COV19facts
Temporary Web Site (to be relocated to COV19facts.com in near future)
Temporary Web Site (to be relocated to COV19facts.com in near future)
Where are those science-based COVID-19 Policies?
(see article at end of this web page)
That simple, effective, science-based metric for use on policy decisions is "R".
R (effective reproduction number) is the local, temporal version of "R0" (R-naught, or basic-reproduction-number). Whereas R0 is a mathematical region-non-specific calculation used to characterize a virus, R is a metric/number specific to a time and location (search R-naught on Wikipedia for more information).
R can be easily approximated for regions across the country as the "new COVID-19 infections" (N) divided by the "new COVID-19 infections" six days earlier from that date.* R is day and region specific.
For the 50th day of an infection,
R = [N of Day 50] / [N of Day 44]
N is the new COVID-19 infections for a 24-hour period.
R is specific to location and time. Such data can be found at The Weather Channel (web site or App).
The following are answers to questions related to this equation:
What is the source of the equation? The source is the definition of R, which is approximated as the [new infections during a day] divided by [the number of hosts shedding during that day].
How is the number of hosts shedding approximated? The number of hosts shedding is approximately the number of people infected during the time period of 9 to 2 days earlier. This assumes seven days of shedding and a two day incubation period. 6 days earlier is the day in the middle of day 2 and 9. Yes, there will be varying opinions on the selected values of 9, 2, and 6; however, as long as the calculation of R uses the same assumptions for comparison purposes, changes in R are very useful.
What about all that deviation in the data? It is best to use reasonably averaged and representative numbers from tables/graphs of new cases (or total cases) versus time data. It is also possible to calculate (compare to) a similar number based on daily increases in deaths with dates adjusted for average time the host is infected before death. Also, smaller regions should be joined/expanded to reduce random deviations.
What about the impact of Hot Spots on calculations? Hot Spot region numbers within a larger region may be added or removed in calculating R. And most importantly, Hot Spot regions should have different policies as information is learned. Hot Spot regions could be a retirement home, a 20-mile radius around a meat processing plant, a metropolitan area, or a neighborhood in a city.
Date: 4/24/2020; this site will be updated and eventually transferred to the site COV19facts.com.
(see article at end of this web page)
That simple, effective, science-based metric for use on policy decisions is "R".
R (effective reproduction number) is the local, temporal version of "R0" (R-naught, or basic-reproduction-number). Whereas R0 is a mathematical region-non-specific calculation used to characterize a virus, R is a metric/number specific to a time and location (search R-naught on Wikipedia for more information).
R can be easily approximated for regions across the country as the "new COVID-19 infections" (N) divided by the "new COVID-19 infections" six days earlier from that date.* R is day and region specific.
For the 50th day of an infection,
R = [N of Day 50] / [N of Day 44]
N is the new COVID-19 infections for a 24-hour period.
R is specific to location and time. Such data can be found at The Weather Channel (web site or App).
The following are answers to questions related to this equation:
What is the source of the equation? The source is the definition of R, which is approximated as the [new infections during a day] divided by [the number of hosts shedding during that day].
How is the number of hosts shedding approximated? The number of hosts shedding is approximately the number of people infected during the time period of 9 to 2 days earlier. This assumes seven days of shedding and a two day incubation period. 6 days earlier is the day in the middle of day 2 and 9. Yes, there will be varying opinions on the selected values of 9, 2, and 6; however, as long as the calculation of R uses the same assumptions for comparison purposes, changes in R are very useful.
What about all that deviation in the data? It is best to use reasonably averaged and representative numbers from tables/graphs of new cases (or total cases) versus time data. It is also possible to calculate (compare to) a similar number based on daily increases in deaths with dates adjusted for average time the host is infected before death. Also, smaller regions should be joined/expanded to reduce random deviations.
What about the impact of Hot Spots on calculations? Hot Spot region numbers within a larger region may be added or removed in calculating R. And most importantly, Hot Spot regions should have different policies as information is learned. Hot Spot regions could be a retirement home, a 20-mile radius around a meat processing plant, a metropolitan area, or a neighborhood in a city.
Date: 4/24/2020; this site will be updated and eventually transferred to the site COV19facts.com.
EIGHT CYCLES:
If we start by May 1st (2020), we have about eight 3-week cycles to learn and adjust before the second wave of COVID-19. How this time is spent could mean the difference between America emerging greater as a result of COVID-19 versus something much worse. China chose to stifle freedom to effectively halt COVID-19; and they did well for the first wave--how will that policy work for the second wave?
And so, what should be learn? Here are some:
1. What are the business practices and modes of operation that lead to essentially zero propagation of the virus? These can be proven and continued during the second wave. There are no restriction on business types or "stay at home", it is simply the open-ended quest for ways to prosper that will lead to a better-stronger country.
2. What are effective medical approaches to halting the "tipping point" for those 1-in-25 infections likely to have very bad results? Learning will provide improved monitoring and treatment. There is a big HOWEVER on this... However, this involves allowing people to choose to become infected with very close monitoring and rapid response. This is not unethical! For people of low risk (e.g. under 40 years of age and of good health), the risks are low; and survival is higher with a controlled inoculation during the summer than a random one during the second wave.
3. What are the best ways and methods of assistance for those who self-isolate?
If we start by May 1st (2020), we have about eight 3-week cycles to learn and adjust before the second wave of COVID-19. How this time is spent could mean the difference between America emerging greater as a result of COVID-19 versus something much worse. China chose to stifle freedom to effectively halt COVID-19; and they did well for the first wave--how will that policy work for the second wave?
And so, what should be learn? Here are some:
1. What are the business practices and modes of operation that lead to essentially zero propagation of the virus? These can be proven and continued during the second wave. There are no restriction on business types or "stay at home", it is simply the open-ended quest for ways to prosper that will lead to a better-stronger country.
2. What are effective medical approaches to halting the "tipping point" for those 1-in-25 infections likely to have very bad results? Learning will provide improved monitoring and treatment. There is a big HOWEVER on this... However, this involves allowing people to choose to become infected with very close monitoring and rapid response. This is not unethical! For people of low risk (e.g. under 40 years of age and of good health), the risks are low; and survival is higher with a controlled inoculation during the summer than a random one during the second wave.
3. What are the best ways and methods of assistance for those who self-isolate?
EDITORIAL:
Where are those science-based COVID-19 Policies?
2/24/2020
Governors, the president, and other policy makers have identified that they will make decisions on COVID-19 policies based on science and not politics. However, after weeks of these announcements, questions emerge: What is that science? Where is the transparency in those decisions that verify the decisions are based on science?
A meaningful, verifiable, and easy metric would not only justify policies, it could provide the needed guidance to check and adjust policies. That metric is "R" (effective reproduction number), the local temporal version of "R0" (R-naught, or basic-reproduction-number). Whereas R0 is a mathematical region-non-specific calculation used to characterize a virus, R is a metric/number specific to a time and location.
We seek values of R that are less than 1.0 where changes in policies (e.g. social distancing) result in noticeable decreases in R starting a few days after policy implementation. For COVID-19, R0 is about 5.5, making it difficult to achieve the goals of less than 1.0.
R can be easily approximated for regions across the country as the "new COVID-19 infections" for the day-specific value of R divided by the "new COVID-19 infections" six days earlier from that date.* These daily new infection numbers are available from a number of sources and hundreds of regions, including The Weather Channel web site and App.
Some example numbers for April 23rd are R = 1.0 for Philadelphia, 0.6 for New York County, and 0.7 for St. Louis County. And so, what do these numbers mean?
For Philadelphia, R=1.0 properly characterizes that the increase in new daily COVID-19 cases has been constant for about two weeks; for each person overcoming infection a new person is infected. Social distancing has stopped an exponential growth, but non-compliance has allowed those non-symptomatic cases to continue to spread the virus. New York County and St. Louis County have been more effective in social distancing with a resulting decrease in R.
To translate these numbers into "effective" policies [simply] requires not being stupidly insane. Namely, do not repeat the same mistakes and expect different results. More specifically, do not expect R to change if policies are not adjusted.
It takes about two weeks to see the change in R from a policy change, and after about a week of verifying that change it is time to learn from that change and adjust policies. Policies should be adjusted about every three weeks to achieve goals of further reducing active cases of COVID-19, to lessen devastating environmental impact, or to simply let people better enjoy their life.
In some instances, this will mean targeted relaxing of policies to reduce job losses and economic devastation. In other instances, this will mean that the policy matures need to identify virus hot spots (or potential hot spots) with a "cracking down" on those out-of-control locations. In some instances, that "cracking down" will be on communities sensitive to practices that could be interpreted as discriminatory--it cannot be over-emphasized how important it is to have science-based and transparent policy decisions.
Insanity is keeping the same policy for several weeks
Several conclusions can be made from a quick inspection of the infection growth curves, the facts brought forth from the media articles, and the unspoken/unspeakable indicators that are sparingly covered; these conclusions include:
1. COVID-19 is a nasty virus, it is not going away soon (as evident by the fact that social distancing in the US rarely reduces R to less than 0.5), and we can expect a catastrophic second peak starting at about the end of October (based on early Imperial College modeling results, which are amazingly accurate).
2. For the most part, the US has dodged making the pandemic worse as a result of having a shortfall of ventilators; however, it may not make a lot of difference since most COVID-19 patients do not make it off the ventilator.
3. The key (learned during these valuable months bought from social distancing) is timely/fast treatment if a patient reaches the "tipping point".
4. While social distancing has had limited success for stopping the virus, self-isolation has been successful for protecting the most vulnerable individuals.
5. With 3-week turnover in policy changes and observed results, any region has the opportunity to make about eight intelligent "cycles/change" in policy (each with a learning curve) to prepare the population and the economy for the second wave starting in about October.
Why October? The answer is; historically, October is the start of flu season.
Yes, COVID-19 will follow the standard flu season, but, it will only slow down and not go away. The summer slowdown of flu seasons is the result of several factors, including: a) solar radiation and heat slowing down spreading and b) outdoor activities having reduced spreading versus indoor activities. Normal flu(s) has an R0 slightly above 1.2; and so, these "summer effects" take R to well below 1.0 with summer-driven extinction.
COVID-19 has an R0 of about 5.5 with virus shedding for several days and non-symptomatic hosts. It takes much-much more to bring an R0 of 5.5 to local values of R "well below 1.0". COVID-19 will not go away during the summer, but society will have a window to recover and prepare for the second wave.
In addition to expecting moderate assistance from summer's heat and a second wave starting in October, we can expect that policy makers will opt for "herd immunity" this Fall, rather than a prolonged and economically devastating shut down of the national economy. Unfortunately, herd immunity for a R0 = 5.5 virus translates to at least about 80% of the public getting the virus (That's most of us.). Those most vulnerable to COVID-19 should prepare for months of isolation. Those who are less vulnerable would do well to insist that our leaders make transparent decisions based on science.
Our country can be made much stronger from COVID-19, or it can be destroyed. It primarily rests in effective use of those eight "cycles/changes" in policy/practices that we can use to tweak both business practices and social distancing before our seasonal weather turns against us. And the most effective use of those eight cycles will not be a result of great political leaders; rather, it would be with transparency and information and sharing of data with the public.
It has been the freedom and innovation of America that has made America emerge as a superpower for freedom, and great leaders will primarily enable that American spirit. Yes, some innovation will fail; but we have time to identify and build upon innovations that succeeds.
What about that COVID-19 flu shot? The most optimistic sources suggest it will take 12 months. That is after the second wave that could kill millions if not handled well.
One option is for those least vulnerable to COVID-19 to choose to become infected in a controlled manner where they can be closely monitored during the time span when the tipping point can emerge. This approach can be used to decrease mortality from 0.02% to less than 0.002% in healthy members of those less than 40 years of age. And the lessons learned can be used to save hundreds of thousands of the more-vulnerable groups who may be randomly infected during the second wave. There is good reason to believe that the mortality rate can be substantially reduced by Fall if we maximize what we learn during this summer.
More information on calculations, the basis for recommendations mentioned herein, and other recommendations
can be found at the web site http://www.terretrans.com/cov19.html .
* Trivial application of this will result in large deviations in values. Rigor in applications to larger regions and correlation (a second calculation) with new Covid-19 deaths will provide rigor, consistency, and reliability.
Where are those science-based COVID-19 Policies?
2/24/2020
Governors, the president, and other policy makers have identified that they will make decisions on COVID-19 policies based on science and not politics. However, after weeks of these announcements, questions emerge: What is that science? Where is the transparency in those decisions that verify the decisions are based on science?
A meaningful, verifiable, and easy metric would not only justify policies, it could provide the needed guidance to check and adjust policies. That metric is "R" (effective reproduction number), the local temporal version of "R0" (R-naught, or basic-reproduction-number). Whereas R0 is a mathematical region-non-specific calculation used to characterize a virus, R is a metric/number specific to a time and location.
We seek values of R that are less than 1.0 where changes in policies (e.g. social distancing) result in noticeable decreases in R starting a few days after policy implementation. For COVID-19, R0 is about 5.5, making it difficult to achieve the goals of less than 1.0.
R can be easily approximated for regions across the country as the "new COVID-19 infections" for the day-specific value of R divided by the "new COVID-19 infections" six days earlier from that date.* These daily new infection numbers are available from a number of sources and hundreds of regions, including The Weather Channel web site and App.
Some example numbers for April 23rd are R = 1.0 for Philadelphia, 0.6 for New York County, and 0.7 for St. Louis County. And so, what do these numbers mean?
For Philadelphia, R=1.0 properly characterizes that the increase in new daily COVID-19 cases has been constant for about two weeks; for each person overcoming infection a new person is infected. Social distancing has stopped an exponential growth, but non-compliance has allowed those non-symptomatic cases to continue to spread the virus. New York County and St. Louis County have been more effective in social distancing with a resulting decrease in R.
To translate these numbers into "effective" policies [simply] requires not being stupidly insane. Namely, do not repeat the same mistakes and expect different results. More specifically, do not expect R to change if policies are not adjusted.
It takes about two weeks to see the change in R from a policy change, and after about a week of verifying that change it is time to learn from that change and adjust policies. Policies should be adjusted about every three weeks to achieve goals of further reducing active cases of COVID-19, to lessen devastating environmental impact, or to simply let people better enjoy their life.
In some instances, this will mean targeted relaxing of policies to reduce job losses and economic devastation. In other instances, this will mean that the policy matures need to identify virus hot spots (or potential hot spots) with a "cracking down" on those out-of-control locations. In some instances, that "cracking down" will be on communities sensitive to practices that could be interpreted as discriminatory--it cannot be over-emphasized how important it is to have science-based and transparent policy decisions.
Insanity is keeping the same policy for several weeks
Several conclusions can be made from a quick inspection of the infection growth curves, the facts brought forth from the media articles, and the unspoken/unspeakable indicators that are sparingly covered; these conclusions include:
1. COVID-19 is a nasty virus, it is not going away soon (as evident by the fact that social distancing in the US rarely reduces R to less than 0.5), and we can expect a catastrophic second peak starting at about the end of October (based on early Imperial College modeling results, which are amazingly accurate).
2. For the most part, the US has dodged making the pandemic worse as a result of having a shortfall of ventilators; however, it may not make a lot of difference since most COVID-19 patients do not make it off the ventilator.
3. The key (learned during these valuable months bought from social distancing) is timely/fast treatment if a patient reaches the "tipping point".
4. While social distancing has had limited success for stopping the virus, self-isolation has been successful for protecting the most vulnerable individuals.
5. With 3-week turnover in policy changes and observed results, any region has the opportunity to make about eight intelligent "cycles/change" in policy (each with a learning curve) to prepare the population and the economy for the second wave starting in about October.
Why October? The answer is; historically, October is the start of flu season.
Yes, COVID-19 will follow the standard flu season, but, it will only slow down and not go away. The summer slowdown of flu seasons is the result of several factors, including: a) solar radiation and heat slowing down spreading and b) outdoor activities having reduced spreading versus indoor activities. Normal flu(s) has an R0 slightly above 1.2; and so, these "summer effects" take R to well below 1.0 with summer-driven extinction.
COVID-19 has an R0 of about 5.5 with virus shedding for several days and non-symptomatic hosts. It takes much-much more to bring an R0 of 5.5 to local values of R "well below 1.0". COVID-19 will not go away during the summer, but society will have a window to recover and prepare for the second wave.
In addition to expecting moderate assistance from summer's heat and a second wave starting in October, we can expect that policy makers will opt for "herd immunity" this Fall, rather than a prolonged and economically devastating shut down of the national economy. Unfortunately, herd immunity for a R0 = 5.5 virus translates to at least about 80% of the public getting the virus (That's most of us.). Those most vulnerable to COVID-19 should prepare for months of isolation. Those who are less vulnerable would do well to insist that our leaders make transparent decisions based on science.
Our country can be made much stronger from COVID-19, or it can be destroyed. It primarily rests in effective use of those eight "cycles/changes" in policy/practices that we can use to tweak both business practices and social distancing before our seasonal weather turns against us. And the most effective use of those eight cycles will not be a result of great political leaders; rather, it would be with transparency and information and sharing of data with the public.
It has been the freedom and innovation of America that has made America emerge as a superpower for freedom, and great leaders will primarily enable that American spirit. Yes, some innovation will fail; but we have time to identify and build upon innovations that succeeds.
What about that COVID-19 flu shot? The most optimistic sources suggest it will take 12 months. That is after the second wave that could kill millions if not handled well.
One option is for those least vulnerable to COVID-19 to choose to become infected in a controlled manner where they can be closely monitored during the time span when the tipping point can emerge. This approach can be used to decrease mortality from 0.02% to less than 0.002% in healthy members of those less than 40 years of age. And the lessons learned can be used to save hundreds of thousands of the more-vulnerable groups who may be randomly infected during the second wave. There is good reason to believe that the mortality rate can be substantially reduced by Fall if we maximize what we learn during this summer.
More information on calculations, the basis for recommendations mentioned herein, and other recommendations
can be found at the web site http://www.terretrans.com/cov19.html .
* Trivial application of this will result in large deviations in values. Rigor in applications to larger regions and correlation (a second calculation) with new Covid-19 deaths will provide rigor, consistency, and reliability.