Continuing with our Analytics team study of the virus on Western European countries, we present our findings for data up to week 15 (14 April).
As discussed in our previous articles, in order to provide an objective comparison per country, the algorithmic results need to be standardised around the population of each country in order to produce a more accurate deaths per million inhabitants rate. The figure shown below summarises the results.
As seen, Belgium’s mortality rate (red) is significantly higher than any of its neighbours. Germany (blue) and the Netherlands (green) have the lowest mortality rates, and appear to be levelling off. This suggests that the Dutch and German governments testing, health care systems and social distancing strategies appear to be paying off.
It’s not completely clear why Belgium’s mortality rate is so much higher than its neighbours, but a possible explanation may be due to insufficient testing and the virus hitting various elderly care homes. We’ll follow Belgium’s progress over the coming weeks, and report our findings.
As discussed in a previous article, the UK had a one-week head start on its neighbours. Therefore, shifting the UK data left by six days, we obtain an interesting picture of the UK’s situation:
Applying a prediction model to the UK data (dashed magenta line), notice how the UK’s data follows France’s data. Although long term predication models should be viewed with a degree of scepticism (as there are too many unknown factors to consider), the prediction suggests that the UK’s mortality rate should follow France’s mortality rate.
The good news for the UK population, is that the emergency measures in place, appear to be working and are leading to a decline in deaths!
https://www.advsolned.com/wp-content/uploads/2020/03/covid19featuredimage-e1584964680979.jpeg630946adminhttps://www.advsolned.com/wp-content/uploads/2018/02/ASN_logo.jpgadmin2020-04-16 13:38:422020-04-18 11:00:46Covid-19: Netherlands and Germany death rates stabilise, but Belgium still skyrockets
The Covid-19 virus has forced European governments to order millions to lockdown in the hope of limiting the spread of the virus, based on ‘expert scientific advice’. The latest recent review of WHO data by Dutch data modelling specialist, Advanced Solutions Nederland (ASN) reveals that the UK could of adverted strain on services and avoided a sharp rise in Covid-19 cases by taking advantage of being six days behind the infection spread in Northern Europe, but failed to put measures in place in time, due to flawed ‘expert’ predictions.
Central to government policies imposed are predictions being made from statistics that are essentially handling raw data ineffectively. Many models are based on raw measured values that are not adjusted for comparison with neighbouring countries, so called population standardisation, which can give a false perspective of the situation at hand.
– Director of Algorithms and Analytics, ASN, Dr. Sanjeev Sarpal
Ineffective use of modelling to predict virus trend
John Hopkins University (JHU) provide an open database of confirmed cases, deaths and number of recoveries, obtained from data from the World Health Organisation (WHO), various other health intuitions and governments. These datasets are broken down into countries and regions.
Analysis considered data obtained from the following five European countries populations: Germany (83 million), France (67 million) UK (66 million), the Netherlands (17 million), Belgium (11 million).
Our analysts found that by analysing the viral trend by doing a ‘like with like’ comparison of populations rather than the conventional method of non-standardisation, resulted in a totally contradictingset of results, implying that the UK governments response was not informed appropriately.
In order to provide an objective comparison per country, the algorithmics results were standardised around the population of each country in order to produce a more accurate deaths per million inhabitants rate. The figure shown below summarises the results.
Analysing the chart, it can be seen that all central countries considered herein all report first cases within days of each other, and have very similar contamination rate. The UK is the exception, as it is approximately 6 days behind mainland Europe.
By shifting the UK left by six days, we see that the UK also follows the same trend as its continental neighbours. The dashed line represents the algorithmic prediction of the number of confirmed cases for the next two days (short term prediction), which closely follows the other countries.
Thus, it can be concluded that despite the British government having advanced warning, they failed to adequately prepare themselves for the effects of the virus.
No magic long-term prediction model
There are a multitude of data modelling methods, each giving a different result depending on the interpretation required. For the Covid-19 virus, there is no ‘magic model’ that can be used to predict the long-term severity of the outbreak, as there are too many variables to consider, which are almost impossible to model and track as the pandemic unfolds.
External factors, such as emergency laws, increased public hygiene/diligence and better medical care facilities are but a few major factors that affect any long-term prediction model. These critical factors are generally not modelled when making a prediction model. The short-term prediction shown herein, was just for the next two days, but all prediction models must be viewed with a degree of scepticism, as it is not possible to model all of the unique circumstances that present themselves.
ASN’s data analytics team will be closely monitoring the development of the Covid-19 virus, and providing regular updates via our blog.
https://www.advsolned.com/wp-content/uploads/2020/03/covid19featuredimage-e1584964680979.jpeg630946adminhttps://www.advsolned.com/wp-content/uploads/2018/02/ASN_logo.jpgadmin2020-03-24 12:13:182020-03-24 13:41:08Covid-19: UK had 1 week headstart, but failed to act
The Netherlands is regarded by the International Monetary Fund (IMF) as one the richest countries in the world, with high life expectancy, good infrastructure and a liberal society. The Dutch have historically been traders, learning multiple foreign languages and trading with the whole world – a practice that is still continued to this date. The Dutch love to travel, which may have been one of main factors for the Covid-19 virus gripping the Netherlands so severely.
The Covid-19 virus has led all European governments to effectively lockdown their countries in the hope of limiting the spread of the virus. Although some see this as a violation of their civil rights, the Dutch government’s ambition is to limit the spread of virus so that the health system can cope with a controlled flow of infections.
Population standardisation and carnival
New research from the University of Massachusetts,
incubation period (i.e. the time between exposure to the virus and the appearance of
the first symptoms)
for Covid-19 is
just over five days and that 97.5% of people who develop
symptoms will do so within 11.5 days of infection.
John Hopkins University (JHU) provide an open database
of confirmed cases, deaths and number of recoveries, obtained from data from
the World health organisation (WHO), and various other health intuitions and
governments. These datasets are broken down into countries and regions.
Applying our ANNA data modelling algorithms to the raw datasets provided by John Hopkins University (JHU), we were able to plot the mortality rate versus time for the Netherlands, as shown below.
Many models are based on raw measured values that are not adjusted for comparison with neighbouring countries, so called population standardisation, which can give a false perspective of the situation at hand.
The yearly Carnival festivals that takes place around the 23-Feb, attracts large crowds of people (shown on the right). This incubation period of approximately 12 days can be clearly seen in the data for the Netherlands, where the first deaths are reported around 7-March (14 days after carnival), suggesting that if not adequately treated in hospital, the patient will die within a few days.
Our analysis considered data obtained from the following five European countries (populations shown in parenthesis): Germany (83 million), the Netherlands (17 million), Belgium (11 million), UK (66 million) and France (67 million).
In order to provide an objective comparison per country, the algorithmic results were standardised around the population of each country in order to produce a deaths per million inhabitants rate. The figure shown below summarises the results.
Analysing the chart, it can be seen that when viewing the scaled dataset, the Netherlands (green) and France (black) have the highest mortality (death) rate, and Germany (blue) the lowest. France’s high mortality rate may be attributed to many foreigners visiting France for their winter holiday.
A disastrous combination of events
various news reports, the Brabant province in the South of the Netherlands was
a particular hotspot for the virus. Our findings as to the likely reasons why
the contagion rate in Brabant is so high can be attributed to a combination of
the following factors:
The yearly Carnival festivals taking place around the
23 February, which attract large crowds of people.
Frequent foreign travel of people working for large
international business, such as Philips and ASML.
People taking their winter holidays in France and
Had carnival taken place several weeks earlier, the effects on the Dutch population may have very well been lower.
Another hotspot for the virus was Amsterdam, which like Brabant is a hub for international business, and a very densely populated region of the country.
The Covid-19 incubation period for the Netherlands is around 12 days.
When standardising the mortality rate population data per million inhabitants with surrounding countries, the Netherlands and France have the highest mortality rate of all of their neighbouring countries. A likely explanation of the explosive outbreak in the Brabant province of the Netherlands, is due to Carnival festival, the school/winter holiday and international business travel. France’s high mortality rate may be attributed to many foreigners visiting France for their winter holiday.
Despite Germany’s large population of 83 million, the data shows that the German government’s handling of the situation has been very effective indeed. The German health system boasts over 25,000 intensive care beds, and respiration equipment. Comparing this this Netherlands, which just has a little over 1,150 beds, and adjusting for the population differences – Germany still has more than 4.5 times more intensive care beds at its desposal.
In terms of
prevention: Germany’s National Association of Statutory Health Insurance
Physicians, reports that it has capacity for approximately 12,000 Covid-19
tests per day, which surpasses all other European countries.
https://www.advsolned.com/wp-content/uploads/2020/03/covid19featuredimage-e1584964680979.jpeg630946adminhttps://www.advsolned.com/wp-content/uploads/2018/02/ASN_logo.jpgadmin2020-03-23 12:31:342020-04-29 20:07:53Covid-19: Netherlands - a disastrous combination of events
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