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SARS-CoV-2 and Edgar Hope-Simpson's Model of Epidemic Influenza

Dr. Al SahhimMay 10, 2021, 5:22:31 AM


Disclaimer: I am not a medical doctor. I am not an epidemiologist. I am not a biologist. I am not giving medical or nutritional advice. However, I am a mathematician, and hold a PhD with a research background in linear algebra, graph theory, combinatorics, and data analysis. This post is me amalgamating some facts about SARS-CoV-2 that do not make any sense to me in the paradigm associated with SARS-CoV-2. As a result, I present an alternative theory, which was developed by British epidemiologist Dr. Robert Edgar Hope-Simpson. To establish that he is a highly reputable individual and ought not be immediately dismissed as a crank, Dr. Hope-Simpson is responsible for discovering that shingles is caused by a reactivation of chicken pox. The theory to be presented was developed to explain the spread of influenza (particularly Influenza A). This should not be misconstrued to say that I am saying that SARS-CoV-2 is the influenza. I am merely introducing a theory used to explain the spread of the influenza, and see if it might be useful to also explain the spread of SARS-CoV-2. Over the course of his 60+ year career beginning in 1932, Dr. Hope-Simpson had various quibbles with the description of the spread of influenza, and so he developed his theory that he found to be a more adequate explanation of the facts. Dr. Hope-Simpson's entire text in which he presents his theory is The Transmission of Epidemic Influenza, and a PDF is available at Ivor Cummins's website: https://thefatemperor.com/wp-content/uploads/2021/04/11th-The-Transmission-of-Influenza-BOOK.pdf. Hopefully, my writing, though verbose, is done in a way that is accessible to all, and even better, able to be easily shared.


A Collection of Strange But True Facts That Require Explanation.

  1. There is a study analyzing blood samples in Italy in the Fall of 2019 (Unexpected detection of SARS-CoV-2 antibodies in the prepandemic period in Italy) that identified SARS-CoV-2 antibodies in 23/162 (14%) of blood samples of lung cancer patients from September 2019, as well as 27/166 (16%) of the blood samples from October 2019. That sample size is admittedly a little small, but that is conclusive evidence of the presence of SARS-CoV-2 in Italy long before the catastrophic outcomes in Italy, especially Lombardy in March 2020. Similar results were observed in France in December 2019 (SARS-CoV-2 was already spreading in France in late December 2019 and Evidence of early circulation of SARS-CoV-2 in France: findings from the population-based "CONSTANCES" cohort). Accordingly, I would be rather surprised if similar results did not exist throughout America, Canada, Europe, etc. Moreover, as a personal anecdote, I know someone who was in Wuhan, China, in November 2019, and returned to America with a terrible influenza-like illness.
  2. The repeated predictions of doom with spikes in cases, hospitalizations, and deaths for places that remove restrictions (e.g., Florida, Texas, etc.) do not come to pass, while concurrent to these openings, states adhering to masks, lockdowns, etc. (e.g., California and Michigan) experience large spikes. Similarly, based on the observed early presence of SARS-CoV-2 in at least France and Italy, the long delay between initial presence and notably bad outcomes undercuts the claim that masks, lockdowns, etc. are necessary to prevent horrible outcomes.
  3. As we enter Year 2 of SARS-CoV-2, comparisons to spring peaks from 2020 in the Great Lakes and Northeast are very interesting. What we see is that in all these states, there is a high degree of synchronicity with the date of the case peak in 2020 and the date of the case peak in 2021. Connecticut and Rhode Island are the two notable states with a sizeable time gap between 2020 and 2021. I think these results are not surprising in the context of the theory I'll present later, and the predictions I will make from it. All numbers are 7 day rolling averages taken from usafacts.org. I'm also only going to look at cases, because usafacts unfortunately does not include hospitalization, and death reports suffer from a systemic problem: they list net new deaths reported by day, which is different from total deaths on a day. To illustrate, Ohio reported a net of -327 deaths on April 5, 2021. In other words, if there were X new SARS-CoV-2 deaths reported on April 5, 2021, there were (X+327) deaths previously attributed to SARS-CoV-2, which were retracted. As another example, if Jimmy dies on March 8, but his SARS-CoV-2 death is reported on April 30, many outlets will include him in the April 30 tally. Jennifer Cabrera @jhaskinscabrera on Twitter has been terrific about reporting the date of death data for Florida.
    • In 2020, Michigan's cases peaked on April 8, and in 2021, their cases peaked on April 13.
    • In 2020, New York's cases peaked on April 9, and in 2021, their cases peaked on April 1.
    • In 2020, New Jersey's cases peaked on April 9, and in 2021, their cases peaked on March 30 (they have some spotty reporting in mid-March).
    • In 2020, Massachusetts's cases peaked on April 28, and in 2021, their cases peaked on April 11.
    • In 2020, Connecticut's cases peaked on April 22, and in 2021, their cases peaked on March 30.
    • In 2020, Rhode Island's cases peaked on April 26, and in 2021, their cases peaked on March 30.
    • In 2020, Vermont's cases peaked on April 10, and in 2021, their cases peaked on April 4.
    • In 2020, Ohio's cases peaked on April 21, and in 2021, their cases peaked on April 11.
    • In 2020, Indiana's cases peaked on April 30, and in 2021, their cases peaked on April 17.
    • In 2020, Pennsylvania's cases peaked on April 11, and in 2021, their cases peaked on April 18.
    • In 2020, Delaware's cases peaked on April 28, and in 2021, their cases peaked on April 19.
    • Is this pattern confined to America? No! Across the Great Lakes and St. Lawrence River, Ontario saw their cases peak around April 16 in Spring 2020, and the cases peaked on April 19 in Spring 2021. Similarly, Québec's cases peaked around April 18 in Spring 2020, and Québec's cases peaked on April 14 in Spring 2021.
  4. The influenza disappeared from everywhere on Earth simultaneously, and never returned. The was no influenza season in the Southern Hemisphere during their winter months of June-August, and there was no influenza season in the Northern Hemisphere during the winter of December 2020-March 2021. According to https://apps.who.int/flumart/Default?ReportNo=7, many countries in the Northern Hemisphere saw dramatic single-week declines in the influenza. For example,
    • Australia (comparatively few cases, but the drop is still clear), Canada, Denmark, Finland, France, and Norway all saw dramatic declines from Week 11 (March 9-March 15) to Week 12 (March 16-22). That decline for Australia is especially notable, because mid-March is their autumn, and so, the influenza should be increasing, and not disappear entirely.
    • In the United Kingdom, there was a large observed decline from Week 12 (March 16-22) to Week 13 (March 23-29).
    • Meanwhile both Germany and the United States saw large declines from Week 11 to Week 12, and then another large decline from Week 12 to Week 13.
    • Italy observed a mild influenza season, with large declines from Week 9 (February 24-March 1) to Week 10 (March 2-March 8), and then another large decline from Week 10 to Week 11, and was completely gone by Week 12.
    • The Republic of Ireland saw a large decline from Week 10 to Week 11, and then another large decline from Week 11 to Week 12, with the influenza disappearing from Week 13 onwards.
    • Japan saw more steady declines in influenza numbers, but again the influenza had vanished by Week 13.
    • South Korea has had very interesting past influenza seasons. In 2017 and 2019, South Korea observed surges of influenza A in the early months that fade out around Week 5 (late January/early February), but then in both years were hit with surges of influenza B starting around Week 11 (mid-March) ending around Week 20 (mid-May). In 2018, South Korea had a simultaneous surge of influenza A and influenza B in the early part of the year, with both being roughly gone by Week 15 (mid-April). In 2020, the typical influenza A surge around the new year comparable to 2017 and 2019 was again observed in South Korea, with the large decline from Week 5 (January 27-February 2) to Week 6 (February 3-February 9). But influenza A completely vanished from South Korea by Week 7 (February 10-February 16), and has not been seen since, and in addition to no influenza B surge comparable to 2017 or 2019, there was no influenza B activity whatsoever after Week 6.

Now, the common refrain from the proponent of masks, lockdowns, etc. is that the reason the influenza vanished completely is as a consequence of the masks, "social distancing", lockdowns, etc. But this explanation has problems. The most obvious problem is that we would need to accept that the masks, "social distancing", lockdowns, etc. were sufficiently powerful to eliminate the influenza, but SARS-CoV-2 remained, because people were not wearing their masks, avoiding other humans, or abiding by lockdowns. This claim seems rather specious, at best. Moreover, masks are already common in Japan and South Korea, and they still have annual influenza seasons. The other major problem is that there's a country that is conspicuous by its absence above: Sweden. Like it's nordic neighbours, Sweden experienced substantive declines in the influenza from Week 11 to Week 12, and then again in Week 13, with the influenza vanishing beyond Week 14. And this occurred despite the Swedes being some of the least masked people on Earth. In other words, we would need to accept that masks and/or lockdowns around the world were sufficient to eliminate the influenza at approximately the same time, despite various different measures being implemented at different times, but SARS-CoV-2 was unaffected, and also the influenza somehow simultaneously vanished in a place like Sweden, which did not follow the same path as most other countries in the West and East Asia. That hypothesis is too fanciful to believe.

Candidates to Explain the Empirical Reality.

Having introduced various facts that need to be simultaneously explained by a theory, I'll raise some candidate theories, and why I reject them.

  1. Let's start conspiratorially: SARS-CoV-2 is a hoax, and the "cases" and "deaths" are the influenza, pneumonia, etc. rebranded. Of the facts listed, this theory adequately explains Fact #2, because if something does not exist, predictions about it are bound to be wrong. Fact #3 is also reasonably explained, because we seasonality as we would expect for the standard influenza and pneumonia. Fact #1 would be a problem, because something that does not exist should not be leaving antibodies around, but Fact #4 is the interesting one. On the surface, the universal disappearance of the influenza would appear to be evidence that the influenza has just been rebranded as something else. But the influenza disappeared everywhere, regardless of that country's reaction to SARS-CoV-2. So, if the influenza was being labelled as something else, we would not expect that to also be occurring in places that have not gone along with the conventional reaction to SARS-CoV-2, such as Sweden or various states such as South Dakota. But this is not the case. The influenza has also vanished in these places. And it is also not a matter of a lack of influenza tests. There were about as many tests this winter as you would expect based on previous years, but the positivity rate was down about 99% compared to prior years, as were influenza hospitalizations (see https://threadreaderapp.com/thread/1362795306874793984.html). But one notable benefit is that there was a single pediatric death from the influenza this past season. So, I'm going to reject the hoax theory.
  2. Next, let's consider the conventional wisdom: something to the effect of "SARS-CoV-2 is real, deadly, and spreads from infected person to non-infected person while the infected person is sick (with possible spread by asymptomatics), in many cases through respiratory droplets, and we need masks, lockdowns, and the rest to prevent it from spreading." I'm not sure that this theory adequately explains any of the given facts. If masks, lockdowns, etc. are necessary, then the results of Italy and France in Fall 2019 are require some hard explanation. We would need to accept that SARS-CoV-2 would be able to spread from the infected people to roughly the entire Italian population, due to the complete absence of any masks, "social distancing", or lockdowns in Italy in Fall 2020. Yet, it takes 6 months for any negative outcomes to arise. This could possibly be resolved by speculating that SARS-CoV-2 was given free reign in Italy and France (at minimum) in Fall 2019, but sometime around February, a highly lethal variant developed that caused the very negative outcomes that didn't exist previously. Possible, but that seems rather ad hoc. It is also far from obvious how this theory explains the complete disappearance of the flu around the world, or the fairly consistent synchronicity across the Great Lakes and Northeast. We would need to believe something to the effect of all of the residents of these states started misbehaving around November, leading to the increases approaching the New Year, then they all started behaving around the same time in January to bring about the declines, but then started misbehaving against around February/March in spectacular synchronicity, before finally behaving again at roughly the same time that they started behaving in 2020. That all seems far too fanciful to be true without some very strong evidence to support it. Likewise, there is no clear explanation how Florida and Texas could lift all restrictions and see no adverse outcomes (or the dramatic declines in cases and deaths in Texas upon reopening), especially as their restrictive counterparts experienced the negative predicted outcomes (almost a Hamish-Mordecai twist of fate). And so, on Fact #3, the Texans would need to somehow be behaving better than the Michiganders despite the restrictions in Michigan and the complete freedom in Texas. Again, this seems highly implausible. So, as with the conspiracy theory, I'll reject the received wisdom, as well.

So, if the conspiracy theory and the received wisdom are both rejected, what is left to explain 14 months of data?

The Hope-Simpson Model: Motivation

Before introducing Dr. Hope-Simpson's theory, I'll raise one more problem for the received wisdom: the mere existence of seasonal variation. As Dr. Hope-Simpson notes (p. 5), seasonal variation is a phenomenon that is completely unexplained by the standard theory on the method of spread for not just SARS-CoV-2, but also the influenza. This matter of seasonal variation is so poorly explained by the conventional theory that Dr. Hope-Simpson dedicates an entire chapter (Chapter 8) to dealing with this problem. The clear observation made by Dr. Hope-Simpson is that the influenza displays a migratory pattern from the Northern temperate zone (defined as above the 30° latitude line, which passes through Houston and New Orleans) to the Northern tropical zone (defined as 0° to 29° N) to the Southern tropical zone (defined as 0° to 29° S) to the Southern temperate zone (defined as below the 30° latitude line, passing through southern Brazil, South Africa, and southern Australia) and back north. Dr. Hope-Simpson also observed that his home of Cirencester, England and Czechoslovakia experienced very identical influenza behaviour for the studied years of 1968-1974, including the prevalence of influenza A and B, respectively, and also the predominant Influenza A(H3N2) variant in each season. Additionally, the same type of behaviour is observed when comparing England and Australia. In the 1967-68 season in England, the Influenza A strain was the "Asian" H2N2 strain, but by the 1968-69 season, H2N2 disappeared and was replaced by the "Hong Kong" H3N2 strain. This same result occurred in Australia, with a 6 month lag behind the observations in England. Likewise, as in Cirecester and Czechoslovakia, the predominant variant of A(H3N2) was the same in Australia as it was in England 6 months prior. Then in Chapter 9, Dr. Hope-Simpson goes on to lay out more strange examples dealing with influenza variants. One is the case of three siblings of Ampney Crucis: Christopher (3), Colin (8), and Julia (7) who all slept in the same room, with Christopher and Colin sharing the same bed. In January 1968, all three become sick with the influenza, but when isolating the influenza strain in each of them a bizarre result was observed: the English variant of the 1967-68 season was isolated in both Colin and Julie, but the Tokyo variant of the 1966-67 season was isolated in Christopher.

In Chapter 10, Dr. Hope-Simpson discusses the problem of "antigenic shift", or the "vanishing trick". Historically, there have been numerous occasions where one strain of Influenza A would be prominent across the world, and then vanish in a single season. This occurred with the originally observed Influenza A strain A(H0N1), which disappeared in 1946 and was replaced by A(H1N1) [also referred to as "A(H1N1) old style"]. But this strain also disappeared in 1957 and was replaced by another strain A(H2N2), which lasted until 1968. The switch is strains was very notable, because while an infection from one strain offered protection against a variant of the same strain, no protection was offered against a new strain. What is also important is that the influenza was only isolated in the 1930s. Based on retroactive serology, it is thought that A(H2N2) was also the prominent strain of Influenza A from about 1889 to 1900, and A(H3N2) was the prominent strain of Influenza A from about 1900 to 1918. In other words, these Influenza A strains were ubiquitous for years, before completely vanishing, and then just as mysteriously reappearing 50 years after disappearing. In particular for 1918, A(H3N2) and a co-prevalent A(H1N1) strain [A(H1N1) old style, the same type that was predominant from 1946 to 1956] both simultaneously vanished and were replace by the A(H1N1) strain "A(HswineN1-like)", which saw the great influenza epidemic of 1918 and caused subsequent influenzas until 1929. When A(H3N2) became dominant again starting in 1968, it was again joined by a co-prevalent A(H1N1) old style, beginning in 1977.

The Hope-Simpson Model: Details

Dr. Hope-Simpson continues going through various interesting observations and flaws in the prevailing theory of direct spread of influenza, until we reach Chapter 16, where he lays out the various propositions that comprise his theory.

  1. Proposition 1: The influenza cannot generally be spread by sick people. Dr. Hope-Simpson notes that there have been various influenza outbreaks, where if individual households are considered, as high as 70% of the households record no spread of the influenza within the household (i.e., the number of sick people in the household is 1), which is a point in favour of Proposition 1.
  2. Proposition 2: After infecting a human, the influenza virus exists in a persistent infectious mode within the human. The human is neither further infected, nor able to transmit the virus unless or until the virus is reactivated to an infectious state. This proposition partially explains the seasonal nature of influenza.
  3. Proposition 3: Influenza virus is reactivated to an infectious state by seasonal stimulus, and the formerly infected individual is now able to spread the virus, and generally is not himself made ill in the process. This proposition in combination with Proposition 2 nicely explains the seasonal nature of influenza. Of note, while Dr. Hope-Simpson proposes a seasonal stimulus as the cause of the reactivation, he does not suggest what that stimulus actually is.
  4. Proposition 4: Influenza epidemics may occur "out of season" when influenza carriers rapidly relocate to different regions and the virus is reactivated in the new region.
  5. Proposition 5: The speed of the spread of the influenza virus is a consequence of the season stimulus reactivating the virus.
  6. Proposition 6: The variants (antigenic drift) of a strain of Influenza A derive from the formerly infected human host. After infection, the host develops immunity against the originally infecting strain, and then the virus retreats into a latent mode (Proposition 2). Upon reactivation, the immunity in the host neutralizes the original strain, but possibly allows variants to escape (thought to be most commonly in the young). The variants, not the parent strain, are then transmitted to nonimmune counterparts.
  7. Proposition 7: A strain of influenza that is prominent in an area through infecting a large number of people and persistent infection in others produces a similar immune response in the people and also a similar collection of variants. A singularly fit variant outperforms all other variants and replaces the parent strain as the dominant strain in the area. This proposition explains how an Influenza A strain is able to be dominant for years, and then vanish completely within a single influenza season.
  8. Proposition 8: Each of H1N1 (and it's major mutants HswineN1-like, H0N1, and H1N1 old style), H2N2, and H3N2 retain their era of prevalence until roughly all of the nonimmune population has been infected.
  9. Proposition 9: The major strains H1N1 (as well as the three major variants of H1N1), H2N2, and H3N2 existed long ago and have been continuously recycled throughout human hosts for decades if not centuries.
  10. Proposition 10: After being infected by influenza for the first time, a person retains the virus in a state of persistent infection. Additionally, the person may also retain the virus genome, possibly for life. The viral genome is also subject to the same reactivation by the seasonal stimulus, but is only able to spread and cause a new epidemic when a sufficiently large number of new humans have been born into the population. This proposition explains the long disappearance of an Influenza A strain, as well as its sudden reappearance decades after suddenly disappearing.
  11. Proposition 11: Among the variants of H1N1 ("the first lineage"), they seem unable to have eras of co-prevalence. Between H2N2 and H3N2 ("the second lineage"), they seem unable to be co-prevalent. The only observed eras of co-prevalence are H1N1 old style with H3N2 from 1908 to 1918 and then again from 1977 to 1990.

Problems for the Direct Spread Theory

Dr. Hope-Simpson conclude in Chapter 18 by listing 21 problems need to be explained by a theory of the spread of influenza. Dr. Hope-Simpson contends that his models adequately explains all 21 (although some hypotheses of his position require verification), while the current theory of direct spread explains exactly zero. These problems are

  1. Problem 1: ubiquity
  2. Problem 2: seasonality
  3. Problem 3: antigenic drift (variants)
  4. Problem 4: disappearance of prevailing strain
  5. Problem 5: prompt replacement by new strain
  6. Problem 6: interepidemic survival of virus
  7. Problem 7: concurrent explosion of epidemics over wide areas
  8. Problem 8: time and strains in small areas mirror the whole
  9. Problem 9: cessation of epidemics
  10. Problem 10: absent serial interval (time between initial case and secondary cases transmitted to nonimmune counterparts)
  11. Problem 11: low secondary attack rate
  12. Problem 12: anomalous age distribution (direct spread illnesses such as measles, mumps, and chickenpox have an average age of infection skewed very young; the average age of infection for Influenza A is comparable to the population)
  13. Problem 13: antigenic shift (switch from H1N1 to H2N2 to H3N2 to H1N1)
  14. Problem 14: vanishing of major serotype (H1N1, H2N2, H3N2)
  15. Problem 15: prompt global replacement by new serotype
  16. Problem 16: recycling of major serotypes
  17. Problem 17: viral and serological anachronisms
  18. Problem 18: seasonal antigenic changes
  19. Problem 19: out-of-season epidemics do not spread
  20. Problem 20: annual transequatorial swing
  21. Problem 21: constant speed of epidemics in the present (1900s) as the past (1500s-1800s)

As a concluding note, Dr. Hope-Simpson wonders if the influenza is unique, because as observed, it is a struggle to conclude that the influenza spreads according to the prevailing theory. However, this is not the case for many other illnesses, such as measles, mumps, chickenpox, shingles, smallpox, rubella, etc.

Subsequent Developments

While Dr. Hope-Simpson's theory is interesting, if not revolutionary, an obvious question is what has been done with it since this publication 30 years ago, and his death in 2003? Unfortunately, the answer is not much. In searching through Google Scholar, I came across only 3 publications since 2000 that mention the Hope-Simpson theory: one from 2006, a follow-up publication from 2008, and a short commentary by a different group from 2016 on the timing of the influenza.

In Epidemic influenza and vitamin D by Cannell et al., the authors investigate the "seasonal stimulus", which Dr. Hope-Simpson proposed only as an abstract concept. The authors propose that vitamin D is Dr. Hope-Simpson's "seasonal stimulus". As various points of evidence, they note that Norwegians have consistently higher vitamin D levels than Britons, and Norwegians have consistently lower excess winter mortality than Britons, which is consistent with the Hope-Simpson "seasonal stimulus" theory. Likewise, they note than melanin reduces natural vitamin D production and that African-Americans have higher mortality from influenza than their white counterparts, which is also consistent with the Hope-Simpson "seasonal stimulus" theory. As another point of consistency with the Hope-Simpson "seasonal stimulus" theory, the authors note that studies of influenza in Russia during various parts of the year show that volunteers subjected to the same level of influenza virus are much more likely to develop an infection in winter than summer.

In On the epidemiology of influenza by Cannell et al. (some authors the same as before, in addition to some new authors), the authors return to the topic of vitamin D and the influenza in the context of Dr. Hope-Simpson's theory. One interesting observation that the authors cite is that even though influenza vaccination increased substantially in the 1980s and 1990s for elderly Americans, influenza mortality was unaffected. When discussing the concept of sick-to-well transmission of the influenza, which Dr. Hope-Simpson rejects, the authors cite a shocking result: there are no known studies of sick-to-well transmission of the influenza, and various attempts at sick-to-well transmission that failed to get anyone infected, including an attempt in 1918 to transmit the 1918 influenza by having infected people cough, spit, and breath on more than 150 well people. The authors do modify the Hope-Simpson theory slightly by proposing that among the total pool of the infected there is a subgroup of "good infectors" that are able to transmit the influenza while infected, while Dr. Hope-Simpson rejected that hypothesis.

The Hope-Simpson Model and SARS-CoV-2

Having introduced the Hope-Simpson model for the spread of influenza, we return to the topic of SARS-CoV-2, wondering if the Hope-Simpson model has any application to SARS-CoV-2. In the beginning, four notable facts were introduced that are hard to square with the prevailing theory on the spread of SARS-CoV-2, and the Hope-Simpson model does adequately explain these observations and others. As above, we start from the presumption that the theory under consideration (the Hope-Simpson model, in this case) is correct, and ask if the observations are sufficiently explained.

  1. The observation that SARS-CoV-2 antibodies were found in Italy at least as early as September 2019 is consistent with the Hope-Simpson theory. We could easily propose that SARS-CoV-2 had a previous reign of dominance in the world, similar to the observation of recycling of Influenza A with H1N1, H2N2, and H3N2. Moreover, Dr. Hope-Simpson's theory about a reactivation brought about by a "season stimulus" explains the long delay between observed SARS-CoV-2 antibodies and wide-scale spread in Italy (and other countries) and highly negative outcomes.
  2. The repeated failures of the experts are explained by the Hope-Simpson model, because they are making predictions based on horribly flawed premises of direct spread from the infected to the well.
  3. The cyclical results in Michigan, New York, New Jersey, Massachusetts, Pennsylvania, Ohio, Ontario, and Québec are highly expected by the Hope-Simpson model. It is also worth noting that this observation is very difficult for the direct spread model to explain, because crossing the border from Canada to America has been very difficult for the past year. The air border has never been closed, but the land borders have only been open for "essential" travel. Moreover, it has been the case for getting close to a year that any entrant to Canada is "legally" required to quarantine for 14 days (and for the past few months, anyone flying into Canada "must" spend at least the first 3 days and possibly longer at a government facility). So, any direct spread explanation for these results would likely need to hypothesize an Ontario/Québec-to-Great-Lakes-states direction of spread. However, up until the last few weeks, Canada's per capita case rate was consistently well below that of America, which again poses problem for an explanation with the direct spread hypothesis. Additionally, this synchronicity of both geography and time comes with differing reactions from the governments. Ontario, for example, implemented more lockdowns in April, while Michigan and Pennsylvania not only did not add new restrictions, but actually removed restrictions by increasing occupancy limits.
  4. The Hope-Simpson theory explains the vanishing of both Influenza A and also Influenza B around the world simultaneously in the same way that it explains the "vanishing trick" for the major serotypes A(H1N1), A(H2N2), and A(H3N2). Now, what is notably different here is that both Influenza A and Influenza B vanished in the midst of influenza season (in the Northern Hemisphere) or in the build-up to influenza season (in the Southern Hemisphere) in the span of at most one month. Simply, this is a much greater "vanishing trick" than has ever been seen for any Influenza A strain.

Conclusions and Predictions

After laying out the Hope-Simpson theory, what conclusions should be drawn from it? I think the most obvious is eat more fish and take more cod liver oil. But again, I'm not a medical doctor and this is neither medical nor nutritional advice, just an observation based on the research of Cannell et al., and that vitamin D deficiency has been linked to an increased risk of SARS-CoV-2. Next, the "seasonal stimulus" is very interesting for SARS-CoV-2, because geography seems to have an outsized role in predicting outcomes. On April 4 (https://www.foxnews.com/health/fourth-coronavirus-wave-variants-osterholm-fox-news-sunday), Chris Wallace correctly observed the lack of a relationship between removing lockdowns and mask mandates with surges of new cases and asked Michael Osterholm to explain this fact. Interestingly, Osterholm did not appeal to masks, "social distancing", lockdowns, or even jabs in his answer. His answer was completely dependent on geography (i.e., different areas of the US simultaneously surges, and implicitly, this occurs regardless of intervention). So, given the regional activity and synchronicity that we're seeing in America and Canada, that does mesh nicely with Dr. Hope-Simpson's proposal. The apparent lack of effectiveness of masks, lockdowns, etc. to the point that not even Michael Osterholm is willing or able to appeal to these variables to explain the results implies that there is no justification for continuing with these policies. It's not clear that the outcome should be any different regardless of whether or not the Hope-Simpson theory is correct, but regardless, we do not see the actions predict outcomes. Regardless, it's rather unfortunate that the Hope-Simpson theory has received very little further investigation since it was proposed 30 years ago.

On the question of predictions, one of my main criticisms of the prevailing narrative is that the proponents such as Anthony Fauci, Michael Osterholm, Eric Feigl-Ding, Phil Murphy, Andrew Cuomo, Gavin Newsom, etc. are unable to make even marginally accurate predictions. So, what should we expect going forward, if the Hope-Simpson theory has merit for explaining the spread of SARS-CoV-2? The most obvious expectation is that the curves of 2021 will be similar to that of 2020 in shape, not necessarily scale. So, I'll predict that Florida, Texas, and the rest of the Southeast will see a case surge starting around mid-June and peaking in mid-July, just as they did in 2020, and just as the Hope-Simpson model predicts (with a slight timelag). Again, this does not predict the size of the surge. It could be greater than in the summer of 2020, or it could be lower. In the header image, I've made a plot of Dr. Hope-Simpson's observations (pg. 83) on influenza prevalence for Florida's latitude zone (red, connecting the 15th of each month), plotted against Florida's case curve for the past 14 months (blue). As we can see, the observed peaks align with the peaks expected by the Hope-Simpson model, with a lag of about 3-4 weeks. Likewise, I'll predict that the Rocky Mountain states, Heartland states, and possibly the Midwest states will see a surge starting around mid-September peaking in mid/late-November.

As to predicting the scale of surges, this is much more tricky, because there are likely multiple factors at play. Two important factors are those who already have immunity, which could be reasonably discerned from the known infection levels, but also it's possible that the lockdowns are contributing to an increase in infections due to keeping people inside, and artificially lowering vitamin D levels. I don't know that it's coincidental that as American states start releasing lockdowns and restrictions and Canadian provinces quadruple down on them that Alberta has claimed top spot for cases/capita in North America, Manitoba is #3, and Ontario is #7. Similarly, it's distinctly possible that more people in the Southeast were staying indoors in Spring 2020, artificially lowering vitamin D levels, and increasing the scale of their summer surge. Given the general usefulness of Dr. Hope-Simpson's theory, and the significance of vitamin D in both the influenza and also SARS-CoV-2, these are questions the certainly deserve scholarly attention.