It looks like the USA has gone through “wave 3”. But one thing masked in the aggregate data is regional variation. Whereas major hotspots like California peaked in January, the states in the Upper Midwest peaked around December 1st. In November there were many alarmist headlines about what was going on in places like Wisconsin and North Dakota. But since then the case-rates have faded, and these states no longer graced the headlines.
When does COVID-19 get more dangerous than the flu? The CDC has some deaths listed for COVID-19. It also has deaths recorded for influenza. These are not perfect records, but, they give us a general comparative sense.
The total count in their data for the column I’ve plotting is about half of or so of the current death total for the USA. With that said, COVID-19 seems to be a really marginal disease in terms of mortality for those 24 years and under. For those 85 years or old COVID-19 is killing order of magnitude more than the flu.
Of course, there is morbidity as well as mortality. COVID-19 seems to have a longer course of progression for the symptomatic, and, there is the worry that it may cause lifetime problems in many people who survive from the severe cases (and even possibly the asymptomatic).
But, the number of people who are under the age of 40 who are dying doesn’t seem that high. And yet when I see headlines and profiles in the media, a huge number of feature focuses seem to be about younger people who die of COVID-19. Why? Obviously, because the deaths of the younger are surprising. But, I also think that part of it is the same rationale for the HIV-AIDS campaign: by pretending as if everyone is vulnerable, you obtain mass social mobilization.
I happen to know lots of people will not look at the raw data to understand what’s happening. But enough will to get annoyed.
50% of the deaths in Europe are in care homes. My family is self-quarantining no because we feel at risk, we’re not. But because there are older people in our family from whom we don’t want to be exiled. Does the media think if we admit and highlight the enormous danger that older people in particular face, we’ll conclude that they’re disposable?
There has been a fair amount of anecdotal and a bit of statistical evidence that obesity is somehow associated with individuals who have worse progression of COVID-19. The data out of China I saw wasn’t significant statistically speaking. The problem? There didn’t seem to be enough obese people in their samples. Then anecdotes and some data came out of Europe implicating obesity as a risk factor. And, doctors started reporting a disproportionate number of obese patients in the ICU.
Now we have really good evidence, Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City:
We conducted a cross-sectional analysis of all patients with laboratory-confirmed Covid-19 treated at a single academic health system in New York City between March 1, 2020 and April 2, 2020, with follow up through April 7, 2020. Primary outcomes were hospitalization and critical illness (intensive care, mechanical ventilation, hospice and/or death). We conducted multivariable logistic regression to identify risk factors for adverse outcomes, and maximum information gain decision tree classifications to identify key splitters….Strongest hospitalization risks were age ≥75 years (OR 66.8, 95% CI, 44.7-102.6), age 65-74 (OR 10.9, 95% CI, 8.35-14.34), BMI>40 (OR 6.2, 95% CI, 4.2-9.3), and heart failure (OR 4.3 95% CI, 1.9-11.2)…In the decision tree for admission, the most important features were age >65 and obesity; for critical illness, the most important was SpO2<88, followed by procalcitonin >0.5, troponin <0.1 (protective), age >64 and CRP>200. Conclusions: Age and comorbidities are powerful predictors of hospitalization; however, admission oxygen impairment and markers of inflammation are most strongly associated with critical illness.
I’ve reformated table 3 of the regression below. It’s important to note here that the whole population is infected. The table is assessing the risk out of the infected sample that someone is going to go critical (which means a host of things, but entails hospitalization). Remember that a lot of the comorbidities associated with obesity are in the table. That means the risk of obesity is viewed as an independent variable. One can make some mechanistic arguments about the inflammatory effects of lipids, etc. That’s neither here nor there.
When assessing the risk of various nations is that 3% of Japanese are obese, while 40% of Americans are obese.
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A friend of mine recently quipped that everyone seems to act like probability can be assigned two values 0 or 1. The same sort of logic seems to apply when it comes to talking about the environmental parameters which might affect the progress of COVID-19, such as temperature, humidity, and density. Many people seem to strenuously want to deny there is any plausible evidence that COVID-19 might exhibit seasonality. There is a fair amount of correlational work which suggests that there is an environmental factor shaping the spread and depth of COVID-19. And, we know three out of the four previous coronaviruses exhibit seasonality.
Well, I noticed this note on medRxiv today, Stability of SARS-CoV-2 in different environmental conditions. It’s a very short write-up of their experimental results. I don’t really know much about virology so I can’t evaluate it well, but you can see the figure above. As you increase the temperature the virus titer seems to drop much faster. At a very high temperature of 70 Celsius, they basically can’t detect anything after 1 minute.
Here is one of the better correlational analyses, using some sophisticated techniques, Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal:
Nearly every country is now combating the 2019 novel coronavirus (COVID-19). It has been hypothesized that if COVID-19 exhibits seasonality, changing temperatures in the coming months will shift transmission patterns around the world. Such projections, however, require an estimate of the relationship between COVID-19 and temperature at a global scale, and one that isolates the role of temperature from confounding factors, such as public health capacity. This paper provides the first plausibly causal estimates of the relationship between COVID-19 transmission and local temperature using a global sample comprising of 166,686 confirmed new COVID-19 cases from 134 countries from January 22, 2020 to March 15, 2020. We find robust statistical evidence that a 1◦C increase in local temperature reduces transmission by 13% [-21%, -4%, 95%CI]. In contrast, we do not find that specific humidity or precipitation influence transmission. Our statistical approach separates effects of climate variation on COVID-19 transmission from other potentially correlated factors, such as differences in public health responses across countries and heterogeneous population densities. Using constructions of expected seasonal temperatures, we project that changing temperatures between March 2020 and July 2020 will cause COVID-19 transmission to fall by 43% on average for Northern Hemisphere countries and to rise by 71% on average for Southern Hemisphere countries. However, these patterns reverse as the boreal winter approaches, with seasonal temperatures in January 2021 increasing average COVID-19 transmission by 59% relative to March 2020 in northern countries and lowering transmission by 2% in southern countries. These findings suggest that Southern Hemisphere countries should expect greater transmission in the coming months. Moreover, Northern Hemisphere countries face a crucial window of opportunity: if contagion-containing policy interventions can dramatically reduce COVID-19 cases with the aid of the approaching warmer months, it may be possible to avoid a second wave of COVID-19 next winter.
To be clear. Does this mean weather/climate determine whether COVID-19 will spread or not? No. Rather, I think that weather/climate has some effect on the margin on the R0. I am not sure of the exact reason, but if the virus degrades much faster in hot climates, that could be one explanation of why spreading is more limited. It also does not seem to be the case that tropical countries are going to avoid mass healthcare crises. Rather, as these countries formulate policies to decrease R0, it may not be as long of a haul.
I believe that many are worried that if there is some relationship between temperature and COVID-19, people will think they are safe in a particular climate. The way to deal with this is not to ratchet up skepticism to an inordinately high extent. Rather, it is to be more clear and careful in how one presents the data.
Similarly, I think density has some impact. But, South Korea, Japan, and Taiwan show that density does not seal one’s fate.
We’ve been talking about Coronavirus in our house pretty constantly since early February. I’ve come out into the open and admitted my family is doing self-quarantine to reduce spread (we don’t think we’re sick, but we don’t want to spread it by getting sick). I haven’t been very hopeful in a month due to what I see as complacency. The median/modal case scenarios in my head have been getting worse and worse over the weeks.
Over the last hour, I’ve changed and become a bit more hopeful because I’m optimistic that COVID-19 does have a relationship to weather. I’m not confidently optimistic, but there’s now a glimmer of hope. The reason is due to this preprint, Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19.
So several things we had talked about in our family:
An immediate explanation is bad reporting. But in all these cases? Also, hospital systems get overwhelmed. This seems like it would get out.
The major result from the paper is here:
Further analysis using 2-meter (2m) temperatures from 2020 rather than hPa temperatures yields similar results (Figure 2). In the months of January 2020 in Wuhan and February 2020 in the other affected, there is a striking similarity in the measures of average temperature (5-11 degrees C) and relative humidity (RH, 47-79%) (Table 1). In addition to having similar average temperature, humidity, and latitude profiles, these locations also exhibit a commonality in that the timing of the outbreak coincides with a nadir in the yearly temperature cycle, and thus with relatively stable temperatures over a more than a one month period of time (Supplementary Figure 1). In addition, none of the affected cities have minimum temperatures going below 0 degree C (Supplementary Figure 1).
Here’s the figure:
And here’s a table:
| City | Nov 2019 | Dec 2019 | Jan 2020 | Feb 2020 |
| Cities with community spreading of COVID-19 | ||||
| Wuhan | 18C/44% | 12 C/56% | 7 C/74% | 13 C/66% |
| Tokyo | 17 C/53% | 11 C/52% | 9 C/54% | 10 C/47% |
| Qom | 12 C/52% | 10 C/58% | 7 C/59% | 10 C/47% |
| Milan | 11 C/77% | 8 C/74% | 7 C/69% | 11 C/58% |
| Daegu | 11 C/64% | 5 C/62% | 4 C/68% | 5 C/62% |
| Seattle | 9 C/76% | 6 C/84% | 6 C/84% | 7 C/79% |
| Mulhouse | 7 C/84% | 6 C/82% | 6 C/80% | 8 C/74% |
| Glasgow | 5C/87% | 5 C/89% | 6C/86% | 4 C/84% |
| Large cities tentatively predicted to be at risk in the coming weeks | ||||
| London | 8 C/78% | 8 C/80% | 8 C/80% | 8 C/70% |
| Manchester | 7 C/82% | 6 C/83% | 7 C/83% | 6 C/73% |
| Berlin | 8 C/81% | 5 C/80% | 5 C/81% | 6 C/75% |
| Prague | 7 C/81% | 4 C/78% | 3 C/79% | 6 C/71% |
| Hamburg | 6 C/89% | 5 C/86% | 6 C/88% | 6 C/83% |
| Vancouver | 8 C/75% | 6 C/84% | 5 C/84% | 5 C/78% |
| New York | 8 C/55% | 4 C/72% | 4 C/61% | 5 C/62% |
| Warsaw | 8 C/76% | 4 C/78% | 3 C/78% | 5 C/72% |
| Glasgow | 5C/87% | 5 C/89% | 6C/86% | 4 C/84% |
| Kiev | 6 C/74% | 4 C/83% | 1 C/85% | 3 C/76% |
| St. Louis | 6 C/71% | 5 C/78% | 3 C/77% | 3 C/73% |
| Beijing | 9C/33% | 2 C/43% | 2 C/41% | 5 C/45% |
| Previously predicted city where COVID-19 failed to take hold | ||||
| Bangkok | 31 C/52% | 30 C/45% | 32 C/50% | 32 C/51% |
Read the whole thing and draw your conclusions. Please put your critiques in the comments if you want.
Update: Some warranted pessimism in the comments.