It is perfectly okay to be a bit freaked out by the Zaire ebolavirus...

The Zaire ebolavirus (abbreviated as EBOV) is one of 5 species of ebolavirus and a member of the same viral family as the species Marburg marburgvirus (yeah, I know). Three of the ebolaviruses, EBOV, Bundibugyo ebolavirus (BDBV) and Sudan ebolavirus (SUDV) are associated with Ebola virus disease (EVD) while Reston ebolavirus (RESTV) and Tai Forest ebolavirus (TAFV) are not.

Much has been written this week on why you are perfectly safe from an outbreak of EVD in your (judging by VDU's stats) probably non-West African home town, or while at the fruit shop or on your couch watching the trailer for Guardians of the Galaxy on endless loop while clutching your ticket for tomorrow night (it's reeeal). 

A plane may deliver an infected person to your country, but our healthcare system will catch it. It will be contained, kept in its box like a hyperactive yet frustrated boggart. Nothing to see here. Move along please. These assurances come after the preceding week in which we felt the aftermath of the first symptomatic "EVD on a plane" incident, 2 US healthcare workers becoming infected and the death of a very bright light in the treatment of EVD patients in Sierra Leone. Twitter's ebola hashtag has been afire like a stock exchange ticker ever since 2-weeks ago.

I'm here to say, it's okay to freak out as much as you like...just don't run into traffic, sell the kids or move to an isolated farm and stock up on tinned food. It's not that kinda bat-crazy. (apt)

So what is it about EVD that could warrant you feeling more fearful about it than about other fatal infections that are far more common? 

Don't forget >14,000 deaths due to Clostridium difficile infections per year (1), 200,000 people die of cholera each year (2), >600,000 died of malaria in 2012 (3) and >1,000,000 died from an AIDS-related illness in 2012 (4). Big numbers. How does our fear remain in the face of that tide of microbial mortality?

Could our unreasonable fears be due to any of these?:

• Most diseases on that list are (still) treatable. HIV not so much, and antimicrobial resistance is of course on the rapid rise. Yet EVD kills more than 1 person in 2 and it does so quickly. That's scary. It isn't treatable; although that could be changing if the ZMapp "plantibody" and its pipeline comrades ever get to decent controlled trials.
• Perhaps you just don't fee like you will get cholera or AIDS. They are "somebody else" problems but this jungle virus, well that's all over the TV, the papers, the web and on radio. It must be spreading fast.
• This outbreak has spread widely in 3 countries, and cases are being added in numbers above 100 every few days. Now EVD seems to be spreading from that person who travelled on a plane while ill. Healthcare worker contacts seem to be bearing the brunt in this 4th country (Nigeria). OH, and he vomited on one of the planes he took on his multi-stop journey from Liberia to Nigeria.
• EVD is a haemorrhagic fever. Haemorrhagic fever I said - arrggh!! No wonder they changed that name! It just sounds scary.
• Around 40% of cases show obvious signs of bleeding. Not the movie level, leaking-like-a-sieve stuff though. But someone is bleeding. On the outside. For everyone to see. From a virus infection. 
• EVD starts off like the flu, or the effects of a dodgy curry but it can end up with you laid out, alone and dying, with only your suited carers for close yet distant comfort.
• Entire families get wiped out during these outbreaks. Perhaps not scary, but heart wrenchingly tragic and something we can feel in our own guts.
• Even experienced healthcare workers get infected by this virus. Sure, this is likely attributable to the hot, overwhelming conditions, to the numbers of cases, under-resourced clinics (by big shiny HEPA-filter equipped hospital standards anyway), tiredness and accidents, but it's happening.
• Just because! Humans watch in fascination the most exotic and scary things. Many like to be scared.

So I think it's okay and even perfectly normal to feel uncomfortable, worried, fearful, perhaps even a bit angry that your country is not doing more to help in West Africa, or is doing so a bit late.

Don't add guilt about having those feelings. 

But do make sure you reality check yourself and your friends.

Some of the concerns listed above just won't be a problem for you in your neck of the woods. Most of us were probably born into a very different situation to those being ravaged by the Zaire ebolavirus variant, a virus about which little was known by those who live in the region, until very recently. Perhaps that is partly our fault since we have lately been able to show that ebolaviruses are not new there after all (5). But this multi-country outbreak is likely to have started through practices that have been commonplace yet not ever resulted in this sort of death before. Most who will read this do so from a place of relative privilege. You are not likely to see an EVD case in your life. Do what you can to find out the facts. WHO and the CDC have many great webpages of resources that can help teach the realities. Read them. 

One reality is very clear, West Africa needs help to shut this down. It should have had it sooner.

Fear is fine, but remember your reality.

References..

1. http://blogs.scientificamerican.com/molecules-to-medicine/2014/08/05/ebola-and-priorities-in-drug-development/ (from http://blogs.scientificamerican.com/molecules-to-medicine/2014/08/05/ebola-and-priorities-in-drug-development/) 
2. http://www.who.int/mediacentre/factsheets/fs107/en/
3. http://www.who.int/mediacentre/factsheets/fs094/en/
4. http://www.who.int/gho/hiv/en/
5. http://wwwnc.cdc.gov/eid/article/20/7/13-1265_article

MERS-CoV: maps, totals, sex, age and different populations

This is a static page - the internet address won't change, just the charts as I add new numbers and update them. The page will follow Middle East respiratory syndrome coronavirus (MERS-CoV) detections by day, month and the cumulative tallies, worldwide and focus on subset of the numbers that are public available. That means these graphs are at the mercy of each nation's willingness to provide basic, deidentified (so patients are never publicly identified) information. 

At a minimum these could include:

• a unique case identifier (preferably in collaboration with country where diagnosis was confirmed and used by all)
• age
• sex
• date of illness onset (DOO; when they first became ill; preferred value to use top plot cases along the bottom axis of the graph)
• date of hospitalization (DOH; if no DOO - the I plot using this*)
• date of laboratory confirmation
• town & country of diagnosis
• whether a healthcare worker
• whether underlying disease (comorbidities) were present
• animal contact if a possible or known zoonotic disease

*If no DOO or DOH - then I plot using the date of reporting.

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Data visualization ("viz") 1.
This breaks down the living (yellowish) and dead (red; when they died) people from which MERS-CoV was confirmed by a laboratory, either as viral RNA-positive using RT-PCR or by the detection of an antibody response. The graphs also show the counts as per day, per week and per month to cover a range of different 'ways' of looking at the numbers. In the Monthlies graph, I have nested a Table that shows how many healthcare workers (HCWs) have been infected and that that equates to as a proportion (%) of all detections from that country.

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Data viz 2.
This is a cumulative curve, It adds the newest case numbers to the total from the timepoint before and so it shows the growth of cases - in this case, of all MERS-CoV detections worldwide. I've marked some pints of interest. These are usually clusters or outbreak and cause a sudden rise in case numbers, seen as a steep curve; a change in rate of case growth. As the cluster or outbreak resolves, the curve "slows down" which can be seen as it levelling off to a horizontal line.

Data viz 3.
This is a series of 'heat' maps with time. They plot the density of MERS-CoV human cases in terms of colours - the more cases in a region, the more warm (red) the colour is. The fewer cases, the more cool (green) it is. There is a scale to show you some more detail.

Data viz 4.
MERS by region of the Kingdom of Saudi Arabia. This is another way to track which region or province is the hottest spot. It does not account for the creation of "MERS-specific clinics or hospitals to which cases from other regions may be being transported.

Data viz 5.
MERS by age and sex. This includes a table of the current global total number of MERS-CoV detections and highlights the gaps in my line list of age and sex data. 
There is a global age and sex bar graph (male-blue;female-pink-sorry; green-no sex data). 
Next are age/sex pyramids for the world, the Kingdom of Saudi Arabia, pre-Jeddah and then the Jeddah and South Korea hospital outbreaks.
These graphs highlight the different distributions during times of sporadic cases or times of clear outbreaks. They also highlight that more cases are male and show some difference between cases and fatal cases as well as differences between Saudi Arabia and an outbreak in another country-highlighting how important the health of the community is to the impact of the same virus.

Data viz 6.
This is an odds and ends viz of some subpopulations. 1st there is a panel looking at the number of comorbidities over time (orange line) against the total case numbers (pale brown mountain), globally; 2nd is the number of times an animal (brown), or specifically a camel (yellow), is mentioned alongside a case; 3rd is a plot of the cases identified as having a role in healthcare, again against a backdrop of the total MERS cases worldwide. This lets us see increased spillovers and, usually with hindsight, associate them in time with a spike in cases. It also shows the intimate relationship between MERS and the healthcare environment as healthcare worker numbers spike along with an overall rise in cases.

Data are derived from the World Health Organization, FluTrackers and various Ministries of Health.
The chart above, as with all on VDU, is made for general interest only. It is also freely available for anyone's use, just cite the page and me please. 

Where has all the MERS-CoV action been...?

Sorry but no prizes to be awarded here.

...the Kingdom of Saudi Arabia is where nearly 90% of all laboratory-confirmed detections of the Middle East respiratory syndrome coronavirus (MERS-CoV) have originated from, as best we can tell. 

This is based on data sourced from the World Health Organization, various Ministries of Health around the world, FluTrackers and the scientific literature. All public data sources. 

While the tally sits around 847 cases and 291 deaths, right now we are experiencing a multi-week lull in new case announcements. Great news for the region and the world, which is dealing with many assaults, biological and otherwise, right now.

Where the wild MERS-CoVs are.
Click on chart to enlarge.

Ebola West Africa numbers in context...[AMENDED]

A quick glance at how the suspect, probable and laboratory confirmed (susp/prob/conf) cases of Ebola virus disease (EVD) stack up in the 3 countries with local spread of Zaire ebolavirus.

Please note that I have separated Guinea-2014, Sierra Leone-2014 and Liberia 2014 only to highlight that each country in the single "West Africa" outbreak (involving a single viral variant as far as we know) has greater case numbers than those found in many of the earlier outbreaks.

I have not yet listed the case imported to Nigeria here.

Click on chart to enlarge.

Now for something (not so) completely different: H7N9 maps...

Now it's time to mess around with influenza A(H7N9) virus mapping using Tableau.

I've (only just) realised the my esteemed peer, Shane Granger has been using Tableau to do this for ages (see here), and that this will be duplicating his excellent work. So I'll try my best to consciously differentiate my maps from his - but there's only so far you can go with that and there will be overlap. 

The page below is a very early first play with H7N9. It's just detections broken across 2013 and 2014, by province most likely to have been the source of the infections (as far as I can tell) in mainland China. 

If I can master this I'll try and add more details in the future. For now, these numbers a a little out of date but he trends are similar. This charts 449/452 detections.

Middle East respiratory syndrome coronavirus (MERS-CoV): Age and Sex [UPDATED]

THIS PAGE IS NO LONGER UPDATED

This data visualization has been added to the single
MERS-CoV page now be found at..
http://virologydownunder.blogspot.com.au/2014/07/middle-east-respiratory-syndrome_17.html

A new static page on which I will update the MERS-CoV numbers as they relate to the age and sex of the people laboratory confirmed as infected.

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Middle East respiratory syndrome coronavirus (MERS-CoV) by week and month...

To follow up yesterday's daily numbers chart, here we have the number of MERS-CoV detections by week (Chart 1) and by month (Chart 2). 

Not a lot of change from my last posts of these 18-June and 23-June - we are currently in our 5th straight day without any new detections being reported - and prior to this drought, there had been very few other detections for a while so we can now very clearly see the Jeddah-2014 (Kingdom of Saudi Arabia) major hospital-base outbreak peak's beginning and end.

We're also in the second half of Ramadan, putting us past the maximum likely incubation period for those visitors to the holy places that may have acquired MERS-CoV infection at the beginning go the month. A pretty good indication that MERS-CoV is not spreading among the community. It is still strange to me that a region that was yielding sporadic cases up until very recently, is now not yielding any such cases. Perhaps it's the improvements initiated under Dr Fakeih's watch, or maybe the hot, dry weather? It could be that camel contacts are reduced or that festivals are not as frequent in the extreme heat.  It would be great to see some scientific literature emerge on the Jeddah-2104 outbreak, on seroprevalence, on camel testing, gene/genome sequencing, studies of other animals or transmission investigations. Things have been very quiet on the publication front for some time now and we still know very little detail about the largest flurry of (known) cases to have occurred since 2012.

We'll wait and watch and see, I suppose.

MERS-CoV detections, worldwide (but mostly in the Kingdom of Saudi Arabia), by week.
Click on chart to enlarge.

MERS-CoV detections, worldwide (but mostly in the Kingdom of Saudi Arabia), by month.
Note the yellow star which highlights a 10-fold higher scale for 2014 y-axis (left-hand side) than in the 2013 numbers. Even 2014's puny June surpassed any month in 2013.
Click on chart to enlarge.

Middle East respiratory syndrome coronavirus (MERS-CoV) daily numbers...

Because I miss my charts, this is a quick one, made the old fashioned way (Excel and Adobe Illustrator).

MERS-CoV detections by date of illness onset (orange; when available, otherwise date of hospitalization or reporting) or by reporting date only (blue), each day since 22-March-2014. 
Click on image to enlarge.

A few things to note from this chart:

• I've arbitrarily chosen to bracket the Jeddah-2014 outbreak as starting in the week beginning 17-March-2014 (MERS Week #106) and ending in the week beginning 19-May-2014 (MERS Week #114). There don't seem to be Jeddah-originating cases in the week after that, and case numbers are low (<5/day, similar to the same period in 2013) from then onwards...although this is not an exact science. For example, does one count those cases from the Al Qunfudah cluster that were moved to Jeddah hospitals? But it's a guide.
• This chart has the daily case numbers (orange) based mostly on the date of illness onset. This highlights (again) the ongoing paucity of recent MERS-CoV detections which is great news for the Kingdom of Saudi Arabia (KSA). The map below previously posted here, does highlight an interesting questions. How are the small number of cases reported in June/July so widespread and where are the infections being acquired from? It's not spring (the camel calving hypothesis suggests human cases take off during the active birthing period; perhaps this is just the "ticking over", non-Spring norm for animal>>human acquisitions?) and there are no hospital outbreaks. Is this community spread? All the indicators we have point away from that. It is also a very busy time in the KSA right now with Ramadan having attracted visitors for some weeks. We have not yet seen cases appearing among those with underlying illness, an indicator or sentinel population for MERS outbreaks because they show the more obvious result of an infection. So these cases must be ongoing sporadic camel (or other animal, including goats which appeared in the recent WHO disease outbreak news) to human acquisition. Right? I'm looking forward to some widespread camel testing results from the KSA and some human seroprevalence studies would be very relevant too. Not sure what's taking so long for the latter to appear.
• The plot of detection based on date of reporting (blue) is somewhat messed up by the found113 detections for which we have no date breakdown (see here for more detail or search VDU for found113). This means the detection all get assigned into 3-June, the date the KSA Ministry announced them. Yuck. It looks like those details are never going to materialize either. At least, my personal efforts to get date data from Prof Tariq Madani have failed, despite his public assurance that more detailed data could be made available to scientists who wanted it, and the WHO seem to have moved on to posting more contemporary cases in detail, skipping over the same level of detail for the found113.

The very good news is that Ramadan has not coughed up a plague of new MERS cases. The bad news is, we still don't really know the source of the cases that have been continuing to emerge in the KSA. Without knowing that we really don't have a handle on this disease, or this virus, at all.

Location of June/July MERS-CoV detections in the KSA.
Click on map to enlarge.

Ebola virus disease (EVD) cases, clusters and outbreaks mapped out...

The West African region epidemic (top map), including countries with imported cases and the totals from past Ebola virus disease outbreaks and the few imported monkey cases (the US & Philippines [hence zero human cases]) plotted by total numbers and country (bottom map).

These maps are best viewed alongside my Ebola virus disease numbers page found here.

The WHO create multi-page Situation Reports [2] and brief Situation Summaries.[3] They are currently presented on Wednesday and "additional updated figures" will be posted "as they become available any day of the week" (via eMail to journalists from WHO).

My totals include all countries that have hosted a case in, or sourced from, a West African nation. Countries include Guinea, Liberia, Sierra Leone, Nigeria (now EVD-free), Senegal (now EVD-free), the United States of America, Spain and Mali.

This is a static page but as of 21-Jan-2015 (AEST) I have copied the maps to the bottom of my main EVD EBOV|Makona (west African Zaire ebolavirus variant) outbreak page at:  http://virologydownunder.blogspot.com.au/2014/07/ebola-virus-disease-evd-2014-west.html

Latest data taken from: 
http://apps.who.int/gho/data/view.ebola-sitrep.ebola-summary-latest?lang=en

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A note about the proportion of fatal cases (PFC): 
On these graphs, my PFC calculations for West African countries and the DRC are based on dividing the total number of suspect/probable/confirmed deaths by the number of total suspect/probable/confirmed cases for the same date. This is crude and may be a sizable underestimate of the true PFC.

It may be better to use the deaths at the most recent date divided by the total cases from 9-16-days  earlier (this number is not precisely known) to better account for the lag in time between presenting to a treatment facility and dying (for those who do not recover). 

Some estimates suggest the true PFC may be closer to 70%-80%. I don't have enough data (or smarts) to be able to calculate the lag for now so please be aware that the PFC above is likely an underestimate.

• The figure above, as with all on VDU, is made for general interest only. It is also freely available for anyone's use, just cite the page and me (Dr. Ian M Mackay, PhD) please. It may be that I have misinterpreted the language in the reports (sometimes a little tricky to wade through) or miscalculated some totals based on the way data have been presented. 
• Sometimes there are very country-specific differences in what gets presented to/via the World WHO DONs/SitReps which make this process less clear than it could be. I recommend you have a read and compare the data from each of the countries for yourself to understand these issues. 
• As I've talked about previously,[1] these numbers are all volatile for a variety of reasons, some Ebola-specific, so regard this chart for its trends only.
• I am only able to plot what is publicly available. To date, this does not include granular data with dates of onset, or daily data of any kind. The WHO have these data and you will see them become more available through their Situation Reports found here http://www.who.int/csr/disease/ebola/situation-reports/en/

References... 

1. Ebola virus disease and lab testing...
   http://virologydownunder.blogspot.com.au/2014/04/ebola-virus-disease-and-lab-testing.html
2. Ebola virus disease outbreak Situation Reports (SitRep)
   http://www.who.int/csr/disease/ebola/situation-reports/en/
3. Ebola virus disease outbreak Situation Summaries (SitSumm)
   http://apps.who.int/gho/data/view.ebola-sitrep.ebola-summary-latest?lang=en

Mucking about with MERS and maps...

Yes, some more of that stuff with the Tableau presentation stuff.

This one shows the MERS-CoV cases on a map by the region they were most likely acquired in. So that means it doesn't show all the site to which cases arrived via export, just their likeliest point of origin.

Because we have no individual case details about the 113 "found" Saudi Arabian cases from 3-June, I've taken some liberties using the hints provided by the World Health Organization to put this figure together. It will be a little out on the (29) of the found113 that occurred between 5 May 2013 and 28 February 2014 but its the best I can manage with that big data gap.

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