Random Analytics

Analytics. Potentially interesting, sometimes challenging or topical. Never rehashed or forced

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)

“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.

Random Analytics: Ebola by Classification and Year (to 30 Mar 2014)

“We are facing an epidemic of a magnitude never before seen in terms of the distribution of cases in the country” Mariano Lugli, Coordinator, Medecins Sans Frontieres, Conkary, Guinea (Mar, 31, 2014).

Ebola Zaire, one of the deadliest variants of the virus has once again broken out in Africa. For the first time in its history it has emerged in Guinea and subsequently spread to Liberia. Unlike many of the former outbreaks it is not isolated to one or two villages or regions but spread out across Guinea into several prefectures.

The current outbreak, which is ongoing, has seen 112-persons infected with 70-deaths in Guinea and in neighbouring Liberia up to seven-persons with two deaths. Both outbreaks are fluid and those numbers may increase or decrease as confirmation of case numbers solidifies or the virus spreads further. See the latest World Health Organisation (WHO) Disease Outbreak News (DON) from both Guinea and Liberia for the latest details.

As part of a post-graduate study program that I am undertaking I am building a five-minute lesson in which I plan to explain the Ebola Haemorrhagic Fever (EHF), in layman terms to pre-service teachers in five graphs and six dot points supplied by WHO. Here are the dot points and the first graph (with more to follow).

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.

01 - Ebola_CasesbyClassYear_140401

***** Please note that this infographic of the Ebola Haemorrhagic Fever (EHF) was updated with public source information to 1200hrs 1 April 2014 (EST) *****

The first 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 119 confirmed cases so far the 2014 outbreak numbers are already seventh 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.

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 and National Geographic.

Random Analytics: Shane Watson First Chance Average (to Cape Town 2013/14)

Shane Watson, who debuted for Australia back in 2005, has just returned to the Australian team for the last test at Newlands, Cape Town. He scored a quick-fire 25 in the second innings but as was reported via the ABC he had to bowl at training prior to re-admission back to the team. Some of the reasoning behind the bowling test might be his batting average (which currently stands at 36.26) but a closer look at his First Chance Average might explain more.

The First Chance Average (FCA) is something that I have recently developed for use in Test cricket and is loosely based off the Earned Run Average statistics utilised in baseball. The FCA is calculated using the score the batsman would have got if a legitimate chance had been taken by the opposing team. Legitimate chances include dropped catches and missed stumping’s (at this stage).

Let’s look at Shane Watson’s First Chance statistics.

1-AvgVsFCA_SWatson_140306

Watson’s standard Test average is currently 36.26 after a 40 and a 25 at Newlands, South Africa) versus his FCA which sits at 27.76 (-8.49).

Special Note: My FCA calculation for Watson is out by up to 31-runs as he was dropped by Danish Kaneira on debut. Unfortunately the Australian Broadcasting Corporation, ESPN and Dawn.com didn’t record his score at the time just stating that he was given a chance. I even reached out to Peter English one of the ESPN commentators who were there on the day but unfortunately he couldn’t remember.

2-140301_Tweet_PeterEnglish

Now a look at Watson’s First Chance Deviation (FCD) and Chances.

3-FCDEtChances_SWatson_140306

The FCD (in blue) is the percentile of First Chance Deviation runs against the total Test Runs scored by a batsman.

Shane Watson has currently scored 3,408 runs but would have scored 743 less if opposing teams had of taken his offered chances given him a current FCD of between 21.8 – 22.7% (if you add on a potential 31 additional runs for this first drop which no one noted). His career FCD low was recorded during the West Indies tour of Australia in 2009 (5.1 – 10.0%) and FCD career high was during the India tour of 2010 (28.5 – 30.7%)

The chances that I have been able to record are in red. Watson has had an Average Chance (AC) high of 1.0000 (one chance every innings) recorded in his first test during the Pakistan tour of Australia in 2005 and an AC low 0.1053 (one chance in almost every ten innings) recorded during the West Indies tour or Australia in 2009.

Finally a look at Shane Watson’s Test batting averages.

4-Stats_SWatson_140306

I think Shane Watson’s batting career can be summed up by his overall statistics if you consider him an all-rounder. His Test average is currently 36.26 as compared to Jacques Kallis who finished up with 55.37. If you look at his First Chance Average that reduces to just 27.76 with his first non-chance century being scored in Kennington in August 2013 almost eight years after he debuted (he was famously dropped for 99 (120*) against Pakistan in Melbourne for then dropped on 0 (126) against India in Mohali).

FINAL THOUGHTS

I was surprised by the traditional batting average differential between Jacques Kallis (who I consider the best all-rounder of recent times) and Shane Watson. If you consider his FCA then you would have to be concerned about his Test longevity.

Random Analytics: David Warner First Chance Average (to 24 Feb 2014)

Note: I first published this blog using the acronym Earned Run Average (ERA) and Earned Run Differential (ERD). I have subsequently amended the acronyms to First Chance Average (FCA) and First Chance Differential (FCD). See: Random Analytics: Shane Watson First Chance Average (to Cape Town 2013/14) for the detail.

David Warner’s recent form has been fantastic. Two centuries plus two half centuries in the Australian leg of the Ashes and one century plus two half centuries in the first two tests in South Africa are a good return.

However I haven’t been completely convinced that Warner is in the best of form and while discussing the subject over a beer a mate of mine suggested using a Moneyball metric to test the theory.

I could be wrong but here might be a cricket first, looking at David Warner’s Test Earned Run Average statistics (and thanks to Daryn Webster for the suggestion and Adrian Storen for the sanity check).

1 - AvgVsERA_DWarner_140226

First chart looks at Warners standard Test average (currently 42.88 after a 70 and a 66 at Port Elizabeth, South Africa) versus his Earned Run Average which sits at 34.92 (-7.96).

The Earned Run Average (ERA) is calculated using the score he would have got if a legitimate chance had been taken by the opposing team. In this case I’ve only had to consider dropped catches and missed stumping’s as legitimate chances but I could foresee a missed referral being added in the future. As an example in the 2nd Innings at Port Elizabeth, Warner was put down by Duminy in the 16th over on 36. Thus although he scored 66 for the match his Earned Runs were just 36.

2 - AvgVsERA_DWarner_Summer2013~14_140226

The next chart looks at Warners standard Test average for the Australian and South African summer series. Although Warner has had an outstanding summer with the bat his average over seven tests stands at 60.46 yet his ERA is a much lower 41.00 (-19.46).

3 - ERDevEtChances_DWarner_140224

The final chart looks at two datasets.

The first (in blue) is the Earned Run Deviation (ERD) which for Warner has increased from a career low of 8.8% at the start of summer to now hit a career high of 18.6%.

The Earned Run Deviation (ERA) is calculated 1/Total Test Runs x Earned Run Deviation. In Warner’s case he has currently scored 2,187 runs but would have scored 406 less if opposing teams had of taken his offered chances.

The second dataset (in red) are the chances that David Warner has been given.

On the positive side his twelve chances have a first chance average of 33 but a multiple chance of 38.7, thus demonstrating he doesn’t throw away his wicket early. On the negative side:

  • He has had 2/3rd (8/12) of all his chances in the last two series;
  • His Average Chances (AC) over his career was 0.27 (a chance every fourth innings). Over the recent summer this has doubled to 0.57 (a chance every second innings);
  • His summer 2013/2014 Earned Run Deviation is 32.2%.

FINAL THOUGHTS

Australia’s coach, Darren Lehmann, when asked if Warner was too reckless has recently statedThat’s just the way he is, and we’re very comfortable with that“.

Warner’s current form is excellent so any coach would be hard pressed to have to drop him.

Saying that Warner’s Earned Run Average, Earned Run Deviation and Average Chances are all moving in the wrong direction. Unless he can turn that around in the short term he might find his luck running out.

UPDATES

6 Mar 2014: Updated title to First Chance Average and added note plus link to Shane Watson’s FCA.

Random Analytics: H7N9 by Employment (to 250 confirmed)

The Avian Influenza A(H7N9) continues its steady attrition.  According to Flutrackers there have been 358-cases of H7N9. With Wave 1 (45) and Wave 2 (32) fatality counts as confirmed by Xinhua my unofficial fatality total stands at 77 (a Case Fatality Rate of 21.5%).

While updating the most recent case details to my personal H7N9 Db today, a 29-year old female from Changsha, Hunan I noticed that we had reached an interesting milestone. Of the 358-cases thus far I have now been able to confirm 250 of their job titles.

Let’s look at the data to data.

1 - JobTitle_H7N9Top20_140218

Looking at the Job Titles we still find that the leading data item (occupation) is Farmer (35.6%), then Retired (24.4%) then the two paediatric titles of Primary School (5-12) and Child (0-4) with a combined total of 21 (8.4%). I’ve now been able to record 40 different titles with the top half accounting for 61.2% of the entire data, the bottom half just 8.6% and unknown 30.2%.

Some further points of interest:

  • In Wave 1 (to case #136) Farmers represented 28/136 of all cases (20.6%). Currently in Wave 2 there have been 222 cases of which 61 were Farmers (27.5%);
  • The current average age of the H7N9 impacted Farmer is 62-years while the average age of all H7N9 victim is 54.5-years;
  • The average age of a H7N9 Retiree is 70.4;
  • In Wave 1 Paediatric cases (0-15) represented 7/136 of all cases (5.1%). Currently in Wave 2 there have been 15-cases (6.8%) which shows a slight increase;

2 - JobFamily_H7N9_140218

When we role all the Job Titles into a Job Family the top-3 groups are Non-Participatory (26.5% comprising children, retirees and the unemployed), Farming, Fishing & Forestry (25.4%) and then Food Preparation & Serving (5.3% including catering, chef/cook, food sales, live poultry trade & market vendor).

Of interest:

  • The first two groups have remained largely unchanged in 2014 but the Food Preparation & Servicing group has been steadily declining in recent weeks (down from 6.9% recorded on the 1st February);
  • Only one Healthcare Practitioner (an ER Surgeon from Shanghai) has been recorded;
  • Along with the single Healthcare Practitioner recorded, no Healthcare Support (Enrolled Nurses, Vet Assistants or Orderlies) or Protective Services (Police, Ambulance, Fire & Wildlife Rangers) have yet been recorded equating to just 0.3% of all cases. A marked contrast to MERS which as Ian M. Mackay noted on 5 February 2014 Health Care Workers accounted for 18% of all cases and 2.7% of all deaths;
  • The average age of all H7N9 victims without a job title is 57.

3 - MainJobs_H7N9_140218

Last chart is a look at some main Job Titles in a running total. I’ve included child cases up to the age of 15-years in response to some of Ian M. Mackay’s concerns about an increasing paediatric count.

Given that those unknown job titles cases have an average of 57-years I believe that Retirees are somewhat underreported but given the older age of Chinese farmers it’s hard to estimate a breakdown without some local knowledge (of which I don’t possess).

FINAL THOUGHTS

Without wishing for more H7N9 cases I’ll plan for another employment update as I confirm the first 300 Job Titles.

There is a lot of interesting data in the first 250 Job Titles that I have been able to confirm. I only wish we had some more clarification on the almost 1/3rd of missing data items.

I’ll continue to scrabble for information as it comes in. Public sourced journals with detailed case studies are excellent sources and I am sure we will be seeing some of the Wave 2 case studies in coming weeks and months.

Random Analytics: Abbott’s Promise. 1-million jobs in 5-years (to Jan 2014)

“The next Coalition Government will create one million jobs in five years and two million jobs in 10 years,” he said. “This pledge is achievable given our record and policies.” Tony Abbott (27 Nov 2012)

First of all, Labor side should be commended for its employment story during its term in office (2007-2013) where 955,200 jobs were created. Saying that one of my key criticisms of the then Employment Minister, Bill Shorten was that the spotlight was always on total job creation rather than looking at full-time and part-time job breakdowns. During the Labor years 450,400 part-time jobs were created against 504,800 full-time ones.

Currently, there is a lot of discussion in Australia around employment and unemployment at the moment. In the past month many companies have announced large job cuts either in the immediate or near future. Recent examples with direct jobs lost include Holden (2,900), Toyota (2,500), Forge (1,400), Rio Tinto (1,100), Qantas (1,000), Electrolux (544) and just today Alcoa (980). The seasonally adjusted unemployment rate hit 6% for the first time since July 2003 (when it peaked at 6.1%).

The RBA is has for some months forecast that the unemployment rate would hit 6.25% during 2014, then steadily improve from 2015. That view remained unchanged in its most recent Economic Outlook.

Thus it would be unfair to immediately thrust blame on to Tony Abbott and the recently elected Coalition government as many in the opposition camp are doing.

To that end I thought I might shine a light on the Abbott promise. 1-million jobs in five years. Here is a look at the data for the first four months to January 2014.

1-AUSEmployGainsLossestoJan2014_140218

First chart is a look at employment gains and losses since the Coalition took power in September 2013. Two points:

  • The total jobs are slightly negative, that is 9,949 jobs lost; and
  • The sample size is way too small to start analysing and unemployment figures from the ABS are generally considered a lagging indicator.

2-JobCreationtoJan2014_140218

Second and last chart looks at job creation in three parts. Total job creation (green), full-time employment (blue) and part-time employment (maroon).

In effect there have been 56-thousand full-time jobs lost against 46-thousand part-time jobs gained for a gross loss of 9,949 jobs.

Final Thoughts

Bill Shorten’s recent commentary around 54,000 job losses (or one job every three minutes) might make a good sound grab but actually only reflects full-time employment losses over a very short timeframe.

I think it’s disingenuous of him as the former Minister to use total employment figures then but now only concentrate on one set of numbers.

That aside I wonder if the RBA has underestimated the unemployment nadir at 6.25% which will make it much harder for Tony Abbott to hit his 1-million jobs in five years promise.

Only time will tell. I’ll keep you updated.

Random Analytics: A H7N9 family cluster in Zhongshan, Guangdong?

Ian M Mackay wrote an update on his Virology Down Under article on Wednesday where he nailed a Wave-2 data-point that I had completely missed. H7N9 snapdate: age with time. Key excerpt:

The interesting line to watch is that of the youngest age group (0-19-years) which has lifted to comprise 50% of cases in the week beginning 27-Jan. Also, the proportion of cases in the oldest age group (70->90-years) has dropped down in the past 2 weeks.

There have been a rash of children in recent announcements; 8 of the last 45 cases have been <10-years of age. For a virus with a median case age sitting at 58-years, this is quite a departure.

 Is this due to an increase in familial clusters? Does it herald a shift in the way the virus is spreading? Interfamilial transmission may provide a hint at increasing transmission efficiency. It might also be a sign of increased testing augmenting clinical observation of close contacts of ill family members.

It was such an interesting thought I started to dig a little deeper into the recent data to see if there were any possible interfamilial patterns that, as yet, might not be confirmed as family clusters but would have a high likelihood of being so.

Consider this.

Looking at the Flutrackers.com case list and case number #285 (37M) and #289 (2F). Data points:

  • Onset within 5-days of each other;
  • Hospitalised 2-days apart;
  • Confirmed one day apart;
  • Both are named Liang, although the original translation was Liang Yijun which might stand for ‘someone Liang’. As Crawford Kilian put it Liang is one of the top 100 Chinese surnames;
  • Both come from Sanjiao Town (original reports had them at Triangle Town but I linked that to Sanjiao Town via local hotel addresses).

The important point to my thinking is that these two cases are the first reported in Zhongshan in both waves. Zhongshan is different from other cities in that it doesn’t have County level administration but rather six inner districts and 18 smaller surrounding towns. Sanjiao Town has a population of just 121K, which by Chinese standards is miniscule.

I don’t believe in coincidences and there is a lot of data which is missing from this picture.

Yet, as we see a lot of P2P denial occurring could we also be seeing the first of many unconfirmed family clusters?

Is this the ‘tip of the iceberg’?

Random Analytics: H7N9 in Hangzhou, Zhejiang (to 4 Feb 2014)

According to the latest updates from Flutrackers.com there have been 299 cases of Avian Influenza A(H7N9) to 1200hrs EST (my time in Brisbane, Queensland) with an unofficial fatality count of 71. The Case Fatality Rate (CFR) plus a comparison between Wave 1 (to case #136) and Wave 2 (from 8 October 2013 to the present) stands at:

Wave 1: 136-cases, 45 known fatalities and a CFR of 33.1%;

Wave 2: 163-cases, 26 known fatalities and a CFR of 16.0%;

Total: 299-cases, 71 known fatalities and a current CFR of 23.7%.

Since mid-January the province of Zhejiang has moved into triple figures for H7N9 cases. At around the same time the provincial capital, Hangzhou became the first city to reach more than 50-cases, surpassing Shanghai as the most impacted city by H7N9.

Given those unfortunate statistics I thought it might be worthwhile to crunch some data on Hangzhou, Zhejiang.

Firstly, let us look at the 119 Zhejiang H7N9 onsets by prefecture level city.

1 - CasesbyCity_Zhejiang_140205

Two points:

Lisa Schnirring from CIDRAP stated in the 3 February H7N9 Update that:

Southern provinces lead second-wave cases

Six of the latest cases are from Guangdong province, continuing a strong second-wave tilt toward the mainland’s southernmost areas. In the first wave, locations north of that area were driving most of the outbreak activity: Shanghai, Jiangsu province, and Zhejiang province.

Not sure I agree with that.

Zhejiang has experienced 73-cases in the second wave which is much higher than Fujian (13) and Guangdong (49) below it. Of the 73-cases, Hangzhou alone had 23.

On the second point the infographic also (interestingly) highlights that 90.8% of Zhejiang’s cases are concentrated in the north of the province, emphasising a north/south provincial divide. I can’t suggest a reason for that outside of population density.

Next, let’s look at cases by month of onset with an emphasis on Hangzhou.

2 - CasesbyMonthofOnset_Hangzhou_140205

During April 2013 (the bulk of first wave cases) there was a significant spike in numbers from Hangzhou (28.9%) as compared to Shanghai for the same month (18-onsets at 18.6%).

As you can see from the provisional data for Hangzhou in January the case load is less both in terms of numbers (25) and as a percentile of total cases (18%), although the overall numbers are greater.

Lastly, a look at the second wave case load within Hangzhou.

3 - CasesbyDistrict_Hangzhou_140205

Here is the biggest surprise (IMO). Although farmers make up 10 of the 23 second wave cases in Hangzhou all of the cases (minus one in Fuyang City and three which are unconfirmed) are not in the outlying cities and districts of Hangzhou but in the more tightly congested metropolitan areas of the prefecture level city. It seems the Chinese peri-urban divide is a significant risk factor in catching H7N9, at least in Hangzhou.

Final Thought

H7N9 has almost been around a year and as we verge on the 300th case I think we have spent more than enough time doing provincial level analytics when we now can and should be spending a little more time getting granular with our analysis.

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