Random Analytics

Charts, Infographics & Analytics. No Spinning the Data. No Juking the Stats

Random Analytics: Ebola in Liberia (to 30 Jul 2014)

I have now been doing Random Analytics since October 2012 and analytics on Ebola itself was my sixth post (having been interested in the subject since I read The Hot Zone in the mid 1990’s. I’ve been doing complete posts on the subject recently, by impacted 2014 country, by African exposure, by top 10 outbreaks and by classification and year but this outbreak is moving faster than I can keep up with. I’ll now just concentrate on infographics that don’t get done in the mainstream narrative (or my flublogist colleagues don’t do). If you want to follow the detail more closely then follow Crawford Kilian, Ian Mackay or Maia Majumber (who does the dangdest infographics in the flublogist space).

Ebola in Liberia (to 30 July 2014)

01 - Ebola_Liberia_140730

***** Please note that this EVD infographic was updated with public source information to 2100hrs 30 July 2014 (EST) *****

As I go to print the overall Ebola situation can be found in the latest World Health Organisation advice (circa 27 July 2014). This infographic comes from the latest information from the Liberian Ministry of Health and Social Welfare via reliefweb. Situation Report No. 72 on the EBOLA Virus disease epidemic in Liberia as of 29th May to 28 July 2014. Detail:

Highlights

Lofa County

Bakedou

Citizens and CHSWT have reached a compromise to allow health workers to investigate and follow up cases. Unofficial: About 15 persons are currently ill.

Voinjama

One of the two patients in the holding room left against medical advice and relocated to Zorzor Curran Hospital. However, specimen was taken.

Bong County

There no lab technician to collect specimen

Bomi County

There is no trained personnel to manage cases – Also, there is no lab technician to collect specimen

Nimba County

Family of confirmed case continuously refuses to allow patient to be carried to treatment facility

Random Analytics: MH17

1 - MH17_Infographic_140722

The above infographic displays the known nationalities of the 298 passengers and crew aboard Malaysian Airlines flight MH17 which departed from Amsterdam and was en-route to Kuala Lumpur. Details of the nationalities are primarily sourced by the Malaysian Airlines statement (released 18 Jul 2014) and further updates via ABC (19 Jul 2014) and the BBC (22 Jul 2014).

Depending on where you are in the World you have either awoken or are going to sleep after hearing about the terrible tragedy that has occurred near Shakhtars’k, Ukraine. Via the Australian Broadcasting Corporation. Live: Australians among dead after Malaysia Airlines jet shot down by missile over Ukraine. Excerpt:

At least 27 Australians have died on board a Malaysia Airlines passenger jet that was shot down by a ground-to-air missile over Ukraine, killing all 298 passengers and crew.

Flight MH17 was en route from Amsterdam to Kuala Lumpur when it came down in rebel-held territory near Ukraine’s border with Russia. US vice-president Joe Biden says it was “blown out of the sky” and that it was “not an accident”.

Prime Minister Tony Abbott has told Parliament it appears that Russian-backed rebels shot the plane down.

Ukrainian wire taps appear to have captured pro-Russian separatists claiming responsibility for downing the jet, but there has been no official confirmation.

 

To the Lost.

Random Analytics: 44th Parliament 94(a) and 94(b) ejections (to 8 Jul 2014)

1-BronwynBishop_viaABC

Here is a look at the Standing Order 94(a) and Standing Order 94(b) ejections for the 44th Parliament of the Commonwealth of Australia House of Representatives to the end of the 14th week (8 July 2014).

2 - 94aEjectionsbyPerson_140714

The first chart details the list of those ejected from the house under Standing Orders 94(a). At the end of the 14th sitting week there were 144 ejections, of which 141 (98.0%) were from the Australian Labor Party, two (1.4%) were from the Liberal National Party and just the one (0.6%) for the National Party.

The Top-3 from the ALP:

3-NickChampion_viaAPH

Nicholas David “Nick” Champion MP (20-ejections) is the ALP Member representing the electorate of Wakefield (SA). He won the seat at the 2007 Australian federal election.

4-MarkDreyfus_viaAPH

Mark Alfred Dreyfus QC MP (12-ejections) is the ALP Member representing the electorate of Isaacs (WA). He won the seat at the 2007 Australian federal election. He is the former Attorney-General, Minister for the Public Service and Integrity, Minister for Emergency Management, and Special Minister of State in the Second Rudd Ministry.

5-PatConroy_viaAPH

Pat Conroy MP (11-ejections) is the Member representing the electorate of Charlton (NSW). He won the seat at the 2013 Australian federal election.

From the Coalition:

6-EwenJones_viaAPH

Ewen Jones MP (2-ejections) is the Member representing the electorate of Herbert (QLD). He won the seat at the 2010 Australian federal election.

There have only been two Standing Order 94(b) ejections thus far. They are Mark Dreyfus (ALP) and Wayne Swan (ALP).

7 - 94aEjectionsbySittingDay_140714

The final chart is a look at the Standing Order 94(a) ejections by sitting day. As you can see they tend to pick up in the second last week of each large sitting period or at the end of the calendar year. In this update I’ve been able to include some colour as three ejections have come from the Coalition.

Image Sources: Bronwyn Bishop’s image care of the ABC while photos of individual members are taken from their official Australian Parliament House photo.

Random Analytics: MERS by Occupation (to >375)

Since my last Middle Eastern Respiratory Syndrome update (21 May 2014) there have been a number of key developments and even some improvements in data quality coming out of the Kingdom of Saudi Arabia.

The big announcement since my previous post was the addition of 113 legacy cases by the KSA Ministry of Health. Of the 113-cases, 42 were Health Care Workers and are included in the following charts.

Almost as importantly, the Saudi’s are now sharing their data with the World Health Organisation (WHO). The Disease Outbreak Notifications, or DON’s, are very comprehensive, light-years away from the Health Ministries updates of the past. With some irony I was pleased to see that Iran and the Kingdom featured together in the most recent DON. Perhaps we are seeing a form of MERS diplomacy occurring.

Here are the latest MERS by Occupation charts.

***** Please note that all infographics for this MERS-CoV article are using publically sourced information to 1200hrs 4 July 2014 (EST) *****

 

01 - MERSbyJobTitle_140704

This first chart looks at those infected with MERS by Job Title or Function.

Key Notes:

  • Retired: The largest group. There are 150-retirees (39.5% of known job titles) represented in this chart but only 2 have been confirmed (1.3%). The bulk of the retirees represented in the chart are included if they did not have a job title or function attributed to them AND if their ages are greater than the official retirement age for their home country.
  • Health Care Workers (HCW): The second largest group. Includes all types of unidentified workers in the Health sector (i.e. Nurses and Doctors).
  • Nurse: I have been able to identify 23-Nurses and in at least two cases, their speciality (ER & ICU).
  • Farmer: Includes both Owners (9/75%) and Employees (3/25%). I suspect the higher weighting toward owners is due to the fact that they are all nationals (from KSA, Qatar and the UAE). The three farm employees that have been identified are all resident workers. Just a thought here. Rich owners get to see the doctor while residents might have a range of barriers which reduce their ability to receive primary care services or choose to work through what they might believe is a bad flu.
  • Pilgrim: Of the 11-Pilgrims I have been able to identify I believe at least 8 were Umrah linked while three were potentially due to the Haj.
  • Doctor: Six identified, including one surgeon and one ICU specialist.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_140704

Key Notes:

  • With the inclusion of an additional 42 Health Care Workers the Non-Participatory (156/18.6%) group (Paediatrics, Students, Retiree’s and the Unemployed) move from the largest to the second largest Job Family.
  • Health Practitioners/Technical Operations (159/19.0%) or HCW as there more commonly known become the largest Job Family represented. This number includes the Nursing Assistant that was identified in Iran (but more on that later).
  • Paediatrics (18/2.1%) numbers have declined since the last update when they represented just 2.8% of the data then. Still seems low and Maia Majumder picked up on this in a recent post.
  • Pilgrim/Tourist (14/1.6%) has seen a slight increase due to some Umrah inclusions recently.
  • Healthcare Support (6/0.7%) numbers remain static so not sure if the Saudi announcement of legacy cases conflates HCW and HCSpt numbers.
  • Construction (2/0.2%) is a new inclusion from the previous update. Given the amount of building going on the in the Middle East, especially in Qatar this number seems on the very low side. I’d expect to see this number increase with more robust reporting.

Last chart.

03 - MERSbyMainJobTitle_140704

The last chart looks at those overall main job families that are most impacted by MERS, specifically Farmers, Travellers, Paediatrics, Retirees, HCW & HCSpt (combined), Other and Unknown.

Key Notes:

  • Farmer (1.7%): With only 14 confirmed cases apart from 2013 you can barely see them across an entire year, quarter or month. Numbers seem low.
  • Traveller (1.7%): Like farming, numbers seem low.
  • Paediatrics (2.1%): As suggested previously, no new paediatric cases since my last update so the numbers have declined somewhat.
  • HCW & HCSpt (19.7%): Health Care Workers and I have also included Health Care Support Workers in this grouping as well. Numbers up on previous update due to the additional 42-cases.
  • Other (2.3%): All other occupations that have been publically released. I’ve actually reduced the number in this group by one from the last update due to improved reporting from Saudi Arabia.
  • Unknown (54.7%): Unknown occupations. Up slightly but with improved reporting I’m hoping that this will reduce (over time).

Final Thoughts (on the difference between a Health Care Worker and Health Care Support

Last month I tweeted that the Iranian Nursing Assistant (FT #827) should be counted as a Health Care Support worker rather than a HCW. I then got a number of return tweets from the likes of Helen Branswell, Ian M Mackay and others who disagreed with that line of thought.

When Helen and Ian ‘guide and advise’ it’s probably worth not disregarding that advice. Upon some personal review I decided that perhaps I had taken a too hard Workforce Planning line to my job functions without fully considering the clinical implications.

I have subsequently reviewed my thinking and have re-organised my data along the following lines.

Health Practitioners/Technical Operations (nee HCW) are any job title or function that is included in the Bureau of Labor Statistics SOC Occupations 29-0000 Healthcare Practitioners and Technical Occupations PLUS any clinical function that is included within the 31-0000 Healthcare Support Occupations, such as Nursing Assistants.

I am continuing to track Health Care Support personnel (there are four job titles already identified in the MERS data including Health Clinic Admin Officer, Health Domain Worker, Hospital Employee, Hospital Receptionist) as I believe the differentiation from HCW is important but I am including their data in job family charts and infographics.

In the end, I made a bad call and I thank those of you who took the time to correct my thinking.

Flublogia is certainly a community and one I truly appreciate being involved in.

Random Analytics: Australian Mining Employment Update (to end June 2014)

Some years ago while working within the Workforce Planning fraternity I quickly understood that June was a tough month for employment, no matter what sector you either guided or worked in. It might have something to do with the seasons given that companies traditionally cut staff in the coldest months. Alternately it might be impacted on the business cycle. After more than a decade in Workforce Planning I’ve found that, whatever your view, business reflects more pragmatic ends.

Last month I suggested that June is often a horror month for mining employment.

My prediction was borne out (no matter the reason). What I didn’t guess was that the job gains versus the job losses would be the worst in more than two-years, potentially making June 2014 the worst month in mining employment since the commencement of the Global Recession (effectively Nov/Dec 2008).

Here are the charts for Australian Mining Employment through to the end of June 2014.

1 - MiningJobsByState_Infographic_Jun2014_140701

 

The opening infographic looks at job gains and losses by State or Territory for the month of June 2014.

Data-points: All key mining states (with the exception of the Northern Territory) have lost significant numbers of operational mining (human) capital. The only positive is that Tasmania picked up an additional 50-positions.

2 - MiningGainsLosses_Chart_140701

The first of my mining employment charts looks at the previous 24-months from a mining employment gain and loss perspective. The positive employment numbers are split into those that reflect infrastructure (tan) and operational (blue) gains. Job losses are represented in red.

The biggest data-point for June 2014 is that the losses in employment are the greatest in two-years, effectively making June 2014, officially, the worst month for two years but, in effect, the worst month for mining employment since the Global Recession which kicked off in Nov/Dec 2008.

3 - MiningSectorGainsLosses_Chart_140701

 

The next employment chart looks in more details at the main resource types (Iron Ore, Coal, Gold/Copper, Zinc/Lead/Nickel, CSG/LNG and Uranium) by either a job gain or a job loss.

Of the 3,939 job losses for the month of June the key points are:

  • 100% were operational job losses (effectively, zero in construction);
  • 79% were included in my tally of key resources;
  • 37.5% of the total operational losses in June were in the Coal sector, the 24th month of consecutive losses in that sector;
  • 0% of the losses were in mining construction.

It should also be noted that the figures above do not include projected job cuts from BHP Billiton who are currently reviewing their iron ore business in West Australia.

4 - MiningSectorSentiment_Chart_140701

The last chart tracks employment gains and losses sentiment.

Given that I haven’t commented on this chart for some time the key points are that August –November 2012 and Q4 FY 2012/2013 plus July 2013 were very negative times for mining employment (effectively corresponding with declining commodity prices). We are currently undergoing a similar period with soft mineral pricing from late 2013 through to even more depressed prices in the last six weeks of the financial year.

One last point on the uptick in June 2014. There were lots of positive announcements for mining in June from government (i.e. positive EIS announcements) but little in the way of immediate positive employment benefits.

Juking the Stats (June 2014)

Whitehaven Coal is cutting jobs at its coal handling and preparation plant at Gunnedah in response to “tough” coal market conditions. At the same time they are not releasing much in the way of details or numbers of the people they are putting out of work and potentially on the bench for an extended time. I look forward to a future update.

Even Money Prediction

After two years of consecutive coal mining employment losses due to depressed thermal and metallurgical coal prices the continued low iron ore pricing will see a similar (and consecutive) drip feed of employment decline in that resource group.

Random Analytics: Ebola 2014 (to 25 Jun 2014)

Here are some charts and infographics of the 2014 Ebola Virus Disease outbreak.

Ebola Outbreak (Guinea Prefectures 2014)

01 - Ebola_Guinea_140625

 

***** Please note that this EVD infographic was updated with public source information to 0900hrs 25 June 2014 (EST) *****

The above infographic looks at the breakdowns by Prefecture of EVD cases and fatalities within Guinea. Data sourced from Ebola virus disease, West Africa – update 23 June 2014.

Ebola Outbreak (Sierra Leone Districts 2014)

02 - Ebola_SierraLeone_140625

 

***** Please note that this EVD infographic was updated with public source information to 0900hrs 25 June 2014 (EST) *****

The above infographic looks at the breakdowns by District of EVD cases and fatalities within Sierra Leone. Data sourced from Ebola virus disease, West Africa – update 23 June 2014 and the latest Sierra Leone Ministry of Health update (via FluTrackers).

Ebola across Africa

03 - Ebola_AcrossAfrica_140625

 

***** Please note that this EVD infographic was updated with public source information to 0900hrs 25 June 2014 (EST). EVD types are 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.

Ebola (Top 10 Outbreaks by Case Numbers)

04 - Ebola_Top10OutbreaksByCaseNo_140625

***** ***** Please note that this EVD infographic was updated with public source information to 0900hrs 25 June 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 344 in Guinea, 81 in Sierra Leone and 12 in Liberia the EBOV17 coded outbreak has now become largest (437) based on case numbers. The second largest outbreak (SUDV4) was of Ebola Sudan in Uganda (2000) when 425 became infected and 224 died. The recent outbreak is the first to migrate across international land borders. 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: EBV outbreaks in order from lowest to highest. 10th: EBOV8 (Gabon/South Africa), 9th: EBOV9 (Gabon), 8th: EBOV11 (Republic of Congo), 7th: BDBV01 (Uganda), , 6th: EBOV15 (Democratic Republic of Congo), 5th: SUDV1 (technically Sudan but would now be South Sudan), 4th: EBOV6 (Zaire but now the DRC), 3rd: EBOV2 (Zaire but now the DRC), 2nd: SUDV4 (Uganda) and the current, now deadliest outbreak EBOV17 (Guinea/Liberia/Sierra Leone).

Ebola (Cases by Classification and Year)

05 - Ebola_CasesbyClassYear_140625

***** Please note that this EVD infographic was updated with public source information to 0900hrs 25 June 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). From 24 June this latest outbreak has become the most significant in terms of case numbers, eclipsing the 1976 dual outbreaks which saw 603 cases and 431 deaths (a combined Case Fatality Rate of 71.5%).

Currently the provisional Western African outbreak has seen 604 cases and 350 deaths (a CFR of 57.9%).

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).

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, FluTrackers, H5N1 and Virology Down Under.

Random Analytics: Ebola 2014 (to 9 Jun 2014)

The latest outbreak of Ebola which had been on the decline in early May has now returned with a vengeance. According to the latest update from the World Health Organisation (Regional Office for Africa) there have been 437-clinical cases and 232 fatalities. Guinea has borne the brunt of the disease with 344-clinical cases (215-deaths), the second impacted country Liberia has had 12-clinical cases (11-deaths) and newly impacted country of Sierra Leone has had 81-cases (6-deaths). These numbers are still likely to change.

According to my notes, this outbreak is now the worst on record in terms of case numbers, extending beyond the 425-cases (224-deaths) experienced during the Ebola Sudan outbreak in Uganda back in 2000-2001. Another two grim milestones is that this is the first Ebola outbreak to cross a land border and the first outbreak to impact on three separate countries.

Here are four charts/infographics looking at the most recent outbreak.

Ebola Virus Disease Outbreak (Guinea Prefectures 2014)

01 - Ebola_GuineaOutbreak_140609

***** Please note that this infographic of the EVD was updated with public source information to 1200hrs 9 June 2014 (EST) *****

From the World Health Organisation Regional Office for Africa. Ebola virus disease, West Africa (Situation as of 5 June 2014). Excerpt:

Guinea

Between 2 and 3 June 2014, 11 new cases (8 confirmed, 1 probable and 2 suspected) and 3 new deaths were reported from Conakry (7 new cases and 1 death), Guéckédou (2 new cases and 1 death), Telimele (1 new case and 0 death) and Boffa (1 new case and 1 death). This brings the cumulative total number of cases and deaths attributable to EVD in Guinea to 344 (laboratory confirmed 207, probable 81 and suspected 56) including 215 deaths.

The geographical distribution of these cases and deaths is as follows: Conakry (65 cases and 27 deaths; Gueckedou, 193 cases and 143 deaths; Macenta, 44 cases and 26 deaths; Dabola, 4 cases and 4 deaths; Kissidougou, 7 cases and 5 deaths; Dinguiraye, 1 case and 1 death; Telimele, 23 cases and 5 deaths; and Boffa, 7 cases and 4 deaths. In terms of isolation, 31 patients are currently hospitalized (6 in Conakry, 9 in Guéckédou, 15 in Telimele and 1 in Boffa).

Sierra Leone

Between 2 and 5 June 2014, 9 new suspected cases were reported bringing the total number of EVD clinical cases to 81 (31 confirmed, 3 probable, and 47 suspected) including 6 deaths. Kailahun district is the epicentre of the outbreak in Sierra Leone. Eleven (11) cases are currently in isolation at Kenema Hospital. The number of contacts currently being followed-up is 30. Community resistance is hindering the identification and follow-up of contacts.

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

Ebola across Africa

02 - Ebola_AcrossAfrica_140609

***** Please note that this infographic of the EVD was updated with public source information to 1200hrs 9 June 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.

Ebola (Top 10 Outbreaks by Case Numbers)

03 - Ebola_Top10OutbreaksByCaseNo_140609

***** Please note that this infographic of the EVD was updated with public source information to 1200hrs 9 June 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 344 in Guinea, 81 in Sierra Leone and 12 in Liberia the EBOV17 coded outbreak has now become largest (437) based on case numbers. The second largest outbreak (SUDV4) was of Ebola Sudan in Uganda (2000) when 425 became infected and 224 died. The recent outbreak is the first to migrate across international land borders. 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: EBV outbreaks in order from lowest to highest. 10th: EBOV8 (Gabon/South Africa), 9th: EBOV9 (Gabon), 8th: EBOV11 (Republic of Congo), 7th: BDBV01 (Uganda), , 6th: EBOV15 (Democratic Republic of Congo), 5th: SUDV1 (technically Sudan but would now be South Sudan), 4th: EBOV6 (Zaire but now the DRC), 3rd: EBOV2 (Zaire but now the DRC), 2nd: SUDV4 (Uganda) and the current, now deadliest outbreak EBOV17 (Guinea/Liberia/Sierra Leone).

Ebola (Cases by Classification and Year)

04 - Ebola_CasesbyClassYear_140609

***** Please note that this infographic of the EVD was updated with public source information to 1200hrs 9 June 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 437 clinical cases so far the 2014 Ebola Zaire outbreak is now the second 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: 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: Australian Mining Employment Update (May 2014)

For a number of professional and personal reasons I have not completed, nor updated my Mining Workforce Planning Scan since September 2013 (some ten months ago). As it happens an article by Australian Mining last week reminded me that it is something I should have a look at again, especially given the recent bad news in regard to mining employment. 2014: Mining jobs cut so far… Excerpt:

Shaky commodity prices and the end of the boom have resulted in a bad year for job losses, and BHP and Rio have warned of more to follow. Cutbacks have ranged from small belt-tightening measures, such as 36 job losses at Illawarra Coal, to the level of catastrophic collapse such as when mismanaged contractor Forge Group folded leaving 1300 out of work early this year.

Coal has been the worst hit in mining, with Vale announcing the closure of all operations in the Hunter Valley for care and maintenance, among a series of other cutbacks by BHP and Rio Tinto. Queensland has also suffered, with Queensland Resources Council president Michael Roche saying that 10 per cent of mines in the state are now in a “very precarious position”.

Industry analysts have blamed oversupply on the global market for the plunge in the price of coal, with hard-coking coal having dropped to $US120 per tonne from $US330 in 2011. Iron ore has also suffered price-wise, falling from $US135 per tonne down to $US110 in April; however the industry seems to have escaped any major job losses.

There is no doubt that after that article was written the news got worse with further cuts, especially in New South Wales coal mines. With iron ore falling another $15 to just above $95 at the end of May how long before we see another round of severe job cuts in the Pilbara similar to September 2012?

The Australian article then lists a large number of job losses in 2014. But what do these job losses look like compared to job gains across the last 12-months. Also given that Australia has become so currently reliant on mineral exports such as iron ore, coal and future reliant on growth in exports of LNG and Uranium how are job gains or losses looking for these resource types.

Re-commencing the first of a monthly series in Mining Employment updates here is a look at how that story from Australian Mining translates into two charts.

1 - MiningGainsLosses_Chart_140601

The first chart looks at the previous 12-months from a mining employment gain and loss perspective. The positive employment numbers are split into those that reflect infrastructure (tan) and operational (blue) gains. Job losses are represented in red.

The obvious take away from this chart is that outside of August and September 2013 the losses have far outweighed the gains, with job cuts underrepresented to some extent as some companies chose to supress actual details. The data for May includes:

  • 392 operational jobs added;
  • 1,000 infrastructure jobs added;
  • 1,944 jobs lost.

2 - MiningSectorGainsLosses_Chart_140601

The second chart attempts to breakdown the job gains and losses by key resource types (Iron ore, Coal, Gold, Copper/Zinc, CSG/LNG & Uranium). Similar to the previous chart the only period of overall job growth occurred in these resource types happened between July and September 2013. The May details were:

  • Iron Ore: 120 lost;
  • Coal: 350 added and 828 lost;
  • Gold: Newcrest opened the Cadia East Gold Mine (see Juking the Stats) and 94 lost.

Juking the Stats (May 2014)

The most notable manipulator of employment information in May was Newcrest which opened its Cadia East Gold Mine with the assistance of NSW Premier Mike Baird. The mine is expected to support 1,900 direct and indirect jobs (source: MineWeb). No doubt it has created some employment but given that Newcrest NEVER shares it job loss data (Telfer July 2013 and November 2013 most recently) and its financials have been less than satisfactory I felt that conflating direct and indirect employment might be a way to also conflate its entire Cadia Valley Operations footprint). I have requested further details directly from Newcrest and am awaiting a reply before including Gold employment numbers for May.

Other Jukers include:

  • Perilya who ended their MacMahon contract which will result in job losses but no specifics were provided;
  • BHP Billiton has confirmed that there will be job losses (a source suggests in the hundreds) at Worsley Alumina but have not confirmed details.

Final Observation(s)

May/June tend to be months where companies clean house, especially around employment so June may prove to be another awful month for further job cut announcements. On that note, I will return next month with updated employment charts and a revised employment sentiment chart which will hopefully add another dataset for your consideration.

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.

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