Bayesian Model for Covid-19 to Achieve Immunity by Parsimony of Exponential Functions Minimizing the Inoculum

A ribonucleoside analog MK-4482/EIDD-2801 blocks SARS-CoV-2 transmission in ferrets and might be able to diminish transmission until vaccineinduced or naturally acquired protective herd immunity is reached [1]. As skinner pointed out, behavioral problems have to be solved through behavioral engineering [2]. Cybernetics has full application in the present condition. As in alcohol consumption, smoking, drugs, gun crimes, wars, and sexually acquired diseases, the teleological Aristotelian causes are not tobacco, drugs, and any other issue, but the aberrant behavior. The situation is not trivial and involves non-classic logic and other mathematical logics [3,4]. The neural topography corresponds to the nucleus accumbens. The latter is the battlefield, and the subject’s obsession is the rise of the neurotransmitter dopamine [3,4]. In general, people are very demanding from their governments; nevertheless, at the same time, they are deeply tolerant with their aberrant behavior promoting the dissemination of the SARSCoV-2 [4,5]. This paper examines how to deal with this problem from a scientific perspective, considering probability methods and classical and doxastic logic, using the Parsimony Principle aiming to reach immunity by minimizing the inoculum.

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Application of the Parsimony Principle of Exponential Functions to the Production of COVID-19 Immunity, According to Bayesian Theorem and Cybernetics, to Tame Anti-scientific Doxastic Logic and the Pandemic

10.36811/ijbm.2020.110021 ◽

2020 ◽

pp. 33-47

Author(s):

Nery Lamothe ◽

Mara Lamothe ◽

Daniel Lamothe ◽

Pedro J. Lamothe

Keyword(s):

Behavioral Problems ◽

Herd Immunity ◽

Behavioral Problem ◽

Cell Subpopulation ◽

Policy Enforcement ◽

Exponential Functions ◽

Doxastic Logic ◽

Bayesian Theorem ◽

Parsimony Principle ◽

Behavioral Engineering

The purpose of this work is to provide evidence to the scientific community that there is solid scientific knowledge available to tame the pandemic, which is mainly a behavioral problem that requires cybernetics through behavioral engineering. Scientifically it is clear that the problem of the pandemic originates in human behavior and misinformation. Behavioral problems are addressed by cybernetics through behavioral engineering. Aristotelian causes of the pandemic are aberrant behavior. This is the field of battle and the obsession of the subject is the rise of the neurotransmitter dopamine. The question is not what is the probability that a patient with COVID-19 has a certain symptom or sign? Rather it is to calculate the probability that a patient with a certain sign or symptom has COVID-19. Without grasping the differential equations modeled by Kermack and McKendrick, it is impossible to have an idea of what is happening in the pandemic. Our straightforward theoretical approach is to use the wild unmodified SARS-CoV-2 to produce immunity by the simple expedient of diminishing the amount of the inoculum to the minimum minimorum. The problem with allowing people, deliberately attempting herd immunity, is that it has the dire effect that a high percentage will necessarily die. It is a matter of competence between two exponential functions. On one hand the exponential reproduction of the virus, and on the other hand, the exponential production of antibodies and activation of T cells. The aim is to diminish the amount of the inoculum to the minimum minimorum capable of infecting the minimum susceptible cell subpopulation. In this manner, herd immunity could be reached, which would allow a parsimonical response in the viral exponential growth that would not overwhelm the exponential immune response. It is expected that susceptible subjects could be infected in a variolation modality through the universal use of masks, maximizing the distance, rather than in a noregulated exposure of a putative low-risk segment of the population. In the logic of the decision, we must distinguish a desideratum from what is physically, economically, legally, and politically implementable. It is a matter of policy-making supported by science and law instead of doxastic logic based on misinformation and bigotry. It is a matter of policy enforcement by cybernetics, by behavior engineering, not of a recommendation. The guidelines, if they are to be implemented, depend on the application of cybernetics, and behavioral engineering. The apodictic inference from fallacies, in a doxastic and desiderative logic, is the origin of disinformation. Keywords: COVID-19 Inoculum; Bayes Theorem; Cybernetics; Variolation; Herd immunity

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Dynamic causal modelling of COVID-19

Wellcome Open Research ◽

10.12688/wellcomeopenres.15881.2 ◽

2020 ◽

Vol 5 ◽

pp. 89 ◽

Cited By ~ 6

Author(s):

Karl J. Friston ◽

Thomas Parr ◽

Peter Zeidman ◽

Adeel Razi ◽

Guillaume Flandin ◽

...

Keyword(s):

Bayesian Model ◽

Causal Model ◽

State Of The Art ◽

Herd Immunity ◽

Nonlinear Effects ◽

Model Inversion ◽

Neuronal Ensembles ◽

Technical Report ◽

Per Se ◽

Dynamic Causal Model

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

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‘Dark matter’, second waves and epidemiological modelling

BMJ Global Health ◽

10.1136/bmjgh-2020-003978 ◽

2020 ◽

Vol 5 (12) ◽

pp. e003978

Author(s):

Karl Friston ◽

Anthony Costello ◽

Deenan Pillay

Keyword(s):

Bayesian Model ◽

Model Comparison ◽

Herd Immunity ◽

Population Immunity ◽

Bayesian Model Comparison ◽

Overwhelming Evidence ◽

The Usa ◽

Conventional Models ◽

Support Programmes ◽

The Uk

Recent reports using conventional Susceptible, Exposed, Infected and Removed models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities exceeding the first wave. We used Bayesian model comparison to revisit these conclusions, allowing for heterogeneity of exposure, susceptibility and transmission. We used dynamic causal modelling to estimate the evidence for alternative models of daily cases and deaths from the USA, the UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany and Canada over the period 25 January 2020 to 15 June 2020. These data were used to estimate the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed and (iii) not infectious when susceptible to infection. Bayesian model comparison furnished overwhelming evidence for heterogeneity of exposure, susceptibility and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain large differences in mortality rates. The best model of UK data predicts a second surge of fatalities will be much less than the first peak. The size of the second wave depends sensitively on the loss of immunity and the efficacy of Find-Test-Trace-Isolate-Support programmes. In summary, accounting for heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

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Dark matter, second waves and epidemiological modelling

10.1101/2020.09.01.20185876 ◽

2020 ◽

Cited By ~ 2

Author(s):

Karl Friston ◽

Anthony Costello ◽

Deenan Pillay

Keyword(s):

Bayesian Model ◽

Model Comparison ◽

Herd Immunity ◽

Viral Transmission ◽

Population Immunity ◽

Bayesian Model Comparison ◽

Overwhelming Evidence ◽

Conventional Models ◽

The Uk ◽

Dynamic Causal Model

Background Recent reports based on conventional SEIR models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities that far exceed the first wave. These models suggest non-pharmaceutical interventions would have limited impact without intermittent national lockdowns and consequent economic and health impacts. We used Bayesian model comparison to revisit these conclusions, when allowing for heterogeneity of exposure, susceptibility, and viral transmission. Methods We used dynamic causal modelling to estimate the parameters of epidemiological models and, crucially, the evidence for alternative models of the same data. We compared SEIR models of immune status that were equipped with latent factors generating data; namely, location, symptom, and testing status. We analysed daily cases and deaths from the US, UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany, and Canada over the period 25-Jan-20 to 15-Jun-20. These data were used to estimate the composition of each country's population in terms of the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed, and (iii) not infectious when susceptible to infection. Findings Bayesian model comparison found overwhelming evidence for heterogeneity of exposure, susceptibility, and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain the large differences in mortality rates across countries. The best model of UK data predicts a second surge of fatalities will be much less than the first peak (31 vs. 998 deaths per day. 95% CI: 24-37)--substantially less than conventional model predictions. The size of the second wave depends sensitively upon the loss of immunity and the efficacy of find-test-trace-isolate-support (FTTIS) programmes. Interpretation A dynamic causal model that incorporates heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

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