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.
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.
click to enlarge
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. Read More
Michael Fumento became prominent with his provocative book, The myth of heterosexual AIDS. On the whole I think Fumento’s point, that HIV-AIDS was not a major issue outside of “at-risk” groups in the United States, was the correct one.
I grew up as part of a generation that was taught about HIV-AIDS in a very apocalyptic manner. One of my health teachers even suggested that HIV-AIDS might lead to the extinction of the human race. When I saw Fumento make his case on a local public affairs television show, it was clear to me that despite everything I’d been told, he was probably correct. To counter his facts and figures the other guests appealed to anecdotes and vague predictions of the future.
COVID-19 is just the latest, albeit the most extreme, in a long series of epidemic hysterias I have covered going back to the “heterosexual AIDS explosion” (“Now No One is Safe from AIDS”) of the 1980s, avian flu, Ebola I and Ebola II, the Zika virus and others. They are known scientifically as “mass psychogenic illness,” and even more specifically as “moral panic” – the same type of hysteria that led to centuries of witch hunts.
Thus I was writing such articles as “Hysteria, Thy Name Is SARS” in 2003 while highly respected journals such as the New Scientist were screaming “SARS Could Eventually Kill Millions.” It ultimately killed only 774, and zero Americans, before simply disappearing in a hot July.
…
Yes, identified cases are still going up (albeit at a slower rate than before, per Farr’s Law), but that may just be an artifact. Indeed, it’s possible the epidemic is coming close to a worldwide plateau – in real terms, at least. The hint is in the category of “serious and critical cases.” It peaked in late February, with a steady decline to less than half that number. This in and of itself good news, of course. But why?
One of the major issues when discussing pretty much anything is the tendency to aggregate nations into a single unit and then compare to other nations that are not comparable. For example, the United States is a federal republic of 330 million people. New York state is not Washington state. And neither is Texas.
The same applies to Italy, which is a diverse nation of 60 million. The normal way to understanding Italian variation is from north to south. But, during the recent COVID-19 outbreak one aspect that is important to note is that Lombardy and Veneto in the Po river valley have taken very different tracks. Lombardy is about twice as populous as Veneto but has five times as many confirmed cases of Covid-19. And 15 times the death toll (8905 vs. 631 dead as of April 5th).
A Italian-speaking friend, who has been tracking the Italian press notes that the big difference seems to be that Veneto is attempting to implement the test-and-trace philosophy that South Korea rolled out. And, in Veneto, they aggressively test people who are not symptomatic to catch silent spreaders who don’t exhibit Covid-19 (in contrast to Lombardy where they tend to test once symptoms present and not even always then).
Below is a recent interview with professor Andrea Crisanti, quickly translated from Italian, where he outlines his philosophy and the path he sees forward for getting COVID-19 under control.
A big debate on the internet is whether China is covering up the number of cases of Covid-19 in Hubei, and more specifically Wuhan. Right now JHU says that China has 82,000 confirmed cases, as opposed to 300,000 in the USA. Both are underestimates, but there are those who believe that the Chinese death toll is not 3,000, but in the millions! I think a more sober take is that they could be underreporting by an order of magnitude. That being said, many epidemiologists believe that China’s numbers are roughly correct. And certainly, some demographic patterns to be robust and holding up (e.g., the proportion of the aged that die).
But there’s another way to estimate how many people were infected: look at the variation in the genome sequences of SARS-coV-2 itself. The genetic variation patterns in viruses that underwent massive rapid demographic expansion will be different from those that are subject to constant population size.
Here, we use a phylodynamic approach incorporating 53 publicly available novel coronavirus (nCoV) genomes to the estimate underlying incidence and prevalence of the epidemic. This approach uses estimates of the rate of coalescence through time to infer underlying viral population size and then uses assumptions of serial interval and heterogeneity of transmission to provide estimates of incidence and prevalence. We estimate an exponential doubling time of 7.2 (95% CI 5.0-12.9) days. We arrive at a median estimate of the total cumulative number of worldwide infections as of Feb 8, 2020, of 55,800 with a 95% uncertainty interval of 17,500 to 194,400. Importantly, this approach uses genome data from local and international cases and does not rely on case reporting within China.
…
…. We began by running the Nextstrain nCov pipeline to align sequences and mask spurious SNPs. We took the output file masked.fasta as the starting point for this analysis. We loaded this alignment into BEAST and specified an evolutionary model to estimate:
* strict molecular clock (CTMC rate reference prior) * exponential growth rate (Laplace prior with scale 100) * effective population size at time of most recent sampled tip (uniform prior between 0 and 10)
We followed Andrew in using a gamma distributed HKY nucleotide substitution model. MCMC was run for 50M steps, discarding the first 10M as burnin and sampling every 30,000 steps after this to give a dataset of 1335 MCMC samples.
The file ncov.xml contains the entire BEAST model specification. To run it will require filling in sequence data; we are not allowed to reshare this data according to GISAID Terms and Conditions. The Mathematica notebook ncov-phylodynamics.nb contains code to analyze resulting BEAST output in ncov.log and plot figures.
It’s been many years since I used BEAST but it’s a complicated piece of software and has a lot of options and parameters. I’m very curious about how robust the estimate is when considering sentences such as “assume that variance of secondary cases is at most like SARS with superspreading dynamics with k=0.15.” Bedford and his colleagues know 1,000 times more about this than I do, but I am really curious about other groups looking at the data and running their models.
If all of the results are in the range of the order of magnitude of above, I think some of us really have to update our priors about how much misreporting the Chinese are engaging in…
Update: Lots of sequences here. I may try to brush up on my BEAST skills…
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.
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.
It’s the beginning of the official spring of 2020, and the United States of America is now in the midst of a massive upsurge in positive test results for COVID-19, the illness caused by SARS-Cov-2. Right now, New York City is the major focus. Seattle, which was an early outbreak hotzone has taken a backseat. The frequency and ubiquity of the positive test results suggest to many that this virus has been in these United States for a while.
One issue that keeps coming up: what are the environmental covariates of COVID-19? These are early days yet, but peculiar patterns such as Italy’s high death rate, and Germany’s low death rate, are not understood yet. One issue brought up rather early by President Donald J. Trump, is that weather warming might mitigate the impact of the virus. And there is a seasonality with many respiratory diseases.
Four human coronaviruses that cause colds and other respiratory diseases are more revealing. Three have “marked winter seasonality,” with few or no detections in the summer, molecular biologist Kate Templeton, also at the University of Edinburgh, concluded in a 2010 analysis of 11,661 respiratory samples collected between 2006 and 2009. These three viruses essentially behave like the flu.
The flu is not a coronavirus, but it’s the most famous seasonal illness.
One of the stranger things about the spread of COVID-19 is the relatively slow spread of the disease in many tropical locations. This is glaring in Southeast Asia, which has extensive contact with China (and some early introductions of COVID-19). In contrast, COVID-19 exploded outside of China first in Iran, and then in Italy.
Some early papers suggested there was no correlation with temperature or perhaps a very modest one. Others made a stronger case. The problem is with data. During the early days of the pandemic, there weren’t many data points, and those came from China. Now we have more data, and more analyses are coming out.
…While influenza virus has been shown to be affected by weather, it is unknown if COVID19 is similarly affected. In this work, we analyze the effect of local weather on the transmission of the 2019-nCoV virus. Our results indicate that 90% of the 2019-nCoV transmissions have so far occurred within a certain range of temperature (3 to 17C) and absolute humidity (4 to 9g/m3) and the total number of cases in countries with mean Jan-Feb-March temperature >18C and and absolute humidity >9 g/m3 is less than 6%. Current data indicates that transmission of 2019-nCoV virus might have been less efficient in warmer humid climate. We could not differentiate which of the two environmental factors is more important, however, given the tight range of absolute humidity (4 – 9g/m3) across which the majority of the cases are observed, and previous associations between viral transmission and humidity, we believe that absolute humidity might play a bigger role in determining the spread of 2019-nCoV. Theoretical calculations suggest that absolute humidity is always lower than 9 g/m3 for temperature less than 15C and for temperatures between 15 and 25 C, the relative humidity has to be >60% for absolute humidity to be >9g/m3. Therefore if humidity plays a bigger role than temperature, then the chances of 2019-nCoV transmission slowing down due to environmental factors would be fairly limited for regions above 35 degree N due to environmental factors. On the other hand, Asian countries experiencing monsoon from mid-June can see a slowdown in transmission. On the contrary if temperature is more important, then most of the northern hemisphere should see a slow down in the spread of the 2019-nCoV with the approaching summer temperatures. Our hypothesis is based on currently available data and its validity will automatically be tested in the next few weeks with reporting of new cases across the world. The relation between temperature and humidity and 2019-nCoV cases should be closely monitored and if a strong environmental dependence in the spread of 2019-nCOV exists then it should be used to optimize the 2019-nCoV mitigation strategies. Our results in no way suggest that 2019-nCoV would not spread in warm humid regions and effective public health interventions should be implemented across the world to slow down the transmission of 2019-nCoV.
The idea that absolute humidity (basically the amount of water vapor that is present in the air) matters comes in part from a 2009 paper, Absolute humidity modulates influenza survival, transmission, and seasonality. If flu is spread through droplets that are aerosolized, then more absolute humidity means water accrues to the droplets, and they don’t stay in the air as long. Though there is still some controversy about the details of how COVID-19 spreads, often it’s through droplets from coughing or sneezing (though the possible spread from asymptomatic people is troubling, as they would not be coughing or sneezing).
A critique of their data easily presents itself. Russia, at this moment, seems highly likely to be masking their cases. The pandemic is in early stages, and literally every day the media declares that India has the potential to be the next major epicenter. Pretty soon, within four weeks, we’ll probably see if every region of the world is going through the exponential increase that we’re seeing in the United States of America, making the climate modifier model moot. But we’re not there yet.
Figure 4 from the preprint presents their primary result (recapitulating earlier work), that most of the infections seem to occur at a particular temperature/humidity range:
You see here that the infections are occurring in the range of absolute humidity between 4 and 8 g/m3. There are all sorts of reasons these are artifacts, but this clearly comports with intuition when you look at the map of where infections are. As is clear in the preprint, the authors are not claiming that climate is the only variable that constraints or shapes the spread of the disease. To name some off the top of my head, density, cultural practices (e.g., physical greetings that require contact), age structure, and frequency of comorbidities and other infections probably matter.
Using a temperature and humidity table I computed when cities get “warm enough” to reduce the risk of COVID-19 transmission (I ignored the cold as a mitigator because I don’t think we really have enough reliable data):
Metro Area
The month when it gets humid enough
New York
June
Los Angeles
June
Chicago
May
Dallas
April
Houston
April
Washington DC
May
Miami
(all year within the zone)
Philadelphia
June
Atlanta
May
Boston
June
San Francisco
(all year outside of zone)
Seattle
July
Milan
June
London
June
Tehran
June
Mumbai
(all year within the zone)
Cairo
June
Karachi
(all year within the zone)
Lahore
July
The key point to note is that absolute humidity is dependent upon relative humidity and temperature. Very dry cities, such as Cairo and Tehran don’t do so well, because even though they get warm rapidly in spring, they remain dry. There should be a huge difference in Pakistan, between balmy Karachi, and Lahore inland, which is drier and more continental.
Unfortunately, San Francisco is too cool all year, though the whole region has many microclimates, so I wouldn’t overgeneralize. Seattle summers tend to be dry and only moderately warm.
Another major wild-card here is that air-conditioning is now very popular and widespread. This reduces absolute humidity in the environments that many people live in. Rural residents of tropical countries, who have less access to air-conditioning (and live at lower densities), may actually be relatively lightly impacted by COVID-19 compared to their jet-setting urban compatriots, who work in air-conditioned offices.
