Random Analytica

Random thoughts, charts, infographics & analysis. Not in that order

Tag: Disease

Random Analytics: MERS-CoV in the Middle East (to 3 Jun 2014)

1 - MERSinMidEast_Infographic_140603

***** Please note that this infographic of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was updated with public source information to 1800 4 June 2014 EST *****

I was always planning on updating my MERS-CoV infographic at the end of May but the own-goal by the Saudi Ministry of Health, having suppressed the details of at least 113-cases and 92-deaths and the sacking of the deputy Health Minister Professor Ziad A. Memish made this update an absolute necessity.

The MERS-CoV in the Middle East infographic displays cases and deaths according to each reporting country (rather than onset country which has become confused over the course of the disease). The primary data source is the latest ECDC update and the most recent figures released by the Saudi Arabian MoH (to 3 June 2014).

Many journalists and flublogists have already started to comment on the deeper meanings behind the ‘exemption’ of Professor Memish from work and the suppression of data over a very long period. One of the best articles I have read on it today came from Crawford Kilian via H5N1. How MERS Could Topple the House of Saud, and Beyond. Excerpt:

A recent book argues that Saudi Arabia and its Gulf neighbours are “rentier states,” living off the revenues from oil. Some of the oil money is distributed in a kind of ethnic socialism: native-born Saudis and Emiratis get cheap housing, education and fuel, as well as undemanding government jobs. In return, they allow the monarchies to do as they please.

Part of this “ruling bargain” is to import cheap labour in vast numbers, for everything from housecleaning to business management. The money and working conditions are atrocious, but usually better than those available at home. One of the benefits of the ruling bargain is a good health-care system, and the Saudis have an extensive one. In many ways, it is indeed good. The previous health minister, Abdullah Al-Rabiah, is a Canadian-trained surgeon who recently separated conjoined twins.

But that was after he got the sack. As health minister, Al-Rabiah had presided over the rise and spread of MERS as a Saudi disease. While cases were seen in Jordan in March and April of 2012, the virus was first identified in a Saudi patient a few months later. Ever since then, the vast majority of cases have affected either Saudis or visitors to the Kingdom; the other Gulf monarchies have seen cases too, but far fewer.

Al-Rabiah’s strategy was to say as little as possible about the cases and to spin what he couldn’t conceal. While the World Health Organization and other agencies worried about what was going on, the Saudi Ministry of Health stonewalled them. But the minister couldn’t conceal the fact that cases were breaking out right inside Saudi hospitals.

I would agree with most of what Crawford is saying with the exception that the previous health Minister Abdullah Al-Rabiah wasn’t spinning the data, he was ‘Juking the Stats’.

So, what is the difference between spinning the data and juking the stats and why is this important in our understanding of MERS?

The answer is that when a government, organisation, company or individual spins the data what they are doing is looking at relatively ‘clean’ data and then using that information to either spin the results or emphasise a point for a positive or negative outcome. You might not realise this but most Western governments spend a lot of time and treasure on doing this as they try to drive home a political message. A fair amount of my time as a Workforce Planner was spent spinning data (aggressive forecasting of human resources in future quarters as an example).

What the House of Saud has been doing is the authorisation and implementation of ‘Juked Stats’ policy.

In my humble opinion, what this effectively means is that the Minister, the deputy Minister, various minions, governmental hospitals and private hospitals that receive government funding were given a number of MERS reports to state for official publication and that the World Health Organisation would not be informed of the real numbers (which would then become a Disease Outbreak Notification).

Two key points:

Point 1: The fact that we now find that 20% of cases and >30% of deaths went unreported since May 2013 is a clear indication that the Saudi’s had a clear policy of underreporting for political reasons. The fact that Professor Memish got sacked a day after the juked figures were revised was (again IMO) a way to quietly point the figure at the patsy so the regime could say it had cleaned house.

Yet, even as a doctor, Professor Memish was a very highly placed bureaucrat who had been politically vetted by the regime who asked him to deliver a result. When the disease spun out of his control and Memish couldn’t deliver the requirement the regime quietly ‘exempted’ him from the story. Having myself been involved in the ‘Juking of Stats’ I can state without qualification that if my numbers had of been bad my boss would have been quietly let go (with a decent payout) and the CEO would have moved on. That’s the game.

Point 2: The data errors go back as far as May 2013 yet it is interesting that the Saudi’s have ‘come clean’ on their data errors just a month after the first case hit America. That detail alone might be worthy of some deeper investigative journalism.

To finalise, the Saudi’s are telling us that they have now come clean on their data errors. Given they have never been clean to date I still don’t believe them.

Post Note: As Ian Mackay just reminded me, I should also state that I don’t believe the new Saudi data because of a conspiracy theory, because conspiracies require a brain-trust and this looks like just an ongoing cock-up!

Random Analytics: MERS-CoV in the Middle East (to 26 May 2014)

1 - MERSinMidEast_Infographic_140527

***** Please note that this infographic of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was updated with public source information to 1300hrs 27 May 2014 EST *****

MERS-CoV has just hit Iran for the first time so I was trying to get the most up-to-date information on the virus spread but all the numbers are a little dated or tainted at the moment so I thought to make sense of it myself.

The above infographic is a look into the MERS-CoV with specific emphasis on its cases within the Middle East. The data is taken primarily from the latest ECDC update along with the current update from the Saudi Arabian MoH (to 26 May 2014) plus a little guesswork from myself (see appended). Unfortunately, given the five day delay in the most recent ECDC update (and errors within that update including an incorrect total of deaths in KSA) I wasn’t able to match the 680-cases (as per FluTrackers) to the public sourced data.

Here is my best guess today:

Middle East:

  • Saudi Arabia: 562 cases/179 deaths (official KSA MoH total)
  • United Arab Emirates: 67 cases/9 deaths
  • Jordan: 17 cases/5 deaths (+8 cases since 16 May ECDC update & 1-fatality)
  • Qatar: 7 cases/4 deaths
  • Kuwait: 3 cases/1 death
  • Oman: 2 cases/2 deaths
  • Iran: 2 cases/0 deaths (+2 cases since 16 May ECDC update)
  • Egypt: 1 case/0 deaths
  • Yemen: 1 case/1 death
  • Lebanon: 1 case/0 deaths

Europe:

  • UK: 4 cases/3 deaths
  • Germany: 2 cases/1 death
  • France: 2 cases/1 death
  • Italy: 1 case/0 deaths
  • Greece: 1 case/0 deaths
  • Netherlands: 2 cases/0 deaths

Africa:

  • Tunisia: 3 cases/1 death

Asia:

  • Malaysia: 1 case/1 death
  • Philippines: 1 case/0 deaths

Americas:

  • United States of America: 2 cases/0 deaths

The ECDC notes in its 18-24 May Update that:

Nineteen cases have been reported from outside the Middle East: the UK (4), France (2), Tunisia (3), Germany (2), USA (2), Italy (1), Malaysia (1), Philippines (1), Greece (1) and Netherlands (2). In France, Tunisia and the UK, there has been local transmission among patients who had not been to the Middle East, but had been in close contact with laboratory-confirmed or probable cases. Person-to-person transmission has occurred both among close contacts and in healthcare facilities.

No one’s numbers agree so I’m looking forward to the next ECDC update so I can work out the anomaly. That aside, given the newly reported cases in Iran I felt the infographic needed to be updated just to highlight its continuing international spread.

Random Analytics: MERS by Key Occupation (to +300)

After some great suggestions by Anil Adisesh I have found a way to include three levels of complexity into my epidemiological database while only presenting two levels of charts. More work for me but I needed the push from the medical community to make it happen.

For those who have previously looked at my epidemiological/occupational charts (mainly H7N9/MERS) they will see a big uptick in numbers as I add Retirees (Provisional) to my count. The detail will be in my notes but I am now treating those over a certain age (dependent on national legislation) as retirees unless the State in question gives me some the World Health Organisation suggested occupational details.

Presented are the updated Middle Eastern Respiratory Syndrome – Corona Virus (MERS-CoV) by Occupation charts:

***** Please note that all infographics for this MERS-CoV article are using publically sourced information to 1800hrs 21 May 2014 (EST) with n=652 *****

01 - MERSbyJobFunction_140521

This infographic looks at those infected with MERS-CoV by Job Title. Some job titles do ‘roll-up’ in terms of function, thus a Nurse and a Nurse (ICU) are shown under the same horizontal data-point (see Key Notes).

Key Notes:

  • Retirees (1) there are 137-retirees represented in this chart but only 1/137 can be confirmed (less than 1%). According to the Saudi Arabian Shoura Council the official retirement age for citizens is now extended from 60 to 62 (effective 21 May 2014) but this does not include female citizens (55?) or residents (reducing from 60). The other country of interest is the United Arab Emirates where the retirement age is 65. In terms of my occupational data anyone over the age of 63 (KSA) and 66 (UAE) is considered retired unless their occupation data is known.
  • Farm Owners (*2*) includes Camel Breeders & Farm Owners (Camels). Doctor (*3*) includes Doctor (ICU). Farm Employee (*4*) includes Shepherd. Nurse (*5*) includes Nurse (ICU). Hospital Employee (*6*) includes Hospital Domain Worker.
  • Health Care Workers (*7*) excludes Paramedics (6), Dermatologists (1) and Pharmacists (1).***** Please note that this infographic of MERS was updated with public source information to 1800hrs 21 May 2014 (EST) with n= 652 *****Key Notes:
  • This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Next chart, Job Families:

02 - MERSbyJobFamily_140521 

This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Key Notes:

  • The most represented by the data are Non-Participatory. It should be noted that I don’t believe that this data is a true representation and there is a lot of ‘hidden’ Pilgrims, Employees (unspecified) and Tourists in this data;
  • Healthcare Prac/Tech Ops are overrepresented in the known data, due to significant outbreaks in hospitals across Saudi Arabia. This is now (in my mind) a proven data point as the new Health Minister, Adel M. Fakieh, has effectively suppressed any new data on HCW infections since he took over the job but they still represent 16.7% of my data;
  • Healthcare Support gets a mention (finally). At less than 1% (confirmed) I think the actual number is much higher given the good data comes out of the UAE. The Saudi’s (again) are not discussing HCW but having worked in an Operating Theatre myself as a non-HCW (I was an Anaesthetic Secretary) it is easy to see how these workers become infected;
  • Paediatric(s) only make up 2.8% of the data inputs. Seems low, especially compared against H7N9 but I’m no virologist, just an amateur flublogist.

Last chart.

02 - MERSbyMainJobTitle_140521

The last chart looks at those overall main job families that are most impacted by MERS-CoV (specifically Farmers, Travellers, Paediatrics, Retired, HCW’s, Other and Unknown).

Key Notes:

  • Farmer (2.0%): It is thought that MERS-CoV is initially spread by the handling of camels. Not represented by the known data so probably underrepresented or the wrong narrative.
  • Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Again, not strongly represented by the data. I suspect it is suppressed data.
  • Paediatrics (2.8%): Any children reported between 0 – 14 years of age are here. Data doesn’t seem to show strong family clusters but I wonder given the P2P data we do know (amongst HCW’s) seem to show strong secondary infections amongst close working colleagues.
  • HCW (16.9%): Health Care Workers of any description but doesn’t include Health Support workers. Often a good indicator of secondary infections potency. Subject Matter Expert(s) on HCW infections are m’coll’s Ian Mackay and Maia Majumder.
  • Other (3.1%): All other occupations that have been publically released.
  • Unknown (53.2%): Unknown occupations. It should be noted that I have ‘guessed’ 136 occupations as ‘Retired’.

Final Thoughts

I now call on Professor Crawford Kilian to add Adel M. Fakieh to the Supari Prize list along with the previous Saudi Health Minister. As many have noted although we saw an initial uptick in publically sourced data from the Saudi MoH the data has now precluded data around occupation (HCW specifically) and expatriate status. This is now pure suppression of data and will no doubt lead to more cases ‘exporting’ from the Kingdom to other countries, including the United States which has now experienced two exported cases and one secondary infection.

Random Analytics: MERS-CoV in the Middle East (to 16 May 2014)

1 - MERSinMidEast_Infographic_140517

***** Please note that this infographic of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was updated with public source information to 2100hrs 17 May 2014 EST *****

The above infographic is a look into the MERS-CoV with specific emphasis on its cases within the Middle East. The data is taken primarily from the latest ECDC update (see appended) plus the most recent update from the Saudi Arabian MoH (care of Al Jazeera). Since I last updated this infographic back in November 2013 the cases have exploded, especially in Saudi Arabia and the United Arab Emirates. New Middle East countries have also been added since November including Egypt, Lebanon and Yemen.

The European Centre for Disease Prevention and Control has released its latest update on the MERS-CoV. Epidemiological update: Middle East respiratory syndrome coronavirus (MERS-CoV) for 16 May 2014. Excerpt:

ECDC notes the decision of Margaret Chan, the Director General of WHO, on 14 May 2014 not to call the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak a Public Health Emergency of International Concern (PHEIC) as the conditions have not been met yet. This decision was based on the advice of the WHO Emergency Committee under the IHR on MERS-CoV. However the committee indicated that, based on current information, “the seriousness of the situation had increased in terms of public health impact, but that there is no evidence of sustained human-to-human transmission.”

Since April 2012 and as of 16 May 2014, 621 cases of MERS-CoV infection have been reported globally, including 188 deaths.

On 11 May 2014 a second imported case of MERS-CoV was confirmed by the United States’ Centers for Disease Control and Prevention.

On 13 May 2014, National Institute for Public Health and the Environment (RIVM) in the Netherlands reported the first imported case of MERS-CoV in the country. On 15 May 2014 a second case, who travelled with the first case, was reported.

Confirmed cases and deaths by region:

Middle East:

  • Saudi Arabia: 511 cases/160 deaths
  • United Arab Emirates: 67 cases/9 deaths
  • Qatar: 7 cases/4 deaths
  • Jordan: 9 cases/4 deaths
  • Oman: 2 cases/2 deaths
  • Kuwait: 3 cases/1 death
  • Egypt: 1 case/0 deaths
  • Yemen: 1 case/1 death
  • Lebanon: 1 case/0 deaths

Europe:

  • UK: 4 cases/3 deaths
  • Germany: 2 cases/1 death
  • France: 2 cases/1 death
  • Italy: 1 case/0 deaths
  • Greece: 1 case/0 deaths
  • Netherlands: 2 cases/0 deaths

Africa:

  • Tunisia: 3 cases/1 death

Asia:

  • Malaysia: 1 case/1 death
  • Philippines: 1 case/0 deaths

Americas:

  • United States of America: 2 cases/0 deaths

Most cases have either occurred in the Middle East or have direct links to a primary case infected in the Middle East. Local secondary transmission following importation was reported from the United Kingdom, France, and Tunisia.

Random Analytics: MERS by Occupation (to 150 confirmed)

According to Flutrackers there have been 580-cases of MERS-CoV that have been clinically diagnosed. The fatality count is a little harder to gauge given the poor data that comes out of the Middle East but Dr Ian Mackay via his blog Virology Down Under believes the number to be 161 out of 571-cases (a Case Fatality Rate of 28.2%). His total does not include the three deaths that were announced by the Saudi Ministry of Health (MoH) overnight.

In the past 24-hours the Centers for Disease Control and Prevention (CDC) have announced the second imported case of the Middle Eastern Respiratory Syndrome (MERS-CoV). All the details have not yet been confirmed but the second case, like the first was in a returning Health Care Worker.

Since the 28th April the Saudi MoH has cut all details about cases involving Health Care Workers and resident/citizen status. Even so, I have been able to detail 151-job titles including the most recent Health Care Worker who travelled from Saudi Arabia to the United States and was hospitalised in Florida.

1 - JobTitle_MERS_Top20_140513

 

Looking at the Job Titles the stand out point is that 47.7% of the data is associated with the occupation of ‘Health Care Worker’. This could be anyone occupied as an anaesthetist, dentist, doctor, lab-tech, midwife, nurse, optometrist, pharmacist, surgeon or even veterinarian. One of my chief complaints about occupation data being given (outside of outright suppression) is that the term ‘Health Care Worker’ lacks detail and is little more than a ‘throw-away line’.

That aside the leading occupations are Health Care Worker (47.7%), Nurse (13.9%), High School (12-14) at (4.6%), Primary School (5-11) and Pilgrims (both at 3.3%).

Points of interest:

  • There are currently 9 different Health Care Workers job titles that have confirmed… They include Nurse (21); Paramedic (6 – currently counted as a HCW), Doctor (4), Nurse (ER – 1), Health Domain Worker (1), Doctor (ICU), Pharmacist (1 – currently counted as a HCW), Respiratory Therapist (1) and a Surgeon (1).
  • The current average age of the 22 confirmed Nurses is 37-years;
  • Of the 22 confirmed Nurses, 17 are expatriate workers and five have not been confirmed by the relevant Ministry as either a resident or citizen;
  • Nine Farm Owners have been confirmed but only two Farm Employees which might suggest that Farm Owners (citizens) are being tested but their Farm Employees (often poor residents) have not been tested or their details are being suppressed.

2 - JobFamily_MERS_140513

The key data-point in the Job Family chart is that 74% of the occupation data is unknown. When we roll up all the Job Titles that we do know into their relevant Job Families the top-3 groups are:

  • Healthcare Prac/Tech Ops (67.7%) comprising Doctors, HCW, Nurses, Specialists and Surgeons);
  • Paediatric (10.6%) a new group that I have created recently to split apart Non-Participatory into more useful datasets. The Paediatric groups comprises Child (0-4), Primary School (5-11) and High School (12-14);
  • Farming, Forestry and Fishing (7.3%) comprising Camel Breeder, Farm Employee, Farm Owner and a Shepherd.

Of interest:

  • I just cannot believe that only four Pilgrims and four Tourists have become infected. I think this is a key area of data suppression and probably due to the fact that the Hajj and Umrah bring in huge tourism revenues into the Kingdom. According to Albawaba the Hajj alone added 3% to Gross Domestic Product in 2012 (or roughly $16.5-billion);
  • Paediatric cases only make up 2.8% of the data. I don’t see a lot of virology commentary on this but it seems low given that MERS seems to be able to spread within family and hospital clusters.
  • The MERS data has thrown up the first Healthcare Support worker, a Hospital Receptionist who was diagnosed in Jeddah, KSA. I also suspect the Health Domain Worker to be in this category but without specific detail I left it in the Healthcare Prac/Tech Ops Family.

FINAL THOUGHTS

With just one in four cases being confirmed for occupation I’m not sure that any reporting on the subject adds much to the level of public knowledge about the causes and progress of MERS outside of the fact that the spike in HCW in April points to a lack of infection control in Saudi hospitals.

Suppression is a strong term but as noted in the opening paragraph the Saudi’s have not provided any detail into occupation or resident/citizen states since the 28th April.

The Saudi’s have their reasons for this (and they cannot be good). Sometimes a lack of data can be just as enlightening as lots of it.

Random Analytics: Comparing H7N9 and MERS by Key Occupations

“Theatre Staff Nurse | King Faisal Specialist Hospital, Riyadh! Excellent tax-free income | 54 days annual leave | Free & secure furnished accommodation | Free medical insurance & emergency dental | and much more!” Online advertisement (7 May 2014).

There has been volumes written recently about the amount of Health Care Workers who been impacted by the Middle Eastern Respiratory Syndrome (MERS-CoV) in the past month after a surge of Saudi Arabian cases in April. Given the discussion I thought I might add my two cents worth.

Here are two charts, focusing on the H7N9 outbreak in China and one for MERS:

01 - H7N9_MainOccupations_140507

***** Please note that this infographic of H7N9 was updated with public source information to 1800hrs 7 May 2014 (EST) with n=435 *****

01 - MERS_MainOccupations_140507

***** Please note that this infographic of MERS was updated with public source information to 1800hrs 7 May 2014 (EST) with n=506 *****

H7N9 & MERS by Key Occupation

The first chart displays three key occupations, an age cohort and two other groups for H7N9 (China). The groups include:

  • Farmer (21.4%): It is thought that H7N9 is spread primarily by the handling and eating of incorrectly cooked poultry so the publically sourced information on occupation type has focussed on farming as a key vector for H7N9.
  • Retired (14.3%): Chinese retirees have been adversely impacted by this disease, it is thought because they take on significant family responsibilities such as shopping (at live markets) and cooking.
  • Paediatrics (6%): Any children reported between 0 – 14 years of age.
  • HCW (0.5%): Health Care Workers of any description. Often a good indicator of secondary infections potency.
  • Other (18.6%): All other occupations that have been publically released.
  • Unknown (39.3%): Unknown occupations.

The second chart displays three key occupations, an age cohort and two other groups for MERS-CoV (Middle East). The groups include:

  • Farmer (1.4%): It is thought that MERS-CoV is initially spread by the handling of camels.
  • Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Although not a primary occupation while on pilgrimage or holiday you are not working so created a new non-employment type for this activity.
  • Paediatrics (2.8%): Any children reported between 0 – 14 years of age.
  • HCW (20.4%): Health Care Workers of any description. Often a good indicator of secondary infections potency.
  • Other (1.6%): All other occupations that have been publically released.
  • Unknown (72.9%): Unknown occupations.

Looking in the Wrong Direction

To the best of the Flublogia community’s knowledge, the Chinese medical authorities have worked very diligently on updating the World Health Organisation on key information. Many WHO updates have included an update on individual cases exposure to chickens and if there occupation was farming. Some provincial governments also release more detailed information which includes the person’s occupation. One of my issues with the Chinese occupational data is the level of detail, especially around farming (my whinge can be found here).

As for the MERS occupational data the first point that needs to be made is there is not enough data (publically available occupation data for MERS sits at just 27%). What data that is there is dominated by Health Care Workers who represent 74% of the known occupations (at least according to my Excel and I dig a little harder for this stuff than most other flublogists that concentrate on the medical side). Two points to be made here:

  1. The fact that Health Care Workers dominate the occupational space in MERS-CoV is important but given that HCW are usually secondary infections it is not as important as finding a possible primary occupational vector. An example of a primary occupation might include farming or racing, especially those that might relate to the camel industry.
  2. If you are going to explore the surge in Health Care Worker cases then start to limit the use of the term ‘Health Care Worker’. Health Care Workers are legion in titles, roles, functions and job families. Without going into detail about grades a great example would be a Nurse with specialities in A&E, Aged Care, Child Health, Community, ICU, General, Midwifery, Neo-Natal, Paediatric, Psych and Theatre. I commenced this blog with a live job ad for King Faisal Hospital in Riyadh looking for Australian and New Zealand Theatre nurses (Registered, two years minimum Theatre experience).

To sum up, the attention given to Health Care Worker cases in the current MERS-CoV outbreak and the almost 7 out of 10 lack of detail on other occupations might make for scary charts but it is not the main game. Stop MERS in the field and you stop MERS in hospitals.

Upcoming Occupational Data Issues

I also wanted to list some other ongoing concerns I have with the level of information coming out about H7N9 and MERS-COV as it relates to occupation data.

H7N9

  1. The level of occupation data supplied by China has dropped significantly in 2014. I note that to the end of 2013 that information was publically available for 75% of all cases but over the past four months that figure has dropped to 60%. Of the past 50-cases occupation have only been supplied on 5 (just 10%).

MERS-CoV

  1. The Kingdom has buried any mention of pilgrims catching the disease and the only real confirmations come from those who travelled to or through Saudi Arabia and returned to Europe, Tunisia or Malaysia. 1.2% for pilgrims and tourists is way to low and I would expect that a fair section of the unknown(s) is in this category. I’ll continue to chase up.
  2. I noted that I could account for five ‘Farm Owners’ but of all the cases I can only identify one ‘Shepherd’. Where are all the farm employees on this list? (My guess is they could lack access to medical facilities as they are more likely to come from poorer migration countries).
  3. On that note, The Guardian recently released a story detailing the almost 1,000 construction workers who have already died building the FIFA World Cup facilities in Qatar. One of the main causes of death listed is a heart attack. Many of these workers live in cramped and neglected accommodation (if it can spread in hospitals…) As yet I haven’t seen any construction occupations included in the publically available information. Could there be Qatari migrant workers dying of MERS and not being investigated.
  4. Although the previous KSA Ministry of Health news releases were noted for their lack of content one change that should be noted is that under the new system the difference between citizen and resident cases is no longer being noted. In a country that is made up of 20% foreign workers linking this data (along with expatriate country) can be critical in seeing patterns, such as issues with secondary hospital infections.

There is more but that’s enough for now.

Random Analytics: Ebola 2014 (update to 23 Apr 2014)

“This study demonstrates the emergence of a new EBOV strain in Guinea,” New England Journal of Medicine (22 April 2014).

The latest outbreak of Ebola Zaire, which is ongoing, has now reached 242-clinical cases and taken the lives of 147. Guinea has borne the brunt of the disease with 208-clinical cases (136-deaths) and Liberia 34 clinical cases (11-deaths, revised down from 13). Previously reported cases in Mali and Sierra Leone have either been confirmed as Lassa fever or Ebola Virus Disease (EVD) negative. Although recent cases are tapering off these numbers are still likely to change.

The World Health Organisation has a comprehensive update issued on the 22 April 2014.

As part of a post-graduate program I am undertaking I have built a five-minute Ebola Virus Disease (EVD) lesson utilising just four graphs and six-dot points.

1. Ebola Virus Disease Outbreak (Guinea/Liberia 2014) * UPDATED *

01 - Ebola_GuineaOutbreak_140423

***** Please note that this infographic of the EVD was updated with public source information to 2000hrs 23 April 2014 (EST) *****

From the World Health Organisation. Ebola virus disease, West Africa (Situation as of 22 April 2014). Excerpt:

As of 18:00 on 20 April, the Ministry of Health (MOH) of Guinea has reported a cumulative total of 208 clinical cases of Ebola Virus Disease (EVD), including 136 deaths. To date, 169 patients have been tested for ebolavirus infection and 112 cases have been laboratory confirmed, including 69 deaths.  In addition, 41 cases (34 deaths) meet the probable case definition for EVD and 55 cases (33 deaths) are classified as suspected cases.  Twenty-five (25) health care workers (HCW) have been affected (18 confirmed), with 16 deaths (12 confirmed).

Clinical cases of EVD have been reported from Conakry (53 cases, including 23 deaths), Guekedou (122/87), Macenta (22/16), Kissidougou (6/5), Dabola (4/4) and Djingaraye (1/1). Laboratory confirmed cases and deaths have been reported from Conakry (37 cases, including 19 deaths), Guekedou (60/38), Macenta (13/10), Kissidougou (1/1) and Dabola (1/1). These updated figures include 3 new cases isolated on 20 April from Conakry and Guekedou, 2 of whom are laboratory confirmed.  Five new deaths have also been reported among existing cases; all 5 of the deaths were patients with confirmed EVD.  Twenty-one (21) patients were in isolation in Conakry (12), Guekedou (8) and Macenta (1), while 16 patients who recovered from their illness were discharged from hospital.

Notes: The map graphic was taken from Wikipedia (then amended).

2. Ebola across Africa * UPDATED *

02 - Ebola_AcrossAfrica_140423

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 23 April 2014 (EST). EBOV = Ebola Zaire, SUDV = Ebola Sudan, BDBV = Ebola Bundibugyo and TAFV = Ebola Ivory Coast *****

The Ebola across Africa infographic details the country specific outbreaks of the EVD since it was first discovered in 1976 (with a 1972 retrospective case from Zaire included). As the map shows the bulk of the outbreaks have occurred within central Africa and the most deadly, Ebola Zaire causing the most cases in the Democratic Republic of Congo (formally Zaire). The most recent outbreak has actually occurred in West Africa, originating from Guinea and is a new isolate of Ebola Zaire (Gueckedou and Kissidougou).

As an additional point of interest I have also added the Health Expenditure per capita for each country in 2012 $USD (source: World Bank).

Notes: The 1976 – 2004 outbreaks of Ebola Sudan occurred in the bottom half of Sudan (now South Sudan). Zaire was renamed the Democratic Republic of Congo in 1997.

3. Ebola (Top 10 Outbreaks by Case Numbers) * UPDATED *

03 - Ebola_Top10OutbreaksByCaseNo_140423

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 23 April 2014 (EST) *****

The next chart displays the top 10 outbreaks in order of case numbers and each horizontal bar is filled with the flag of the country where the outbreak occurred. With clinical cases reaching 208 in Guinea and 34 in Liberia the EBOV17 coded outbreak has now become sixth largest (242) based on case numbers. The largest outbreak (SUDV4) was of Ebola Sudan in Uganda (2000) when 425 became infected and 224 died. The only other recording of an EVD that jumped borders prior to this outbreak was the 10th worst outbreak (EBOV8) when a doctor caught the disease in Gabon and subsequently took an international flight to South Africa where he became ill and infected other health workers.

Notes: In order from lowest to highest. 10th: EBOV8 (Gabon/South Africa), 9th: EBOV9 (Gabon), 8th: EBOV11 (Republic of Congo), 7th: BDBV01 (Uganda), 6th: EBOV17 (Guinea/Liberia/Mali), 5th: EBOV15 (Democratic Republic of Congo), 4th: SUDV1 (technically Sudan but would now be South Sudan), 3rd: EBOV6 (Zaire but now the DRC), 2nd: EBOV2 (Zaire but now the DRC) and 1st: SUDV4 (Uganda).

4. Ebola (Cases by Classification and Year) * UPDATED *

04 - Ebola_CasesbyClassYear_140423

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 23 April 2014 (EST) *****

The final chart shows cases by classification (Ebola Zaire, Sudan, Bundibugyo, Reston and Ivory Coast) by year and then split into those recovered or those deceased (following in a red variant). As you can see the initial outbreak in 1976 of the both Ebola Zaire and Ebola Sudan was the most significant year with 603 cases and 431 deaths (a combined Case Fatality Rate of 71.5%). With up to 242 clinical cases so far the 2014 Ebola Zaire outbreak is now the fifth worst in terms of case numbers.

Notes: Several years had just one case. They are 1972 (a retrospective fatality of Ebola Zaire in Zaire), 1977 (a single case of Ebola Zaire in Zaire), 1988 (an accidental infection of Ebola Zaire in Porton Down, UK) and 2011 (a single fatality of Ebola Sudan in Uganda). The 2014 numbers are currently provisional.

Key Facts: (source: Fact Sheet 103, WHO, last updated March 2014)

  • The Ebola virus causes Ebola virus disease (EVD; formerly known as Ebola haemorrhagic fever) in humans;
  • EVD outbreaks have a case fatality rate of up to 90%;
  • EVD outbreaks occur primarily in remote villages in Central and West Africa, near tropical rainforests;
  • The virus is transmitted to people from wild animals and spreads in the human population through human-to-human transmission;
  • Fruit bats of the Pteropodidae family are considered to be the natural host of the Ebola virus;
  • No specific treatment or vaccine is available for use in people or animals.

Acknowledgements:Data for this infographic was sourced from official reports from the World Health Organisation. I have also utilised resources from the CDC, CIDRAP, H5N1, Virology Down Under and National Geographic.

Random Analytics: Ebola Outbreak in Guinea/Liberia (to 21 Apr 2014)

“Our priority is to continue to care for the people infected with the Ebola virus,” Henry Gray, Médecins Sans Frontières (MSF) Emergency Coordinator, Guinea (18 April 2014).

The latest outbreak of Ebola Zaire, which is ongoing, has now infected up to 230-persons and taken the lives of 142. Guinea has borne the brunt of the disease with 203 infections (129-deaths) and Liberia 27 infections (13-deaths). Previously reported cases in Mali and Sierra Leone have shown to be negative. These numbers are still likely to change.

The World Health Organisation has a comprehensive update issued on the 17 April 2014 (care of FluTrackers).

As part of a post-graduate program I am undertaking to build a five-minute Ebola Virus Disease (EVD) lesson utilising just four graphs and six-dot points. Here is the final chart! I’ll update the other three chart(s) to align the information as the next update becomes available:

New Chart – Ebola Virus Disease Outbreak (Guinea/Liberia 2014)

01 - Ebola_GuineaOutbreak_140421

***** Please note that this infographic of the EVD was updated with public source information to 2345hrs 20 April 2014 (EST) *****

The most impacted area of this EVD outbreak is in the Guekedou Prefecture with the outbreak spreading over the border to neighbouring Liberia.

Notes: The map graphic was taken from public source data from Wikipedia (and amended).

Key Facts: (source: Fact Sheet 103, WHO, last updated March 2014)

  • The Ebola virus causes Ebola virus disease (EVD; formerly known as Ebola haemorrhagic fever) in humans;
  • EVD outbreaks have a case fatality rate of up to 90%;
  • EVD outbreaks occur primarily in remote villages in Central and West Africa, near tropical rainforests;
  • The virus is transmitted to people from wild animals and spreads in the human population through human-to-human transmission;
  • Fruit bats of the Pteropodidae family are considered to be the natural host of the Ebola virus;
  • No specific treatment or vaccine is available for use in people or animals.

Acknowledgements:Data for this infographic was sourced from official reports from the World Health Organisation. I have also utilised resources from the CDC, CIDRAP, H5N1, Virology Down Under and National Geographic.

Random Analytics: Ebola across Africa (to 14 Apr 2014)

***** Note: If you would like a more updated version of this series of charts then please check out Random Analytics: Ebola across Africa (to 1 Oct 2014) *****

“We are pleased to say we have controlled the spread of the epidemic,” Francois Fall, Foreign Minister, Guinea (14 April 2014).

The latest outbreak of Ebola Zaire, which is ongoing, has now infected up to 200-persons and taken the lives of 121. Guinea has borne the brunt of the disease with 168 infections (108-deaths), Liberia 26 infections (13-deaths) and there are six suspected cases in Mali. These numbers are still likely to change.

The World Health Organisation has a comprehensive update issued on the 14 April 2014.

As part of a post-graduate program I am undertaking to build a five-minute Ebola Virus Disease (EVD) lesson utilising just five graphs and six-dot points. Here is the latest chart along with previous updated chart(s):

New Chart – Ebola across Africa

01 - Ebola_AcrossAfrica_140414

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 15 April 2014 (EST). EBOV = Ebola Zaire, SUDV = Ebola Sudan, BDBV = Ebola Bundibugyo and TAFV = Ebola Ivory Coast *****

The Ebola across Africa infographic details the country specific outbreaks of the EVD since it was first discovered in 1976 (with a 1972 retrospective case from Zaire included). As the map shows the bulk of the outbreaks have occurred within central Africa and the most deadly variant, Ebola Zaire causing the most cases in the Democratic Republic of Congo (formally Zaire). Although the reasons are unclear the most recent outbreak has actually occurred in West Africa, originating from Guinea. As an additional point of interest I have also added the Health Expenditure per capita for each country in 2012 $USD (source: World Bank).

Notes: The 1976 – 2004 outbreaks of Ebola Sudan occurred in the bottom half of Sudan (now South Sudan). Zaire was renamed the Democratic Republic of Congo in 1997.

Chart 2 – Ebola (Top 10 Outbreaks by Case Numbers) * UPDATED *

02 - Ebola_Top10OutbreaksByCaseNo_140414

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 15 April 2014 (EST) *****

The next chart displays the top 10 outbreaks in order of case numbers and each horizontal bar is filled with the flag of the country where the outbreak occurred. With confirmed/suspected cases in Guinea (168), Liberia (26) and Mali (6) the EBOV17 coded outbreak has now become sixth largest based on case numbers. The largest outbreak (SUDV4) was of Ebola Sudan in Uganda (2000) when 425 became infected and 224 died. The only other recording of an EVD that jumped borders prior to this was in the 10th worst outbreak (EBOV8) when a doctor caught the disease in Gabon and subsequently caught an international flight to South Africa where he became ill and infected other health workers.

Notes: In order from lowest to highest. 10th: EBOV8 (Gabon/South Africa), 9th: EBOV9 (Gabon), 8th: EBOV11 (Republic of Congo), 7th: BDBV01 (Uganda), 6th: EBOV17 (Guinea/Liberia/Mali), 5th: EBOV15 (Democratic Republic of Congo), 4th: SUDV1 (technically Sudan but would now be South Sudan), 3rd: EBOV6 (Zaire but now the DRC), 2nd: EBOV2 (Zaire but now the DRC) and 1st: SUDV4 (Uganda).

Chart 3 – Ebola (Cases by Classification and Year) * UPDATED *

03 - Ebola_CasesbyClassYear_140414

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 15 April 2014 (EST) *****

The final chart shows cases by classification (Ebola Zaire, Sudan, Bundibugyo, Reston and Ivory Coast) by year and then split into those recovered or those deceased (following in a red variant). As you can see the initial outbreak in 1976 of the both Ebola Zaire and Ebola Sudan was the most significant year with 603 cases and 431 deaths (a combined Case Fatality Rate of 71.5%). With up to 200 confirmed/suspected cases so far the 2014 Ebola Zaire outbreak is now the fifth worst in terms of case numbers.

Notes: Several years had just one case. They are 1972 (a retrospective fatality of Ebola Zaire in Zaire), 1977 (a single case of Ebola Zaire in Zaire), 1988 (an accidental infection of Ebola Zaire in Porton Down, UK) and 2011 (a single fatality of Ebola Sudan in Uganda). The 2014 numbers are currently provisional.

Key Facts: (source: Fact Sheet 103, WHO, last updated March 2014)

  • The Ebola virus causes Ebola virus disease (EVD; formerly known as Ebola haemorrhagic fever) in humans;
  • EVD outbreaks have a case fatality rate of up to 90%;
  • EVD outbreaks occur primarily in remote villages in Central and West Africa, near tropical rainforests;
  • The virus is transmitted to people from wild animals and spreads in the human population through human-to-human transmission;
  • Fruit bats of the Pteropodidae family are considered to be the natural host of the Ebola virus;
  • No specific treatment or vaccine is available for use in people or animals.

Acknowledgements: Data for this infographic was sourced from official reports from the World Health Organisation. I have also utilised resources from the CDC, CIDRAP, H5N1, Virology Down Under and National Geographic.

Random Analytics: Top 10 Ebola Outbreaks by Case Numbers (to 11 Apr 2014)

 

“This is one of the most challenging Ebola outbreaks that we have ever faced. And the reasons why this is one of the most challenging outbreaks is that, first we see a wide geographic dispersion of cases. So this has come in from a number of districts as well as a large city in Guinea, Conakry.” Dr Keiji Fukuda, WHO (10 April 2014).

The latest outbreak of Ebola Zaire, which is ongoing, has now infected 157-persons and taken the lives of 101 in Guinea while in neighbouring Liberia up to 25-persons have been infected with 12-deaths. As I write this post there are unconfirmed reports of the outbreak spreading to Mali while other countries have been ruling out cases through intensive testing. Both outbreaks are fluid and those numbers may increase or decrease as data solidifies or the virus spreads further. See the latest World Health Organisation (WHO) Disease Outbreak News (DON) from the two countries (correct as at 10 April 2014) for the latest details.

As part of a post-graduate program I am undertaking to build a five-minute Ebola Virus Disease lesson utilising just five graphs and six-dot points (as supplied by the WHO). Here is the latest chart along with previous chart(s) and my dot-points:

New Chart – Ebola (Top 10 Outbreaks by Case Numbers)

02 - Ebola_CasesbyClassYear_140411

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 11 April 2014 (EST). *****

The next infographic addition displays the top 10 outbreaks in order of case numbers and each horizontal bar is filled with the flag of the country where the outbreak occurred. With confirmed/suspected cases in Guinea (157) and Liberia (25) for a total of 182 the outbreak (coded EBOV17) has now become sixth largest based on case numbers. The largest outbreak (SUDV4) was of Ebola Sudan in Uganda (2000) when 425 became infected and 224 died. The only other recording of an EVD that jumped borders prior to this was in the 10th worst outbreak (EBOV8) when a doctor caught the disease in Gabon and subsequently travelled on a plane to South Africa where he infected health care workers.

Notes: In order from lowest to highest. 10th: EBOV8 (Gabon/South Africa), 9th: EBOV9 (Gabon), 8th: EBOV11 (Republic of Congo), 7th: BDBV01 (Uganda), 6th: EBOV17 (Guinea/Liberia), 5th: EBOV15 (Democratic Republic of Congo), 4th: SUDV1 (technically Sudan but would now be South Sudan), 3rd: EBOV6 (Zaire but now the DRC), 2nd: EBOV2 (Zaire but now the DRC) and 1st: SUDV4 (Uganda).

Chart 2 – Ebola (Cases by Classification and Year) * UPDATED *

01 - Ebola_Top10OutbreaksByCaseNo_140411

***** Please note that this infographic of the EVD was updated with public source information to 0800hrs 11 April 2014 (EST) *****

The updated infographic shows cases by classification (Ebola Zaire, Sudan, Bundibugyo, Reston and Ivory Coast) by year and then split into those recovered or those deceased (following in a red variant). As you can see the initial outbreak of the both Ebola Zaire and Sudan in 1976 was the most significant with 603 cases and 431 deaths (a combined Case Fatality Rate of 71.5%). With up to 182 confirmed cases so far the 2014 outbreak numbers are already fifth in terms of case numbers.

Notes: Several years had just one case. They are 1972 (a retrospective fatality of Ebola Zaire in Zaire), 1977 (a single case of Ebola Zaire in Zaire), 1988 (an accidental infection of Ebola Zaire in Porton Down, UK) and 2011 (a single fatality of Ebola Sudan in Uganda). The 2014 numbers are currently provisional.

Key Facts: (source: Fact Sheet 103, WHO, last updated March 2014)

  • The Ebola virus causes Ebola virus disease (EVD; formerly known as Ebola haemorrhagic fever) in humans;
  • EVD outbreaks have a case fatality rate of up to 90%;
  • EVD outbreaks occur primarily in remote villages in Central and West Africa, near tropical rainforests;
  • The virus is transmitted to people from wild animals and spreads in the human population through human-to-human transmission;
  • Fruit bats of the Pteropodidae family are considered to be the natural host of the Ebola virus;
  • No specific treatment or vaccine is available for use in people or animals.

Acknowledgements:Data for this infographic was sourced from official reports from the World Health Organisation. I have also utilised resources from the CDC, CIDRAP, H5N1, Virology Down Under and National Geographic.