Happy 4th birthday...

This blog!

I'm sure somene one said that a year in social media equates to 7 cat years. Maybe it 9.

Anyhoo, I'll be hunting down a cake for us all today - or maybe a muffin at the canteen.

My heartfelt thanks to those who have made writing for this blog so much fun. 

I hope we all keep learning about viruses together. Anything is worthwhile if you learn something from it. ...and have a reference to prove it was based on data...and cite that reference...and it gets cited by others...and peer reviewed....

Happy 4th!!

Some changes to my Ebola virus disease (EVD) graphs...

To perhaps provide clearer info and to accommodate the changes in the epidemic, namely the reduction in cases and the focus on ridding Guinea, Liberia and Sierra Leone from any and all cases of EVD, I've made some tweaks to my Tableau data visualizations (or dashboards). Briefly...

The dots take their leave.

Was this.

Gone are the dots in my cumulative chart, to be replaced by a third "area under the curve" style graph. 

This brings out the importance of the confirmed cases-more on why that matters later. This week Cedric Moro @Moro_Cedric) asked why we seem to have a relatively large number of suspect and probable cases released in each report World Health Organization situation report (WHO SitRep) or summary (SitSumm). I imagine this is due to the turnaround time once the sample arrives, occasions when results may need to be repeated to confirm strange results, time between seeing a patient and sampling them for Ebola virus testing...but there are probably more obvious reasons. Chime in.

Is now this.

Plot the right data for now.

I'm not an epidemiologist - yes, I know you epidemiologists out there already know that. But I like to play with numbers and pretty colours. So this week I got some information that I didn't have before - the reason why use of cumulative curves was frowned upon by the excellent numbers communicator, Prof Hans Rosling (@HansRosling). 

I had read previously that Prof Rosling was no fan of cumulative curves in graphically explaining progress in ridding west Africa of EVD. But I like them - I've even explained, in my epidemiologically unprofessional opinion - how a flat plateau on a cumulative curve clearly shows the stalling of an outbreak or epidemic. Turns out I either didn't read all of that quote, or the text I read didn't contain the key fact. 

It's not really that cumulative curves are at fault, it's what they are plotting that can mislead. The important thing to plot, especially now that cases are fewer and laboratory capacity is in place, are the confirmed cases, not the total cases which include suspected+probable+confirmed cases.

Confirmed cases are Ebola virus, unconfirmed cases may never be.

In the last WHO SitRep (14-Jan-15) it was noted..

All 54 EVD-affected districts (those that have ever reported a probable or confirmed case) have access to laboratory support within 24 hours of sample collection.

"Access" doesn't mean a result will appear 24 hours after sampling though. But even with this shorter access period, suspected and probable cases are in fact still making up a decent proportion of the total cases reported in even the most recent reports. For example...

• In Guinea the numbers between 14-Jan-2015 and 15-Jan-2015 saw suspected cases rise by 3, probables stayed the same and confirmed cases lifted by 8; 27% of the total cases reported between this pair of reports were not confirmed to be Ebola virus infections, at the time of reporting. 
• In Liberia over this period, suspected cases rose by 29, probables by 2 and confirmed case numbers did not change-so none of the 31 cases were laboratory confirmed as an Ebola virus infection. 
• In Sierra Leone over this period, suspected cases rose by 10, probables remained the same and confirmed cases lifted by 16; 38% of the total cases were not confirmed to be Ebola virus infections.

If we compare those figures to 2 SitReps from well before the WHO had declared the 24 hour laboratory support, dated 24-Sept-2014 and 26-Sept-2014, we find that Guinea only had 8% of its tally unable to be confirmed, Sierra Leone was at 12% not confirmed while 87% of EVD-like cases added to Liberia's tally between reports were not confirmed as due to and Ebola virus infection. 

This may not be a fair comparison of course and it's not one that accounts for every report - just the 2 pairs of reports I arbitrarily chose as being from 'now' and 'back then'. Nonetheless, I expected there to be a bigger and more obvious difference in the proportion of cases that were now being quickly confirmed-I thought that percentage would have gone up as the unconfirmed cases were less frequent. Instead, it seems that the proportion is not that much better. Perhaps this is an indication of the other diseases which mimic EVD early on, that normally emerge at this time of year or have emerged because of the state of healthcare in the countries blasted by the EVD epidemic. As I said above, it may also just be the time it takes to observe, collect a good history and make a clinical decision before a sample is collected. It may also be that laboratory turnaround times (including testing, verifying and reporting) take a bit longer than we naively expect from reading that quote from the WHO above.

More visualizations of confirmed case numbers.

So for the reasons above, I've added the changes I've mentioned and I've also duplicated some of the "total case" graphs by creating versions that only include confirmed cases. 

In the example below I'm showing that it looked like Liberia was experiencing an uptick in cases for 2 consecutive reporting weeks (blue bar graph, right column). I tweeted about this during the week. In fact, those rises were due to unconfirmed cases. The confirmed case plots (green titles in the right-hand column of graphs) show the consistent decline in new EVD cases we had been hearing about. 

Live and learn.

Graphs plotting total EVD cases (including suspected, probable and laboratory confirmed;
brown title bars, left-hand column) versus graphs plotting only the laboratory confirmed cases
(green title bars, right-hand column). 
Data are from WHO SitReps and SitSumms
Click on image to enlarge.

A model of MERS-CoV acquisition (ver1)

With thanks to David Spalten (@dspalten) for discussion and considerations and AtRG for advice.

First we heard about Middle East respiratory syndrome coronavirus (MERS-CoV)-related viruses in bats in South Africa, then we read of antibodies in camels that reacted to MERS-CoV more than the most likely (known) other CoV to infect cattle, and most recently we were absorbed by the discovery of a probable parental strain of the MERS-CoV in the faeces of a Taphozous perforatus insectivorous bats.

We've also heard that most patients have not had direct or obvious contact with bats and we also know that pasteurised camel milk products should be safe. But that still leaves many stones unturned.

So if we can assume that the most likely route of acquisition of MERS-CoV is through the upper respiratory tract and that the spillover events come from animals (I'm including human-to-human exposures in this figure) then we need to consider how that might happen. I've included the animals above as well as baboons as they seem highly mobile, interact well with humans, visit mountains and caves (where bats are likely to hang out") and are found in the KSA. I've added ingestion but I don't really imagine how this could result in a respiratory infection, and MERS-CoV gastrointestinal involvement seems infrequent.

I don't live in the Kingdom of Saudi Arabia or in the Middle East and I do not profess to know much of the environment so what follows is "remote guestimation" at best. But I've thrown together some of the possible routes and animal players into a figure which may have some degree of reality buried in there somewhere. It may also spark an idea or two among those who do know what they're talking about.

So, here is my model of how humans may indirectly get a zoonotic infection from a primary or secondary animal host...

A model of MERS-CoV acquisition. Click to enlarge.

I'd be most happy to take suggestions for improvement of the figure. I know some of you like to use the graphics from the blog and Virology Down Under (which I strongly support, asking only for a specific reference to their source) so if they can be made more robust, I am very happy to do so. Get to me via the comment section below or on Twitter (see top right).

Editor's rant: Why testing the few may not benefit the many...

Prospective screening without regard for whether the person is sick. That's what I think we need more of, in order to truly understand respiratory viruses and acute respiratory infections (ARIs).

And I don't just mean the scary ones like MERS-CoV or influenza A(H5N1) virus or H7N9 or H7N7 (zoonotic flu). 

I also mean the rhinoviruses, influenza A(H3N2) virus, H1N1 (seasonal flu), endemic coronaviruses (CoV; 229E, OC43, NL63, HKU1), metapneumoviruses (MPV; I use the plural because there are 4 genotypes and who knows how many immunogenetically distinct clades), respiratory syncytial viruses (RSV; same plural), adenoviruses, enteroviruses, Saffold viruses, parechoviruses, polyomaviruses, bocaviruses...etc.

Sure, there have been studies in birth cohorts and in the community among "normal"'healthy people. But they sometimes have limitations that may blur our view of what is really happening in the community. For example, such studies may:

• Exclude certain diseases that viruses are involved in.
• Only sample when there are signs and symptoms of disease. 
• Only call "disease" when a certain number or combination of symptoms are present (this one irks me no end - pedantically, if your body deviates from its physiological norms, you are diseased).
• Sample too infrequently to catch whats going on between sampling points. We don't get one virus, recover, then get another - we're a virus's favourite hang out - but because of our awesome immune system, only some of those infections make us ill enough to stop and groan.
• Employ insensitive detection methods (cell, tissue or organ culture). In fact, if a study used culture you might as well ignore those data - they will have missed many fastidious viruses (those that don't grow easily or in the cell lines used) rendering any conclusions associating detection of a virus and disease weak or wrong.
• Sample for too short a period or just focus on a particular season etc. 
• Only include a pet virus or a few viruses or just those viruses known about at the time. 

Much of our understanding of each virus comes from hospital-based studies. People in this environment, whether admitted (inpatients) or presenting but being allowed back home (outpatients), represent the "tip of the iceberg" of the disease spectrum. The pointy end. The most severe cases. We may make the assumption that the viruses circulating in the community are represented by what's happening in a hospital environment - or vice versa. But how often have we tested that? Do we know if there is a lag or lead time? Does it differ by climate? Could we go further and perhaps use those numbers to predict what the burden of disease in hospital will be this "season"?

And then there's the ongoing testing issue. Research dollars generally do not fund epidemiology. Certainly not ongoing epidemiology. Even big hospitals and private testing labs cannot afford the personnel and cost of testing all respiratory samples for all "likely" viral pathogens, all the time. "Likely" having been defined with the caveats above. 

And so our epidemiology data have holes. Big ones. We read of complaints about some countries not being able to identify a viral/bacterial cause (not that a POS lab test does prove cause) of pneumonia or encephalitis...but many patients in more "developed"countries also leave hospital without ever being attached to a lab-confirmed positive result. We could reduce that, even if we could not specifically treat them. And therein lies another issue. We test for some viruses based on historical precedent - do those precedents accurately stand up today? Do we even have the data to answer that? If you are a health professional, have a look at what your local testing lab offers - does it cater for the most likely causes of ARI or just what's been used before? 

Click image to enlarge. Respiratory virus infections among the community
and in hospital-based populations. Generally more males than females
present to hospital  with clinically-defined acute respiratory infections
(ARIs). Infections in the hospital setting are shown in red, those in the
community in orange.  Most ongoing virus testing is from
hospital-based populations as is our contemporary
understanding of viral season.

Notifiable viral diseases are kept track of, and if they occurred by themselves without interaction with, or interference from, other viruses that might be enough. But they don't. 

The "One World" concept of infectious disease study - looking at animals and humans and the environment together - is great; what about the concept of "One Virus"? The days of a study looking at just one virus and from that, without testing for any other respiratory virus that may cause the same signs and symptoms, concluding what that virus is capable of, it's severity and how many cases of disease are lessened by a drug for it, in a human should be far behind us. But they are not. 

So, do we really know the viruses that call us home? And if the answer is no, how can we possibly hope to be prepared for the next virus that emerges, the next local viral outbreak of ARI or encephalitis or gastroenteritis, or the next pandemic? How can we protect our population from viral threats if we're always on the back foot?

If I ruled the world, we would do more testing we'd try not to bias our attentions toward any 1 virus, we'd screen everything for everything, we'd sample the community, we'd make the data publicly available in real time, we'd understand ARI epidemiology better and we'd use all those data to prioritize some antiviral drug development or other viral interventions. At the very least, we'd re-jig our testing panels and create a new paradigm or 2.

But I don't rule the world - nor do I have input into these sorts of decisions - perhaps you do?

Feel free to weigh in below.

Editor's Note #6.

I've finally started to rebuild the rhinovirus (HRV) page. For someone whose main interest is these little dudes...well, it was in a pretty woeful state. How embarrassment. Expect to see it take shape over the coming weeks.

New MERS-CoV Page takes shape.

VDU is a "work-in-progress" kinda site. The latest work is to develop a new coronavirus page dedicated to the exploits of the latest of the betacoronavirus to infect humans. 

It has a way to go and will probably never have the colourful charts of the H7N9 page - data from the Arabian Peninsula has been nothing like the quality of those from China - but I'll bring it up to speed as I read through the literature, and then leave it as a resource for those wanting a grounding in what used to be (sniff) HCoV-EMC.

Editor's Note #5.

I thought I might add a new bit to this bloggy thing I'm doing (6 weeks old and I'm changing it already). 

So, see below for the first "Stuff from the Literature" comment. I'll try and find a paper or two and break it into manageable chunks. 

As always, this is firstly an effort by me to learn something new, and secondly to try and communicate that to others. It won't be definably regular.

Off the air.

VDU has had some FTP issues with its server so no updates across the weekend. 

The University of Queensland quickly fixed the problem today.

Editor's note #4

VDU's Editor asked to comment on recent WHO statement about H7N9 severity on BBC World News' Newsday show with Rico Hizon. 

Video currently viewable at the Australian Infectious Diseases Research Centre at The University of Queensland.