A model for analysing and comparing outbreaks of illness and disease - covid-19 et al

I have been quite vocal on my criticism of the reaction to the pandemic. My reasoning has been many, but the major reason is the data doesn't appear to make covid-19 as significant as is being portrayed by the press. If we ever try to attack a narrative from a specific angle, rest assured a plethora of people will attempt to discredit what you say - some examples;

  • The experts know what they are talking about.
  • These official organisations are publishing the data which shows there to be a pandemic.
  • If it is in mainstream news it must be true.
  • The government has experts who are saying this is true.
  • How could this be planned or coordinated?
  • Why would they let people suffer?
  • Look at all the deaths of the old people?

Fundamentally, I see the goal of a model is to understand whether the response or preparation to a pandemic threat was appropriate or not.

Some quick counters to the many people defending the coronavirus pandemic

Experts exist who are contradicting what is the government narrative

A previous post on my blog, listed around 12 eminent experts questioning the significance of the coronavirus and/or the response by governments and organisations with measures such as mask wearing and social distancing all but useless. To name one - Professor Dolores Cahill. For me, when somebody as significant as Dolores speaks on this - the deal is done. Not for the mainstream news and government who choose to use their "experts".

The countless mistakes made by government(s) on covid-19

  • Double counting admitted by the UK government on covid-19. (Telegraph).
  • Numerous examples and questions on the validity of testing (UK). (Medical experts on Social Media, YouTube, Twitter, Decentralised video streaming platforms).
  • Many governments and national health agencies advising their medical professionals to record covid-19 as the cause of death despite underlying health conditions existing.
  • Complete ignorance of existing trends - the best example was the well cited Lombardo region in Italy (Bloomberg). This region has a history of respiratory related illnesses, a high distribution of elderly people etc, etc.
  • Recommended guidance contradicting medical guidance. 
  • Mounting evidence that hydroxychloroquinine is an effective treatment of covid-19. 
  • Significant concern on the use of ventilators as a methodology to treat covid-19. One New York ER consultant said he felt like they had been given the wrong solution to treat covid-19 and ventilators was contributing to their deaths. He described covid-19 more akin to extreme altitude sickness.


The self-fulfilling prophecy theory

  • An over-emphasis on diagnosing and detecting covid-19 is mandated.
  • An over-provision of capacity to handle peak in covid-19 cases is made in healthcare (nightingale hospitals).
  • Many who would normally get access to healthcare for serious conditions die through lack of access to care.
  • Those who died from co-morbidities are classed as covid-19 deaths.

Bare-faced illogicality and contradictions

One of the things I have seen and heard about, are the number of young children taking their lives due to the effects of the lockdown. Indeed, it appears more children have died through misadventure or suicide than covid-19. Yet parents are campaigning actively for social distancing in schools.

People are dying - you don't care about people dying from coronavirus/covid-19

Any death is tragic, but the one certain thing in life is death, as they say. "The government are trying to protect the old people". This is a position which disgusts me - the government has never cared about old people. Their care in most old people's homes is abhorrent, their pensions are stolen from them through inflation - the hidden tax. Look at recent examples over trying to scrap the licence fee reduction. My opinion is - government doesn't give a shit about old people.

There are tragic examples of many dying and suffering from coronavirus so this isn't a denier article. https://www.dailymail.co.uk/tvshowbiz/article-8394435/Kate-Garraway-cuts-solemn-figure-revealing-husband-Derek-Draper-comatose-forever.html 

The availability of data is poor

I build databases and reporting solutions. This is known as business intelligence and whilst there are countless contributors to effective reporting - the most important is the granularity of data. In simple speak, this means there is enough detail for conclusions to be drawn from it. One of the biggest problems with this coronavirus (covid-19) pandemic is there is a distinct lack of granularity. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports 

What I would expect, as a data analyst, would be details of minor/major conditions, ages, genders. We now, have to supplement covid-19 data with other data sources to try and add that granularity. Doing so introduces error, so the simple point is - why is this data not being published with sufficient granularity?

Koch's postulates aren't fulfilled on covid-19

Big Caveat, Koch's postulates can never be fulfilled for viruses but there are variations on this. The key point is, many experts have questioned whether covid-19 exists at all (not coronavirus). Please do your own research on this but start with Dr Andy Kauffman.

The Neil Ferguson model

Rather than get technical, suffice to say this is a concern his numbers were overstated and his model was shite.




Studies are being released which challenges the official narratives

Mainstream media is now publishing articles showing studies on mistakes made on covid-19.

Nothing you have provided here proves anything?

Quite right. Not a jot proves what I have said is right. We could go through every example cited and say they are all just opinion and conjecture. Why would we simply believe the media and government's interpretation? Just once, has Matt Hancock explained about the multitude of people claiming they haven't had access to cancer treatment? No, we know that politicians avoid transparency and yet now we trust them? Watch most interviews by Andrew Neil with politicians.

So, how do we establish truth with relation to covid-19?

Creating a consistent verifiable model

Without going into too much technical jargon, an important feature of any effective system is to be able to take new factors into account. This flexibility is what allows us to consider different scenarios. Any system must be capable of taking on additional data items.

A consistent model, should be able to permit data to be treated. Treating data means that, for example, if we don't have the necessary granularity, we should be able to apply other data against our source to approximate impacts.

  • The number of deaths in the UK of Covid-19 is 40,000.
  • We know that normally, deaths are split upon specific age groups.
  • We can apply those age groups against that 40,000 figure.

We don't stop there, we need to consider the impact of existing conditions and have to try to determine statistical significance at every step.

Modelling other outbreaks, pandemics and epidemics

One of the major questions I have, is Covid-19 worse or more severe than other pandemics? One classic statistic, even cited in the UK's government's 50 page document was the current pandemic to the average previous 5 years' flu pandemics. To go into the many errors made in trying to use this as a good measure is out of scope, but I state it as being disingenuous to say the least.

So, we have to be able to model previous pandemics.

Incorporating known measures?

We know Goodhart's law - "When a measure becomes a target, it ceases to be a good measure.". This is like the R value. Firstly, R can be calculated via different methodologies, but the government's insistence on using R as the major tool in managing the covid-19 crisis reminds me of the futility of their 4 hr A&E wait times. I was working at a hospital producing reports on A&E when the 4hr target came in. What happened? Hospitals would have somebody discuss the emergency with the patient within 4hrs, but keep the patient there for up to 12 hrs. Try taking your child to A&E, it is like a day-trip.

Having said this, an effective model should be able to incorporate known measures. This prevents those who are obsessed about a measure such as R not being able to criticise the model.

Comparing pandemics to other pandemics

Once we have our pandemics modelled, the obvious thing to do is to compare the models to understand if there is significant difference or not. Those not so well-versed scientifically. Imagine you have hundreds of pensions to choose from, ideally we want a system which can compare all the available pensions and come up with a top 5 list. Remember, this then permits us to compare Sweden to the UK for example (social distancing).

Self-discovery - Artificial Intelligence/Machine Learning?

Do we need a system which can self-discover the many scenarios we as humans may initially try to model? Of course. Imagine if a system could trawl the internet to find all the news articles reporting suicides during lockdown and incorporate those figures into our model whilst using a baseline of "normal" suicide instances outside of lockdown?

Publication - the data needs to be reusable and available

For the WHO to publish pdf documents rather than downloadable data at the correct granularity is at the very least, incompetence. Any verifiable system should be transparent in the data it consumes, the methodology used and the outputs should be available for others to use and analyse.

Tying up the loose ends

This post detailed the suspicions held by me and others regarding covid-19 and it then went on to propose a methodology to try and pinpoint the true impact of covid-19. I can see, that building a model such as this is a big deal - a big project. For this reason, I cannot see how many organisations have the will or resources to build a model such as this. The Guardian newspaper does have data scientists (I attended a conference on this) but the manner of their reporting - their blind faith in the numbers in spite of all the qualified opinion questioning those numbers is troubling.

The ultimate question is, why are individuals so happy to accept the official data when it is patently clear they haven't got the right methodologies to adequately understand the nature of a situation?

It would be a very interesting project to work on, I have been wondering if a simplified data model and reporting solution could be created relatively easily and then improved upon. My main challenge is that I have been working on my property platform for ages and once I get that working - I need a bit of a break, but this is the only way we are going to have a mechanism to challenge official narratives.

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