Introduction
Some aspects of respiratory illness are solely the domain of Medical Doctors. Medical Researchers have the education, background and experience to investigate and assess issues relating to public health and limited clinical issues. These individuals have specific knowledge unique to their position, education and capabilities and are the unquestioned leaders in treating for respiratory illness.
Engineers and architects can also contribute. Some Medical Doctors can design and build their own equipment, but it is not always the best use of their time and efforts. Engineers under the supervision of medical professionals often design medical instruments. Likewise, technical personnel such as engineers can study the mechanics of respiratory illness spread to good effect freeing Medical Doctors to use their unique skills to treat patients and assess medical issues.
The original Aanoit Covid-19 project was Flu and Cold Spread Software (FACSS just the facts Joe Friday used to say) respiratory spread data consolidation software designed to create a central database of respiratory spread datum. Covid-19 reporting was suspect at the beginning of the pandemic. There were different definitions of what constituted a Covid-19 death. There was a disparity of testing supplies throughout the country. Some locations reported local infection rates rarely if at all. Different locations reported data in different formats.
Because of reporting consistencies, Aanoit decided only mortality data would be tracked. The reliability of data improved after a few weeks, largely because politics and testing availability had less effect on mortality data.
The original FACSS project was solely for the purposes of consolidating and normalizing data into a consistent format. Toward the end of summer 2020 format difficulties and reporting reliability was improved and Aanoit began to concentrate on spread modeling simulation software. The intention was to develop reusable concepts and algorithms for others. This is not spread-prediction software.
Spread prediction software benefited from advanced educational organization support and federal funding. Their advantages included large computational resources, state-of-the-art equipment, generous funding and leading edge computer scientists. However, research leaders dedicated those resources to helping larger urban areas where the effort could affect the greatest population.
Because the author is from a remote area of the country and rural reporting was so different from urban areas Aanoit’s specialty became modeling small community spread. Early work of the author revealed a few mistaken premises. Some of the mistaken premises included:
- R0 is a constant – as a definition it is a constant, but under tightly controlled conditions, but not in real world applications. An alternative is to use an effective R0 to compensate for varying conditions. For example, if one doubles the number of contacts the effective R0 Obviously, its value does not double because it is an exponent. Less obviously, its value is based on statistical methods which are much less intuitive and more difficult to determine.
- People are easily modeled. – I have never been more impressed with the fact that of the six billion + people in the world no two are the same. No two countries are the same. No two states are the same. No two cities are the same. Well you get the idea.
- It’s just the people and the virus. – My latest work reveals the premise that a good model for respiratory illness spread must also include geology, geography, meteorology, season, economics, politics and a myriad of other factors. Some generalizations can help reduce the data load and computational complexity. However, any generalizations must be applied carefully and tested rigorously.
- The virus is a constant – I expected a variant strain in the beginning, but we now have a delta dominant variant along with other variants co-existing in the same areas each with different effect. Once country might be relatively Covid-19 free, while there is a raging Covid-pandemic just across a political border, even though the political situation is the same in both countries.
- Covid-19 is going to go away – This is the worst news of all. My original research indicated that the 1918 Spanish flu was essentially gone by 1920. All we know is that it no longer generated newspaper stories. The flu doesn’t go away. There are still examples of the 1918 Spanish Flu H1N1 that we have used for comparison to SARS-CoV-2. And there is no reason to believe it won’t flare up again, especially with global warming melting pre-historic snow and ice. The Spanish Flu reporting was probably unreported because things were so much better than one or two years before. Thanksgiving 2021 update: I just heard reports of a South African variant that may be even more problematic than the delta variant with more unique features and greater infectivity.
I will add other considerations to the above list as time goes on. Unlike bacterium, a virion is not alive and has no innate source of animation, but its host animates it and in sometimes mysterious manners.
Aanoit has been working on the mechanics of Covid-19 spread for the past 20+ months. The owner of Aanoit has extensive experience developing and testing medical equipment for some of the finest companies in the United States. Some fine on-line courses from MIT have also enhanced Aanoit’s skills.
Research Papers
Sixty Days of Hell is the story of New York City in the two months beginning on the ides of March, 2020. It is a story of scientific discovery. Early predictions regarding Covid-19 spread were quite effective in getting headlines but often had order(s) of magnitude errors. One notable exception was New York City for which predictions were actually understated.
High New York City infection and mortality rates early in the pandemic had an adverse effect on empirical predictive software and public decisions. The fact that NYC is a media hub only exacerbated the influence her high numbers had on the rest of the United States and the World. It truly seemed that Armageddon had arrived.
The author of that story originally compared the 8.5 million New York City residents with the country of Sweden. He originally thought Sweden’s response was that good because they implemented no mitigation strategies but had better statistics than NYC. Instead, further examination revealed that that New York City’s results were that bad.
Both locations had a high proportion of elderly residents. Both areas were predominantly living indoors. Both had good medical systems. Both were relatively affluent. Yet New York City’s population suffered from the worst Covid-19 mortality rates in the United States and significantly worse than Sweden.
The author’s first response was that mitigation did not work. The high New York mortality rates numbers did not support the effects of mitigation. The people of Sweden primarily live in the major cities. Sweden had fewer deaths even though there were more people.
The author labored for months while attempting to create models of spread for differing conditions such as rural areas, suburbs, company towns, resort areas, tourist areas, agriculture, ranching, tech centers and other variations of demographics, topographic and societal variations.
The author was able to successfully compare data and make models for various areas with one exception. The one anomaly was New York City and her high death rate in the beginning. Working with the contemporary theories of close confines, mass transit and mass gatherings there was no accounting for the high mortality rate. It was looking more and more as if the results were from a non-physical source such as divine disapproval with the city in general.
Aanoit’s models are not empirically. The effort was to understand the underlying reasons for various effects. Therefore, model iterations required returning to actual data to comparing the results. Only months of back and forth comparison of models between rural, urban and metropolitan areas revealed the final truth about NYC’s epic siege.
Wanting to take a break the author went back into his Facebook archives. While researching a different subject regarding civil unrest the author retrieved a personal post regarding window serenades in March 2020. There was the probable answer to the New York City Conundrum! Less than a week later, the model and calculations were finished. Now there was an explanation for the extremely high rate of NYC fatalities in the first 60 days of her Covid-19 siege.