Playlist

COVID-19: Basic Reproduction Number

by Sean Elliott, MD

My Notes
  • Required.
Save Cancel
    Learning Material 3
    • PDF
      Slides Coronavirus Epidemiology I.pdf
    • PDF
      Slides Coronavirus Basic Reproduction Number.pdf
    • PDF
      Download Lecture Overview
    Report mistake
    Transcript

    00:01 COVID-19 statistics.

    00:04 In this session, we're going to talk about the common numbers that you will have seen to describe and predict the behavior of the COVID-19 pandemic caused by the SARS-Coronavirus II.

    00:13 The most familiar number probably is the basic reproduction number, known as the “R naught.” This is the average number of secondary infections produced by an index case or a typical first case of an infection in a population, where everyone is susceptible.

    00:30 It is used to measure the transmission potential of the disease, in this case of COVID-19.

    00:36 If R naught, is greater than 1, then the number of infected people will likely increase exponentially and an epidemic or pandemic can ensue.

    00:46 If the R naught is less than one, then the number of those secondary cases from the index case, will not be sufficient to sustain an outbreak and it will likely recede on its own.

    00:57 The problem of course as with any of these indices, is that the R naught, is not sufficiently valid, to definitively forecast an outbreak.

    01:06 It certainly can be affected by other factors, but it is very important as an early warning system, sort of, giving the indication of the possibility, of an epidemic or a pandemic.

    01:19 The R naught then can be used even to compare and contrast, different types of epidemics or pandemics, even such as we see here, comparing the current COVID-19 pandemic, with an annual influenza epidemic.

    01:34 So, on the left COVID-19 pandemic, R naught values have been from 2 to 2.5, certainly, in the early days of the pandemic, compared to a typical year with an influenza, where the on R naught has ranged anywhere from 0.9 up to 2.1.

    01:51 Those years where the R naught was 0.9 for influenza, were largely fizzles, that there were not significant numbers of secondary cases and the epidemic was not extreme.

    02:02 Versus those years of influenza where the R naught was 2.1, where there was a rip-roaring epidemic with tons of secondary cases.

    02:10 So, the R naught, can vary widely and in fact, those ranges you see both for COVID-19 and for influenza, the R naught varies widely because there are other host and viral factors, which can affect it.

    02:23 Certainly, if you think about it are not can be affected by interventions, right, so, as we have seen with COVID-19 and as we hope to see every year with influenza intervening with public health interventions such as, wearing masks, social distancing, physical distancing, closing the schools, avoiding social gatherings, all those can have a major impact, on the exposures for the virus, both for SARS-Coronavirus II and for influenza and therefore on the numbers of secondary cases.

    02:57 After those public health measures are initiated, then we can look and see what happens and that number is called, the “Effective reproductive number.” As you would hope this is typically much lower than the R naught, meaning that we've had an effective cluster of interventions.

    03:16 So, the effective reproductive number, if it's greater than 1, means that we were not very good at our interventions and that the outbreak is likely going to overwhelm further healthcare resources, to overwhelm the healthcare system.

    03:34 In if for example you can see this playing out in lifetime in February of 2021, the R naught for COVID-19 in the United Kingdom was 0.6 to 0.9, so less than 1, meaning that there was going to be a successful or predicted successful diminishing of the outbreak numbers in the United Kingdom and that in fact was what was seen after a very strict lockdown measures were implemented.

    04:01 So again, diminishing spread with the R naught.

    04:05 Unfortunately, things change and variance and in this case, we're talking about virus factors now.

    04:12 Variants of the SARS-Coronavirus II, have shown increasing, differing levels of infectivity and are of major concern.

    04:21 So, despite the United Kingdom having an initial response, it's both R naught and its effective reproductive numbers below 1, unfortunately, the delta variant the B.1.617.2, first reported in India in late 2020, entered into the United Kingdom and changed its epidemic picture completely.

    04:44 The R naught and the effective reproductive number both rose, to be above 1.

    04:49 This is played out in other parts of the world as well.

    04:53 So, here's where viral factors can negatively affect, the R naught and the effective reproductive number and how does that happen? Well, it likely has to do with variants or mutations, which occur in the SARS-Coronavirus II itself.

    05:11 This virus itself has shown itself quite able to undergo mutations, especially in the spike protein, which will increase its infectivity or its transmissibility.

    05:23 Of course, it's very difficult to know exactly what effect a single mutation or a cluster of mutations might have, in the transmissibility of the virus.

    05:33 But, by using the R naught and the effective reproductive number, then we can at least follow the small changes and predict, that there's going to be a change in the behavior of the pandemic.

    05:45 Another number, which you you'll hear, another estimate I should say, is the “Case fatality rate,” which actually should be, “Case fatality ratio.” This this estimates the mortality ratio, among documented cases and it's calculated by taking the number of deaths, divided by the number of documented cases.

    06:05 As noted, we should not really consider that a rate, but unfortunately, rate, is the usage or the term, which most individuals in the social and common media and literature have used and so, you'll hear us use rate.

    06:19 Ratio of course, is the more accurate statistical number.

    06:24 This number the “CFR” is not a constant and it's also a very poor measure, during an epidemic.

    06:30 You could pause this this session right now, just think about reasons for that.

    06:34 But of course, what you're coming to come up with, is that, the CFR will typically overestimate the true death rate, why is that? Because our denominator the total number of cases, is imperfect, we don't know the number of asymptomatic cases, we don't know the number of true cases that were not tested and not confirmed and so our “n” our denominator, is likely far larger than what we are using to calculate the CFR and again, as we say here most death by infection are also noted going to be noted because they may not be diagnosed or they may be attributed to something else or they may be wrongly attributed to infection with SARS-Coronavirus II, causing COVID-19.

    07:23 So again, perhaps a more accurate estimate of the mortality rate, is the infection fatality rate the “IFR.” And this is the number of deaths due to infections, divided by the total number of infected people.

    07:37 So, inserting here, is going to be a better attempt, to confirm that deaths and infections are specifically due, to the disease of interest, in this case COVID-19.

    07:49 So, the IFR, should be a better measurement, it will almost always be lower than the CFR, just due to the imperfections in calculating the CFR.

    08:00 The IFR, will include both documented and undocumented or asymptomatic cases and it's going to be typically estimated at the end of a pandemic.

    08:14 Although of course estimates will occur throughout the epidemic, but it is largely looking in retrospect, that one can accurately calculate the IFR.

    08:23 Why is it more accurate? Because it requires documentation of the infection, typically, through antibody studies, the IFR is going to vary with the distribution of other factors, comorbidities, so age, health issues, pre-existing health issues, other qualities of the infected individuals, as well as qualities of the medical care they receive or have access to.

    08:47 So, you can imagine then that that for COVID-19, that the, true mortality rates have and will vary greatly, across different countries and age groups.

    08:58 The CFR currently is being estimated at 2.2% with COVID-19, the IFR the infection fatality rate is estimated at being somewhere less than 0.5%, but neither of these is going to account for the full burden of the COVID-19, because they're not going to include mortality, which is caused by other things, such as, delayed care of other medical conditions, certainly, the individual with chronic lung disease or even asthma, who may have an exacerbation, due to something other than COVID-19, may still have prevention of care, because they can't get into the emergency department.

    09:38 Perhaps the health system itself is overburdened, certainly, we're seeing this in many parts of the world, were, the sheer vast numbers of COVID-19 patients, has overwhelmed the medical setting, clinics, hospitals, emergency departments and there is insufficient medication, or healthcare personnel, ventilators, IV fluids and thus, those who do make it into the healthcare system, encounter an overburden system, which has a very decreased quality of care.

    10:10 Social determinants of health, this is a very difficult and yet a very important factor which will impact on both CFR and IFR.

    10:19 So those individuals who lost their jobs because of all the social closures.

    10:24 Decreased social interactions, causing perhaps mental health variabilities, maintenance of education, maintenance and mental health et cetera.

    10:34 All these have the ability to intervene or impact on the CFR or the IFR.

    10:40 So, as an example, again in real time, these are still estimates, but the fatality rates, in individuals with COVID-19 who also have comorbidities and you see them listed here.

    10:54 Cardiovascular disease, the mortality rate, the fatality rate, over 10%, those with diabetes, especially those with insulin dependent diabetes, 7.3%, those individuals with chronic respiratory disease, 6.3%, death rate from COVID-19, pre-existing hypertension or other cardiovascular disease, 6.0%, those with cancer immunodeficiencies, almost 6.0%, those who had no pre-existing conditions, so zero comorbidities, age, obesity, any other medical conditions, that the estimate of the fatality rate with COVID-19 is just 0.9, while that is still much higher, than it is for influenza, where the estimate is 0.1%, yet that is far less significantly horrible, than as we see with cardiovascular disease.

    11:43 So, it is through using these statistical predictive numbers, that we can both follow as well as predict areas of the world, which are going to be negatively affected by the COVID-19 pandemic and it is certainly the wish and the concern, to continue to use these successfully to drive resources and to do a better job of impacting on COVID-19.


    About the Lecture

    The lecture COVID-19: Basic Reproduction Number by Sean Elliott, MD is from the course Coronavirus.


    Included Quiz Questions

    1. The average number of secondary infections produced by one infectious individual in a population where everyone is susceptible
    2. The number of cases generated in the current state of a population
    3. The average number of individuals who are likely to get infected
    4. The doubling time of the number of active cases
    5. The average number of secondary infections produced by one infectious individual in a population where some of the population is already immune
    1. It is intended to be an indicator of the contagiousness or transmissibility of infectious agents.
    2. It is not dependent on the host or viral factors.
    3. It is a constant during an outbreak of an infectious disease.
    4. The basic reproductive number R0 will increase as more cases are diagnosed.
    5. It is intended to predict how long an outbreak will last.
    1. < 1
    2. 1
    3. > 1
    4. < 0.5
    5. > 2
    1. Cardiovascular disease
    2. Cancer/immunosuppression
    3. Diabetes
    4. Chronic respiratory diseases such as COPD
    5. Hypertension
    1. It is a more accurate measurement than the CFR.
    2. It only includes documented cases.
    3. It overestimates the true death rate.
    4. It is a constant with regard to age distribution, comorbidities, and access to medical care among individuals.
    5. It is always higher than the case fatality rate.

    Author of lecture COVID-19: Basic Reproduction Number

     Sean Elliott, MD

    Sean Elliott, MD


    Customer reviews

    (1)
    5,0 of 5 stars
    5 Stars
    5
    4 Stars
    0
    3 Stars
    0
    2 Stars
    0
    1  Star
    0