[oldalum]

estimated without effective mitigation measures. WHO's early estimate was 50% lower. I think the significance is in underscoring how quickly the infection can get out of hand when effective mitigation measures are relaxed or dropped.

study

Abstract

The SIR (‘susceptible-infectious-recovered’) formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number [i]R[/i][i]o[/i] inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled [i]R[/i][i]o[/i] = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of [i]R[/i][i]o[/i] = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of ‘super-spreaders’ on the inference of [i]R[/i][i]o[/i] are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled [i]R[/i][i]o[/i] are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in [i]R[/i][i]o[/i]> 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

[oldalum]

estimated without effective mitigation measures. WHO's early estimate was 50% lower. I think the significance is in underscoring how quickly the infection can get out of hand when effective mitigation measures are relaxed or dropped.

study

Abstract

The SIR (‘susceptible-infectious-recovered’) formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number [i]R[/i][i]o[/i] inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled [i]R[/i][i]o[/i] = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of [i]R[/i][i]o[/i] = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of ‘super-spreaders’ on the inference of [i]R[/i][i]o[/i] are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled [i]R[/i][i]o[/i] are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in [i]R[/i][i]o[/i]> 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

[M T]

I think such numbers are silly when applied to very diverse conditions.  For instance, is 4.5 appropriate for agrarian areas where there is little contact, and equally appropriate in the mega-dense, pack-them-into-mass-transit, cities?   Coming up with a number for the whole world is more dependent upon the mixture of population densities & interpersonal distances than of the disease.   (Is the elephant more like tree trunk or a snake?)

To me, a better number to estimate would be something like the probability of droplet/aerosol spread to someone with whom an infected person spends 15 minutes within 6' in an indoor situation without mask on each day of the illness.  Then get the same number for when one of them is wearing a mask and when both are.  Then the same number for outdoor conditions..

Call such a number E (exposures) for an individual per day.   E_0, E_1, E_2 (for 0,1,2 masks).  If you're within 6' of 3 people for 5 minutes (or 45 people for 20 seconds), that's the same as being within 6' of 1 person for 15 minutes.   (I don't think this is the best possible measure, but these are the units that are being used for "close contact")   Compute the average E_n for a population, using surveys to tell you what percentage of exposures are 0 mask, 1 mask, or 2 mask.  Now calculate the number of new infections, and then the number of infections per 1K exposures for infected persons.   Shutdowns drive exposures toward 0.  Mask usage drives E_0 down without driving down the total exposures.

Suppose E_0 was 10 for some population.  If 100 of that population was sick on any day, that's  a total of 1000 infected exposures.

Something like the contact tracing apps that detect the nearby presence of others could help in estimating the values of the sum of E (but don't tell you about masks).

I would expect that infections per 1K E_0 (or E_1 or E_2) infected exposures should be roughly constant across populations and over time.  Recognize that the number of exposures would be dependent upon the weather.  (I could be wrong about it being constant.  Things like temperature, sunlight (both on the exposure, and as something that impacts Vit. D levels) could certainly impact such a measure.)

Then you can integrate that with estimates of exposures in different societies and living conditions to estimate the rate of spread in rural Utah as well as New York City and Starkville (i.e., college town).   You could also then judge whether somewhere like SCC is really doing a good job at reducing transmission compared to other counties, or is it the per-capita infection rate more a function of the combination of the different lifestyles in the county vs the combinations in other counties.

You can also estimate the number of additional exposures from opening up sectors (gyms, indoor restaurants, whatever).  If one had a good estimate of new infections daily per daily infected exposures, one could estimate the number of new cases resulting from additional exposures from opening that sector for whatever the current number of infections.

[M T]

I think such numbers are silly when applied to very diverse conditions.  For instance, is 4.5 appropriate for agrarian areas where there is little contact, and equally appropriate in the mega-dense, pack-them-into-mass-transit, cities?   Coming up with a number for the whole world is more dependent upon the mixture of population densities & interpersonal distances than of the disease.   (Is the elephant more like tree trunk or a snake?)

To me, a better number to estimate would be something like the probability of droplet/aerosol spread to someone with whom an infected person spends 15 minutes within 6' in an indoor situation without mask on each day of the illness.  Then get the same number for when one of them is wearing a mask and when both are.  Then the same number for outdoor conditions..

Call such a number E (exposures) for an individual per day.   E_0, E_1, E_2 (for 0,1,2 masks).  If you're within 6' of 3 people for 5 minutes (or 45 people for 20 seconds), that's the same as being within 6' of 1 person for 15 minutes.   (I don't think this is the best possible measure, but these are the units that are being used for "close contact")   Compute the average E_n for a population, using surveys to tell you what percentage of exposures are 0 mask, 1 mask, or 2 mask.  Now calculate the number of new infections, and then the number of infections per 1K exposures for infected persons.   Shutdowns drive exposures toward 0.  Mask usage drives E_0 down without driving down the total exposures.

Suppose E_0 was 10 for some population.  If 100 of that population was sick on any day, that's  a total of 1000 infected exposures.

Something like the contact tracing apps that detect the nearby presence of others could help in estimating the values of the sum of E (but don't tell you about masks).

I would expect that infections per 1K E_0 (or E_1 or E_2) infected exposures should be roughly constant across populations and over time.  Recognize that the number of exposures would be dependent upon the weather.  (I could be wrong about it being constant.  Things like temperature, sunlight (both on the exposure, and as something that impacts Vit. D levels) could certainly impact such a measure.)

Then you can integrate that with estimates of exposures in different societies and living conditions to estimate the rate of spread in rural Utah as well as New York City and Starkville (i.e., college town).   You could also then judge whether somewhere like SCC is really doing a good job at reducing transmission compared to other counties, or is it the per-capita infection rate more a function of the combination of the different lifestyles in the county vs the combinations in other counties.

You can also estimate the number of additional exposures from opening up sectors (gyms, indoor restaurants, whatever).  If one had a good estimate of new infections daily per daily infected exposures, one could estimate the number of new cases resulting from additional exposures from opening that sector for whatever the current number of infections.