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Work and recreation in times of Covid-19: The Covid Buzzer

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The last months, due to Covid-19 factories, offices and institutions have been closed or been working on less power. Now, factories and offices want to open again. In the meantime, it is clear that Covid-19 will be with us for a long time. Employers want a safe as possible environment for their employees and visitors, as well as healthcare institutions and tourist and cultural providers. In addition, companies have a legal obligation to ensure a safe environment. And to take measures where necessary, so that the social distance is respected.

The Social Distance Badge is the device with which the 1.5-meter distance can be maintained. Simple, safe and without privacy issues

Work and recreation in times of Corona

For whom, what, why?

  • Factories
  • Construction and installation sites
  • Logistics: ports, warehouses
  • Transport (train, airports, busses)
  • Offices and Facility management
  • Healthcare
  • Touristic, sports and cultural sites and events

Factories

Factories and offices want to open again. Where offices can still choose by letting employees work from home, factories do not have this luxury. At assembly lines, many people are working together, concentrated to carry out their work. Then the 1.5 meter social distance may be ignored.

Construction and installations, logistics and transport

Employees are also unable to work from home during construction and installations. Many employees work side by side and close together. The employee will often need his focus to perform his work accurately and pay attention to other forms of safety, for example to prevent him from slipping or falling down. Employees can then forget to maintain the 1.5 meter social distance. The Social Distance badge helps to remind the employee to keep distance.

Offices and facility management

In the last months, many employees of offices have worked at home. But for social bonding and the well-being of employees, it’s important that employees are regularly present in the office. And not every office has the option to offer working from home.

Employees walk from workplace to company restaurant and meeting and back again. And meanwhile, facility managers walk through the entire building. Then it is important to keep the 1.5-meter distance, since 1 employee can spread the Corona-19 virus in the entire building.

Healthcare

For some groups, Covid-19 is particularly dangerous: the elderly and people who already have a condition. It is important for them that social distance is maintained. By clients, staff and visitors. In addition, some residents must be reminded to keep 1.5 meters.

Tourist, Cultural, Recreational, Sports sites and events

Visitors get mixed up, are enthusiastic and forget to keep their distance. Or are there visitors who do not take the 1.5 meter into account and thus pose a risk. With the Social Distance Badge, you can safely open again.


Guaranteed 1,5 meters distance

All employees (and customers or visitors) receive the SD badge upon entrance. This is worn around the wrist or neck. The SD Badge continuously scans its environment for other badges via a so-called Ultra Wide Band signal. This signal provides the most accurate distance measurement. As soon as 2 (or more) badges are less than 1.5 meters apart, they give a warning sound and/ or light signal. This allows everyone to keep enough distance. As soon as there are another safe 1.5 meters, the signal will stop.

Read more and order now: https://covidbuzzer.com

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Covid-19: Netherlands and Germany death rates stabilise, but Belgium still skyrockets

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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.

The UK

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!

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Covid-19: UK had 1 week headstart, but failed to act

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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 contradicting set 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.

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Covid-19: Netherlands – a disastrous combination of events

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, suggests that the median 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.

Confirmed deaths for the Netherlands: source JHU database

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.

Population standardisation trends: deaths per million inhabitants

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

Analysing the 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.
  • School holiday.
  • People taking their winter holidays in France and Italy.

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.

Conclusions

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.

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