Just How Lethal Is SARS-CoV-2 (i.e. COVID-19) Part A?

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JMJ

 

 

 I have been carving out time to research and respond to Smith's recent email.  In the course of this I have been digging further into understanding the current Pandemic in light of Previous Pandemics and outbreaks.

The results of the research was making my response to Smith longer and it has shaken some of my earlier understandings of the pandemic to such a degree that it now merits a separate treatment.

So what was shaken?  I originally believed that the CFR for Spanish Influenza was around 10%, however it really is between 2.5 to 3.0%. 

Which is uncomfortably close to that of COVID-19?

So - I'm going to do a deep dive into the stats to place SARS-CoV-2 into its proper niche in the pantheon of pathogens.

P^3

Key Statistics

For the purposes of this article, I will be using the WHO methods noted at the article linked her:WHO: Estimating Mortality  From COVID-19.

I will also be quoting heavily as it is easier to just take their explanations instead of translating everything.  WHO quotes will be italicized and purple in colour.

COVID-19 case and death definitions

Countries have varying approaches to COVID-19 case definitions. Consequently, the numerator and the denominator of any formula used to calculate fatality rate will vary according to how they are defined. WHO recommends using the surveillance case definitions which are available in the WHO interim guidance on Global surveillance for COVID-19 [5].

A COVID-19 death is defined for surveillance purposes as a death resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease (e.g. trauma). There should be no period of complete recovery between the illness and death [6].

Infection Fatality Rate (IFR)

The true severity of a disease can be described by the Infection Fatality Ratio:

 

Serological testing of a representative random sample of the population to detect evidence of exposure to a pathogen is an important method to estimate the true number of infected individuals [7,8,9]. Many such serological surveys are currently being undertaken worldwide [10], and some have thus far suggested substantial under-ascertainment of cases, with estimates of IFR converging at approximately 0.5 - 1% [10-12].

As serological studies require an investment of time and resources, there are many situations in which they may not be conducted timely, or even at all. Nevertheless, it remains crucial to monitor trends in severity in real time. In such situations, estimates need to be made with routinely available surveillance data, which generally consist of time-series of cases and deaths reported in aggregate.

Case Fatality Ratio (CFR)

Case fatality ratio (CFR) is the proportion of individuals diagnosed with a disease who die from that disease and is therefore a measure of severity among detected cases:

 

Reliable CFRs that can be used to assess the deadliness of an outbreak and evaluate any implemented public health measures are generally obtained at the end of an outbreak, after all cases have been resolved (affected individuals either died or recovered). However, this calculation may not hold in an ongoing epidemic, because it makes two assumptions:

Assumption 1: The likelihood of detecting cases and deaths is consistent over the course of the outbreak.

Early in an outbreak, surveillance tends to focus more on symptomatic patients who seek care, so milder and asymptomatic cases are less likely to be detected, leading to overestimation of CFR; this overestimation may decrease as testing and active case finding increase. One method to account for this is to remove from the analysis those cases that occurred before the establishment of robust surveillance, including application of clear case definitions (a method called left censoring).

Assumption 2: All detected cases have resolved (that is, reported cases have either recovered or died).

During an ongoing epidemic, some of the active cases already detected may subsequently die, leading to underestimation of CFR estimated before their death. This effect is accentuated in fast-growing epidemics (e.g. during the exponential growth phase of COVID-19).

Calculating CFR during an ongoing epidemic

CFR calculated using the above formula during ongoing epidemics provides a conditional, estimate of CFR and is influenced by lags in report dates for cases and deaths [13]. This leads to a wide variation in CFR estimates over the course of an epidemic, which tends toward a stable, final estimate of CFR as active cases are resolved.

One simple solution to mitigating the bias due to delays to case resolution during an ongoing outbreak is to restrict the analysis to resolved cases:

 For the purposes of this article I will distinguish between the two stats as CFR1 and CRF2.

Total Population Mortality Rate (TPMR)

This is  Smith's acronym for the per-capita fatality rate.

TPMR in % =Number of deaths from disease / Population of Country or Region x 100.

Reproduction Number (R0)

 In epidemiology, the basic reproduction number, or basic reproductive number (sometimes called basic reproduction ratio or basic reproductive rate), denoted ${\displaystyle R_{0}}$ (pronounced R nought or R zero),^[20] of an infection is the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.^[16] The definition assumes that no other individuals are infected or immunized (naturally or through vaccination). Some definitions, such as that of the Australian Department of Health, add the absence of "any deliberate intervention in disease transmission".^[21] The basic reproduction number is not the same as the effective reproduction number ${\displaystyle R}$ (usually written ${\displaystyle R_{t}}$ [t for time], sometimes ${\displaystyle R_{e}}$),^[22] which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. ${\displaystyle R_{0}}$ is a dimensionless number and not a rate, which would have units of time⁻¹,^[23] or units of time like doubling time.^[24]

${\displaystyle R_{0}}$ is not a biological constant for a pathogen as it is also affected by other factors such as environmental conditions and the behaviour of the infected population. ${\displaystyle R_{0}}$ values are usually estimated from mathematical models, and the estimated values are dependent on the model used and values of other parameters. Thus values given in the literature only make sense in the given context and it is recommended not to use obsolete values or compare values based on different models.^[25] ${\displaystyle R_{0}}$ does not by itself give an estimate of how fast an infection spreads in the population.

The most important uses of ${\displaystyle R_{0}}$ are determining if an emerging infectious disease can spread in a population and determining what proportion of the population should be immunized through vaccination to eradicate a disease. In commonly used infection models, when ${\displaystyle R_{0}>1}$ the infection will be able to start spreading in a population, but not if ${\displaystyle R_{0}<1}$. Generally, the larger the value of ${\displaystyle R_{0}}$, the harder it is to control the epidemic. For simple models, the proportion of the population that needs to be effectively immunized (meaning not susceptible to infection) to prevent sustained spread of the infection has to be larger than ${\displaystyle 1-1/R_{0}}$.^[26] Conversely, the proportion of the population that remains susceptible to infection in the endemic equilibrium is ${\displaystyle 1/R_{0}}$.

The basic reproduction number is affected by several factors, including the duration of infectivity of affected people, the infectiousness of the microorganism, and the number of susceptible people in the population that the infected people contact. 

 ...

In reality, varying proportions of the population are immune to any given disease at any given time. To account for this, the effective reproduction number ${\displaystyle R_{e}}$ is used, usually written as ${\displaystyle R_{t}}$, or the average number of new infections caused by a single infected individual at time t in the partially susceptible population. It can be found by multiplying ${\displaystyle R_{0}}$ by the fraction S of the population that is susceptible. When the fraction of the population that is immune increases (i. e. the susceptible population S decreases) so much that ${\displaystyle R_{e}}$ drops below 1, "herd immunity" has been achieved and the number of cases occurring in the population will gradually decrease to zero.^[36][37][38]

Source: Wikipedia - Basic Reproduction Number

Conclusions

 IFR is the best statistic for evaluating the lethality of a disease and is generally less than CFR. However, it is not readily available and requires a statistically significant study in order to apply it to an entire population. I have read studies done in Germany that showed the IFR of SAR-CoV-2 to be ~0.4%.

CFR is good interim statistic as it is based on more reliable data and is useful for estimating the risk in the event of a more severe case as well as the efficacy of the treatment methods.  So we would expect countries with comparable levels of development / healthcase systems, in the same general region to have close CFR's.  Such is the case between Canada and the USA.

R0 is useful for assessing the risk that a pathogen poses to a 'naive' population- mean little or no inherent immunity. 

TPMR gives a comparative indication of the progression of the disease through a population and the efficacy of the methods to slow its spread and treat it. In comparing countries, the number of cases per 100k vs number of deaths per 100k provides a good basis for comparing country to country measures.

All of these stats are affected by the context and pandemic life-cycle state in which the disease manifests, although IFR and R0 would have a smaller variance as it is more closely related to the pathogens properties.

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