I think it's everywhere at this point. It's just not *detected* everywhere... You aren't going to try prevention? I'm not judging, I'm just asking. This statement interests me because I was sensing it before it was said explicitly:
The WHO chief, Tedros Adhanom Ghebreyesus, has warned that the world is in “uncharted territory” and has “never seen a respiratory pathogen that is capable of community transmission, but which can be contained with the right measures”. He said containment must be the top priority for all countries and that there is “no one-size-fits-all approach”.
Source:
A summary of the biggest developments in the global coronavirus outbreak
www.theguardian.com
I took a graduate-level class in SIR disease transmission modeling in the R language. Usually there is either a snowball effect where almost everyone gets it, or the disease doesn't get far at all. You don't really need a graduate course to know that. This in between thing is tempting decisionmakers to go for prevention and it's hard to tell if that is reasonable or not. It might be something to hope for rather than something that is known. Here is another excerpt from a very interesting article I turfed up:
The clearest sign of the progress in modeling comes from flu forecasts in the U.S. Every year, about two dozen labs try to
model the flu season, and have been coming ever closer to accurately forecasting its timing, peak, and short-term intensity. The U.S. Centers for Disease Control and Prevention determines which model did the best; for 2018-2019, it was
one from Los Alamos.
Los Alamos also nailed the course of the 2003 outbreak of SARS in Toronto,
including when it would peak. “And it was spot on in the number of people who would be infected,” said Del Valle: just under
400 in that city, of a global total of about 8,000.
The Covid-19 outbreak in China is quickly spreading worldwide, sparking quick calculations on how deadly this new disease is. One measure is called a case fatality rate. While the formula is simple, it’s difficult to get a precise answer.
The computers that run disease models grind through calculations that reflect researchers’ best estimates of factors that two Scottish researchers
identified a century ago as shaping the course of an outbreak: how many people are susceptible, how many are infectious, and how many are recovered (or dead) and presumably immune.
That sounds simple, but errors in any of those estimates can send a model wildly off course. In the autumn of 2014, modelers at CDC
projected that the Ebola outbreak in West Africa could reach 550,000 to 1.4 million cases in Liberia and Sierra Leone by late January if nothing changed. As it happened, heroic efforts to isolate patients, trace contacts, and stop unsafe burial practices kept the number of
cases to 28,600 (and 11,325 deaths).
To calculate how people move from “susceptible” to “infectious” to “recovered,” modelers write equations that include such factors as the number of secondary infections each infected person typically causes and how long it takes from when one person gets sick to when the people she infects does. “These two numbers define the growth rate of an epidemic,” Vespignani said.
Source:
Like weather forecasters, modelers who project how bad a disease outbreak might become are used to uncertainties and incomplete data, and Covid-19 has those everywhere you look.
www.statnews.com
This modeling process looks frustrating. By the time the model is right, it looks like it will be describing what already happened instead of offering a useful projection. But, it also seems like they could make a tool for a person like me to input my behavior, get back a customized projection of my odds of getting covid-19, then toggle my behaviors to discover viable ways to decrease my likelihood.
