I\u2019ve written about validating scientific findings previously.\u00a0 With the current COVID-19 (coronavirus) situation, there have been numerous published claims of various \u201cfacts\u201d, which are based on models.\u00a0 It was only a couple of months ago that the news carried projections of 8 billion people being infected and 80 million people dying.\u00a0 That later number was reduced to 40 million.\u00a0 In the US, there were projections of up to 3 million people in the United States dying, which number has been continuously reduced to possibly a maximum of 200,000, but with a possibility of the number being much lower.\u00a0 The current number of reported deaths is just over 60,000 as I write this blog.<\/p>\n\n\n\n
The \u201cfacts\u201d two months ago were 80 million worldwide would\ndie, and now that number is currently at 226,882 world-wide who have died (updated\nApril 30, 2020) with a projected total to be in the mid to upper 100,000s. This current trend indicates that the total\nnumber will be less than 1% of the original projection! What happened? There are a lot of questions that need to be\nanswered, but that needs to be done by the developers of the model. <\/p>\n\n\n\n
The issue that will be addressed below is why models and the\nsubsequent results need to be understood in order to correctly explain what the\n\u201cfacts\u201d presented actually mean.<\/p>\n\n\n\n
First, the information provided as \u201cfacts\u201d were not \u201cfacts\u201d\nbut projections based on someone\u2019s model of the situation. A comment years ago by a friend, Professor\nBob Shannon of Texas A&M explained it well. \n\u201cAll models are WRONG, some are useful!\u201d A strong statement, which we will explore. <\/p>\n\n\n\n
Why are all models wrong? \nThe answer is that models are based on assumption. (I have spent considerable time working in\nmodeling.) The model is only as correct as the mathematical description of the\nobject being evaluated, the accuracy of the assumptions being made, the\ninclusion of all the key variables, and an estimate of the probably of the variables\noccurring. Usually models are built,\ntested, modified, tested again, and finally run multiple times over a set of\nprobabilities. The resultant answers\nyield a possible projection with a probability range. There are usually results that provide the\nextremes as well as the most probable. \nTherefore, the ANSWER is not a single number but a variable with a\nprobability range based on certain assumptions. \nNotice the word \u201cassumptions\u201d, it is plural. The results of the model are only as good as\nthe assumptions. If you do not know the\nassumptions, you are unable to evaluate the results from the model. In addition, models need to be improved as\nthe analysis continues. This is why it\nis called modeling. There are some\nsuggestions that the basic virus impact model has not changed. [Ref. #1] That in itself is unusual. Models need to be continually updated to reflect\nlearning from earlier versions.<\/p>\n\n\n\n
A commentary by Holman W. Jenkins, Jr. [Ref. #2] provides\nthoughts about the media not being able to understand multivariate. This is the fact that things are not simple\n\u201cif A, then B\u201d. A short version of this\nis: \u201cWhen it rains, Jack always wears a hat\u201d. \nDoes this imply that Jack wearing a hat causes it to rain? Of course not. But there are other variables. Does Jack need to keep his head covered due\nto a skin problem? Does Jack always wear\na hat rain or shine? This is simple\nexample. But, when reporting reduces a\nstory to a single number, it loses the contributing factors. A better understanding of how models work is\nrequired to be able to accurately report on it.<\/p>\n\n\n\n
Yes, there is a need to evaluate situations that may cause a\nsingularity \u2013 also called a black swan event. \nAt one-time, black swans were considered fiction, then people found\none. It was rare at the time. Now they are not that rare. Recently, the 100-foot rogue waves were first\nconsidered fiction and then black swan events. \nThanks to satellite imaging, we now know that they happen relatively\noften is certain part of the world with certain conditions. The point being that looking at the results\nof a model, one needs to consider the possibility of such event, but not use\nthat as the final answer. Is it possible\nthat 8 billion of the current 8.8 billion people could get the virus? A 91% world infection rate? Possibly yes, but that would not be the most\nprobable and require a significant reevaluation of the modeling assumptions. <\/p>\n\n\n\n
What we, as the public, need to hear and understand is what\nthe assumptions were in developing the models. \nThe first model is what is the total impact and how is it spread over\ntime. The second model is what is being\nproposed and what is that impact. The\nconcern on the current situation that required governmental interaction was the\npotential for a huge number of cases that would overwhelm the medical\nsystem. By using a distancing model of 6\nfeet and a requirement of sheltering in place, the rate of infection is slowed\nand occurs over a longer period so the medial facilities would not be\noverwhelmed. It is not indicating that\nthe fatality rate is lower due to these regulations. It has only been delayed. If a vaccine is created, it will lower the\nfatality rate. To be presenting anything\notherwise is an indication of not understanding what the models are\nsaying. Large number projections may get\npeople nervous and provide revenue for media, but the large number projections\nend up forcing the improper allocation of resources. <\/p>\n\n\n\n
The Mayo Clinic responded to the projections that the COVID-19 would require major resource allocation. \u00a0This resulted in the cessation\/postponement of elective surgeries, cancer treatments, and other related medical procedures.\u00a0 This large projected number of serious ill people did not happen.\u00a0 The result was the Mayo Clinic is furloughing and\/or giving pay cuts to about 1\/3 of its 70,000 employees. [Ref. #3] This does not include the impact on related, externally contracted workers.\u00a0 And, that does not even address the impact on the patients who were unable to have their procedures.\u00a0 <\/p>\n\n\n\n
Could all of this over allocation of resources be based on the lack of knowledge of understanding what is involved is establishing guidance based on unknowns in models?\u00a0 One needs to know what is involved in the assumptions, variables, and probabilities.\u00a0 After the models are run, there is a final decision.\u00a0 Does the answer make sense, or could there be elements missing or misstated?\u00a0 91% of the world being infected, raises a very serious question about the validity of the model with me.\u00a0 <\/p>\n\n\n\n
If the news media responds to analyses with single number\nanswers, when these answers are not accurate, can there be any guaranty of\ndeveloping a true understanding of the problem? \nI doubt it. The consequence of\nthis type \u201cfactual presentation\u201d is that the general public loses trust in any\nstatements that are published. With that\nis also a loss of confidence in leadership. \nScientific facts need to be presented accurately with the assumptions\naccompanying the results. Integrity in every\nstep of the entire process is required.<\/p>\n\n\n\n
References:<\/p>\n\n\n\n
I do not provide an explicit email address due to that email\naddress becoming overloaded with spam and advertising. I can be reached via email: \u201cideas at\nnano-blog dot com\u201d. Replace the \u201cat\u201d and\n\u201cdot\u201d with the appropriate symbols. <\/p>\n","protected":false},"excerpt":{"rendered":"
I\u2019ve written about validating scientific findings previously.\u00a0 With the current COVID-19 (coronavirus) situation, there have been numerous published claims of various \u201cfacts\u201d, which are based on models.\u00a0 It [..]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-370","post","type-post","status-publish","format-standard","hentry","category-science"],"_links":{"self":[{"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/posts\/370","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=370"}],"version-history":[{"count":1,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/posts\/370\/revisions"}],"predecessor-version":[{"id":371,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=\/wp\/v2\/posts\/370\/revisions\/371"}],"wp:attachment":[{"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=370"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=370"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.nano-blog.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=370"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}