Top-Down Investigation Bottom-Up Quantification Method (TBM)

Epidemiological & Public Health Vigilance System

[Fourth article in a series. 1) Leadership, Management, and Strategic Planning in the Covid Era, 2) The Ineffective Messaging of “Myocarditis in Young, Male Athletes”, 3) Sudden Kidney Failure Began with Government Incentives, not with COVID, 4) Top-Down Investigation, Bottom-Up Quantification Method for Epidemiological Vigilance, 5) Differences in Pneumonia, COVID, and All-Cause Death Profiles by Age Group.]

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The first article in the series called for better leadership and messaging in the health freedom movement. The second article detailed the magnitude of myocarditis versus kidney failure and which should get more visibility. The third article explained that sudden kidney failure excess deaths began when COVID treatments and “vaccines” began and not when COVID disease began. The recommendation was to conduct a detailed forensic investigation in order to conclusively determine the causes of excess deaths.

This fourth article provides a project plan for forensic investigation and quantification to determine the cause and quantity of negative health occurrences. Nothing in the plan proposed in this article is new to engineering, though it may be new to epidemiology. The implementation is cheap, easy, necessary, and fits the express missions of state and federal government health agencies. Any good engineer would characterize this plan by the colloquialism, “It’s a no-brainer.”

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Issue

The Communicable Disease Center (CDC) opened on July 1, 1946 with the primary mission of preventing malaria from spreading across the United States.¹ In 2025, the current U.S. Centers for Disease Control and Prevention (CDC) agency expresses the following mission, “… to protect America from health, safety and security threats … fights disease and supports communities and citizens … increases the health security of our nation … saves lives and protects people from health threats … conducts critical science and provides health information that protects our nation …”²

Agents of the CDC have not acted in accordance with their express public mission statement. In fact, they have acted adversely to their mission statement since at least 2020 and likely for many decades before that.

Rather than continue negative commentary listing all the criminal acts committed by agents of three-letter agencies (TLAs) intending to harm The People, a short one-page purpose statement is offered followed by a cursory 11-page project plan to do the job they should have been doing since their inceptions.

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MISSION

• Make America Healthy, Free, and Great Again

OBJECTIVES

• Deliver the most robust epidemiological investigation method ever created and maximize the utility of government health data

ISSUES

1. Current epidemiological methods, including evidence-based medicine (EBM), are woefully inadequate in investigating causality of, and quantifying, excess death and injury

2. A true assessment of safety and effectiveness of medications, including vaccines, has not been performed because certain public health data is not utilized and is kept hidden from the public

In research papers, confidence intervals and p-values are used to draw inferences about a larger actual population for which the data is unavailable to the researcher. These inferences are often wildly incorrect due to confounding variables, lack of controls, polymodal distribution functions, and paradoxes in data.

SOLUTIONS

Utilize all of the pertinent government data. In order to determine causality, investigate from the high-level abstract data down to the low-level particularized individual records. Then quantify the dead and maimed by mathematically operating on data aggregated by cause-of-death, age, and other variables.

IMPLEMENTATION

Adopt the Top-Down Investigation, Bottom-Up Quantification Method (TBM) and develop the sub-process Automated Learning in Public Health Analysis (ALPHA), both centered on the public health mission. ALPHA is an early warning system and an hierarchical forensic investigation tool. The CDC, FDA, and NIH have not shown that they have anything close to the utility of the TBM.

EBM is not a project plan. It is simply a rigid framework for scientific study. TBM is a project plan that determines causality and then quantifies deaths from externalities such as pathogens that enter a locale.

TBM borrows from electrical engineering (EE) workflows and solves the deficiencies of EBM. The EE workflow, “Top-Down Design, Bottom-Up Verification,” evolved to meet the business challenges of billions of transistors switching billions of times per second costing millions of dollars per prototype run.

ALPHA utilizes more EE methods such as Monte Carlo analysis. The availability of compute resources is vast, while cost is mere. There is no reason to omit Monte Carlo sweeps on all available government data.

NOTE:

Early TBM and ALPHA prototypes were used to generate world-leading Covid and vaccine findings years before EBM-style research papers found the same evidence. Future access to immunization registry databases will greatly improve TBM results. In one man-week, all vaccine debate can end. Government blockade of the immunization registry is a dereliction of a legal duty to act for the safety of The People.

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Invitation

State and federal health agencies are welcome to contact Summa Logica LLC, John Beaudoin, Sr., to discuss strategy, tactics, or details of the plan. Nuanced organizational behavior and management techniques of the plan are not intuitive. Thus, government implementation without consultation from Summa Logica LLC is less likely to succeed. An open offer is on the table to perform the work under NDA, in government offices, on the government network. This would allay all privacy issues. The government then has no excuse to withhold government health data. Government citation of HIPAA or other laws would only the pharmaceutical industry from The People attaining the TRUTH at the expense of mass deaths among The People.

The TBM Method

It is critical for the researcher to understand that the objective is to determine cause and then quantify those affected. The investigation process from the top-level data abstraction of All-Cause deaths down to the thousands of pages in an individual case file is to establish causation, and not necessarily to learn the mechanism of action at the cellular or molecular level. The Top-Down Investigation, Bottom-Up Quantification Method (TBM) is not a science experiment. EBM and the scientific method are only incidental to the mission to Make America Healthy Again, Free, and Great Again. TBM is a workflow project plan to achieve an objective, not prove an hypothesis. Know the difference.

If a researcher discovers stark increases in specific causes-of-death, he is tempted to rely on the numbers as proof of causality between a medication or procedure and the cause-of-death. This cannot be proof. Variables are unknown. The Top-Down Investigation half of the flow is not complete until individual medical records are reviewed by professional clinicians such as physicians, medical examiners, or other experts. The sampling of individual records is crucial to achieve conclusive evidence of causation. Only after causation is established can Bottom-Up Quantification results be firmly stated in public communications. Both halves of TBM can be performed in parallel. The latter does not rely on the former. My point here is that emphatic statements about the quantity of people who died from a disease or medication cannot be substantiated or believed unless the causal trace has been completed to the record-level forensic inspection.

Top-Down Investigation

1. Obtain, verify, and clean-up official RLSD databases to begin analysis

   1. Vital Records - Death records without redactions

   2. VAERS Reports

   3. Medicare/Medicaid databases - without redactions

   4. Immunization Information System (IIS) records without redactions (IRB de-identification with primary key can be used if records are to be used offsite from government offices)

2. All-Cause HotSpot Detection

   1. Using SQL or another language, automate the creation of vectors of the number of deaths/day for variable inputs such as age ranges, sex, place of death, ICD-10 codes, and others

   2. Create heat maps with 24 half month periods of rows and age groups in columns in order to visualize hot spots. Use a baseline of 2015-2019 or more years, if available. Employ multiple methods of establishing baseline and then conditioning the cells to change color and intensity based on excess for each period and age group (e.g., raw data increase over expected value by trend, percent increase over 5-year average, percent increase over expected value by trend, number of standard deviations from mean established from baseline years, et al).

3. All-Cause HotSpot Investigation

   1. Depending on staff, choose the hottest cells to manually investigate in the database. Each person can manage to review a few hundred records if the causes-of-death can be easily scanned visually.

   2. Determine which causes are more prevalent. Look for anomalies and patterns, whether in-hospital death or in a care home, involving sudden blood loss anemia or stroke, for example, or noticing that no one under age 70 appeared in the hotspot.

4. Automated Learning in Public Health Analysis Including Monte Carlo Sweeps Across Variables

   1. Sections 1, 2, and 3 above are for a preliminary new engagement where immediate response is required within a few days to a few weeks. The permanent engagement plan should be deployed in parallel. Begin deployment of the Automated Learning in Public Health Analysis (ALPHA) system.

   2. Adapt ALPHA to the target environment given the databases available

      1. Front-End - develop an input screen for the user to choose input parameters of a study (e.g., lower bound age, upper bound age, ICD-10 Code A, ICD-10 Code B, ICD-10 Code C, sex, where died, autopsy performed, autopsy available, state, days from vaccination date)

      2. Intermediate Package - Through SQL and perhaps Python languages, program the environment to apply the input variables and create an output data stream (vector) for every day from the beginning date of the data set (e.g., January 1, 2015) to the latest date (e.g., December 31, 2023). The data streams can then be sent out to researchers for their use (No personal information is in the data streams. Simply numbers per day for 9 years)

      3. Monte Carlo Analyses - Automate the Front-End to perform sweeps across all permutations of variables. Program metrics that define a scale for anomalous findings, perhaps by Z-score, by standard deviations, or by deviations from trend. The system will be set up to rank and flag serious anomalies such that it will find historical anomalies and also find real time anomalies that must immediately be investigated. This is an early warning system and a forensic investigational tool. There is nothing like it in the world.

      4. Back-End - Set up as outputs a series of graphs and tables for human visualization. There should be a standard set and an API to allow people to work with services such as Tableau to generate their own visuals. The graphs and tables should seek to discover and untangle all known and common data paradoxes as well as to quantify excess or deficit deaths.

         1. Annual Bar Graph stated as raw numbers (January 1 start date)

         2. Annual Bar Graph stated as PoC (Prevalence-of-Cause) (January 1 start date)

         3. Fiscal Bar Graph stated as raw numbers (July 1 start date)

         4. Fiscal Bar Graph stated as PoC (July 1 start date)

         5. 9-year Waveform Line Graph as raw numbers 51-day rolling average

         6. 9-year Waveform Line Graph as PoC 51-day rolling average

         7. Cumulative Annual Line Graph stated as raw numbers (January 1 start date)

         8. Cumulative Annual Line Graph stated as PoC (Prevalence-of-Cause) (January 1 start date)

         9. Cumulative Fiscal Line Graph stated as raw numbers (July 1 start date)

         10. Cumulative Fiscal Line Graph stated as PoC (July 1 start date)

         11. Daily Annual Line Graph stated as raw numbers (January 1 start date)

         12. Daily Annual Line Graph stated as PoC (Prevalence-of-Cause) (January 1 start date)

         13. Daily Fiscal Line Graph stated as raw numbers (July 1 start date)

         14. Daily Fiscal Line Graph stated as PoC (July 1 start date)

         15. 9-year Waveform Area Plot of Excess deaths (use 51-day RA in excess comparisons)

         16. Table showing annual excess deaths by year and total of excess deaths across all years (include slope & intercept)

         17. Repeat Waveform graphs above, but with 2nd and/or 3rd cause involved

             1. Plot (All-Cause) line and (All-Cause minus specified cause) line on same graph

             2. Plot (Cause A) line and (Cause A minus those records containing both Cause A & Cause B) line on the same graph

             3. As necessary, look at multiple cause involvement

      5. Perform frequency domain and time domain waveform analyses to find and quantify seasonality or cyclical signals

         1. Using Discrete Fourier Transforms (DFT), determine the Signal to Noise Ratio (SNR) of data streams (vectors). For example, more people die in winter than in summer in northern climates, but if separated by age groups, only the very old will have a high SNR. The young and middle-aged SNR will be low, indicating only noise and no seasonality. Pneumonia and other respiratory illnesses are expected to have higher SNR, but at what ages? Are heart attacks seasonal?

         2. The value of using DFT analysis to quantify seasonality in the form of SNR provides the downstream ability to untangle multiple signals if present in the data. Through further mathematical operations, it is possible to remove one signal to see what is left over and quantify the remaining signal.

         3. Time domain analysis of signals both mathematically and visually can provide deep insight into extraction of externality signal forms. Explain using example depictions from The Real CdC (Beaudoin, 2024) applied to pneumonia and acute renal failure help explain the topic.

Figure 1

Figure 1 is Figure 19.1 from The Real CdC (Beaudoin, 2024). Three diminishing waves of a seasonal respiratory virus-like illness (gray plot) entered this ideal seasonal society (top black line plot) and resulted in the lowest of the three plots representing total deaths. Notice the return to baseline in the summers even during the externality. If pneumonia deaths result from a new seasonal virus, then the resultant plot should look like the bottom plot in Figure 1. One important thing to consider is that if the first wave enters late in a season, then the first wave may be less than the second wave. All waves after the second wave should be smaller in each successive season.

Figure 2

Figure 2 is Figure 19.3 from The Real CdC (Beaudoin, 2024). In this case, an externality (gray line plot) enters society and increases linearly similar to what a “vaccine” rollout might look like. The bottom plot depicts the total deaths resultant from such a linear externality.

Figure 3

Figure 3 depicts the actual “pneumonia, unspecified” involved deaths from three (3) states, Minnesota (MN) at top, Massachusetts (MA) middle, and Connecticut (CT) at bottom for ages 75 and older. All three (3) states clearly show a seasonal externality entering late in the season in 2020 end of winter, beginning of spring. Notice that all return to baseline or below baseline every summer. Also notice that MN’s first wave is very small compared to MA and CT. Thus, MN’s second wave is the largest. And all three (3) states’ third waves are smaller than second waves. One would expect the same graphs to result in younger ages.

Figure 4

Figure 4 depicts “J18” pneumonia for ages 18-54. The expected observations are that summer 2020 returned to baseline and that there appears to be a seasonal aspect to all three (3) states. However, there is a significant issue in the amplitudes. The all get much larger from the second to the third waves. Why would more people ages 18-54 die involving pneumonia in the third wave than in the first or second waves? No disease in history kills more the third time around. It’s as if people ages 18-54 did something to their bodies to make them more susceptible of dying from pneumonia.

At this point, one would flag this as a health emergency and proceed to steps 5. and 6. below to perform forensic investigations in detail for samples of previously young, healthy people who died from pneumonia. What medications were they given in hospital? What vaccinations or gene drug therapies did they receive and when? Omission of such an investigation is a criminal omission of required legal duty.

Figure 5

Figure 5 is a reminder of a linearly increasing externality where the troughs rise. Imagine two externalities that combine.

Figure 6

Figure 6 depicts deaths involving “N17” “Acute renal failure” in ages 75+. The graph is obviously a combination of a linearly increasing signal due to troughs rising in time and seasonal signal of winters rising and falling.

5. Detailed Correlation of Records

A year of Massachusetts death records contains ~ 60,000 records. The purpose of data analyses and anomaly searches in the Top-Down Investigation phase of TBM is to reduce the locus of examination to a plausibly investigable quantity. Imagine ~60,000 records pared down to 300 records that are the most likely to conclusively yield causal attribution of death to a medication or procedure. Ten (10) experts can forensically review 300 case files in detail in a reasonable time period (a few weeks).

A record-level forensic inspection must be performed by experts to ascertain causality to an acceptable and court-ready standard of proof.

(A more detailed representation of this section would manifest a three hundred (300) page manual. This is a mere cursory overview.)

5. 1. VAERS - VAERS is not to be used for statistical analyses. Though HHS.gov cautions against using VAERS to establish causality, the information in VAERS reports can be used to corroborate facts gleaned from other official records. Correlate other records with VAERS reports to verify and ascertain such facts such as:

      1. Injection date

      2. Death date

      3. Onset of symptoms date

      4. Medications administered

      5. Vaccine manufacturer and lot number

      6. Medical history

      7. Description of circumstances

      8. Other

   2. Medicare/Medicaid - In the Top-Down Investigation phase of TBM, inspection of Medicare and Medicaid records is to again select the best candidates for detailed case-level review. For example, in order to pare 10,000 records down to 200, simply filter the databases for the records of those who died within two (2) days of immunization. The resultant subset of a few hundred records is low-hanging fruit for full hospital record and autopsy report forensic investigation. Causal attribution to a medication or procedure, if any, will likely be found in the detailed records, especially if they were young and healthy people. Included in Medicare and Medicaid records are dates and times of:

      1. Vital signs

      2. Medications

      3. Diagnoses

      4. Procedures

      5. Surgeries

      6. Imaging

      7. Diagnostics

      8. Blood labs

      9. Other

   3. Vital Records (Death Records) - If an official record expressly states that the vaccine was a cause of death, then causation has been established in the death. Still, it is good to find more evidence through correlation to other records. Additionally, death records are correlated with other databases to ascertain more pertinent information about a decedent or patient. While vaccine dates are not generally on the death records (though some are expressly stated), those vaccine dates can be gleaned from other records. Correlation of death records to VAERS reports and Medicare and Medicaid records has already produced hundreds of documented vaccine-caused deaths that state and federal systems missed or ignored. See The Real CdC (2024), THE CDC MEMORANDUM (2024), and THE CONNECTICUT MEMORANDA SERIES Vol. I & II (2024).

   4. Immunization Information System (IIS) - The IIS or equivalent database from any state contains the immunization date, location, manufacturer, lot number, name of recipient, age of recipient, and other information. Access to the IIS is hardly ever granted to researchers for the purported reason of keeping private the information of the decedent. This excuse does not pass muster in the context of an offered Non-Disclosure Agreement, research to be performed onsite in state offices, and no breach of records beyond state computer networks. There is no breach of privacy in this TBM workflow. If someone dies in a car accident, their blood alcohol level, narcotics blood levels, stomach contents, year and make of vehicle, and names of drivers and passengers are usually available to the press and the public. In a balance of harms analysis of public interest versus individual liberty and privacy, courts usually bend toward public interest, though a noted exception is where pharmaceutical products are concerned. Vaccination dates are not allowed to be known by the public. This is not to protect the public, but to protect the pharmaceutical companies to the detriment of the public. Notwithstanding the fact that data transparency and data privacy can be easily and concomitantly be secured, state and federal governments refuse transparency of IIS data. This must end.

6. Forensic Investigation of Individual Records - MOST IMPORTANT - Establish Causation

Seek, obtain, and forensically analyze the detailed hospital and emergency response records of those who appear otherwise healthy and who died or were injured by an unexpected cause, for example, thrombocytopenia, pulmonary embolism, cardiac arrhythmia, sudden kidney failure, aortic dissection, GI ischemia, or stroke. Begin in the 18-44 age range, then subsequently pursue records from older age groups in five (5) year increments until causation is evinced beyond reasonable doubt in a substantial number of records.

6. 1. Build a timeline

      1. Hourly vital signs and nurse’s notes

      2. Daily labs and all doctor’s notes

      3. Imaging and other diagnostics

      4. Medications and procedures as they happened usually recorded to the minute

      5. Other pertinent file information

   2. Enter a templated summary profile of likely causes and expressed causes for use in the ALPHA system

   3. Provide a detailed case report detailing and defining causes-of-death, details of medications or procedures involved, causal attribution of death to medication or procedure, and steps taken to alert the proper authorities.

Bottom-Up Quantification

Much of the work of quantification may already have been done through the Monte Carlo sweeps performed during the Top-Down Investigation phase. Using various model calculations for excess deaths based on actual minus expected deaths, provide reports of quantification across pertinent variables. The quantities can now be justified based on the causal traceability established in the expert case file reviews of the Top-Down Investigation phase of TBM.

Record-Level Source Data (RLSD) allows multi-variable analyses such as COVID AND Pneumonia compared to COVID NOT Pneumonia or compared to Pneumonia NOT COVID. This aids in quantification.

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Concluding Remarks

Epidemiology seems to rely almost exclusively on EBM for methodology and inferential statistical methods (ISM) for modeling. Epidemiologists, including many doctors who take a class in epidemiology, self-describe as “scientists.” ISM modeling and EBM methods they call “science” are not symbiotic with real world project planning and real time project execution to achieve successful disease investigation and quantification.

“Public health" requires strategic plans for disease response and vigilance. TBM is a project planning methodology for public health response and vigilance. TBM utilizes far more modeling techniques than ISM. If the objective is to determine causality and quantify those affected by the cause, then EBM and ISM fall short of that mark. Poor methodology and modeling are the likely reasons that the vaccine debate lingers after more than a century of research and publication. TBM can solve that debate in a week.

One example of incorrect modeling and methodology is the use of randomized controlled trials (RCT) and peer reviewed research papers to determine everything. If all you have is a hammer, everything looks like a nail. For example, hubris was on display in the use of RCT and peer review to determine mask effectiveness. Before performing RCTs with dozens of confounding variables, ask an engineer. The question is easily answered by engineers who develop the specifications for masks, design the masks, develop the manufacturing process for the masks, and develop the quality assurance test fixtures for the masks. Doctors might read the spec on the box before donning. Or ask a surgeon the tensile strength of his new scalpel. He can only tell you how it feels in his hand and if it bends upon cutting into ligaments, cartilage, or bone. Each expert should stay in their lane and doctors have no useful opinion about masks better than a drywall and plaster contractor.

The TBM methodology laid out herein was prototyped via the work on Massachusetts, Minnesota, and Connecticut death records databases. Unique insights and findings no one else in the world found are in publications from Summa Logica LLC. Peer-reviewed research papers into Covid-19 and vaccine issues have been one (1) to two (2) years behind the results provided by the TBM methodology.

The TBM methodology produced new visualizations of anomalies likely caused by serious adverse externalities. For example, Time-Window Shifting, Prevalence-of-Cause, and Simpson’s paradoxes found anomalies that no other researcher in the world found. TBM found massive numbers of excess Acute Renal Failure involved deaths back in the middle of 2022. TBM also showed back in mid-2022 that lymph node cancer and bone marrow cancer were excessive and climbing. Lymph node cancer in Massachusetts was ~400% of normal in 2023.

TBM also showed in mid-2022 that, in Massachusetts, greater excess deaths in 2020 involved respiratory ICD-10 codes, while greater excess deaths involving clotting and bleeding occurred in 2021. Again, a disease does not change how it kills on a year boundary. The societal profile of causes-of-death changed starkly upon introduction of a new technology gene therapy drug rebranded as a “vaccine.”

In addition to offering new techniques in finding data paradoxes, new visualizations to determine timing of anomalies, new profiles of seasonality in waveforms using time and frequency domain analyses, new methods of age profiling excess deaths, and other world firsts in epidemiological findings, TBM provides insight into the behavioral aspects of records including fraud detection, custom and practice change detection, and other effects on data integrity.

Most importantly, TBM produces a path to find the best records for deep inspection, which yields important answers derived from evidence such as vaccine dates from VAERS reports and Medicare/Medicaid reports, number days since vaccination, express statements by medical examiners that the vaccine is a cause-of-death, and medical files that contain times of medications, blood anomalies, and other diagnostics.

There is no method in the world that comes close to TBM. One engineer produced more accurate and ground-breaking findings than hundreds of thousands of public health employees and PhD epidemiologists around the world. EBM and ISM cannot prove anything conclusively. However, TBM, using RLSD investigation, can conclusively determine causality and quantify those affected.

Adopt TBM. The People will then have an answer for the TRUTH of “safe and effective” in one man-week and a methodological pathway for “public health” that will last ages.

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Risks

The greatest risk in deploying TBM is that a government somewhere may use it against The People as a means to create a false alarm in society — to create fear to control society. Transparency is the answer to alleviate such risk.

Probably the most difficult thing to believe is that one person, with good friends and support in the research community, found all these new epidemiological methods and discoveries.

There is a great risk that TBM may sound too good to be true. TBM is real and has been proven effective. TBM should have been implemented decades ago.

The risk is that ONE MILLION AMERICANS MURDERED BY GOVERNMENT RECOMMENDATIONS will be discovered by many in the future and they will absolutely attribute this holocaust mass murder to whomever was in government at the time. All will be revealed.

I recommend that “they” fix it or be affixed with it, forever in the history of the world and in the life everlasting.

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References

1. (2023 April 19). Our History — Our Story. David J. Sencer CDC Museum: In Association with the Smithsonian Institution. Found at https://www.cdc.gov/museum/history/our-story.html# on 2025 January 15.

2. (2024 January 26). CDC’s Mission. Mission and Org Charts. About CDC. Found at https://www.cdc.gov/about/divisions-offices/index.html on 2025 January 15.

Beaudoin, J. (2024). The Real CdC: COVID FACTS FOR REGULAR PEOPLE. Summa Logica LLC, Winchester, New Hampshire. Available at TheRealCdC.com.

Beaudoin, J. (2024). THE CDC MEMORANDUM: NOTICE OF CRIMINAL LIABILITY. Summa Logica LLC, Winchester, New Hampshire. Available at TheRealCdC.com.

Beaudoin, J. (2024). THE CONNECTICUT MEMORANDA SERIES - NOTICE OF HOSPITAL HOMICIDES & ACUTE RENAL FAILURE DEATHS Vol. I. Summa Logica LLC, Winchester, New Hampshire. Available upon request from author.

Beaudoin, J. (2024). THE CONNECTICUT MEMORANDA SERIES Volume II - COVID-19 “VACCINE”-CAUSED DEATHS & DEATHS FRAUDULENTLY LABELED “COVID-19”. Summa Logica LLC, Winchester, New Hampshire. Available upon request from author.

Regards,

John Paul Beaudoin, Sr.

President & CEO

Summa Logica LLC