Posted on 04/04/2020 6:43:59 AM PDT by blam
A new Oxford study said that millions of people in the United Kingdom (and therefore, in other countries) have likely already gotten the coronavirus, recovered from it, and are immune. But the mainstream media doesn’t want this information to get out, and some went to work quickly telling people “not to believe it.”
A newer model, which predicts the progression of the novel coronavirus, set off governments’ reactions around the globe and has systematically ruined lives across the Western world (not because of the virus, but the reaction to it). pandemic produced by researchers at Imperial College London set off alarms across the world and was a major factor in several governments’ decisions to lock things down. But a new model from Oxford University is challenging its accuracy, the Financial Times reports.
The head of the study, professor Sunetra Gupta, an Oxford theoretical epidemiologist, said she still supports the U.K.’s decision to shut down the country to suppress the virus even if her research winds up being proven correct. But she also doesn’t appear to be a big fan of the work done by the Imperial College team. “I am surprised that there has been such unqualified acceptance of the Imperial model,” she said.
The acceptance of the original model was to ensure people would be quickly living a life in fear and one without a source of income to combat the totalitarian measures that have already been implemented and are still coming our way. Some media outlets say this newer model relies on assumptions so we should disregard it, yet the original model that has forced lockdown and an economic crash is relying on the same thing. Assuming no one has had the infection, and forcing everyone into a frenzied panic to prevent it.
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(Excerpt) Read more at shtfplan.com ...
If you do the math, you will realize that the artical is likely fake news. First case was IDed on Jan 19. The doubling rate was 4/5 days. I did the math a few days back and came up with a max of 4 million Americans might have been infected. Thats a little more then 1% of us. Has it spreads the doubling rate increases but social distancing counter acts that so I sticking to the Max of at most 4 million.
This is why we need the statistics on the people who need hospitalization plus the people who need the ventilators plus the people who die. Perhaps an extremely tiny percentage of people even over age 70 who have no comorbidities and are not obese are even getting very sick. We deserve to know these facts. I wonder if we will ever be told them.
The p= number isn't known, so you see plots of different vectors from p=0.1 to p=0.001.
The R-naught numbers tell you how many people an infected person would pass the virus on to. It's a contagion number.
The bulging curves on the x coordinate line reflect the time when people would have been infected based upon the various p= numbers.
The black vector of reported deaths is the only actual hard data on the graph.
https://www.medrxiv.org/content/10.1101/2020.03.24.20042291v1
“Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic”
The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases:
(I) an initial phase of slow accumulation of new infections (often undetectable),
(II) a second phase of rapid growth in cases of infection, disease and death, and
(III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave.
Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern.
Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population.
We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death.
Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections.
Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries.
There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease.
This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity.
There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these.
Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data.
These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.
Hopefully the knowledge gathered from this pandemic will be a game changer for future new viruses.
Yep. It’s focusing a huge amount of resources on that goal.
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