Ebola round-up #3

A very busy week, both on the response side – with commitments from the US, Europe, Africa, China and the UN amongst others – and on the science side.  On the latter front, of particular note was the release of a New England Journal of Medicine article that represents a collaboration between national governments, the WHO and academics in London.  Key to this work’s authoritativeness is the use of individual-level data to look in detail at the epidemic.  Additionally, the CDC published its first take on the scientific front with a model based on a mix of historic and current outbreak inputs. The plan for this post is first to outline some of the methodological approaches of these two papers, and then to summarize how their predictions/estimates fit together, and fit with other literature.  And then I’ll just keeping adding more topics until I run out of time/room/internet bandwidth.

  • WHO NEJM paper. The WHO NEJM paper has the huge strength of having real data from individual cases in this outbreak, courtesy of working with the governments in each of the three countries.With this data they are able to provide classic outbreak epi figures like the weekly rates of new infections, geographic locations and symptomology. The benefit of all this real data is also the downside – there is very little modelling or projection here.  It’s a well thought-through, very helpful discussion of the data, but not digging into ‘what if’ questions.  For that, we’ll need to turn to:
  • CDC Metlzer paper. The CDC MMWR paper feels like a mixed bag.  On the one hand it carefully models the reported case data from Sierra Leone and Liberia (not Guinea, for data quality reasons?), validating its model based on data up to the end of August on data from first two weeks of September.  On the other hand, it attempts to estimate the under-reporting rate, based on how many people would be expected to be hospitalized given case numbers on August 28th (last day of data used), and compared that to how many were actually hospitalized.  It’s good that the authors have tried to account for missing cases – I haven’t seen anyone else try this yet and its an inventive approach. But this isn’t the most convincing analysis I’ve ever seen – as the authors note, there are several possible biases that would invalidate their assumptions.  I’d love to see triangulation of this assumption using other methods. Their model is an SIIR (susceptible-incubating-infectious-recovered) model with an incubation period of 6 days and an infectiousness period of 6 days – based on past outbreaks in Uganda and DRC – and there are three categories of patient: those in hospital, those at home but with safe care, and those at home without safe care.  The latter group are far more likely to infect than the others.  It then proceeds to consider various scenarios and how they might reduce future spread.  As I will discuss below. Oh, and they provide the model online in Excel format for you to adjust based on your favourite parameter estimates.  Which I can recommend.

A. Modelling epidemic parameters

1. Data quality. As the Metlzer paper suggests, underreporting may mean that as few as 40% of all true cases are coming to light. This seems supported by the large number of cases and deaths found during the “Ose to Ose Ebola Tok” (house to house Ebola talk) sweep conducted by Sierra Leone during the three-day lockdown last weekend. How much longer case reporting and contact tracing continues to be comprehensive remains to be seen; a recent twitter conversation highlighted that eventually the cost-benefit balance may shift from case-finding to screening:

At which point what the data can, and cannot, tell us will change.

2. Reproductive rate.1 The NEJM paper this week provides a great deal of solid evidence on how many new cases are arising from each infection.  The headline figure reported from this work is that the R0 is highest in Sierra Leone at 2.02, lower in Liberia at 1.83 and lowest in Guinea at 1.71. But the more meaningful2 figures right now are those for Rt over the last month of data: 1.38 in SL, 1.81 in Guinea and 1.51 in Liberia. This is more in line with the observation that Liberia’s epidemic is growing fastest, and that Guinea has seen a recent resurgence in cases after a few months where growth was almost flat.

3. Epidemic trajectory. This topic is the one open to the most speculation, since any epidemic curve that hits exponential growth will look very similar; the big questions are: (i) when it will peak out and fall off; (ii) how fast exactly is it growing?  The former question is very hard to estimate since peaks are only hit when (a) the proportion of susceptible contacts begins to seriously decline (“natural” decline) or (b) when control measures kick in (“intervention-led” decline; see section below).  For now, no-one thinks the “natural” limit is going to be reached any time soon, and to understand the impact of interventions, a baseline model without interventions is needed.  Which is what most of the news stories have been covering.  The variation in predictions can be frustrating, but as this very clear article describes, it depends on the date to which numbers are being projected, and the assumptions being made about the serial interval – the length of time between someone getting infected and their contacts getting infected3.  To add a third layer of nuance, it can matter how long one is infectious for (longer time, more chance to infect, so this feeds into Rt); and estimates on this have varied by model:

In this case, the CDC estimates used historical data, while the NEJM used data from the current one.  We should therefore probably trust the latter, except that the NEJM team only has data for a subset of those infected.  So, uncertainty remains.

In order to try and sort out what has been done, I have built a small table that outlines estimates/assumptions and predicted epidemic sizes in the absence of intervention.  I have simplified considerably – most models look at each country separately but I have generally collapsed them together.  But this should give you a flavour:

Ebola case numbers estimatesFootnote: this table is limited, imperfect, and subject to revision.  If I have misrepresented any study, please let me know and I’ll change the numbers.  Refs: EbolaTeam; Majumder; Meltzer; Nishiura; Rivers et al.

My read is that most of these estimates are in the same ballpark – even numbers out by a factor of two only reflects a week or two’s delay in an exponential epidemic – with the exception of the under-report adjusted figures.

4. Case fatality rate.  The good news is that estimates of the CFR are coming together; the bad news is that they are coming together at a higher level than the previously publicized figure of ~50%.  The original figure was arrived at calculating the proportion of of Ebola cases (suspected, probable or confirmed) up to today who have died by today [M1].  Unfortunately, as many people noted, this biases the results downwards since anyone infected within the recent past will not yet have had time to recover.  One way around this is to only include people with a confirmed outcome (death or recovery in the case of Ebola) [M2], but this can also introduce a smaller bias since those who die tend to do so sooner than those who recover.  In a perfect world we would only look at those who have had long enough to die or recover – i.e. build a cohort that stops with those infected (or symptomatic – which is easier to measure) by the date X days before the last day of outcome, where X is the maximum time one can take to recover or fail to recover [M3].

The WHO NEJM paper helpfully provides many measures of CFR (see Table 2 or the extensive explanation in the eAppendix if you are following along at home).  Their M1 is 38% for cases reported up to September 14, which is even lower than the previously accepted figure: but this isn’t surprising once the epidemic has taken off and each week brings more cases than were seen in the previous month. Their M2 is 71% – the headline figure in the press – based on all cases to September 14 with a definitive outcome.  They don’t provide an M3, but if they had looked at the final outcomes for those symptomatic before August 18 say (so allowing for incubation and symptomatic periods to have almost certainly passed) that would be the number they got.  Anyway, it’s worth noting that the 71% is almost exactly in line with the most recent calibration of Maia Majumder’s model (see also a presentation on this work) and slightly lower than the 75-85% I noted last week.  And people working in the field seem to feel that this number is credible.

One side note concerning people is that Sierra Leone appears to have a far lower CFR than Guinea or Liberia, based on raw WHO figures.  The closest I have heard to an explanation for this comes to date comes from Ian Mackay, who noted in a series of tweets earlier this week that SL uses a different definition of an Ebola-related death than the other nations:

I’m not clear if this definition is being passed on to WHO (who take their data exclusively from government sources) , but if so it might explain something.  Or there may be another explanation that is behavioural, data quality or something else…

B. Mapping the epidemic

Our guest topic for the week is maps (because, who doesn’t like maps, they just convey so much information so quickly).  Specifically, I wanted to list out some of the sources I’ve seen that map the epidemic, often in real time.  Also, don’t forget that there are many sites doing real-time epidemic curves and similar (see several blogs I mentioned last week for starters).  But this short list is about geography, and the display of data from different sources.

  • WHO Ebola maps: Datasource: national government reports.These are the maps you may well have seen online, and in the NEJM.  As a bonus, the grey/red overlays gives you a temporal sense of where the epidemic has cooled off, and where it is hot right now.  UNICEF had a slightly different approach which was also very readable, but I haven’t seen any recent material from them; given their focus on prevention including social mobilization and water/sanitation this may represent the UN understandably divvying tasks to the most appropriate agency.
  • Healthmap maps: Datasource: various public media sources, scrapped from the web.  Healthmap has been using this approach to track/predict flu for some time, and has expanded into Dengue, vaccination and now haemorrhagic fever. While there is concern that it may not pick things up all the time (e.g. this piece on non-English language news), this automated approach avoids the need for active data requests.
  • Crowdsourced maps for action:  The opposite of healthmap, in some senses; people actively contributing to a central dataset. The first of these I saw was Cedric Moro‘s e-tracking via OpenStreetMaps, based on a WhatsApp group that reports suspected cases or other activity. More recently I found the Humanitarian OSM team’s work using the same infrastructure.  The latter project is aimed squarely at providing real-time data for people moving around – I can only imagine this links to contact tracing or other control measures.

C. Stopping the epidemic

I would love to write lots about developments on treatments and vaccines, and on the health systems shortfalls and improvements.  But others have this covered better than I (for example, CIDRAP produces a well-curated feed of Ebola-related news), and this post is getting unwieldy.  However, I will comment a bit on:

1. Behaviour change.

There have been several calls this week for commitment to behaviour change.  Given my interest in networks I particularly noted this article from NECSI highlighting the role of barriers and quarantine for preventing spread between communities.  One on-the-ground programme of which I am aware is the effort by Irish NGO Goal to train policemen to provide effective and compassionate quarantining of affected households. I sense that there has been a shift in mood recent days from a rather negative view of travel restrictions/quarantines that was in play earlier in the epidemic to more acceptance of their role.  I’m not clear whether this change is due to fear that bottom-up behaviour change won’t stop the burgeoning epidemic, or to the increasingly militaristic, control-based tone of those “fighting” the disease.  I’m also not sure how I feel about the change: I felt the earlier anti-restriction language was a little too dismissive, but I’m always wary of movements to restrict individual rights in the name of public health. And while I’m vaguely aware that there has been a lot of education efforts aimed at reducing transmission risk at funerals and within homes, this doesn’t tend to hog the headlines, and so I don’t have a good feel for the relative weight of such work, or how the balance is changing.  In conclusion, I look forward to seeing more ideas and approaches in the weeks to come, and hopefully to their discussion in the popular and scientific press.

As I noted last week, Rivers et al. have modelled several interventions already.  Majumder et al. also show on Healthmap that very small changes wrought by generic interventions can have considerable impacts on their models – and thus potentially on cases/deaths.  However, my impression is that the scramble to put in place interventions – particularly top-down distancing (e.g. quarantines) and improved basic healthcare (e.g. this call for simple acute-care efforts) – is not leaving time or political space for evaluation of the relative benefits of different interventions.  The good news, for me, is that we now have several good baseline models which can be filled with realistic assumptions of intervention impact, and thus can provide this evidence very quickly.

D. Miscellanea

1. Historical comparisons.  This seems to be becoming a regular slot.  This week, two papers on case fatality rates (CFR).  First, a brief report by Adam Kucharski and John Edmunds in the Lancet shows a similar CFR pattern in the 1976 Yambuku outbreak to that seen this year: real-time values rose throughout the epidemic, while allowing for the time between symptoms and outcome (in that case a mean of 7.5 days).  And second, a meta-analysis of CFRs for all the past 20 Ebola epidemics (full text behind a paywall, I’m afraid).  The study finds:

  1. variation in CFR by strain (Zaire – the current one seen in West Africa – being the highest). I would caution that may relate to geographic distribution of outbreaks;
  2. reduction in CFR within Zaire strain over time.  I would caution that this may relate to outbreak management learning curves;
  3. a mean CFR across outbreaks of 65%, with a 95% confidence range from 55-75%.  So that would make the current outbreak CFR high, but not abnormally high.

2. Journalism worth reading.  For all my pretentions in writing this blog, there are some people who bring everything they write to life so much better than I ever will.  So here are some pieces that caught my eye this week.

Footnote(s)

1 It occurred to me after last week that not everyone has their head buried quite so deep in math modelling as I do. So for those of you who don’t read these terms all the time, a quick overview. The basic reproductive rate, R0, is the number of individuals who will get infected (on average) by a single infectious person dropped into a population where everyone else is susceptible to infection (and typically not making any specific efforts to avoid infection). If the number is greater than one, then the epidemic expands; if less it dies out. R0 can change with epidemic setting (it’s the product of the number of contacts you have, how likely each contact is to be susceptible and how likely a single contact is to cause infection; so might be different in rural Guinea vs Monrovia) but should be invariant for a given epidemic.

Things get more complicated once people have had the infection and recovered, or are vaccinated/otherwise protected, or start taking evasive maneuvers (e.g. for Ebola, not touching bodies at funerals, not touching other people generally). Now the number of infections generated by each infected person is likely to drop, so now we have an “effective reproductive number” or Rt, which can and will change over time. The threshold of one remains the key to stopping the epidemic. There are bells and whistles, but that’s the basics.

2 When I say meaningful, I mean in terms of how the epidemic is expanding and how much work needs to be done to get it under control – i.e. get Rt < 1 on a consistent basis.

3 In fact, the serial interval can be defined many ways, but the idea is that you measure from a set point in an individual’s infection timeline to the same point in the timeline of those infected by them. So you can measure from infection to infection, or symptomatic to symptomatic, etc. These choices can affect estimates, but not by a lot, and in practice the decision is usually driven pragmatically by data availability.

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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.