find a new visualization every week, and include a couple of new ones below.  But I keep coming back to the ECDC‘s approach, which neatly combines space and time to give an immediate idea of how things are changing, at least according to the data available:


An alternative to trying to get everything into one image is to provide multiple time-windows side-by-side.  As MapAction do here for Sierra Leone:

MapAction SL 15Nov2014

I’ll also flag the map of cumulative incidence by county on the Humanitarian Data Exchange’s Ebola page, which also has masses of (well, 41) links to datasets relating to the epidemic.  And what looks like a dynamic version of the standard Ebola map the WHO uses for their SitReps.

C. Stopping the Epidemic


Bah et al’s data from Conakry, combined with a qualitative assessment of clinical experience in Monrovia by Daniel Chertow and colleagues, seem to point to central importance of massive fluid loss (via diarrhoea, and eventually cell destruction) and shock in clinical management (Chertow paper).  Avoidance or management of fluid loss appears to be central to improving outcomes, as was also highlighted in the Bah paper, and in a very different context by doctors at Emory hospital and elsewhere in the United States.2  Knowing what to do is one thing, achieving it quite another: as this NYT piece highlights, intensive care requires lots of staff, especially when they are all in PPE at >30 degree Celcius (and rising over the next couple of months). So the fix needed isn’t knowledge, it’s better ways to meet the known need.

Nevertheless, if medications can be found that reduce the initial dehydration, that would be very helpful.  On which front, the announcement by MSF that they will be hosting three treatment trials – using historical outcomes as the control group3 – is of interest. One trial will look at using serum (concentrated blood) from recovered patients, while the other two will use antivirals proven to help with influenza, pneumonia and other infections.  Fingers crossed.

And of course, care needs to extend well past the door of the Ebola Treatment Unit/Centre (ETU/ETC).  As the latest infected doctor (who was not working at an ETU) was flown from Sierra Leone to the US for care, there was a piece in MMWR this week reminding us that the greatest risk for healthcare workers early in the epidemic arose from non-ETU settings, where PPE is not being routinely used. The setting of precautionary measures at Community Care Centres (i.e. where suspected or infected persons can be supported in the absence of ETU) is particularly worthy of attention.

While all of the above is crucial to treating infected persons, there continues to be strong evidence that hospital treatment alone will not be sufficient to curtail an epidemic as generalized as the current one.  John Drake and colleagues have a paper on the arXiv this week that uses a generational branching process approach and the construction of plausible parameter sets (as opposed to fitting the model to observed data).  They find that anything short of rapid hospitalization of 85% of all cases will not get R0 under 1 any time soon. This is a very big/rather unlikely ask.

Given the consistent finding that treatment alone is unlikely to end the epidemic rapidly, three strategies remain available: (1) change behaviour; (2) contain the at-risk population; (3) vaccinate widely.  I’m afraid I don’t have much new news on (1) right now, although there was a nice article in the New Yorker on how media conversations around Ebola are being built.  But here’s some recent thoughts on (2) and (3).


Contact tracing appears to be quite possibly both the most important and the least-well executed part of the epidemic response to date.  While much input from the CDC and others has provided an infrastructure for contact tracing, twitter sources I can’t immediately track down seem to suggest that on-the-ground effectiveness may be hamstrung by resource availability, client fear (perhaps by association with those who are working on Ebola) and possibly a lack of funds.

Given Nigeria and the DRC’s successes in snuffing out outbreaks, comparisons are being made – with some attributing Nigeria’s success (their Re is estimated to have been below 1 within 15 days of the first case) to their experience in tackling polio, and many attributing the DRC’s to experience of past outbreaks. There are also descriptions of how case management proceeded in Dallas, TX and Ohio, after the 3-case outbreak in Dallas last month.

However, it looks like the big problems are logistical and indeed sociological, rather than a knowledge-gap.  In which case, it’s back to figuring out how to adapt existing models to local situations.


There are many vaccine efforts underway, including safety trials in Germany/Switzerland/Gabon/Kenya and Mali. for the two most advanced candidates; there are many others in development.Trials will start late this year/early next year, probably initially in healthcare workers.

Social epidemiology.

I’m broadening this category out a little to cover anything that provides insight by drawing on human behaviour, or how Ebola draws out existing problems/opportunities within the affected communities.

And for a lengthy, considered and involving read on this topic, I can strongly recommend Kathleen Alexander and colleagues’ pre-acceptance paper in PLoS Neglected Tropical Diseases (Alexander paper). After explaining how the biology of this outbreak looks much like earlier Ebola episodes, the authors consider many of the possible ways in which human behaviour, and its interaction with the environment (including fauna), might have made this event so serious.  Topics covered include climate change and human landscape change (for initial zoonotic infection) and population density, human mobility, civil unrest, cultural practices (including burial and healthcare seeking behaviour), food consumption and fear.  The article focuses on scope-of-inquiry than detailed examination of specific issues, but highlights the importance of examining a wide range of factors for understanding this epidemic, and for early detection of the next one.

On the mobility front, there was an article this week out of CIDRAP, highlighting the potential for increased infection spread as seasonal migration from home farmlands to commercial employment sites begins post-rainy season.  Another reminder of the importance of local knowledge in fighting the epidemic.

And on a historical note, Mark Honigsbaum drew parallels last week between 19th century yellow fever and cholera outbreaks and the Ebola epidemic today.  He highlights the fear of disease importation, the stigmatization of the sick and foreigners, and sacrifices of those who either chose or had not choice but to stay and help.  A timely reminder.

D. Miscellanea

D1. Risk communication

I’m rolling material on movement restrictions, aside from actual evidence of their effectiveness, into here since so much of this topic is about perceptions and communication.

There’s an interesting conversation developing around what those with full information about the epidemic (i.e. who have really studied the evidence) should be saying.  On one side are those who argue for a low-key reaction, based on fear that quarantines and travel bans will reduce our ability to respond to the epidemic and that such bans will lead to stigmatization and is being used for political ends. On anoother side are those who worry that a failure to highlight and act to prevent the possibility of severe (but maybe not as extreme as many think) scenarios, such as a widespread outbreak in the United States, is a significant failure of  the part of the public health services. There may well be other sides I’m not seeing.  My impression is that people are seeing roughly the same information but drawing rather different conclusions, perhaps based on their professional expertise, personal experiences and risk tolerance. But however heated the arguments, I’m glad to see the strong use of evidence on by all: a common language is the first prerequisite for a productive conversation.

In this context, I read two very insightful articles this week.  One by Lisa Rosenbaum in the NEJM noting that officials often prioritize certainty in their predictions (e.g. there’s no way there will be a large-scale outbreak in the US) for fear that expressions of doubt will be interpreted as a lack of expertise.  Rosenbaum raises two key points: first, that conveying uncertainty can be reassuring if done carefully – too sure an assurance can sound like it is hiding the truth; and second, that it is crucial to sensitize the public to the reality that facts may change, and when they do, so will your message (and that this is not a sign of ignorance).  The second wasn’t directly about Ebola, but reminded me that when it comes to changing beliefs, we cannot assume that everyone is simply an island.  The paper by Aris Anagnostopoulos and colleagues looks at how misinformation is spread across a network (in this case Facebook), and shows the importance of homophily (Anagnostopoulos paper). As can be seen with measles outbreaks in the US, those who question public health messages are non-randomly distributed and tend to cluster together.  Thus even when the great majority of people can be inoculated against disease – either through a vaccination or through sound advice – some groups will require special attention and quite possibly different messages to affect change.  This came to mind when reading a report earlier today by Timothy Robertson relating to villagers in rural Guinea over the summer.  The villagers could not be persuaded to allow health workers to enter the village for some weeks despite deaths from Ebola, due to circulating rumours of body snatching, a history of harm arriving from outside, and the absence of a trust relationship between HCW and villagers. The upshot was additional deaths, and then a new approach that allowed villagers to participate in burials by HCWs that began to build a relationship. We keep on learning.

D3. Journalism

It’s been a quiet week or two for me on the generalist literature front.  Laurie Garrett has been writing lots in Foreign Policy while visiting West Africa. Jina Moore‘s still there and also writing frequently.  And I’d note that a prominent journalist David Tam Baryoh just spent 10 days in jail in Sierra Leone, purportedly for being a little too pointed about the government’s Ebola response.

E. Getting involved

For those of you in the local area, there is going to be a weekend-long hackathon in Cambridge, MA on 21-23 Novemeber. And it sounds like Massachusetts isn’t the only place jumping on this:

Specifically, I see a link to a hackathon in San Francisco this coming weekend. As well as data hacking, universities around North America have been thinking about engineering hacks, including coloured bleach for disinfection and more ergonomic PPE.  Lots of ways of using whatever your skills might be to lend a hand.  And there’s always donation if time is short.

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 whose tweets and posts I’m building this from.

There are previous posts in this series: #1, #2, #3, #4, #5, #6; and a summary of data/research sources.

1 I also liked this article on how we don’t really know the mortality rate of Ebola in the US yet – too few cases to draw trustworthy conclusions…
2 Inside baseball alert. Weitz and Dushoff also neatly show the importance of using a non-exponential incubation time, suggesting a multi-class gamma function instead.
3 On which topic, see this discussion in Nature on the discussion of the ethics of control groups in Ebola trials.


Ebola, -isms and dialogue

This story doesn’t really fit with the series on Ebola science (new one of those almost ready to roll), but it got me thinking, and I wanted to give my thoughts a chance to breathe.  Hope it’s of interest.

A couple of weeks ago, Anthony England (from the UK) posted a picture of Africa and where the current major Ebola outbreak is occurring, to highlight the situation of Susan Sherman, a Kentucky teacher who had been working in Kenya, but whose school was sufficiently concerned about her potential exposure to Ebola that it asked her to stay away from work for 21 days. She resigned, apparently due to feeling an absence of trust and respect.

The picture above, and the subsequent sales of t-shirts containing the image (for the benefit of the Ebola response effort), did not go down well in West Africa.  In the context of travel and visa bans for citizens of Liberia, Sierra Leone and Guinea, and the local response campaign of “I am a Liberian, not a virus“, such a visual appeared tar whole countries with a single brush.  An adjusted version of the image was suggested by Cédric Moro:

Reading the twitter conversation between England and Moro, it was clear that no such message was intended, and England has subsequently clarified his image to make a more nuance point:

My first reaction to this narrative was that it represented twitter at its best: the rapid dissemination of efforts to improve knowledge (by both parties), the overcoming of an initial unintended consequence, and progress to a better final outcome.  Synthesis, growth, happiness, etc. But then I can be a crazy optimist like that.

However, a second important point occurred to me subsequently while reading about #shirtgate (follow this link with care, many trigger warnings) this week.  A short version of the tale: scientist working on the Rosetta project comet landing wears a shirt made by a (female) friend to help promote her products, which offends many, he apologises profusely the next day.  Reading a blogpost in another context, I was reminded of “unexamined decisions”, how as someone not directly impacted by many of society’s -isms, it is easy not to see how your actions might be seen, or what unintended consequences might arise.  In the context of the Ebola outbreak, there is now clearly “ebolaism” at play, which builds clearly on existing stereotypes regarding race, poverty and “exotic” groups (i.e. those not like me).  Which reminded me of the importance of community engagement in the research field I work in (social determinants of health): how preconceptions and post-conceptions of the researcher need to be passed back past the groups under study, to see if our impressions are correct.  As an external researcher, I should always assume that I am missing some meaning in the data I am analysing, and seeking to understand these lacunae.  And this goes for all of us working on Ebola, for sure.

As Bob Hoskins/British Telecom once said, it’s good to talk.

P.S. If you are interested in thinking deeply about research engagement, I can strongly suggest the work of Heather Lanthorn, for example see this recent post.

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”.