Ebola roundup #4

After a brief hiatus, here’s another roundup of Ebola science I’ve seen over the past 14 days. I think fortnightly reports may be the way forward from here on out.

A. Modelling epidemic parameters1

A1. Data quality

A couple of weeks ago I wrote that it seemed like most contacts that were identified were getting traced.  Last time around we started to get into what proportion of cases were actually being identified at all. Caitlin Rivers dug into the county level data in Sierra Leone and Liberia, to show which regions have low case confirmation rates – often despite the WHO reporting that they have adequate lab capacity (Caitlin also has a great post highlighting the importance and difficulty of contact tracing to find these suspected cases).  Ian Mackay proposes that the gap between cumulative case report #s and death/lab confirmations represents a loss of control – a “control gap”. I’m not quite as pessimistic as I read Ian to be: the graph he shows seems to also be congruent with rising case numbers and a fixed lag between suspected and confirmed case reports.  But both the level of, and rate of change in, data quality remains unclear to me.

A2. Epidemic trajectory

The reporting front. As Maia Majumder shows in a recent blog post, the rate of increase in reported cases in Liberia appears to be falling. As she notes, however, it is unclear if this is a function of changes in Rt or the limitations of surveillance and/or healthcare systems. The WHO reports ongoing deterioration in Monrovia, worsening data collection and the continued spread of infection to the far east of the country. In Guinea, cases remain steady, which is raising concern.  The epidemic is focused around Conakry and in the far south-east by the SL and Liberia borders, where the epidemic began early this year.

The modelling front. As I posted a table last week, others (the European Centre for Disease Prevention and Control, to be precise) were moving rapidly to produce a wonderful figure showing weekly case reports to date, and the projected figures going forwards.2 Notably, as I suggested last time out, while the predicted numbers in the absence of intervention vary greatly in absolute terms, they all point in roughly the same direction on a log scale.

A new paper this week looking at 72 linked Sierra Leonean viral sequences (from the NEJM paper set of 99) estimated an R0 somewhat higher than the average of existing models (2.18 but with wide confidence bounds). This may reflect the success of the sampled cases in spreading (i.e. observed chains will contain successful spreaders more than a random sample would), especially given the variation the authors find in secondary cases arising from this sample.  (Stadler et al. paper)

A3. Case fatality rate

Over the past couple of weeks, Sierra Leone’s oddly low case fatality rate (CFR) has come under scrutiny.  It appears that the previous Minister of Health had a policy that only confirmed cases who died within healthcare facilities counted as Ebola-related deaths. Those who didn’t make it to a hospital did not count (~50% of the first 2000 confirmed cases).3 The upshot: the method has now been changed, and the old data will be updated at some point in the future.  But the cumulative CFR in SL remains far below that of Guinea and Liberia.  So far there has been only a small up-tick in SL CFR based on WHO numbers – see 2nd figure on this blog post – but the latest numbers from the MoH in Sierra Leone are considerably higher, so expected change in the near future.).

B. Visual descriptions of the epidemic

A new entrant: the ECDC make really nice maps that merge the current and the historic totals in a simple but effective manner.  Well worth checking out.  See also last week’s post and its section on maps.  The figures produced in the WHO’s bi-weekly sitreps – particularly their Wednesday ones (e.g. this week and last week [pdfs]) – are also well worth looking through.

I’ll also give a shout-out to Shane Granger‘s latest work from the Liberian government figures, which clearly shows the variation in cases and deaths across the country.

C. Stopping the epidemic

C1. Tools

Social and Behavioral Change Communication. There is much theory on communication for change, some existing materials (e.g. the Health Compass) and active communities discussing and implementing programs (e.g. Springboard).  I’m not convinced that theory is being used to build many of the on-the-ground programs, which is a shame, but something roughly right now may well be more important than something perfect in a month’s – or even a week’s – time. Paint me ambivalent, but also short of time to dive deeply into this field.

Cellphone data. A commentary last week highlighted that if we are planning on restricting movement, or predicting where an epidemic will spread next, call data records (CDR) may provide excellent real-time evaluation of areas at imminent risk of infection (based on rate of communication between areas – for which aggregate data are sufficient – and numbers of cellphones present in multiple locations), and potentially also of the impact of behaviour change interventions (based on mobility of cellphones – with provisos that multiple SIM cards may affect estimates).  CDRs definitely hold potential as a planning and evaluation tool if structures can be created for passing information smoothly and rapidly from providers to researchers and policy makers. (Wesolowski et al. paper)

C2. Strategies4

Quarantine. Since late September Sierra Leone has restricted movement for about one-third of its population to within their home county. In contrast to the 3-day lock-down of the whole country, the aim here is to keep potentially infected (but not yet infectious) people from spreading the disease by moving elsewhere and then becoming infectious.  It is unclear how much additional benefit that will bring over and above existing social distancing measures (e.g. no physical contact), but as with many interventions, the goal here may be reassurance rather than direct risk reduction.

“Community care”.  In the absence of sufficient hospital space, there is a move to provide “home-care kits” to reduce the risk of within-home transmission.  While no-one thinks this is a perfect solution, it might provide stop-gap benefits.  And as Mead Over at the Center for Global Development argued with his usual great cogency, imperfect interventions that get Rt below 1 may be good enough – even if they aren’t ideal.

C3. Social epidemiology(!). I was thrilled to see Daniel Hoffman had put together an ecological analysis of county-level Ebola growth rates and poverty in Sierra Leone (finding: a positive association). As Hoffman (and Caitlin Rivers) note, this is nothing causal, but we might not be surprised to see poorer districts doing worse at controlling an epidemic – especially if poverty is higher in rural areas far from the resource-rich capital.  In any case, it’s great to see people thinking about social determinants of health within countries, as well as Paul Farmer and others at Partners in Health highlighting how poverty made the most-affected countries vulnerable to Ebola in the first place.

D. Miscellanea

D1. Historical evidence

A couple of quick links:

  • Evidence from the 2000-01 Sudan outbreak that treatment needs for children are likely to be different from those for adults. (McElroy et al. paper)
  • Contact tracing based on shared airplane space for viral haemorrhagic fevers can be limited to cleaning and flight crews, those in direct contact and adjacent seats only. (Gilsdorf et al. paper)

D2. International travel restrictions

One resurgent debate – in light of the recent imported case to Dallas, TX – has been over shutting down air-travel to/from SL, Guinea and Liberia.  Views on both sides seem very strong, and not particularly evidence-based.The argument about restricting the arrival of Ebola support I think we can leave to one side – if restrictions were created, exemptions could be made for vetted, relevant resources (including human resources). The main remaining pro-restriction argument appears to be that, unlike border closures, ending air-travel would probably reduce risk in Europe/US/not West Africa. And linked to this, screening at point-of-travel isn’t very useful for catching Ebola cases.  These points seem valid, but not particularly important, since secondary and particularly tertiary infections from imported cases look very unlikely in settings with robust public health systems. On the down-side, restrictions would have potentially significant effects on trade for the West African region (not just the core 3 countries, where economic activity is already hugely affected), in addition to the likely increased stigmatization of nationals of many West (who are we kidding, any and all) African countries, even if they haven’t been in (West) Africa in the past month/year. But how does this trade-off against people reacting worldwide in (quite possibly unreasonable) fear of Ebola, and thus restricting their economic activity.  Am I missing a key point here that swings the argument?

tl;dr:

D3. Journalism

There were a couple of big popular press pieces this week that really helped people understand the epidemic.  Those I noticed included:

As ever, comments very welcome, either here or @harlingg.

Footnotes

1 For more regular and more comprehensive updates than I can produce, I continue to strongly recommend Ian Mackay‘s blog Virology Down Under and ‘s personal blog Mens et Manus for great up-to-date temporal coverage of the epidemic.  You might well also like to read Caitlin Rivers‘ series of posts on her blog that focus on less-considered aspects of the epidemic, often from a quantitative standpoint.
2 This figure also pointed me towards the broader work of the ECDC, which includes providing downloadable data and very regular updates (e.g. this week’s ECDC Ebola update). ECDC are also the publishers of Eurosurveillance, one of the key journals for rapid publication on Ebola.
3 As a side-note, this proportion may be a useful input to models attempting to understand how short of capacity healthcare systems currently are.
4 A review of the Nigeria outbreak by Folorunso Fasina and colleagues earlier this year highlights the role of contact tracing, isolation and quarantine in stopping the chain of infections. I’m not sure how applicable this approach is to the current situation in Guinea/SL/Liberia, but it does highlight the importance of early, forceful action.

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.

Links roundup

A few things that passed across my rss feed/tweetdeck/other input strand recently:

  • Thoughts on predicting which mega-urban areas are most vulnerable to epidemics (h/t Matt Watson, @BioAndBaseball).
  • Kings Fund study finds increasing inequality in behavioural risk factors by income & education (cf Victora’s Inverse equity hypothesis).
  • A new data visualization tumblr from the World Bank.
  • An brief-ish article (paywalled, sorry) that promos a new book on envisioning Public Health ecologically, from two UK Food Policy researchers.