Tuesday 25 March 2014

Editor's Note #16: VDU takes a break...

Hard to believe it's been 1 year since I blogged my first blog. 500 posts ago.

To celebrate, I'm taking some time off!

Feel free to read through earlier material. There  are plenty of posts that remain relevant, unanswered questions to ponder and knowledge to absorb.

Ebola outbreak in Guinea: 13-lab confirmed cases, 73 more suspect, includes 59 deaths

Schematic of an Ebola virus virion.
Its a work in progress but feel free to use.
Just cite Ian M. Mackay, PhD and

Click to enlarge.
There's an outbreak of Ebola virus (species Zaire ebolavirus) haemorrhagic fever going on in Guinea just now (see the map for where that is within Africa).

Haemorrhagic fever? That's the scary stuff that books get written about and movies based on - bleeding from tissues and person-to-person spread to infected healthcare workers and grieving family carers...horrible, scary stuff for those in the thick of it. If anything can be said to be good news to this, it is that usually (to date) cases do not pass several hundred (see Storify article of tonight's Twitter information [5]) because the virus does not transmit as easily as influenza for example (no aerosol route; spread is by bodily secretions);  scary bleeding from everywhere is not as common as the movie make out and there are survivors of infection. 

A very digestible backgrounder on Ebola that will get you up to speed on this virus and disease can be found at Mike Coston's Avian Flu Diary, here [9].

On 22-March, the World Health Organization (WHO) was made aware of an outbreak in the west African country by its Ministry of Health [1]. The outbreak of febrile disease commenced 9-Feb. When the Minister of Health released a statement, 22-Mar, he noted the disease was characterized by fever, diarrhoea, vomiting, fatigue and sometimes bleeding. 

Guinea and surrounds, Africa.
Maps purchased from maptorian.
Click on image to enlarge.
Since then, haemorrhagic fever cases in the country's capital, Conakry, have been shown to be due to something other than the Ebola virus according to testing results from the Pasteur Institute Dakar [3]. 

The Institut Pasteur in Lyon had earlier identified Ebola virus (so far in 13 cases) in Guekedou, Macenta, Nzerekore and Kissidougou districts; they also genotyped some strains using the L gene as a PCR target[10]. This led to identifying the species Zaire ebolavirus.
The Emerging and Dangerous Pathogens Laboratory Network (EDPLN) is working with the Guinean VHF Laboratory in Donka, the Institut Pasteur in Lyon, the Institut Pasteur in Dakar, and the Kenema Lassa fever laboratory in Sierra Leone to make available appropriate Filo-virus diagnostic capacity in Guinea and Sierra Leone [1]
There is no specific treatment or vaccine for Ebola disease. The first Ebola virus outbreak was identified in 1976. Ebola virus is the conversational name of the viruses that are members of the Family Filoviridae, Genus Ebolavirus and exist as 5 species [4]:
  1. Species: Tai Forest ebolavirus ("Tai Forest virus")
  2. Species: Reston ebolavirus ("Reston virus")
  3. Species: Sudan ebolavirus ("Sudan virus")
  4. Species: Zaire ebolavirus ("Ebola virus")
  5. Species: Bundibugyo ebolavirus ("Bundibugyo virus")
"Multidisciplinary teams have been deployed to the field to actively search and manage cases; trace and follow-up contacts; and to sensitize communities on the outbreak prevention and control. Médecins Sans Frontières, Switzerland (MSF-CH) is working in the affected areas and is assisting with establishment of isolation facilities, and also supported transport of the biological samples from suspect cases and contacts to international reference laboratories for urgent testing." [1]
But where was the crack team of experts laden with ultra cool tech capable of diagnosing the tiny beast within minutes, containing it within hours and saving more people from becoming afflicted in days? 

Unfortunately no such crack team or timeline exists, except in the movies anyway. But as we are going to see in the coming days as diagnostic delays cause headaches for those multidisciplinary teams trying to educate the locals and begin helping contain, confirm cases and trace the disease, there is a real need for faster identification of the causes of acute and serious disease outbreaks worldwide. 

Wouldn't it be great if the world's governments and biotech industries could come together to assemble and maintain some sort of rapidly deployable multinational pathogen detection force? Several teams of scientists and healthcare workers assembled and trained by the world's best, and remaining linked to them, carrying with them all the (perhaps bespoke) tech they'd need to identify any pathogen (unbiased molecular methods). They could analyse their data on the fly or rapidly send it to others for help using satellite links. The "Force" could be funded by the contributing States, biotech and Pharma; coordinated by the WHO perhaps. I'd love to see a dedicated set-up for pathogen identification; a kind of virus/bacteria/parasite-hunting fire department that can down its regular tools and jump on a plane ASAP; someone whose number is on every country's speed dial. 

Ahh 'tis to dream. 

In the meantime, I have a lot to learn about Ebola; something that is more nightmare than dream to me.


  1. WHO situation report as of 22-Mar-2014 [PDF]
  2. Epidemic hemorrhagic fever in Guinea after 29 deaths, the Health Minister announces new measures
  3. Guinea: fever cases detected in Conakry are not due to Ebola.
  4. Prof Vincent Racaniello's Virology blog on Ebola virus naming
  5. Storify: Early timeline of the events in the Guinea Fever outrbreak
  6. WHO webpage on the Guinea Ebola outbreak
  7. WHO primer on Ebola haemorrhagic fever
  8. Avian Flu Diary (Mike Coston) on WHO Twitter Messaging On Ebola
  9. Avian Flu Diary (Mike Coston) on A Brief History Of Ebola
  10. Guinea Ebola outbreak believed to be deadly Zaire strain

Monday 24 March 2014

Avian influenza A(H7N9) virus cases hit 400

While everyone was looking at Guinea and the Ebola Zaire outbreak, that stealthy H7N9 has gone and infected a total of 400 people that we know of. It is of course, just another milestone and not an indication of anything changed about the virus. In fact the trend for few cases per day is continuing. One constant in s sea of change and new things.

Another constant, the up-to-date nature of the FluTrackers case list - check it out here

I have to run - much to learn about Ebola!


  1. FluTrackers H7N9 case list

Google Flu Trends: What did you expect?

I posted this on Crawford Kilian's H5N1 blog in response to his positing yet another story whacking Google Flu Trends for its "failure".

In case you can't tell - I'm a little sick of the number of electrons being wasted on writing the same thing about this paper in Science. I know, there is no shortage of electrons. Still, I hope to see this same degree of ire elicited by and directed toward other places, corporations and States who have trouble providing data to the public within the expected realms of accuracy. I'd also hope for more focus on what and how we test now and how representative that is of what a virus is doing; or what we might be missing.

I think Olson et al said it well when noting GFT's earlier failure to predict the H1N1 2009 pandemic's influenza-like illness activity..
"Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection"
The post...

Okay. Google Flu Trends (GFT) was not 100% accurate. Wow. Who'd would have thunk it? Who could possibly have guessed this would happen? The disappointment is clearly widespread. A predictive computer-based system set up for devising regulatory guidelines, formulating vaccine formulations, ensuring suitable laboratory testing capacity and preparation or national surveillance guidelines failed. Wait. What? It wasn't setup for any of that! It’s really just a pretty thing you can go look at to get an estimate of flu activity near you; much easier to wade through than some country's public health efforts. Estimate. When did we expect an estimate to be perfect?

Come on people-interpreting-this-paper. GFT isn't a failure unless you were honestly expecting it to be 100% correct.

Of course it couldn't ever be that. THERE. WAS. NO. VIRUS. TESTING. Not done by GFT anyway. Some lab testing went into it apparently, but even that was a sliver of a slice of a shard. And if you know anything about respiratory virus testing, then you know that even the testing we do, represents only a tiny fraction of the amount of virus-positive cases out there, extrapolating from those. That testing even varies from place-to-place in type, quantity and extent of reporting. The choice of what to test (sampling) is itself biased in a number of ways, not the least of which is that we favour testing pretty sick people or those that feel crook enough to present to a Doctor. We’re comparing GFT’s “fail” to an estimate. You’re all comfortable with using that to lambaste GFT? You’re comfortable to call that a total fail?

"The folks at Google figured that, with all their massive data, they could outsmart anyone."

Really? Is that what the folks thought? Did Google really get bitten by the flu bug?; can Google truly not track the flu? Certainly catchy headlines one and all. I guess no-one would read something entitled "Google Flu Trend's estimates not in agreement with some national testing data which also represents only a portion of those who get infected". I can see where that might not be a real mouse-wheel turner.

GFT was and could only ever be a predictive system. Just like that shiny App you have on your phone that predicts the weather forecast. Let's drag "big weather" through the interwebs flailing it at every turn so we can suitably express our righteous indignation at its failure to predict the rain we wanted on the weekend. It failed! OMG! Now I have to water my lawn to stop it from drying up. But that's all I have to do. No-one died when the clouds held their watery payload. My child was no more or less safe because the weather bug bit the Bureau of Meteorology here in Queensland. I didn’t have to get a new lawn because it is now 24-hours drier.

Does GFT's overestimate of the number of predicted cases by 0.5-2 fold (depending on the story you read) really have a real-world impact on anyone? Seriously? Keep in mind that its estimates still followed the trend of flu activity pretty closely; they peaked when actual flu was peaking, just not (my other estimates) perfectly. But apparently someone 100% concordance between lab sampling and GFT estimate data.

GFT has been doing a perfectly good job given what it is and what it could ever hope to be in its current setup. Perhaps centralizing and plotting the WORLD'S lab-based data alongside Google “flu”-related search-result data would be a useful next step for GFT. Then we could make up our own

In the meantime, keep it in context people.


Sunday 23 March 2014

More camel mentions among MERS-CoV cases...

Click on image to enlarge.
Special thanks to Professor Andrew Rambaut ([4]; @arambaut) for keeping such great track of the number of human cases in which camels has been mentioned.
Also thanks to Prof Rambaut and Ellen Knickmeyer  (@EllenKnickMeyer) for putting up with my stupid questions.

The current tally is now 11 human cases with a link to camels; 3 more than my earlier post on this topic.

The charts still show that cases outside of the Kingdom of Saudi Arabia (KSA) are proportionately more likely to identify human contact with camels than are MERS cases acquired within the KSA. 

The first case from Qatar with a camel link was from Sept-2012; from the United Arab Emirates (UAE) on Oct-2012; from Oman 20-Dec-2013. The very first (index) case of MERS-CoV to be announced to the world on Sept-2012, that from a 60-year old man living in Bisha in the KSA, also had contact with his 4 pet camels which we learned of in an article in the New York Times ([1] and later in an article late February 2014 [3]). 

I have not added the case of a Qatari male who owned a camel and goat farm [5], because the report in Eurosurveillance notes he claimed no direct contact with sick animals. I do wonder about contact with healthy or asymptomatic animals though. 


  1. New York Times article on Prof Memish et al's mBio paper.
  2. Prof Memish et al's mBio paper (does not mention camels though)
  3. Alagaili et al's paper noting camels were a contact of 60M index MERS case
  4. Professor Andrew Rambaut's MERS-CoV case list
  5. Eurosurveillance contact study of 45-year old male from Qatar (FluTracker's Case #6)

Friday 21 March 2014

MERS-CoV in camels... [CORRECTED]

The top pie chart shows the distribution
of all human cases containing the word 
"camel" in their case notes, by the site where
the human was likely to have acquired their 
MERS-CoV infection. The bottom bar 
graph shows those data in terms of the
 proportion of cases at that site for which
"camel" contact was possible.
Click on chart to enlarge.
Thanks very much to Nicholas Evans (@neva9257 via Twitter) for asking me to back up my gut feeling about there having been more camel-links among MERS-CoV cases outside the Kingdom of Saudi Arabia (KSA) compared to inside.

I live to serve and so using those data I have to hand I've made a couple of charts. I'll keep these updated from now on too. 

I'd be grateful if anyone wanted to shout out human cases where camel contact was mentioned. I currently have 8 in total on my list of 201 lab confirmed MERS-CoV cases. (see the figure up there for where my cases are sourced). There may be many I have missed though.

One obvious question arising from the bottom bar graph is why does such a low proportion of camel-associated cases occur in the KSA but not elsewhere

For the sake of simplicity, I'll exclude the possibility that MERS-CoV jumps off its camel hosts at a border. Because the latest 68M from UAE may well have acquired his infection while visiting his camels in the KSA I have now listed him as a KSA acquisition...until I hear differently). We also know that camels in the KSA get actively infected (see earlier posts, listed below, on these findings [1,2,4]). 

So do these charts, by highlighting that so few camel links are to be found in the KSA (site of >80% of MERS-CoV human cases), discount camels as a source of infection? I don't think so. We have some very compelling evidence for camels hosting MERS-CoV [4], for camels being present in mass gatherings [5], and nothing but an absence of epidemiology to counter their role as a host and source.

I suspect the graph shows that MERS cases in the KSA won't admit to camel contact. Alternatively, perhaps contact, in its many possible direct and indirect forms is not being adequately sought or listed in case reports and in "gumshoe epidemiology" efforts (Ian Lipkin's comment, [6]). But why would camel contact not be listed, reported or collected? Perhaps it is seen as a bad thing? There may be stigma associated with acquiring an illness from a camel. Or perhaps stigma attached to the way in which that illness was acquired.

Perhaps it is a simpler explanation. There is likely to be fear, or a real risk, of social and economic fall-out of "naming and-shaming" camels as a major source of infection/disease. Camels fill many important and significant roles in the lives of those around the Arabian peninsula; from food, drink, religion to tourism and fun. But not identifying camel links in the spread of MERS-CoV, if indeed more links do exist, won't stop KSA's locals from acquiring infection and MERS. 

If there is a deficit in reporting camel exposures in the KSA, for whatever reason, it does one thing particularly well; it delays the understanding of how to protect people and reduce their exposure to MERS-CoV. I think that understanding is probably inevitable, so it may be better for the KSA Ministry of Health to get out in front of the issue; be proactive in finding the source of infections and openly discuss and plan for the implications. But I may be seen as living in a world of unicorns and fairies (again) to suggest that will eventuate. My cynicism is based on 2-years and 201 cases of a virus that's been very well virologically and molecularly detected and characterised outside the KSA, while its basic aetiology and epidemiology inside the KSA has left much to be desired.

I would very much like some locals to weigh in on this topic. Here (in the comments below) or by email or on Twitter. The bar graph simply highlights a discrepancy that could be cleared up with a better understanding (perhaps just by me) of what may  underlie the difference in the apparent roles for camels among countries sharing borders.


  1. Dromedary camels are a host of MERS-CoV...
  2. Middle East respiratory syndrome coronavirus (MERS-CoV): camels, camels, camels!
  3. MERS in the UAE....[UPDATED]
  4. Dromedary camels are a host of MERS-CoV...
  5. Middle East respiratory syndrome coronavirus (MERS-CoV) cases rise in march: Festival-related?
  6. Receptor for new coronavirus found: Virus might have many animal reservoirs.

MERS in Kuwait...

It's a "MERS-in.." kinda day. 

The World Health Organization todayannounced a fatal case of confirmed MERS-CoV infection diagnosed in Kuwait [1].

Summary of the case details:
  1. The infected person was a 60-year old male
  2. A Syrian national
  3. Hospitalised 13-Feb-2014
  4. Died 6-Mar-2014
  5. Lab confirmed 9-Mar-2014
  6. He had comorbidities
A few things here worth noting I think:
  • There was quite a gap (24-days) between being hospitalised (not sure when the case started showing signs of illness) and having a laboratory confirmation (also 3-days after death)
  • While this is the 3rd case identified within Kuwait, it seems to be the first case that could be nailed down as having been acquired in Kuwait. I tend to try and list my numbers and maps by where the case was acquired rather than where they were diagnosed. Two previous MERS-CoV cases (FluTrackers #158 and #159; includes 1 case with camel contact noted) diagnosed in Kuwait had travelled outside Kuwait blurring the ability to see where they had picked up the virus

  1. WHO Disease Outbreak Notification 20-Mar-2014
  2. FluTrackers thread on Kuwait MERS-CoV case #1
  3. FluTackers on Kuwait MERS-CoV case #2

MERS in the UAE....[UPDATED]

For the second time this month, there has been a case of Middle East respiratory syndrome coronavirus (MES-CoV) infection confirmed in the United Arab Emirates (UAE; Abu Dhabi to be precise). 

What added to my confusion (as you'll know if you were following me on Twitter this morning) was that both cases, apart from being from Abu Dhabi, were also 68-year old males and both have had camel contact. 

Today's 68M UAE case frequently visited his camel farm in the Kingdom of Saudi Arabia (KSA; had just returned from there 5-days earlier, thus in my mind making this a likely KSA acquisition) while the earlier 68M UAE case owns his farm in the UAE where he contacted animals including camels which he breeds.

This raises another question from me; why do we see proportionately more camel contact outside the KSA than we do inside the KSA (I haven't done the maths so this may just me my unfounded gut feeling)? Is it something simple like better epidemiological investigations conducted by Qatari and UAE investigators or are things, yet again, different somehow inside the KSA than they are outside the KSA? 

Surely there are some clues in there for investigators to use either to either improve how the epidemiology investigations are conducted or to look beyond camels in the KSA at other sources of acquisition?

Wednesday 19 March 2014

Any differences in the sex of avian influenza A(H7N9) virus cases in different areas of China?

a) Male (blue) and female (lavender) lab-confirmed H7N9 human cases broken into the Province or Municipality of likely acquisition. b) The proportion of total H7N9 positives at each site of acquisition that are female (lavender).  The proportion of females in Wave 1 (Range of weeks beginning 18-Feb-13 to 20-May-12) and Wave 2 (07-Oct-13:current) are also shown as a horizontal line for comparison.
Click on chart to enlarge.

This new chart idea was just a look-see at whether there is anything out of the ordinary about the sex distribution of H7N9 human cases in the different areas of China. These are total numbers from both Waves of H7N9 season.

I've included case numbers in Part a) as well as proportion of females in part b) to show that a value of 100% must be place in context of only 1 POS!

Nothing much to see here folks.

Tuesday 18 March 2014

MERS-CoV: sex, age and accumulating death

A few more charts, just to fill out the set for today's Middle East respiratory syndrome coronavirus (MERS-CoV) update.

First Chart.
Click on chart to enlarge.
The first chart shows what everyone knows; MERS, as it has been for the past 2-years, is a severe disease principally of the people of the Kingdom of Saudi Arabia (KSA). 

The route of human acquisition of MERS-CoV remains unknown and will not soon be discovered judging by the lack of any evident plan in the most recent Editorial on MERS-CoV from the KSA's lead author, Prof Ziad Memish. An even less addressed topic is why this disease has such an impact in this particular country given that neighbouring States share aspects of lifestyle, belief and habit.

Second chart.
Click on chart to enlarge.
The second chart reinforces that MERS, in the severe form we see in hospitals, is principally a disease of men (66% of all case are male;  77% among the fatal cases) aged 50 and above (median age is currently 53-years). Something this chart does not show is the that MERS-CoV is a particularly opportunistic virus causing serious disease and death particularly among those who present with an underlying disease (at least a third of cases have a comorbidity of some sort).

Third chart.
Click on chart to enlarge.
In the third chart we can see the human cases by month. Nothing to add for 2012 or 2013 but that steady climb in 2014 should be watched. Why is it there? Why, 2.04 years since we learned of MERS-CoV thanks to the endeavours of an Egyptian scientist named Dr Ali Zaki, are there no public conversations on what is/could/should be done to staunch the trickle of new infections and deaths? Will we see a take-off of cases in April 2014 as we did in 2013? What is happening in Riyadh (where most cases have been of late)? I've added in the Janadriyah festival too because why not?

And in the fourth chart we can see that trickle of new cases but they have thankfully not (yet) been matched by an equivalent rise in fatalities judging by the proportion of fatal cases (PFC) which has dropped a little. The PFC still sits at the "killer virus" level of 42% of all laboratory confirmed cases dying. Not my phrase. 

To generalise, MERS-CoV infection is mainly a cause for serious concern among a particular adult population within the KSA. 

A question I'd like to see answered by studies from the KSA is what is the epidemiology and clinical spectrum of human coronaviruses 229E, NL63, HKU1 and NL63? I believe that would be an interesting study yielding results  that may well put MERS-CoV in a very different context.

Yet another reason for every State to test its population for respiratory viruses I suppose, because then one has a baseline for the known viruses which can help judge the impact of newly identified or emerging viruses.

Influenza viruses in Queensland, Australia: 03-Mar-2014:09-2014.

Map of Queensland's Hospital and Health service
areas. Adapted from
Click on image to enlarge.
Sure enough, as promised on the 12-Mar, the new Queensland flu numbers are out (I post a week after the next new numbers come out publicly; its just the deal I have). So this follows on from last Wednesday.

This is the next week's numbers which follow on from my earlier post on the increased number of influenza cases and the media reports of influenza A(H1N1)pdm09 virus  predominance.

The Queensland Health Statewide Communicable Disease Surveillance Report for the week 03-Mar:09-Mar has some extra detail, this week. The extra detail outlines that most (860; 94%) of this year's 918 influenza notifications (2.3X the 5-year year-to-date mean value) to date are located in the following Hospital Health Service (HHS) areas (see the map above):
  • Metro South: 192 (21%)
  • Metro North: 176 (19%)
  • Gold Coast: 110 (12%)
  • Cairns and Hinterland: 106 (12%)
  • Townsville: 73 (8%)
  • Darling Downs: 52 (6%)
  • Sunshine Coast: 50 (5%)
  • Cape York: 46 (5%)
  • West Moreton: 28 (3%)
  • Mackay: 27 (3%)
The median age of cases is 41-years and 50% are male. The highest rate of notifications is in the 50-59-year age group at 24.8/100,000.

Percentages represent the proportion of all 80 
notifications for this reporting period.IFAV-Influenza A virus; IFBV-Influenza B virus.
Click on image to enlarge.
This report also has some typing (Flu A or B) and subtyping data (H3N2 or H1N1).

These data are very much appreciated  since this is ahead of the traditional "flu season" reports. 

Many thanks to all associated with the Communicable Diseases Unit, Queensland Health, for adding this detail in. 

The chart above makes it very clear that H1N1 dominates the Qld influenza landscape so far. Specifically...
  • 80 notifications with signs and symptoms during the reporting period
  • 68 were typed as influenza type A viruses (85%)
    • 13/14 were subtyped as H1N1 (pdm09 I presume; 93% of the FluAs that were subtyped)
    • 1/14 H3N2
  • 12 were typed as influenza type B viruses

Respiratory viruses: the viruses we detect in the human respiratory tract

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Middle East respiratory syndrome coronavirus (MERS-CoV) cases rise in march: Festival-related?

It's been nearly 3-weeks since my last update so I'm well overdue to look at what's changed.
Click on image to enlarge.

A quick post first up showing the accumulating cases by the region in which the person probably caught their infection and the week in which they became ill - or the week in which the case was publicly reported by a Ministry if no onset date was provided.

Why the recent uptick in cases; ~9 reported this month so far? 

There have been a few things going on in Riyadh (a city of 6 million, in which 10/11 most recent cases have been acquired) that might link people with camels (the likely intermediate or primary host of MERS-CoV in the region). 

I have listed a few other events listed that may also be pertinent for MERS-CoV acquisition &/or transmission, from whatever source, in a post 29-Jan [5]. To add to that there is this:
  • The 17-day long 29th annual Janadriyah Festival kicked off Feb-12. It showcases the national heritage and culture of the Saudi lifestyle. Includes camel rides and racing [3,4] and is visited by millions of people from all over the Kingdom of Saudi Arabia (1.4mil by 26-Feb) [2][3]. But not just locals; tourists come from all other regions of the Arabian peninsula too, including the UAE which a "guest of honor" this year. Of note: the recent 68-year old male UAE MERS-CoV case had exposure to animals from his own farm and is not noted as having traveled.

Saturday 15 March 2014

H7N9: the dotted lines that make sense of things...[CORRECTED]

Click on image to enlarge.
The latest H7N9 case-per-day chart shows that the trickle of human cases of confirmed avian influenza A(H7N9) virus infection is becoming a drip. The tap? My money is still mostly with the market closures. What precisely in the markets is the source of human H7N9 acquisition? Dunno, but the consensus seems to be poultry; songbirds also look pretty good though. It doesn't have to be, and is unlikely to be, just 1 thing of course. We know that this virus, as with other avian influenza viruses, can be shared around among bird species. It can even go into a human and that isolate be used to infect a bird again. See my recent post on some of this.

Click on image to enlarge.
What's also particularly intriguing, among the many interesting aspects of H7N9's acquisition and spread among humans, is that we're seeing much more "shouldering" in the Wave 2 epidemic curve than we did in Wave 1's.

Instead of the precipitous decline we saw back in 2013, we're seeing a drop down to ~10 cases per day, but then a slower decline the rest of the way. Is this because we started human cases from more sites this time around?; because markets took longer to close after the cases numbers began to climb?; is it related to markets being closed at different times, in different ways, in different locales? Who knows?
Cases by region acquired, per week, with different
 regions highlighted by coloured lines and the 
total case number in the background (grey).
Wave 1 and Wave 2.
Click on image to enlarge.

Dr Katherine Arden suggested I have a look at what's happening in each Province or Municipality and see whether any particular place can shoulder the blame for the shouldering. And that does seem to be the case if you look at the adjacent chart. Guangdong province seems to be the major culprit contributing to the shoulder effect. 

Cases by region acquired, per week, with different
 regions highlighted by coloured lines and the
total case number in the background (grey).
Wave 2 only.
Click on image to enlarge.
In the zoomed-in version that focusses on Wave 2 alone, we can see that the Wave 2 "peak" has in fact 2 peaks; the 1st peak dominated by Zhejiang province cases and the 2nd driven by a surge in Guangdong provincial cases. Guangdong cases took longer to drop away, and are in fact still being reported, possibly because the major poultry markets there were closed later than in Shanghai and Zhejiang province and only temporarily for a clean. Or perhaps the bird outbreak @influenza_bio and I discussed has a source in Guangdong province?

It's all speculation beyond the data we can actually plot.

Friday 14 March 2014

Google Flu Trends: not so perfectly predictive?

I'm no expert at the algorithms that go into the search giant's Google Flu Trends (GFT) predictive website so take what follows as a very superficial opinion. It does not surprise me at all that a recent paper in Science [1], backing up previous chatter on this subject [2], finds GFT is is not very accurate. Specifically, it has been overestimating peak influenza levels compared to more traditional laboratory-confirmed cases (itself only a subset of all cases) and influenza-like illness presentations to Doctors (a non-specific method of trying to identify influenza from a swarm of other ILI-capable viruses). 

A note: this recent paper is more a look at big data and whether it deserves our complete trust yet (it doesn't, is the message) than it is an analysis of how best to predict influenza virus activity in the future.

It would be fantastic if we could have a predictive system that could work around the need for actual testing of sampled people and give us an informed guess as to what flu was doing, how long it would be doing it, how severely it would do it and when it might start and stop doing it...I just don't have a lot of faith in predictive things like this. Perhaps I've just entered into a grumpy middle-aged male phase of my life....but I think that if we want to find out what's happening, we don't need to look too much beyond simply (not so simple when it comes to lab capacity and funding of course) upping the level of testing and typing that we do.

Even now, the current situation in Queensland of a 2-fold increase in influenza virus notifications compared to the mean of the past 5-years does not really show up so clearely on GFT.

Given that so many variables will contribute to a person's choice to search for "flu" (or whatever related text GFT includes in its algorithms), it makes perfect sense that a website showing flu activity in your area that is based on that component of the results, will be an over-estimate, especially during the peak times of flu activity. Why then? Imagine the impact on search when the media is most active in trying to get your pageviews using headline banners with "killer flu" or "early flu season" in them. People don't just chat over the back fence in response to those headlines any more, they go looking to the internet to provide their answers, news and sometimes poorly communicated facts. This will not just indicate that the have the flu, it will reflect concern that they may get it at some point in the future.

GFT also taps "real" flu data from real testing labs and Doctors clinics. This means its performance is probably not "off the rails" wrong, just overly influenced by non-infectious factors at peak times.

Are the inflated results positively affecting flu vaccine uptake I wonder? That would be a good thing. Might even have an impact on the size of the peak season.

Of course, no one knows what the actual numbers of flu cases in the community are; because flu is not always a serious disease that leads us to get a sampled collected and tested. The serious disease gets to a hospital and does get tested. these get added to notifications. Sure, influenza virus can cause a more serious outcome than many other respiratory virus infections, especially in certain groups, including death on occasion. There are also many mild infections that fly "under the radar". Those numbers won't be accounted for anywhere except through modelling. Perhaps the overestimate isn't that much of an overestimate; very hard to actually know that.

It's that damn iceberg tip again. 


  1. http://www.sciencemag.org/content/343/6176/1203.summary?rss=1
  2. http://www.nature.com/news/when-google-got-flu-wrong-1.12413

Thursday 13 March 2014

The decline of H7N9 Wave 2: some thoughts on why it may be different from Wave 1...

Influenza virus and influenza the disease certainly give scientists a run for their limited money when it comes to predicting what either will do from year-to-year, country-to-country or outbreak-to-outbreak. 

And just when you think you know enough, things change. 

This morning my Twitter stream was fed by a sparkling rivulet of informed comment by @influenza_bio ("A biologist"follow him if you don't already) on the subject of why H7N9 cases are falling. @influenza_bio groups together a few great points:
  1. H7N9 cases are declining.
    Agreed, I think Wave 2 ended almost a month ago.
  2. Overall, influenza-like illness (ILI) visits in China have declined.
    A clear parallel, but is it causal? ILIs provide a general guide to influenza circulation (general, because other viruses cause ILI which is basically fever + upper and or lower respiratory signs and symptoms - so a very broad but useful good guide
  3. Is the drop in human H7N9 cases linked to the end of a (silent) outbreak in birds (poultry, waterbirds, songbirds, both...)?
    Finding data on specific bird migration dates in the region is difficult. See here and here for some generalizations. Seems very reasonable.
  4. Live bird market closures cannot be the only cause of a drop in H7N9 cases otherwise we'd expect to see cases in other areas continue to rise (presumably areas where markets are not closed).
    If we compare Zhejiang to Guangdong, then we can see that the delay in closing Guangdong's bird markets seems to have manifested as a delay in slowing of human cases; most recent H7N9 case acquisitions have indeed been in Guangdong (a major poultry producing area in southern China) whereas cases in Zhejiang which, like other eastern coastal regions shut their markets earlier and "permanently", generally speaking, have dried up.
  5. If H7N9 human case decreases were linked solely to weather, then how could we explain the peak in 2013 which extended into late April whereas it looks to have peaked well before that, in early Feb, in 2014?
    Given that the seasons have not differed between the years (or have they?), I'd suggest we look more at the start of the 2 Waves; Wave 2 commenced earlier in 2014 than did Wave 1 in 2013, but the precipitous decline of both outbreaks of human notifications seemed to have been more closely tied to market closures than dates on a calendar. Of course markets are stocked with H7N9 infected birds and that which links to outbreaks at the supply end unless poultry acquired their infections at markets and then spread that between markets by bird movements which can extend right across southeast China. Why did it start earlier is a key question for me.
@influenza_bio finishes with the comment that...

As I've learned from @influenza_bio, many factors go into humans acquiring a particular influenza virus at a particular time/season, and probably no single thing is responsible for all events for any given outbreak. Phew. But that's why we don't have influenza infections all the time and it underpins why they peak at a certain time.

Human acquisition of influenza virus is related to:

  • How a person is exposed to the virus (aerosol from upper respiratory tract coughs and sneezes or self-inoculation from contact with contaminated surfaces)
  • Whether the virus survives long enough to be inhaled/self inoculated which is in turn linked to virus subtype and strain and environmental temperature and humidity (see some more on that in a guinea pig model here)
  • The host and their immune state and general health, smoking, underlying diseases etc
  • How much virus enters the host and where it "lands" and makes a footing in the host's respiratory tract
  • The spaces we share with infected people and how fast and well the air is filtered/exchanged in those spaces
  • The virus subtype in terms of what receptor it prefers and where those might be located throughout the respiratory tract.
  • For avian influenza in humans there is also the type and length of exposure to the animal hosts and their environment

Not an all inclusive list I'm sure, but you get the point. Influenza viruses are a complex beast, made more so by the fact that any given subtype could be represented by a range of strains indicating a variety of stabilities, preferences for receptors, antiviral susceptibilities etc. 

So I complete agree with @influenza_bio, more bird surveillance would indeed be a very important step in understanding what is happening in and perhaps predicting the risk of, human outbreaks of this and other avian influenza viruses.