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.