Oh if this is relevant i think may help as it seems the Australian flu season appears to have been headed off by the hand-washing, quarantine and social distancing measures designed to control COVID-19.
We've seen a similar effect in Germany , except since we were mostly past the flu season already, it just stopped sooner. That's the fun thing about hygiene: it has good health effects across the board, you have nothing to lose if you advocate it.
Hi all could i ask for a summary of this thread please, its a few pages now and data heavy and im not that wise or gifted to grasp what has been shown..
Well, what do you want to know?
Overview
* It is hard to project how many people are going to die, because
-- we do not know how many people will be infected in the end
-- we do not know how many of the infected will die
-- we do not know how the case numbers relate to the dead and infected
We have estimates for the stuff we do not know, and hot discussion about whose estimates are more correct. Studies are emerging right now that give us data so we can know.
CFR = Case Fatality Rate = deaths/cases
This is the percentage of cases that are dying. This depends highly on what counts as a case, i.e. how well a specific country is detecting cases. Countries in the containment phase that manage contact tracing well and have appropriate testing resources should detect close to every infected person with symptoms. (I assume that's happening when 90% or more of a region's PCR tests come back negative.) Countries in the mitigation phase and where testing capacity is stretched will detect far less. Since you usually detect most of the deaths (but even there, some countries didn't initially know how many people did outside of hospitals!), the CFR is higher if you find fewer cases. Cases are typically confirmed with a genetic PCR test which is very specific. (The WHO test will only detect bat coronaviruses, and there's only one of these going around right now.)
Age is the major risk factor.
The case fatality rates from the big Chinese outbreak have stood up well, in my opinion. They're under 0.5% for ages up to 50, 1.3% (50-60), 3.6% (60-70), 8.0% (70-80), 14.8% (over 80). The 14.8% matches for the German data I looked at a while back, the others were somewhat lower (~4% in 60-80), but that may have changed as more data has come in, I should re-process that (see below).
Most of the people who die of Covid-19 are old or very old. So if you look at the mortality of a population (how many people will die?), you need to adjust this for how many old people you have. Any study that doesn't do that can't tell you anything robust about mortality.
People die a lot later than when they become cases, so to find the mortality of the cases you have now, you theoretically have to wait until all of them are recovered or dead. This is a big problem if you want to be first to publish, and basically makes some Diamond Princess studies very questionable, because the study is based on 7 deaths and now we have 13 or 14.
IFR = Infection Fatality Rate = deaths/infected = lethality
This is the percentage of people infected with the virus that are dying. To determine this number, we need to find out who has been infected. We know the number of cases, but we don't know how many infected people never became cases because they never got tested. (The lethality is obviously also heavily age-specific!)
Happily, when your body fights an infection, it creates antibodies (such as IgG and IgM), and these persist for months after the infection. We can simply survey a population with an antibody test and find out who already got infected.
The problem is that antibody tests are hard to develop. They have a poorer record of distinguishing Covid-19-antibodies from other coronavirus antibodies that we might have because we caught a cold. As Z.W.Wolf just explained, this becomes a problem if you are trying to find a small number in a big sample.
Test Error
A test can have 4 outcomes:
- person was infected, test is positive: true positive
- person was not infected, test is positive: false positive (error!)
- person was not infected, test is negative: true negative
- person was infected, test is negative: false negative (error!)
Specificity = "true negatives"/"not infected"
Sensitivity = "true positives"/"infected"
100%-Specificity = false positive rate (not infected counted as infected)
100%-Selectivity = false negative rate (infected people you miss)
Obviously, we need to know these values to analyse test results for error, but there's also error inherent in the way we discover them. If we test 30 samples known to not be infected (because they're from last year), and all results are negative, do we have 100% specificity? Not quite, because if the true specificity is 90.5% = 9.5% false error rate, there's still a random chance of 5% to get that result. So all we can say is that we're 95% confident that the true specificity is 90.5%-100%. That's the 95% confidence interval, and that's what you usually see as error bars on good graphs.
But it gets worse: if our lab used samples from healthy people (such as blood donors) taken during the summer (when fewer people have colds) to determine specificity, that wouldn't apply if we test everybody in April, right after the flu season: we'd expect this sample to have more false positives that the sample used to evaluate the test. (If you don't know how the manufacturer selected their samples, they may have selected them to make their test look good.)
So, to do this immunological survey that we need to find out who has been infected, we need our antibody test to be really good, or we need to at least confirm the positives with a really good test to be sure we don't count false positives.
Herd immunity
If we want to predict how our hospitals are coping, we don't really need the IFR: we can look at the hospitalization rate, or predict that from the case rate, and base our short-term public health measures off that. But if we have the IFR, we can take our number of deaths, divide by the IFR, and get an estimate of how many people are infected right now. And then we hope that everyone who has been infected is immune to the virus. With "common cold" coronaviruses, most people retain immunity for a while (maybe 2 years?) after they have had an infection.
And that hinders the spread of the virus. If the virus spreads to 3 people on average (R0=3), then we need to reach the point where 2/3 are immune: the virus wants to spread to 3 people, but only 1 person gets sick. That means the epidemic can no longer grow, and as R0 drops below 1, it fizzles out. (This is why anti-vaxxing "works" if not too many people do it.)
The hope is that if we have a lot more infected people than we thought, we might not have to wait for a vaccine to make that happen. Unfortunately, it doesn't much look like that's going to the case, but we'll know for sure when some robust studies have been published. This is underway, but takes time to do properly.
If we assume we have no vaccine and that 70% of the old people get infected, we take 70% of the lethality (IFR) and we have a good idea of how many people are going to die. This is another reason why the "it's harmless" believers like to see a low IFR: if the overall mortality isn't that high, it's somehow ok if grandma dies. (If you're looking forward to retirement, you ought to think twice about creating a society that kills old people off without remorse.)
Mortality = deaths / population
Here is where the "it's harmless" people come in. "People die every day, if we don't have a lot of people dying from Covid-19, why bother?" If you live in a region where the deaths haven't gotten out of hand, you can't disprove them. But the data from other regions and countries makes it clear that Covid-19 doesn't just cause people to die "who would have died anyway", it's significantly more, and the EuroMOMO data I posted in #183 proves that.
The way this works is via
excess mortality, which is a bit of a shortcut: you take a baseline mortality computed from past years that had no outbreaks (mild flu season), and you compare the actual mortality to that baseline. The deaths that are significantly above the baseline are the excess mortality, and they are considered to be caused by the current outbreak (typically influenza). The excess mortality from Covid-19 is clearly visible in many countries.
Prevalence, incidence rates, and case numbers
Prevalence and incidence rate are ratios, typically with respect to 100 000 people.
The news reports absolute numbers of cases and deaths. The trouble with that is that these numbers are roughly in line with population figures. They don't help you answer the question if a small town has fewer cases because it is smaller than e.g. New York, or if it has fewer cases because the virus spreads less rapidly. If you make a "corona map" of the US, it just looks a lot like a population map if you use absolute numbers. If we want to compare populations of different sizes, we need to use rates.
Contact Tracing
Health officials try to ask every confirmed infected person whom they might have infected. High-risk contacts are typically those who you have been closer than 6 feet to for more than 15 minutes. These contacts are then isolated, and tested if they have symptoms. Contact tracing and isolation breaks chains of infection and contains the spread. A Covid-19 contact tracing team typically consists of five people who are on the phone a lot. This seems awfully low-tech, but's proven to work. The South Korean super spreader cluster was contained with contact tracing. Germany's first outbreak was contained with contact tracing, which bought us much-needed time.
Contact tracing requires adequate manpower and timely access to test results. It contributes to mitigation even if it's overburdened, but it's our best hope to contain the spread in a situation where we relax social distancing measures. We've talked elsewhere about the many outbreaks of infectious diseases occurring all over the globe each year, and most don't turn into epidemics because of contact tracing.
Covid-19 is difficult to trace because there's evidence that people are most infectious on the day before symptoms start. If it takes you a day to decide to see a doctor, and then there's another day delay until you receive the test result, the first people you have infected could already be infectious themselves. This is why isolation of contacts is absolutely crucial, even if they have no symptoms.
That's the factual overview. It feels like I forgot to explain something important, so please ask if you think something's missing.
Personal Outlook
We are conditioned by Hollywood (and maybe human nature) to perceive winning a fight as something highly dramatic that we overcome. That's why war movies make good viewing, and why New York is on the TV so much (and why there's always more strife on reality TV than we see in the real world). But it's healthier to not have drama. It's better to arrest a perp before he gets into the car instead of having a high-speed chase. It's better to do long hours of observation on cold street corners than to have a shootout. It's better to head an epidemic off than to fight its effects.
This requires forethought and planning. It requires trusting in the people who do the planning, especially if it is successful, because then you never get to see the drama that has been headed off. This is difficult because the planning has to be done under uncertainty. We can't wait until we have researched all the answers, because by then our inactivity has created the drama we are planning to avoid. We need to trust the people who are best placed to take a good estimate of where we're headed, and listen to them, and accept that they will turn out to have been more or less accurate in their predictions. (The conspiracy theorists suggest that we should trust those people least who have been most accurate in their planning. The problem with that approach is obvious.)
So especially when our politicians and scientists have been successful, we need to cope with the fact that we have invested a lot of effort with no drama to show for it. This is particularly difficult in Western, more individualistic societies. If we don't see the drama, we tend to want to believe that there is no danger. (If we trust the drama that the media are showing us elsewhere, that's a substitute, but those who don't trust the media have a problem again.)
Statistics become a tool in this: they tell us how much danger we are in. Those concerned with planning want the numbers to be accurate and to show the exact danger we're in, and those who want the danger to not be there want the numbers to confirm their world-view. This is what the fight is about. It's about seeing 15 cases as a small number that will quickly go away, or as a sign that an epidemic is on the horizon. By now, it's obvious which approach is trustworthy.