Uncertainty in the Time of COVID-19, Part 2

Part 2: How Do We Know What We Know?

When a new pathogen first shows up to threaten human lives, ignorance dominates knowledge. The faster we retire our ignorance and maximize our knowledge, the better our response to any novel threat. The good news is that knowledge of what is happening during the current COVID-19 pandemic is accumulating more rapidly than it did during the SARS outbreak, in part because we have new tools available, and in part because Chinese clinicians and scientists are publishing more, and faster, than in 2003. And yet there is still a great deal of ignorance about this pathogen, and that ignorance breeds uncertainty. While it is true that the virus we are now calling SARS-CoV-2 is relatively closely related genetically to the SARS-CoV that emerged in 2002, the resulting disease we call COVID-19 is notably different than SARS. This post will dig into what methods and tools are being used today in diagnosis and tracking, what epidemiological knowledge is accumulating, and what error bars and assumptions are absent, being misunderstood, or are errant.

First, in all of these posts I will keep a running update of good sources of information. The Atlantic continues its excellent reporting into lack of testing in the US by digging into the decision-making process, or lack thereof, that resulted in our current predicament. I am finding it useful to read the China CDC Weekly Reports, which constitute source data and anecdotes used in many other articles and reports.

Before diving in any further, I would observe that it is now clear that extreme social distancing works to halt the spread of the virus, at least temporarily, as demonstrated in China. It is also clear that, with widespread testing, the spread can also be controlled with less severe restrictions — but only if you assay the population adequately, which means running tests on as many people as possible, not just those who are obviously sick and in hospital.

Why does any of this matter?

In what follows, I get down into the weeds of sources of error and of sampling strategies. I suggest that the way we are using tests is obscuring, rather than helping, our ability to understand what is happening. You might look at this, if you are an epidemiologist or public health person, and say that these details are irrelevant because all we really care about are actions that work to limit or slow the spread. Ultimately, as the goal is to save lives and reduce suffering, and since China has demonstrated that extreme social distancing can work to limit the spread of COVID-19, the argument might be that we should just implement the same measures and be done with it. I am certainly sympathetic to this view, and we should definitely implement measures to restrict the spread of the virus.

But it isn’t that simple. First, because the population infection data is still so poor, even in China (though perhaps not in South Korea, as I explore below) every statement about successful control is in actuality still a hypothesis, yet to be tested. Those tests will come in the form of 1) additional exposure data, such as population serology studies that identify the full extent of viral spread by looking for antibodies to the virus, which persist long after an infection is resolved, and 2) carefully tracking what happens when social distancing and quarantine measures are lifted. Prior pandemics, in particular the 1918 influenza episode, showed waves of infections that reoccured for years after the initial outbreak. Some of those waves are clearly attributable to premature reduction in social distancing, and different interpretations of data may have contributed to those decisions. (Have a look at this post by Tomas Pueyo, which is generally quite good, for the section with the heading “Learnings from the 1918 Flu Pandemic”.) Consequently, we need to carefully consider exactly what our current data sets are teaching us about SARS-CoV-19 and COVID-19, and, indeed, whether current data sets are teaching us anything helpful at all.

What is COVID-19?

Leading off the discussion of uncertainty are differences in the most basic description of the disease known as COVID-19. The list of observed symptoms — that is, visible impacts on the human body — from the CDC includes only fever, cough, and shortness of breath, while the WHO website list is more expansive, with fever, tiredness, dry cough, aches and pains, nasal congestion, runny nose, sore throat, or diarrhea. The WHO-China Joint Mission report from last month (PDF) is more quantitative: fever (87.9%), dry cough (67.7%), fatigue (38.1%), sputum production (33.4%), shortness of breath (18.6%), sore throat (13.9%), headache (13.6%), myalgia or arthralgia (14.8%), chills (11.4%), nausea or vomiting (5.0%), nasal congestion (4.8%), diarrhea (3.7%), and hemoptysis (0.9%), and conjunctival congestion (0.8%). Note that the preceding list, while quantitative in the sense that it reports the frequency of symptoms, is ultimately a list of qualitative judgements by humans.

The Joint Mission report continues with a slightly more quantitative set of statements:

Most people infected with COVID-19 virus have mild disease and recover. Approximately 80% of laboratory confirmed patients have had mild to moderate disease, which includes non-pneumonia and pneumonia cases, 13.8% have severe disease (dyspnea, respiratory frequency ≥30/minute, blood oxygen saturation ≤93%, PaO2/FiO2 ratio <300, and/or lung infiltrates >50% of the lung field within 24-48 hours) and 6.1% are critical (respiratory failure, septic shock, and/or multiple organ dysfunction/failure).

The rate of hospitalization, seriousness of symptoms, and ultimately the fatality rate depend strongly on age and, in a source of more uncertainty, perhaps on geography, points I will return to below.

What is the fatality rate, and why does it vary so much?

The Economist has a nice article exploring the wide variation in reported and estimated fatality rates, which I encourage you to read (also this means I don’t have to write it). One conclusion from that article is that we are probably misestimating fatalities due to measurement error. The total rate of infection is probably higher than is being reported, and the absolute number of fatalities is probably higher than generally understood. To this miscalculation I would add an additional layer of obfuscation, which I happened upon in my earlier work on SARS and the flu.

It turns out that we are probably significantly undercounting deaths due to influenza. This hypothesis is driven by a set of observations of anticorrelations between flu vaccination and deaths ascribed to stroke, myocardial infarction (“heart attack”), and “sudden cardiac death”, where the latter is the largest cause of “natural” death in the United States. Influenza immunization reduces the rate of those causes of death by 50-75%. The authors conclude that the actual number of people who die from influenza infections could be 4X-2.5-5X higher than the oft cited 20,000-40,000.

How could the standard estimate be so far off? Consider these two situations: First, if a patient is at the doctor or in the hospital due to symptoms of the flu, they are likely to undergo a test to rule in, or out, the flu. But if a patient comes into the ER in distress and then passes away, or if they die before getting to the hospital, then that molecular diagnostic is much less likely to be used. And if the patient is elderly and already suffering from an obvious likely cause of death, for example congestive heart failure, kidney failure, or cancer, then that is likely to be what goes on the death certificate. Consequently, particularly among older people with obvious preexisting conditions, case fatality rate for influenza is likely to be underestimated, and that is for a pathogen that is relatively well understood for which there is unlikely to be a shortage of diagnostic kits.

Having set that stage, it is no leap at all to hypothesize that the fatality rate for COVID-19 is likely to be significantly underestimated. And then if you add in insufficient testing, and thus insufficient diagnostics, as I explore below, it seems likely that many fatalities caused by COVID-19 will be attributed to something else, particularly among the elderly. The disease is already quite serious among those diagnosed who are older than 70. I expect that the final toll will be greater in communities that do not get the disease under control.

Fatality rate in China as reported by China CDC.

Fatality rate in China as reported by China CDC.

How is COVID-19 diagnosed?

For most of history, medical diagnoses have been determined by comparing patient symptoms (again, these are human-observable impacts on a patent, usually constituting natural language nouns and adjectives) with lists that doctors together agree define a particular condition. Recently, this qualitative methodology has been slowly amended with quantitative measures as they have become available: e.g., pulse, blood pressure, EEG and EKG, blood oxygen content, “five part diff” (which quantifies different kinds of blood cells), CT, MRI, blood sugar levels, liver enzyme activity, lung and heart pumping volume, viral load, and now DNA and RNA sequencing of tissues and pathogens. These latter tools have become particularly important in genetically tracking the spread of #SARS-CoV-2, because by following the sequence around the world you can sort out at the individual case level where it came from. And then simply being able to specifically detect viral RNA to provide a diagnosis is important because COVID-19 symptoms (other than fatality rate) are quite similar to that of the seasonal flu. Beyond differentiating COVID-19 from “influenza like illness”, new tools are being brought to bear that enable near real time quantification of viral RNA, which enables estimating viral load (number of viruses per sample volume), and which in turn facilitates 1) understanding how the disease progresses and then 2) how infectious patients are over time. These molecular assays are the result of decades of technology improvement, which has resulted in highly automated systems that take in raw clinical samples, process them, and deliver results electronically. At least in those labs that can afford such devices. Beyond these achievements, novel diagnostic methods based on the relatively recent development of CRISPR as a tool are already in the queue to be approved for use amidst the current pandemic. The pandemic is serving as a shock to the system to move diagnostic technology faster. We are watching in real time a momentous transition in the history of medicine, which is giving us a glimpse of the future. How are all these tools being applied today?

(Note: My original intention with this post was to look at the error rates of all the steps for each diagnostic method. I will explain why I think this is important, but other matters are more pressing at present, so the detailed error analysis will get short shrift for now.)

Recapitulating an explanation of relevant diagnostics from Part 1 of this series (with a slight change in organization):

There are three primary means of diagnosis:

1. The first is by display of symptoms, which can span a long list of cold-like runny nose, fever, sore throat, upper respiratory features, to much less pleasant, and in some cases deadly, lower respiratory impairment. (I recently heard an expert on the virus say that there are two primary ways that SARS-like viruses can kill you: “Either your lungs fill up with fluid, limiting your access to oxygen, and you drown, or all the epithelial cells in your lungs slough off, limiting your access to oxygen, and you suffocate.” Secondary infections are also more lethal for people experiencing COVID-19 symptoms.)

2. The second method of diagnosis is imaging of lungs, which includes x-ray and CT scans; SARS-CoV-2 causes particular pathologies in the lungs that can be identified on images and that distinguish it from other respiratory viruses.

3. Thirdly, the virus can be diagnosed via two molecular assays, the first of which uses antibodies to directly look for viral proteins in tissue or fluid samples, while the other looks for whether genetic material is present; sophisticated versions can quantify how many copies of viral RNA are present in a sample.

Imaging of lungs via x-ray and CT scan appears to be an excellent means to diagnose COVID-19 due to a distinct set of morphological features that appear throughout infected tissue, though those features also appear to change during the course of the disease. This study also examined diagnosis via PCR assays, and found a surprisingly high rate of false negatives. It is not clear from the text whether all patients had two independent swabs and accompanying tests, so either 10 or 12 total tests were done. If 10 were done, there are two clear false negatives, for a 20% failure rate; if 12 were done, there are up to four false negatives, for a 33% failure rate. The authors observe that “the false negative rate of oropharyngeal swabs seems high.” Note that this study directly compares the molecular assay with imaging, and the swab/PCR combo definitely comes up short. This is important because for us to definitively diagnose even the number of serious cases, let alone start sampling the larger population to track and try to get ahead of the outbreak, imaging is low throughput and expensive; we need rapid, accurate molecular assays. We need to have confidence in testing.

How does “testing” work? First, testing is not some science fiction process that involves pointing a semi-magical instrument like a Tricorder at a patient and instantly getting a diagnosis. In reality, testing involves multiple process steps implemented by humans — humans who sometimes are inadequately trained or who make mistakes. And then each of those process steps has an associated error or failure rate. You almost never hear about the rate of mistakes, errors, or failures in reporting on “testing”, and that is a problem.

Let’s take the testing process in order. For sample collection the CDC Recommendations include nasopharyngeal and oropharyngeal (i.e., nose and throat) swabs. Here is the Wikipedia page on RT-PCR, which is a pretty good place to start if you are new to these concepts.

The Seattle Flu Study and the UW Virology COVID-19 program often rely on home sample collection from nasal and throat swabs. My initial concern about this testing method was motivated in part by the fact that it was quite difficult to develop a swab-PCR for SARS-CoV that delivered consistent results, where part of the difficulty was simply in collecting a good patient sample. I have a nagging fear that not everyone who is collecting these samples today is adequately trained to get a good result, or that they are tested to ensure they are good at this skill. The number of sample takers has clearly expanded significantly around the world in the last couple of weeks, with more expansion to come. So I leave this topic with a question: is there a clinical study that examines the success rate sample collection by people who are not trained to do this every day?

On to the assays themselves: I am primarily concerned at the moment with the error bars on the detection assays. The RT-PCR assay data in China are not reported with errors (or even variance, which would be an improvement). Imaging is claimed to be 90-95% accurate (against what standard is unclear), and the molecular assays worse than that by some amount. Anecdotal reports are that they have only been 50-70% accurate, with assertions of as low as 10% in some cases. This suggests that, in addition to large probable variation in the detectable viral load, and possible quality variations in the kits themselves, human sample handling and lab error is quite likely the dominant factor in accuracy. There was a report of an automated high throughput testing lab getting set up in a hurry in Wuhan a couple of weeks ago, which might be great if the reagents quality is sorted, but I haven’t seen any reports of whether that worked out. So the idea that the “confirmed” case counts are representative of reality even in hospitals or care facilities is tenuous at best. South Korea has certainly done a better job of adequate testing, but even there questions remain about the accuracy of the testing, as reported by the Financial Times:

Hong Ki-ho, a doctor at Seoul Medical Centre, believed the accuracy of the country’s coronavirus tests was “99 per cent — the highest in the world”. He pointed to the rapid commercial development and deployment of new test kits enabled by a fast-tracked regulatory process. “We have allowed test kits based on WHO protocols and never followed China’s test methods,” Dr Hong said.

However, Choi Jae-wook, a medical professor of preventive medicine at Korea University, remained “worried”. “Many of the kits used at the beginning stage of the outbreak were the same as those in China where the accuracy was questioned . . . We have been hesitating to voice our concern because this could worry the public even more,” Mr Choi said.

At some point (hopefully soon) we will see antibody-based tests being deployed that will enable serology studies of who has been previously infected. The US CDC is developing these serologic tests now, and we should all hope the results are better than the initial round of CDC-produced PCR tests. We may also be fortunate and find that these assays could be useful for diagnosis, as lateral flow assays (like pregnancy tests) can be much faster than PCR assays. Eventually something will work, because this antibody detection is tried and true technology.

To sum up: I had been quite concerned about reports of problems (high error rates) with the PCR assay in China and in South Korea. Fortunately, it appears that more recent PCR data is more trustworthy (as I will discuss below), and that automated infrastructure being deployed in the US and Europe may improve matters further. The automated testing instruments being rolled out in the US should — should — have lower error rates and higher accuracy. I still worry about the error rate on the sample collection. However, detection of the virus may be facilitated because the upper respiratory viral load for SARS-CoV-2 appears to be much higher than for SARS-CoV, a finding with further implications that I will explore below.

How is the virus spread?

(Note: the reporting on asymptomatic spread has changed a great deal just in the last 24 hours. Not all of what appears below is updated to reflect this yet.)

The standard line, if there can be one at this point, has been that the virus is spread by close contact with symptomatic patients. This view is bolstered by claims in the WHO Joint Mission report: “Asymptomatic infection has been reported, but the majority of the relatively rare cases who are asymptomatic on the date of identification/report went on to develop disease. The proportion of truly asymptomatic infections is unclear but appears to be relatively rare and does not appear to be a major driver of transmission.”(p.12) These claims are not consistent with a growing body of clinical observations. Pinning down the rate of asymptomatic, or presymptomatic, infections is important for understanding how the disease spreads. Combining that rate with evidence that patients are infectious while asymptomatic, or presymptomatic, is critical for planning response and for understanding the impact of social distancing.

Two sentences in the Science news piece describing the Joint Mission report undermine all the quantitative claims about impact and control: “A critical unknown is how many mild or asymptomatic cases occur. If large numbers of infections are below the radar, that complicates attempts to isolate infectious people and slow spread of the virus.” Nature picked up this question earlier this week: “How much is coronavirus spreading under the radar?” The answer: probably quite a lot.

A study of cases apparently contracted in a shopping mall in Wenzhou concluded that the most likely explanation for the pattern of spread is “that indirect transmission of the causative virus occurred, perhaps resulting from virus contamination of common objects, virus aerosolization in a confined space, or spread from asymptomatic infected persons.”

Another recent paper in which the authors built an epidemiological transmission model all the documented cases in Wuhan found that, at best, only 41% of the total infection were “ascertained” by diagnosis, while the most likely acertainment rate was a mere 21%. That is, the model best fits the documented case statistics when 79% of the total infections were unaccounted for by direct diagnosis.

Finally, a recent study of patients early after infection clearly shows “that COVID-19 can often present as a common cold-like illness. SARS-CoV-2 can actively replicate in the upper respiratory tract, and is shed for a prolonged time after symptoms end, including in stool.” The comprehensive virological study demonstrates “active [infectious] virus replication in upper respiratory tract tissues”, which leads to a hypothesis that people can present with cold-like symptoms and be infectious. I will quote more extensively from the abstract, as this bit is crucially important:

Pharyngeal virus shedding was very high during the first week of symptoms (peak at 7.11 X 10^8 RNA copies per throat swab, day 4). Infectious virus was readily isolated from throat- and lung-derived samples, but not from stool samples in spite of high virus RNA concentration. Blood and urine never yielded virus. Active replication in the throat was confirmed by viral replicative RNA intermediates in throat samples. Sequence-distinct virus populations were consistently detected in throat- and lung samples of one same patient. Shedding of viral RNA from sputum outlasted the end of symptoms. Seroconversion occurred after 6-12 days, but was not followed by a rapid decline of viral loads.

That is, you can be sick for a week with minimal- to mild symptoms, shedding infectious virus, before antibodies to the virus are detectable. (This study also found that “Diagnostic testing suggests that simple throat swabs will provide sufficient sensitivity at this stage of infection. This is in stark contrast to SARS.” Thus my comments above about reduced concern about sampling methodology.)

So the virus is easy to detect because it is plentiful in the throat, which unfortunately also means that it is easy to spread. And then even after you begin to have a specific immune response, detectable as the presence of antibodies in blood, viral loads stay high.

The authors conclude, rather dryly, with an observation that “These findings suggest adjustments of current case definitions and re-evaluation of the prospects of outbreak containment.” Indeed.

One last observation from this paper is eye opening, and needs much more study: “Striking additional evidence for independent replication in the throat is provided by sequence findings in one patient who consistently showed a distinct virus in her throat as opposed to the lung.” I am not sure we have seen something like this before. Given the high rate of recombination between strains in this family of betacoronaviruses (see Part 1), I want to flag the infection of different tissues by different strains as a possibly worrying route to more viral innovation, that is, evolution.

STAT+ News summarizes the above study as follows:

The researchers found very high levels of virus emitted from the throat of patients from the earliest point in their illness —when people are generally still going about their daily routines. Viral shedding dropped after day 5 in all but two of the patients, who had more serious illness. The two, who developed early signs of pneumonia, continued to shed high levels of virus from the throat until about day 10 or 11.

This pattern of virus shedding is a marked departure from what was seen with the SARS coronavirus, which ignited an outbreak in 2002-2003. With that disease, peak shedding of virus occurred later, when the virus had moved into the deep lungs.

Shedding from the upper airways early in infection makes for a virus that is much harder to contain. The scientists said at peak shedding, people with Covid-19 are emitting more than 1,000 times more virus than was emitted during peak shedding of SARS infection, a fact that likely explains the rapid spread of the virus. 

Yesterday, CNN joined the chorus of reporting on the role asymptomatic spread. It is a nice summary, and makes clear that not only is “presymptomatic transmission commonplace”, it is a demonstrably significant driver of infection. Michael Osterholm, director of the Center for Infectious Disease Research (CIDRAP) and Policy at the University of Minnesota, and always ready with a good quote, was given the opportunity to put the nail in the coffin on the denial of asymptomatic spread:

"At the very beginning of the outbreak, we had many questions about how transmission of this virus occurred. And unfortunately, we saw a number of people taking very firm stances about it was happening this way or it wasn't happening this way. And as we have continued to learn how transmission occurs with this outbreak, it is clear that many of those early statements were not correct," he said. 

"This is time for straight talk," he said. "This is time to tell the public what we know and don't know."

There is one final piece of the puzzle that we need to examine to get a better understanding of how the virus is spreading. You may have read about characterizing the infection rate by the basic reproduction number, R0, which is a statistical measure that captures the average dynamics of transmission. There is another metric the “secondary attack rate”, or SAR, which is a measurement of the rate of transmission in specific cases in which a transmission event is known to have occurred. The Joint Mission report cites an SAR in the range of 5-10% in family settings, which is already concerning. But there is another study (that, to be fair, came out after the Joint Mission report) of nine instances in Wuhan that calculates the secondary attack rate in specific community settings is 35%. That is, assuming one initially infected person per room attended an event in which spread is known to have happened, on average 35% of those present were infected. In my mind, this is the primary justification for limiting social contacts — this virus appears to spread extremely well when people are in enclosed spaces together for a couple of hours, possibly handling and sharing food.

Many missing pieces must be filled in to understand whether the high reported SAR above is representative globally. For instance, what where the environmental conditions (humidity, temperature) and ventilation like at those events? Was the source of the virus a food handler, or otherwise a focus of attention and close contact, or were they just another person in the room? Social distancing and eliminating public events was clearly important in disrupting the initial outbreak in Wuhan, but without more specific information about how community spread occurs we are just hanging on, hoping old fashioned public health measures will slow the thing down until countermeasures (drugs and vaccines) are rolled out. And when the social control measures are lifted, the whole thing could blow up again. Here is Osterholm again, from the Science news article covering the Joint Mission report:

“There’s also uncertainty about what the virus, dubbed SARS-CoV-2, will do in China after the country inevitably lifts some of its strictest control measures and restarts its economy. COVID-19 cases may well increase again.”

“There’s no question they suppressed the outbreak,” says Mike Osterholm, head of the Center for Infectious Disease Research and Policy at the University of Minnesota, Twin Cities. “That’s like suppressing a forest fire, but not putting it out. It’ll come roaring right back.”

What is the age distribution of infections?

The short answer here is that everyone can get infected. The severity of one’s response appears to depend strongly on age, as does the final outcome of the disease (the “endpoint”, as it is somewhat ominously referred to). Here we run smack into another measurement problem, because in order to truly understand who is infected, we would need to be testing broadly across the population, including a generous sample of those who are not displaying symptoms. Because only South Korea has been sampling so widely, only South Korea appears to have a data set that gives some sense of the age distribution of infections across the whole population. Beyond the sampling problem, I found it difficult to find this sort of demographic data published anywhere on the web.

Below is the only age data I have been able to come up with, admirably cobbled together by Andreas Backhaus from screenshots of data out of South Korea and Italy.

Why would you care about this? Because, in many countries, policy makers have not yet closed schools, restaurants, or pubs that younger and healthier members of the population tend to frequent. If this population is either asymptomatic or mildly symptomatic, but still infectious — as indicated above — then they are almost certainly spreading virus not only amongst themselves, but also to members of their families who may be more likely to experience severe symptoms. Moreover, I am led to speculate by the different course of disease in different communities that the structure of social contacts could be playing a significant role in the spread of the virus. Countries that have a relatively high rate of multi-generational households, in which elderly relatives live under the same roof as young people, could be in for a rough ride with COVID-19. If young people are out in the community, exposed to the virus, then their elderly relatives at home have a much higher chance of contracting the virus. Here is the distribution of multigenerational households by region, according to the UN:

Screen Shot 2020-03-15 at 8.39.46 PM.png

The end result of all this is that we — humanity at large, and in particular North America and Europe — need to do a much better job of containment in our own communities in order to reduce morbidity and mortality caused by SARS-CoV-2.

How did we get off track with our response?

It is important to understand how the WHO got the conclusion about the modes of infection wrong. By communicating so clearly that they believed there was a minimal role for asymptomatic spread, the WHO sent a mixed message that, while extreme social distancing works, perhaps it was not so necessary. Some policy makers clearly latched onto the idea that the disease only spreads from very sick people, and that if you aren’t sick then you should continue to head out to the local pub and contribute to the economy. The US CDC seems to have been slow to understand the error (see the CNN story cited above), and the White House just ran with the version of events that seemed like it would be politically most favorable, and least inconvenient economically.

The Joint Mission based the assertion that asymptomatic and presymptomatic infection is “rare” on a study in Guangdong Province. Here is Science again: “To get at this question, the report notes that so-called fever clinics in Guangdong province screened approximately 320,000 people for COVID-19 and only found 0.14% of them to be positive.” Caitlin Rivers, from Johns Hopkins, hit the nail on the head by observing that “Guangdong province was not a heavily affected area, so it is not clear whether [results from there hold] in Hubei province, which was the hardest hit.”

I am quite concerned (and, frankly, disappointed) that the WHO team took at face value that the large scale screening effort in Guangdong that found a very low “asymptomatic count” is somehow representative of anywhere else. Guangdong has a ~50X lower “case count” than Hubei, and a ~400X lower fatality rate, according to the Johns Hopkins Dashboard on 15 March — the disparity was probably even larger when the study was performed. The course of the disease was clearly quite different in Guangdong than in Hubei.

Travel restrictions and social distancing measures appear to have had a significant impact on spread from Hubei to Guangdong, and within Guangdong, which means that we can’t really know how many infected individuals were in Guangdong, or how many of those were really out in the community. A recent study computed the probability of spread from Wuhan to other cities given both population of the city and number of inbound trips from Wuhan; for Guangzhou, in Guangdong, the number of infections was anomalously low given its very large population. That is, compared with other transmission chains in China, Guangdong wound up with many fewer cases that you would expect, and the case count there is therefore not representative. Consequently, the detected infection rate in Guangdong is not a useful metric for understanding anything but Guangdong. The number relevant for epidemiological modeling is the rate of asymptomatic infection in the *absence* of control measures, because that tells us how the virus behaves without draconian social distancing, and any return to normalcy in the world will not have that sort of control measure in place.

Now, if I am being charitable, it may have been that the only large scale screening data set available to the Joint Mission at the time was from Guangdong. The team needed to publish a report, and saying something about asymptomatic transmission was critically important to telling a comprehensive story, so perhaps they went with the only data they had. But the conclusions smelled wrong to me as soon as they were announced. I wrote as much to several reporters and on Twitter, observing that the WHO report was problematic because it assumed the official case counts approximated the actual number of infections, but I couldn’t put my finger on exactly what bugged me until I could put together the rest of the story above. Nevertheless, the WHO has a lot of smart people working for it; why did the organization so quickly embrace and promulgate a narrative that was so obviously problematic to anyone who knows about epidemiology and statistics?

What went wrong at the WHO?

There are some very strong opinions out there regarding the relationship between China and the WHO, and how that relationship impacts the decisions made by Director-General Dr. Tedros Adhanom. I have not met Dr. Tedros and only know what I read about him. However, I do have personal experience with several individuals now higher up in the chain of command for the WHO coronavirus response, and I have no confidence in them whatsoever. Here is my backstory.

I have wandered around the edges of the WHO for quite a while, and have spent most of my time in Geneva at the UN proper and working with the Biological Weapons Convention Implementation Support Unit. Then, several years ago, I was asked to serve on a committee at WHO HQ. I wasn’t particularly enthusiastic about saying yes, but several current and former high ranking US officials convinced me it was for the common good. So I went. It doesn’t matter which committee at the moment. What does matter is that, when it came time to write the committee report, I found that the first draft embraced a political narrative that was entirely counter to my understanding of the relevant facts, science, and history. I lodged my objections to the draft in a long minority report that pointed out the specific ways in which the text diverged from reality. And then something interesting happened.

I received a letter informing me that my appointment to the committee had been a mistake, and that I was actually supposed to be just a technical advisor. Now, the invitation said “member”, and all the documents that I signed beforehand said “member”, with particular rights and responsibilities, including a say in the text of the report. I inquired with the various officials who had encouraged me to serve, as well as with a diplomat or two, and the unanimous opinion was that I had been retroactively demoted so that the report could be written without addressing my concerns. All of those very experienced people were quite surprised by this turn of events. In other words, someone in the WHO went to surprising lengths to try to ensure that the report reflected a particular political perspective rather than facts, history, and science. Why? I do not know what the political calculations were. But I do know this: the administrative leadership in charge of the WHO committee I served on is now high up in the chain of command for the coronavirus response.

Coda: as it turns out, the final report hewed closely to reality as I understood it, and embraced most of the points I wanted it to make. I infer, but do not know for certain, that one or more other members of the committee — who presumably could not be shunted aside so easily, and who presumably had far more political heft than I do — picked up and implemented my recommended changes. So alls well that ends well? But the episode definitely contributed to my education (and cynicism) about how the WHO balances politics and science, and I am ill disposed to trust the organization. Posting my account may mean that I am not invited to hang out at the WHO again. This is just fine.

How much bearing does my experience have on what is happening now in the WHO coronavirus response? I don’t know. You have to make up your own mind about this. But having seen the sausage being made, I am all too aware that the organization can be steered by political considerations. And that definitely increases uncertainty about what is happening on the ground. I won’t be writing or saying anything more specific about that particular episode at this time.

Uncertainty in the Time of COVID-19, Part 1

Part 1: Introduction

Times being what they are, in which challenging events abound and good information is hard to come by, I am delving back into writing about infectious disease (ID). While I’ve not been posting here about the intersection of ID, preparedness, and biosecurity, I have continued to work on these problems as a consultant for corporations, the US government, and the WHO. More on that in a bit, because my experience on the ground at the WHO definitely colors my perception of what the organization has said about events in China.

These posts will primarily be a summary of what we do, and do not, know about the current outbreak of the disease named COVID-19, and its causative agent, a coronavirus known officially as SARS-CoV-2 (for “SARS coronavirus-2”). I am interested in 1) what the ground truth is as best we can get to it in the form of data (with error bars), and I am interested in 2) claims that are made that are not supported by that data. You will have read definitive claims that COVID-19 will be no worse than a bad flu, and you will have read definitive claims that the sheer number of severe cases will overwhelm healthcare systems around the world, potentially leading to shocking numbers of fatalities. The problem with any definitive claim at this point is that we still have insufficient concrete information about the basic molecular biology of the virus and the etiology of this disease to have a good idea of what is going to happen. Our primary disadvantage right now is that uncertainty, because uncertainty necessarily complicates both our understanding of the present and our planning for the future.

Good sources of information: If you want to track raw numbers and geographical distribution, the Johns Hopkins Coronavirus COVID-19 Global Cases dashboard is a good place to start, with the caveat that “cases” here means those officially reported by national governments, which data are not necessarily representative of what is happening out in the real world. The ongoing coverage at The Atlantic about testing (here, and here, for starters) is an excellent place to read up on the shortcomings of the current US approach, as well as to develop perspective on what has happened as a result of comprehensive testing in South Korea. Our World In Data has a nice page, updated often, that provides a list of basic facts about the virus and its spread (again with a caveat about “case count”). Nextstrain is a great tool to visualize how the various mutations of SARS-CoV-2 are moving around the world, and changing as they go. That we can sequence the virus so quickly is a welcome improvement in our response, as it allows sorting out how infection is spreading from one person to another, and one country to another. This is a huge advance in human capability to deal with pathogen outbreaks. However, and unfortunately, this is still retrospective information, and means we are chasing the virus, not getting ahead of it.

How did we get here?

My 2006 post, “Nature is Full of Surprises, and We Are Totally Unprepared”, summarizes some of my early work with Bio-era on pandemic preparedness and response planning, which involved looking back at SARS and various influenza epidemics in order to understand future events. One of the immediate observations you make from even a cursory analysis of outbreaks is that pathogen surveillance in both animals and humans needs to be an ongoing priority. Bio-era concluded that humanity would continue to be surprised by zoonotic events in the absence of a concerted effort to build up global surveillance capacity. We recommended to several governments that they address this gap by aggressively rolling out sampling and sequencing of wildlife pathogens. And then not much happened to develop and real surveillance capacity until — guess what — we were surprised again by the 2009 H1N1 (aka Mexican, aka Swine) flu outbreak, which nobody saw coming because nobody was looking in the right place.

In the interval since, particularly in the wake of the “West Africa” Ebola outbreak that started in 2013, global ID surveillance has improved. The following years also saw lots of news about the rise of the Zika virus and the resurgence of Dengue, about which I am certain we have not heard the last. In the US, epidemic planning and response was finally taken seriously at the highest levels of power, and a Global Health and Security team was established within the National Security Council. That office operated until 2018, when the current White House defunded the NSC capability as well as a parallel effort at DHS (read this Foreign Policy article by Laurie Garrett for perspective: “Trump Has Sabotaged America’s Coronavirus Response”). I am unable to be adequately politic about these events just yet, even when swearing like a sailor, so I will mostly leave them aside for now. I will try to write something about US government attitudes about preparing to deal with lethal infectious diseases under separate cover; in the meantime you might get some sense of my thinking from my memorial to virologist Mark Buller.

Surprise? Again?

Outside the US government, surveillance work has continued. The EcoHealth Alliance has been on the ground in China for many years now, sequencing animal viruses, particularly from bats, in the hopes of getting a jump on the next zoonosis. I was fortunate to work with several of the founders of the EcoHealth Alliance, Drs. Peter Daszak and Billy Karesh, during my time with Bio-era. They are good blokes. Colorful, to be sure — which you sort of have to be to get out of bed with the intention of chasing viruses into bat caves and jumping out of helicopters to take blood samples from large predators. The EcoHealth programs have catalogued a great many potential zoonotic viruses over the years, including several that are close relatives of both SARS-CoV (the causative agent of SARS) and SARS-CoV-2. And then there is Ralph Baric, at UNC, who with colleagues in China has published multiple papers over the years pointing to the existence of a cluster of SARS-like viruses circulating in animals in Hubei. See, in particular, “A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence”, which called out in 2015 a worrisome group of viruses to which SARS-CoV-2 belongs. This work almost certainly could not have picked out that specific virus before it jumped to humans, because that would require substantially more field surveillance and more dedicated laboratory testing than has been possible with existing funding. But Baric and colleagues gave a clear heads up that something was brewing. And yet we were “surprised”, again. (Post publication note: For more on what has so far been learned about the origin of the virus, see this absolutely fantastic article in Scientific American that came out today: How China’s “Bat Woman” Hunted Down Viruses from SARS to the New Coronavirus, by Jane Qiu. I will come back to it in later installments of this series. It is really, really good.)

Not only were we warned, we have considerable historical experience that (wildlife consumption + coronavirus + humans) leads to zoonosis, or a disease that jumps from animals to humans. This particular virus still caught us unawares; it snuck up on us because we need to do a much better job of understanding how viruses jump from animal hosts to humans. Unless we start paying closer attention, it won’t be the last time. The pace of zoonotic events among viruses related to SARS-CoV has accelerated over the last 25 years, as I will explore in a forthcoming post. The primary reason for this acceleration, according to the wildlife veterinarians and virus hunters I talk to, is that humans continue to both encroach on natural habitats and to bring animals from those habitats home to serve for dinner. So in addition to better surveillance, humans could reduce the chance of zoonosis by eating fewer wild animals. Either way, the lesson of being surprised by SARS-CoV-2 is that we must work much harder to stay ahead of nature.

Why is the US, in particular, so unprepared to deal with this virus?

The US government has a long history of giving biological threats and health security inadequate respect. Yes, there have always been individuals and small groups inside various agencies and departments who worked hard to increase our preparedness and response efforts. But people at the top have never fully grasped what is at stake and what needs to be done.

Particularly alarming, we have recently experienced a unilateral disarming in the face of known and obvious threats. See the Laurie Garrett article cited above for details. As reported by The New York Times,

“Mr. Trump had no explanation for why his White House shut down the Directorate for Global Health Security and Biodefense established at the National Security Council in 2016 by President Barack Obama after the 2014 Ebola outbreak.”

Yet this is more complicated than is apparent or is described in the reporting, as I commented on Twitter earlier this week. National security policy in the US has been dominated for many decades by people who grew up intellectually in the Cold War, or were taught by people who fought the Cold War. Cold War security was about nation states and, most importantly, nuclear weapons. When the Iron Curtain fell, the concern about large nations (i.e., the USSR) slipped away for a while, eventually to be replaced by small states, terrorism, and WMDs. But WMD policy, which in principle includes chemical and biological threats, has continued to be dominated by the nuclear security crowd. The argument is always that nuclear (and radiological) weapons are more of a threat and can cause more damage than a mere microbe, whether natural or artificial. And then there is the spending associated with countering the more kinetic threats: the big, shiny, splody objects get all the attention. So biosecurity and pandemic preparedness and response, which often are lumped together as "health security", get short shrift because the people setting priorities have other priorities. This has been a problem for both Democrat and Republican administrations, and demonstrates a history of bipartisan blindness.

Then, after decades of effort, and an increasing number of emergent microbial/health threats, finally a position and office were created within the National Security Council. While far from a panacea, because the USG needs to do much more than have policy in place, this was progress.

And then a new Administration came in, which not only has different overall security priorities but also is dominated by old school security people who are focussed on the intersection of a small number of nation states and nuclear weapons. John Bolton, in particular, is a hardline neocon whose intellectual roots are in Cold War security policy; so he is focussed on nukes. His ascendence at the NSC was coincident not just with the NSC preparedness office being shut down, but also a parallel DHS office responsible for implementing policy. And then, beyond the specific mania driving a focus on nation states and nukes as the primary threats to US national security, there is the oft reported war on expertise in the current exec branch and EOP. Add it all up: The USG is now severely understaffed for the current crisis.

Even the knowledgeable professionals still serving in the government have been hamstrung by bad policy in their ability to organize a response. To be blunt: patients are dying because the FDA & CDC could not get out of the way or — imagine it — help in accelerating the availability of testing at a critical time in a crisis. There will be a reckoning. And then public health in the US will need to be rebuilt, and earn trust again. There is a long road ahead. But first we have to deal with SARS-CoV-2.

Who is this beastie, SARS-CoV-2?

Just to get the introductions out of the way, the new virus is classified within order Nidovirales, family Coronaviridae, subfamily Orthocoronaviridae. You may also see it referred to as a betacoronavirus. To give you some sense of the diversity of coronaviruses, here is a nice, clean visual representation of their phylogenetic relationships. It contains names of many familiar human pathogens. If you are wondering why we don’t have a better understanding of this family of viruses given their obvious importance to human health and to economic and physical security, good for you — you should wonder about this. For the cost of a single marginally functional F-35, let alone a white elephant new aircraft carrier, we could fund viral surveillance and basic molecular biology for all of these pathogens for years.

The diversity of pathogenic coronaviruses. Source: Xyzology.

The diversity of pathogenic coronaviruses. Source: Xyzology.

Betacoronaviruses (BCVs) are RNA viruses that are surrounded by a lipid membrane. The membrane is damaged by soap and by ethyl or isopropyl alcohol; without the membrane the virus falls apart. BCVs differ from influenza viruses in both their genome structure and in the way they evolve. Influenza viruses have segmented genomes — the genes are, in effect, organized into chromosomes — and the virus can evolve either through swapping chromosomes with other flu strains or through mutations that happen when the viral polymerase, which copies RNA, makes a mistake. The influenza polymerase makes lots of mistakes, which means that many different sequences are produced during replication. This is a primary driver of the evolution of influenza viruses, and largely explains why new flu strains show up every year. While the core of the copying machinery in Betacoronaviruses is similar to that of influenza viruses, it also contains an additional component called Nsp-14 that corrects copying mistakes. Disable or remove Nsp-14 and you get influenza-like mutation rates in Betacoronaviruses. (For some reason I find that observation particularly fascinating, though I can’t really explain why.)

There is another important feature of the BCV polymerase in that it facilitates recombination between RNA strands that happen to be floating around nearby. This means that if a host cell happens to be infected with more than one BCV strain at the same time, you can get a relatively high rate of new genomes being assembled out of all the parts floating around. This is one reason why BCV genome sequences can look like they are pasted together from strains that infect different species — they are often assembled exactly that way at the molecular level.

Before digging into the uncertainties around this virus and what is happening in the world, we need to understand how it is detected and diagnosed. There are three primary means of diagnosis. The first is by display of symptoms, which can span a long list of cold-like runny nose, fever, sore throat, upper respiratory features, to much less pleasant, and in some cases deadly, lower respiratory impairment. (I recently heard an expert on the virus say that there are two primary ways that SARS-like viruses can kill you: “Either your lungs fill up with fluid, limiting your access to oxygen, and you drown, or all the epithelial cells in your lungs slough off, limiting your access to oxygen, and you suffocate.” Secondary infections are also more lethal for people experiencing COVID-19 symptoms.) The second method of diagnosis is imaging of lungs, which includes x-ray and CT scans; SARS-CoV-2 causes particular pathologies in the lungs that can be identified on images and that distinguish it from other respiratory viruses. Finally, the virus can be diagnosed via two molecular assays, the first of which uses antibodies to directly look for viral proteins in tissue or fluid samples, while the other looks for whether genetic material is present; sophisticated versions can quantify how many copies of viral RNA are present in a sample.

Each of these diagnostic methods is usually described as being “accurate” or “sensitive” to some degree, when instead they should be described as having some error rate, a rate than might be dependent on when or where the method was applied, or might vary with who was applying it. And every time you read how “accurate” or “sensitive” a method is, you should ask: compared to what? And this is where we get into uncertainty.

Part 2 of this series will dig into specific sources of uncertainty spanning measurement and diagnosis to recommendations.

A memorial to Mark Buller, PhD, and our response to the propaganda film "Demon in the Freezer".

Earlier this year my friend and colleague Mark Buller passed away. Mark was a noted virologist and a professor at Saint Louis University. He was struck by a car while riding his bicycle home from the lab, and died from his injuries. Here is Mark's obituary as published by the university.

In 2014 and 2015, Mark and I served as advisors to a WHO scientific working group on synthetic biology and the variola virus (the causative agent of smallpox). In 2016, we wrote the following, previously un-published, response to an "Op-Doc" that appeared in the New York Times. In a forthcoming post I will have more to say about both my experience with the WHO and my thoughts on the recent publication of a synthetic horsepox genome. For now, here is the last version (circa May, 2016) of the response Mark I and wrote to the Op-Doc, published here as my own memorial to Professor Buller.


Variola virus is still needed for the development of smallpox medical countermeasures

On May 17, 2016 Errol Morris presented a short movie entitled “Demon in the Freezer” [note: quite different from the book of the same name by Richard Preston] in the Op-Docs section of the on-line New York Times. The piece purported to present both sides of the long-standing argument over what to do with the remaining laboratory stocks of variola virus, the causative agent of smallpox, which no longer circulates in the human population.

Since 1999, the World Health Organization has on numerous occasions postponed the final destruction of the two variola virus research stocks in Russia and the US in order to support public health related research, including the development of smallpox molecular diagnostics, antivirals, and vaccines.  

“Demon in the Freezer” clearly advocates for destroying the virus. The Op-Doc impugns the motivation of scientists carrying out smallpox research by asking: “If given a free hand, what might they unleash?” The narrative even suggests that some in the US government would like to pursue a nefarious policy goal of “mutually assured destruction with germs”. This portion of the movie is interlaced with irrelevant, hyperbolic images of mushroom clouds. The reality is that in 1969 the US unilaterally renounced the production, storage or use biological weapons for any reason whatsoever, including in response to a biologic attack from another country. The same cannot be said for ISIS and Al-Qaeda. In 1975 the US ratified the 1925 Geneva Protocol banning chemical and biological agents in warfare and became party to the Biological Weapons Convention that emphatically prohibits the use of biological weapons in warfare.

“Demon in the Freezer” is constructed with undeniable flair, but in the end it is a benighted 21st century video incarnation of a middling 1930's political propaganda mural. It was painted with only black and white pigments, rather than a meaningful palette of colors, and using a brush so broad that it blurred any useful detail. Ultimately, and to its discredit, the piece sought to create fear and outrage based on unsubstantiated accusations.

Maintaining live smallpox virus is necessary for ongoing development and improvement of medical countermeasures. The first-generation US smallpox vaccine was produced in domesticated animals, while the second-generation smallpox vaccine was manufactured in sterile bioreactors; both have the potential to cause serious side effects in 10-20% of the population. The third generation smallpox vaccine has an improved safety profile, and causes minimal side effects. Fourth generation vaccine candidates, based on newer, lower cost, technology, will be even safer and some are in preclinical testing. There remains a need to develop rapid field diagnostics and an additional antiviral therapy for smallpox.

Continued vigilance is necessary because it is widely assumed that numerous undeclared stocks of variola virus exist around the world in clandestine laboratories. Moreover, unsecured variola virus stocks are encountered occasionally in strain collections left behind by long-retired researchers, as demonstrated in 2014 with the discovery of 1950s vintage variola virus in a cold room at the NIH. The certain existence of unofficial stocks makes destroying the official stocks an exercise in declaring “victory” merely for political purposes rather than a substantive step towards increasing security. Unfortunately, the threat does not end with undeclared or forgotten samples.

In 2015 a WHO Scientific Working Group on Synthetic Biology and Variola Virus and Smallpox determined that a “skilled laboratory technician or undergraduate student with experience of working with viruses” would be able to generate variola virus from the widely available genomic sequence in “as little as three months”. Importantly, this Working Group concluded that “there will always be the potential to recreate variola virus and therefore the risk of smallpox happening again can never be eradicated.” Thus, the goal of a variola virus-free future, however laudable, is unattainable. This is sobering guidance on a topic that requires sober consideration.

We welcome increased discussions of the risk of infectious disease and of public health preparedness. In the US these topics have too long languished among second (or third) tier national security conversations. The 2014 West Africa Ebola outbreak and the current Congressional debate over funding to counter the Zika virus exemplifies the business-as-usual political approach of throwing half a bucket of water on the nearest burning bush while the surrounding countryside goes up in flames. Lethal infectious diseases are serious public health and global security issues and they deserve serious attention.

The variola virus has killed more humans numerically than any other single cause in history. This pathogen was produced by nature, and it would be the height of arrogance, and very foolish indeed, to assume nothing like it will ever again emerge from the bush to threaten human life and human civilization. Maintenance of variola virus stocks is needed for continued improvement of molecular diagnostics, antivirals, and vaccines. Under no circumstances should we unilaterally cripple those efforts in the face of the most deadly infectious disease ever to plague humans. This is an easy mistake to avoid.

Mark Buller, PhD, was a Professor of Molecular Microbiology & Immunology at Saint Louis University School of Medicine, who passed away on February 24, 2017. Rob Carlson, PhD, is a Principal at the engineering and strategy firm Biodesic and a Managing Director of Bioeconomy Capital.

The authors served as scientific and technical advisors to the 2015 WHO Scientific Working Group on Synthetic Biology and Variola Virus.

Harry Potter and The Future of Nature

How will Synthetic Biology and Conservation Shape the Future of Nature?  Last month I was privileged to take part in a meeting organized by The Wildlife Conservation Society to consider that question.  Here is the framing paper (PDF), of which I am a co-author.  There will be a follow-up paper in the coming months.  I am still mulling over what I think happened during the meeting, and below are a few observations that I have managed to settle on so far.  Others have written their own accounts.  Here is a summary from Julie Gould, riffing on an offer that Paul Freemont made to conservation biologists at the close of the meeting, "The Open Door".  Ed Gillespie has a lovely, must-read take on Pandora's Box, cane toads, and Emily Dickenson, "Hope is the thing with feathers".  Cristian Samper, the new head of the Wildlife Conservation Society was ultimately quite enthusiastic: Jim Thomas of ETC, unsurprisingly, not so much.

The meeting venue was movie set-like Cambridge.  My journey took me through King's Cross, with its requisite mock-up of a luggage trolley passing through the wall at platform nine and three-quarters.  So I am tempted to style parts of the meeting as a confrontation between a boyish protagonist trying to save the world and He Who Must Not Be Named.  But my experience at the meeting was that not everyone was able to laugh at a little tension-relieving humor, or even to recognize that humor.  Thus the title of this post is as much as I will give in temptation.

How Can SB and CB Collaborate?

I'll start with an opportunity that emerged during the week, exactly the sort of thing you hope would come from introducing two disciplines to each other.  What if synthetic biology could be used as a tool to aid in conservation efforts, say to buttress biodiversity against threats?  If the ongoing, astonishing loss of species were an insufficient motivation to think about this possibility, now some species that humans explicitly rely upon economically are under threat.    Synthetic biology might - might! - be able to offer help in the form of engineering species to be more robust in the face of a changing environment, such as enabling corals to cope with increases in water temperature and acidity, or it perhaps via intervening in a host-prey relationship, such as that between bats and white-nose disease or between bees and their mites and viruses.

The first thing to say here is that if the plight of various species can be improved through changes in human behavior then we should by all means work toward that end.  The simpler solution is usually the better solution.  For example, it might be a good idea to stop using those pesticides and antibiotics that appear to create more problems than they solve when introduced into the environment.  Moreover, at the level of the environment and the economy, technological fixes are probably best reserved until we try changes in human behavior.  After all, we've mucked up such fixes quite a few times already.  (All together now: "Cane Toad Blues".)  But what if the damage is too far along and cannot be addressed by changes in behavior?  We should at least consider the possibility that a technological fix might be worth a go, if for no other reason that to figure out how to create a back up plan.  Given the time scales involved in manipulating complex organisms, exploring the option of a back-up plan means getting started early.  It also means thoughtfully considering which interventions would be most appropriate and urgent, where part of the evaluation should probably involve asking whether changes in human behavior are likely to have any effect.  In some cases, a technical solution is likely to be our only chance.

First up: corals. We heard from Stanford's Steve Palumbi on work to understand the effects of climate change on corals in the South Pacific.  Temperature and acidity - two parameters already set on long term changes - are already affecting coral health around the globe.  But it turns out that in the lab some corals can handle remarkably difficult environmental conditions.  What if we could isolate the relevant genetic circuits and, if necessary, transplant them into other species, or turn them on if they are already widespread?  My understanding of Professor Palumbi's talk is that it is not yet clear why some corals have the pathway turned on and some do not.  So, first up, a bunch of genetics, molecular biology, and field biology to figure out why the corals do what they do.  After that, if necessary, it seems that it would be worth exploring whether other coral species can be modified to use the relevant pathways.  Corals are immensely important for the health of both natural ecosystems and human economies; we should have a back-up plan, and synthetic biology could certainly contribute.

Next up: bats. Bats are unsung partners of human agriculture, and they contribute an estimated $23 billion annually to U.S. farmers by eating insects and pollinating various plants.  Here is nice summary article from The Atlantic by Stephanie Gruner Buckely on the impact upon North American bats of white nose syndrome.  The syndrome, caused by a fungus evidently imported from Europe, has already killed so many bats that we may see an impact on agriculture as soon as this year.  European bats are resistant to the fungus, so one option would be to try to introduce the appropriate genes into North American bats via standard breeding.  However, bats breed very slowly, usually only having one pup a year, and only 5 or so pups in a lifetime.  Given the mortality rate due to white nose syndrome, this suggests breeding is probably too slow to be useful in conservation efforts.  What if synthetic biology could be used to intervene in some way, either to directly attack the non-native fungus or to interfere with its attack on bats.  Obviously this would be a hard problem to take on, but both biodiversity and human welfare would be improved by making progress here.

And now: bees. If you eat, you rely on honeybees.  Due to a variety of causes, bee populations have fallen to the point where food crops are in jeopardy.  Entomologist Dennis vanEngelstorp, quoted in Wired, warns "We're getting closer and closer to the point where we don't have enough bees in this country to meet pollination demands.  If we want to grow fruits and nuts and berries, this is important.  One in every three bites [of food consumed in the U.S.] is directly or indirectly pollinated by bees."  Have a look at the Wired article for a summary of the constellation of causes of Colony Collapse Disorder, or CCD -- they are multifold and interlocking.  Obviously, the first thing to do is to stop making the problem worse; Europe has banned a class of pesticide that is exceptionally hard on honeybees, though the various sides in this debate continue to argue about whether that will make any difference.  This change in human behavior may have some impact, but most experts agree we need to do more.  Efforts are underway to breed bees that are resistant to both pesticides and to particular mites that prey on bees and that transmit viruses between bees.  Applying synthetic biology here might be the hardest task of all, given the complexity of the problem.  Should synthetic biologists focus on boosting apian immune systems?  Should they focus on the mite?  Apian viruses?  It sounds very difficult.  But with such a large fraction of our food supply dependent upon healthy bees, it also seems pretty clear that we should be working on all fronts to sort out potential solutions.

A Bit of Good News

Finally, a problem synthetic biologists are already working to solve: malaria.  The meeting was fortunate to hear directly from Jay Keasling.  Keasling presented progress on a variety of fronts, but the most striking was his announcement that Sanofi-Aventis has produced substantially more artemisinin this year than planned, marking real progress in producing the best malaria drug extant using synthetic biology rather than by purifying it from plants.  Moreover, he announced that Sanofi and OneWorldHealth are likely to take over the entire world production of artemisinin.  The original funding deal between The Gates Foundation, OneWorldHealth, Amyris, and Sanofi required selling at cost.  The collaboration has worked very hard at bringing the price down, and now it appears that they can simply outcompete the for-profit pricing monopoly.

The stated goal of this effort is to reduce the cost of malaria drugs and provide inexpensive cures to the many millions of people who suffer from malaria annually.  Currently, the global supply fluctuates, as, consequently, do prices, which are often well above what those afflicted can pay.  A stable, high volume source of the drug would reduce prices and also reduce the ability of middle-men to sell doctored, diluted, or mis-formulated artemisinin, all of which are contributing to a rise of resistant pathogens.

There is a potential downside to this project.  If Sanofi and OneWorldHealth do corner the market on artemisinin, then farmers who currently grow artemisia will no longer have that option, at least for supplying the artemisinin market.  That might be a bad thing, so we should at least ask the question of whether the world is a better place with artemisinin production done in vats or derived from plants.  This question can be broken into two pieces: 1) what is best for the farmers? and 2) what is best for malaria sufferers?  It turns out these questions have the same answer.

There is no question that people who suffer from malaria will be better off with artemisinin produced in yeast by Sanofi.  Malaria is a debilitating disease that causes pain, potentially death, and economic hardship.  The best estimates are that countries in which malaria is endemic suffer a hit to GDP growth of 1.3% annually compared to non-malarious countries.  Over just a few years this yearly penalty swamps all the foreign aid those countries receive; I've previously argued that eliminating malaria would be the biggest humanitarian achievement in history and would make the world a much safer place.  Farmers in malarious countries are the worst hit, because the disease prevents them from getting into the fields to work.  I clashed in public over this with Jim Thomas around our respective testimonies in front of the Presidential Bioethics Commission a couple of years ago.  Quoting myself briefly from the relevant blog post,

The human cost of not producing inexpensive artemisinin in vats is astronomical.  If reducing the burden of malaria around the world on almost 2 billion people might harm "a few thousand" farmers, then we should make sure those farmers can make a living growing some other crop.  We can solve both problems.  ...Just one year of 1.3% GDP growth recovered by reducing (eliminating?) the impact of malaria would more than offset paying wormwood farmers to grow something else.  There is really no argument to do anything else.

For a bit more background on artemisinin supply and pricing, and upon the apparent cartel in control of pricing both the drug and the crop, see this piece in Nature last month by Mark Peplow.  I was surprised to learn that that the price of artemisia is set by a small group that controls production of the drug.  This group, unsurprisingly, is unhappy that they may lose control of the market for artemisinin to a non-profit coalition whose goal is to eliminate the disease.  Have a look at the chart titled "The Cost of Progress", which reveals substantial price fluctuations, to which I will return below.

Mr. Thomas responded to Keasling's announcement in Cambridge with a broadside in the Guardian UK against Keasling and synthetic biology more generally.  Mr. Thomas is always quick to shout "What about the farmers?"  Yet he is rather less apt to offer actual analysis of what farmers actually gain, or lose, by planting artemisia.

The core of the problem for farmers is in that chart from Nature, which shows that artemisinin has fluctuated in price by a factor of 3 over the last decade.  Those fluctuations are bad for both farmers and malaria sufferers; farmers have a hard time knowing whether it makes economic sense to plant artemisia, which subsequently means shortages if farmers don't plant enough.  Shortages mean price spikes, which causes more farmers to plant, which results in oversupply, which causes the price to plunge, etc.  You'll notice that Mr. Thomas asserts that farmers know best, but he never himself descends to the level of looking at actual numbers, and whether farmers benefit by growing artemisia.  The numbers are quite revealing.

Eyeballing "The Cost of Progress" chart, it looks like artemisia has been below the $400/kg level for about half the last 10 years.  To be honest, there isn't enough data on the chart to make firm conclusions, but it does look like the most stable price level is around $350/kg, with rapid and large price spikes up to about $1000/kg.  Farmers who time their planting right will probably do well; those who are less lucky will make much less on the crop.  So it goes with all farming, unfortunately, as I am sure Mr. Thomas would agree.

During his talk, Keasling put up a chart I hadn't seen before, which showed predicted farmer revenues for a variety of crops.  The chart is below; it makes clear that farmers will have substantially higher revenues planting crops other than artemisia at prices at or below $400/kg. 

The Strange Arguments Against Microbial Production of Malaria Drugs

Mr. Thomas' response in the Guardian to rational arguments and actual data was a glib accusation that Keasling is dismissing the welfare of farmers with "Let them plant potatoes".  This is actually quite clever and witty, but not funny in the slightest when you look at the numbers.  Thomas worries that farmers in African and Asia will suffer unduly from a shift away from artemisia to yeast.  But here is the problem: those farmers are already suffering -- from malaria.  Digging deeper, it becomes clear that Mr. Thomas is bafflingly joining the pricing cartel in arguing against the farmers' best interests.

A brief examination of the latest world malaria map shows that the most intense malaria hot spots are in Africa and Asia, with South America not far behind (here is the interactive CDC version).  Artemisia is primarily grown in Africa and Asia.  That is, farmers most at risk of contracting malaria only benefit economically when there is a shortage of artemisinin, the risk of which is maintained by leaving artemisia production in the hands of farmers. Planting sufficient quantities of artemisia to meet demand means prices that are not economically viable for the farmer.  There are some time lags here due to growing and processing the crop into the drug, but the upshot is that the only way farmers make more money planting artemisia than other crops is when there is a shortage.  This is a deadly paradox, and its existence has only one beneficiary: the artemisinin pricing cartel.  But we can now eliminate the paradox.  It is imperative for us to do so.

Once you look at the numbers there is no argument Mr. Thomas, or anyone else, can make that we should do anything but brew artemisinin in vats and bring the price as low as possible.

I had previously made the macro-scale economic arguments about humanitarian impacts economic growth.  Malarious countries, and all the farmers in them, would benefit tremendously by a 1.3% annual increase in GDP.  But I only realized while writing this post that the micro-scale argument gives the same answer: the farmers most at risk from malaria only make money growing artemisia when there is a shortage of the drug, which is when they are most likely to be affected by the disease.

I get along quite well in person with Mr. Thomas, but I have long been baffled by his arguments about artemisinin.  I heartily support his aims of protecting the rights of farmers and taking care of the land.  We should strive to do the right thing, except when analysis reveals it to be the wrong thing.  Since I only just understood the inverse relationship between artemisinin pricing and the availability of the drug to the very farmers growing artemisia, I am certain Mr. Thomas has not had the opportunity to consider the facts and think through the problem so that he might come to the same conclusion.  I invite him to do so.

Censoring Science is Detrimental to Security

Restricting access toscience and technology in the name of security is historically a losing proposition.  Censorship of information that is known to exist incentivizes innovation and rediscovery. 

As most readers of this blog know, there has been quite a furor over new results demonstrating mutations in H5N1 influenza strains that are both deadly and highly contagious in mammals.  Two groups, led by Ron Fouchier in the The Netherlands and Yoshihiro Kawaoka at The University of Wisconsin, have submitted papers to Nature and Science describing the results.  The National Science Advisory Board for Biosecurity (NSABB) has requested that some details, such as sequence information, be omitted from publication.  According to Nature, both journals are "reserving judgement about whether to censor the papers until the US government provides details of how it will allow genuine researchers to obtain redacted information".

For those looking to find more details about what happened, I suggest starting with Dorveen Caraval's interview with Fouchier in the New York Times, "Security in Flu Study Was Paramount, Scientist Says"; Kathleen Harmon's firsthand account of what actually happened when the study was announced; and Heidi Ledford's post at Nature News about the NSABB's concerns.

If you want to go further, there is more good commentary, especially the conversation in the comments (including from a member of the NSABB), in "A bad day for science" by Vincent Racaniello.  See also Michael Eisen's post "Stop the presses! H5N1 Frankenflu is going to kill us all!", keeping in mind that Eisen used to work on the flu.

Writing at Foreign Policy, Laurie Garrett has done some nice reporting on these events in two posts, "The Bioterrorist Next Door" and "Flu Season".  She suggests that attempts to censor the results would be futile: "The genie is out of the bottle: Eager graduate students in virology departments from Boston to Bangkok have convened journal-review debates reckoning exactly how these viral Frankenstein efforts were carried out."

There is much I agree with in Ms. Garrett's posts.  However, I must object to her assertion that the work done by Fouchier and Kawaoka can be repeated easily using the tools of synthetic biology.  She writes "The Fouchier episode laid bare the emptiness of biological-weapons prevention programs on the global, national, and local levels.  Along with several older studies that are now garnering fresh attention, it has revealed that the political world is completely unprepared for the synthetic-biology revolution."   As I have already written a book that discusses this confusion (here is an excerpt about synthetic biology and the influenza virus), it is not actually what I want to write about today.  But I have to get this issue out of the way first.

As far as I understand from reading the press accounts, both groups used various means to create mutations in the flu genome and then selected viruses with properties they wanted to study.  To clarify, from what I have been able to glean from the sparse accounts thus far, DNA synthesis was not used in the work.  And as far as I understand from reading the literature and talking to people who build viruses for a living, it is still very hard to assemble a functioning, infectious influenza virus from scratch.   

If it were easy to write pathogen genomes -- particularly flu genomes -- from scratch, we would quite frankly be in deep shit. But, for the time being, it is hard.  And that is important.  Labs who do use synthetic biology to build influenza viruses, as with those who reconstructed the 1918 H1N1 influenza virus, fail most of the time despite great skill and funding.  Synthesizing flu viruses is simply not a garage activity.  And with that, I'll move on.

Regardless of how the results might be reproduced, many have suggested that the particular experiments described by Fouchier and Kawaoka should not have been allowed.  Fouchier himself acknowledges that selecting for airborne viruses was not the wisest experiment he could have done; it was, he says, "really, really stupid".  But the work is done, and people do know about it.  So the question of whether this work should have been done in the first place is beside the point.  If, as suggested by Michael Eisen, that "any decent molecular biologist" could repeat the work, then it was too late to censor the details as soon as the initial report came out. 

I am more interested in the consequences of trying to contain the results while somehow allowing access to vetted individuals.  Containing the results is as much about information security as it is biological security.  Once such information is created, the challenge is to protect it, to secure it.  Unfortunately, the proposal to allow secure access only by particular individuals is at least a decade (if not three decades) out of date.

Any attempt to secure the data would have to start with an assessment of how widely it is already distributed.  I have yet to meet an academic who regularly encrypts email, and my suspicion is that few avail themselves of the built-in encryption on their laptops.  So, in addition to the university computers and email servers where the science originated, the information is sitting in the computers of reviewers, on servers at Nature and Science, at the NSABB, and, depending on how the papers were distributed and discussed by members of the NSABB, possibly on their various email servers and individual computers as well.  And let's not forget the various unencrypted phones and tablets all of those reviewers now carry around.

But never mind that for a moment.  Let's assume that all these repositories of the relevant data are actually secure.  The next step is to arrange access for selected researchers.  That access would inevitably be electronic, requiring secure networks, passwords, etc.  In the last few days the news has brought word that computer security firms Stratfor and Symantec have evidently been hacked recently.  Such attacks are not uncommon.  Think back over the last couple of years: hacks at Google, various government agencies, universities.  Credit card numbers, identities, and supposedly secret DoD documents are all for sale on the web.  To that valuable information we can now add a certain list of influenza mutations.  If those mutations are truly a critical biosecurity risk -- as asserted publicly by various members of the NSABB -- then that data has value far beyond its utility in virology and vaccinology.

The behavior of various hackers (governments, individuals, other) over the last few years make clear that what the discussion thus far has done is to stick a giant "HACK HERE" sign on the data.  Moreover, if Ms. Garrett is correct that students across the planet are busy reverse engineering the experiments because they don't have access to the original methods and data, then censorship is creating a perverse incentive for innovation.  Given today's widespread communication, restriction of access to data is an invitation, not a proscription.

This same fate awaits any concentration of valuable data.  It obviously isn't a problem limited to collections of sensitive genetic sequences or laboratory methods.  And there is certainly a case to be made for attempting to maintain confidential or secret caches of data, whether in the public or private interest.  In such instances, compartmentalization and encryption must be implemented at the earliest stages of communication in order to have any hope of maintaining security. 

However, in this case, if it true that reverse engineering the results is straightforward, then restriction of access serves only to slow down the general process of science.  Moreover, censorship will slow the development of countermeasures.  It is unlikely that any collection of scientists identified by the NSABB or the government will be sufficient to develop all the technology we need to respond to natural pathogens, let alone any artificial ones.

As with most other examples of prohibition, these restrictions are doomed before they are even implemented.  Censorship of information that is known to exist incentivizes innovation and rediscovery.  As I explored in my book, prohibition in the name of security is historically a losing proposition.  Moreover, science is inherently a networked human activity that is fundamentally incompatible with constraints on communication, particularly of results that are already disclosed.  Any endeavor that relies upon science is, therefore, also fundamentally incompatible with constraints on communication.  Namely developing technologies to defend against natural and artificial pathogens.  Censorship threatens not just science but also our security.

"National Strategy for Countering Biological Threats"

I recently had cause to re-read the National Strategy for Countering Biological Threats (Full PDF), released last fall by the National Security Council and signed by the President. I think there is a lot to like, and it demonstrates a welcome change in the mindset I encounter in Washington DC.

When the document came out, there was just a little bit of coverage in the press. Notably, Wired's Threat Level, which usually does a commendable job on security issues, gave the document a haphazard swipe, asserting that "Obama's Biodefense Strategy is a Lot Like Bush's".  As described in that post, various commentators were unhappy with the language that Under Secretary of State Ellen Tauscher used when announcing the Strategy at a BWC meeting in Geneva. According to Threat Level, "Sources tell this reporter that the National Security Council had some Bush administration holdovers in charge of editing the National Strategy and preparing Ms. Tauscher's script, and these individuals basically bulldozed the final draft through Defense and State officials with very little interagency input and with a very short suspense." Threat Level also asserts that "Most are disappointed in the language, which doesn't appear to be significantly different than the previous administration." It is unclear who "Most" are.

In contrast to all of this, in my view the Strategy is a clear departure from the muddled thinking that dominated earlier discussions. By muddled, I mean security discussions and policy that, paraphrasing just a little, went like this: "Biology Bad! Hacking Bad! Must Contain!" 

The new National Strategy document takes a very different line. Sources tell this reporter, if you will, that the document resulted from a careful review that involved multiple agencies, over many months, with an aim to develop the future biosecurity strategy of the United States in a realistic context of rapidly spreading infectious diseases and international technological proliferation driven by economic and technical needs. To wit, here are the first two paragraphs from the first page (emphasis added, of course):

We are experiencing an unparalleled period of advancement and innovation in the life sciences globally that continues to transform our way of life. Whether augmenting our ability to provide health care and protect the environment, or expanding our capacity for energy and agricultural production towards global sustainability, continued research and development in the life sciences is essential to a brighter future for all people.

The beneficial nature of life science research is reflected in the widespread manner in which it occurs. From cutting-edge academic institutes, to industrial research centers, to private laboratories in base­ments and garages, progress is increasingly driven by innovation and open access to the insights and materials needed to advance individual initiatives.

Recall that this document carries the signature of the President of the United States.  I'll pause to let that sink in for a moment.

And now to drive home the point: the new Strategy for Countering Biological Threats explicitly points to garage biotech innovation and open access as crucial components of our physical and economic security. I will note that this is a definite change in perspective, and one that has not fully permeated all levels of the Federal bureaucracy and contractor-aucracy. Recently, during a conversation about locked doors, buddy systems, security cameras, and armed guards, I found myself reminding a room full of biosecurity professionals of the phrase emphasized above. I also found myself reminding them -- with sincere apologies to all who might take offense -- that not all the brains, not all the money, and not all the ideas in the United States are found within Beltway. Fortunately, the assembled great minds took this as intended and some laughter ensued, because they realized this was the point of including garage labs in the National Strategy, even if not everyone is comfortable with it. And there are definitely very influential people who are not comfortable with it. But, hey, the President signed it (forgive me, did I mention that part already?), so everyone is on board, right?

Anyway, I think the new National Strategy is a big step forward in that it also acknowledges that improving public health infrastructure and countering infectious diseases are explicitly part of countering artificial threats. Additionally, the Strategy is clear on the need to establish networks that both promulgate behavioral norms and that help disseminate information. And the new document clearly recognizes that these are international challenges (p.3):

Our Strategy is targeted to reduce biological threats by: (1) improving global access to the life sciences to combat infectious disease regardless of its cause; (2) establishing and reinforcing norms against the misuse of the life sciences; and (3) instituting a suite of coordinated activities that collectively will help influence, identify, inhibit, and/or interdict those who seek to misuse the life sciences.

...This Strategy reflects the fact that the challenges presented by biological threats cannot be addressed by the Federal Government alone, and that planning and participation must include the full range of domestic and international partners.

Norms, open biology, better technology, better public health infrastructure, and better intelligence: all are themes I have been pushing for a decade now. So, 'nuff said on those points, I suppose.

Implementation is, of course, another matter entirely. The Strategy leaves much up to federal, state, and local agencies, not all of whom have the funding, expertise, or inclination to follow along. I don't have much to say about that part of the Strategy, for now. But I am definitely not disappointed with the rest of it. It is, you might say, the least bad thing I have read out of DC in a long time.

H1N1 is a "rotten pot", plus the beginnings of vaccine plans

A ProMED mail from yesterday (Archive Number 20090430.1636) has some interesting tidbits.

First, following up on the confusion over the genetic origins of "H1N1 Influenza A", the group at Columbia states:

Preliminary analysis of the genome of the new H1N1 influenza A virus responsible for the current pandemic indicates that all genetic segments are related closest to those of common swine influenza viruses.

...Six segments of the virus are related to swine viruses from North America and the
other 2 (NA and M) from swine viruses isolated in Europe/Asia.

The North American ancestors are related to the multiple reassortants, H1N2 and H3N2 swine viruses isolated in North America since 1998 [2,3]. In particular, the swine H3N2 isolates from 1998 were a triple reassortment of human, swine and avian origin.

Therefore, this preliminary analysis suggests at least 2 swine ancestors to the current H1N1, one of them related to the triple reassortant viruses isolated in North America in 1998.

So, it's composed of all recent pig viruses, but displays some inheritance from human and avian strains from a decade ago.  It's a flu potpourri!  And here I intend the original French meaning of the word potpourri -- "rotten pot".

On the vaccine front, there is a mix of efforts.  It is unclear when a traditional vaccine might show up.  However, the ProMED mail does contain an excerpt of a Scientific American story that suggests Novavax is already working on a VLP synthetic vaccine, possibly confirming my earlier speculation.

On Pandemic Preparendness, Surveillance, and Surprise

After working with Bio-era for several years on pandemic preparedness, pathogen surveillance, and synthetic vaccines, a few things jumped out at me from ScienceInsider's interview with CDC Virologist Ruben Donis.

As part of the discussion on the origin of the present "H1N1 Influenza A", as we are now supposed to call it, Donis notes that "The amazing thing is the hemagglutinins we are seeing in this strain are a lonely branch that have been evolving somewhere and we didn't know about it."

Translation: Despite the increased surveillance since 2005, a key set of genes that are important components of the present virus(es) appeared out of nowhere, or, rather, appeared out of somewhere that the surveillance does not reach.  Must fix.  Immediately.

With respect to vaccine development, Donis suggests that "The virus doesn't grow very well in eggs. We hope the virus will improve [the] ability to grow in eggs so we can produce [a] vaccine very quickly so these secondary and tertiary cases can be controlled."  It is unclear at this point in the interview whether he is referring specifically to "H1N1 Influenza A", or to a larger group of viruses, or something else.  Assuming he means the present (almost pandemic) strain, it is interesting that somebody at CDC already knows the bug doesn't grow well in eggs.  It is also unclear what he means by "we hope the virus will improve [the] ablity to grow in eggs" -- perhaps he is referring to an effort to produce a vaccine via reverse genetics for production in eggs.  Either way, it suggests we may have to rely on newer technologies to produce vaccines (see my earlier posts on synthetic vaccines).

I have heard rumors that DARPA has a program up and running to turn out several million doses of synthetic vaccines (VLPs, primarily) in six weeks.  Here's hoping those are more than rumors.

The interview with Donis ends on a rather somber note:  Even though the flu season is ending in North America and Europe, we can't forget the rest of the planet: "The folks in Buenos Aires are in trouble. They're entering winter now."

This is a long, long way from being over.

More on the genetics of the H1N1 virus

Effect Measure has a nice post on the origin of genes in the present H1N1 strain making the rounds, and it adds some subtlety to the story I relayed a couple of days ago.

In short, the genome appears to be composed of pieces that have all be circulating in pigs for many years, yet some of those genes may have originally come from human and avian viruses.

I took a few minutes last night to add tags to most of my old posts about SARS, H5N1, vaccines, influenza, and infectious disease.  I also fixed a few links still broken from the ISP switch last year, including the SARS outbreak timeline in "Nature is Full of Surprises, and We Are Totally Unprepared".

Update:  Here is another good 2009 H1N1 Flu Outbreak map from Google.