Ordinarily I prefer for my care of HIV to remain scientific and not political as it doesn't just kill gay men or IVDUs. However I find it amazing that we are turning the country upside down for an illness that hasn't come close to killing half as many Americans as the flu this year, yet when HIV was killing more people per day back in the 1990s, only a small group of us took notice and scrambled to help.Not saying COVID19 is insignificant, just keeping things in perspective and pointing out how politics can make people ignore science.
At least this isn't going to kill a giant fraction of the work force in SFO and other places like HIV when it disproportionately killed young people that worked and paid taxes. I could never understand why conservatives didn't try harder to keep young tax payers healthy back then.again, I'm just looking at numbers not saying one life means more than another...
Dr. Kaspar, I'd invite you to read the rules of this Community Exchange, which asks the we maintain a safe space for discussion with respectful, professional dialogue. I'm not sure your last two posts adhere to the spirit of these rules. I think there are pitfalls in trying to compare SARS-CoV-2 to Influenza since we have no vaccine, no therapies, and at least 97% of us have no immunity to SARS-CoV-2. Also, what Thomas McQuaid was trying to show in the chart I believe was the intensity of death from COVID-19 that has been concentrated in recent weeks with nearly 2,000 deaths/day whereas influenza deaths are totaled over a season. The final numbers are not yet in yet, of course, but we've already passed 50,000 COVID-19 deaths (that we know about) in just the past two months whereas influenza killed only 34,000 through the whole of the 2018-2019 season. I'm not sure what you mean to suggest regarding the virus getting here in December or January. The chart he posted is showing deaths, which is known to lag well behind introduction of the first cases and even widespread community transmission. Remember incubation average 5 days (range 2-14), then 7-10 days from mild symptoms progressing to severe, then average 2-3 weeks in ICU before death. So even as we now see cases and hospital admissions declining in some hotspots, deaths are known to lag by a few weeks. While there have been recent reports of a few early deaths in Santa Clara county before the first recorded US death in Seattle, those were sporadic and for some lucky reason didn't result in widespread transmission. Which study is suggesting only a 0.2% case fatality rate for COVID-19? I would agree that is this has been a difficult number to pin down for several reasons:
1) initial focus of limited testing resources on only the sickest patients thus inflating CFR by keeping denominator of confirmed cases low2) potential for many mild/asymptomatic cases to never be diagnosed if they never present to careHowever, I'd be careful in the interpretation of serologic studies at this point. Perhaps you're referring to the (as yet not peer reviewed) 'Santa Clara county study' from Stanford researchers or the even less peer reviewed 'Los Angeles county serology study' by the same authors, not even 'pre-published' yet, but rather just a press release. These studies use assays that are not FDA-approved nor have they even been granted an FDA EUA status and the methods have been criticized roundly. There are several major methodological problems including selection bias of sampling (Facebook ads), no confirmation of positive results to try to see if misclassified as true positives, and suspect mathematical adjustments that seem to ignore Bayesian statistics to get from 1.5% seroprevalence to >4%. I've heard other bay area epidemiologists (from UCSF) note that of the 50 samples out of 3,000 that were positive in this study, nearly 1/3 were negative when tested with another assay (suggesting they were false positives), which would bring the estimates of true seropositive way down. There have been a torrent of COVID-19 serology tests on the market that are of dubious quality, and in this situation, they need to be really good in order to minimize false positives. For example, even a test with 99% sensitivity and specificity will yield as many false positives as true positives if disease prevalence is around 1%. Putting those two studies aside, another from NYC that Gov Cuomo cited (I haven't seen the data yet and not sure which Ab test was used), suggested 21% COVID-19 seropositivity within the City. OK, so if 8.4 million in NYC, that means 1.7 million people were actually infected--presuming ZERO false positives. Johns Hopkins CCSE dashboard reporting 17,126 deaths in NYC alone, which is still a 1.0% case fatality rate, exactly 10-fold higher than influenza. So again, where does the 0.2% fatality rate come from? I think time will tell that is an inaccurate assumption.