By Grant Jacobs 01/02/2020

There’s so much being reported about the ‘Wuhan’ 2019-nCov coronavirus outbreak it’s confusing. Here’s an outline of some aspects of what scientists are looking at, some places to follow the story, and a few tips for reading the science.

Yesterday Tedros Ghebreyesus, the Director General of the World Health Organisation (WHO) wrote, calling for a public health emergency –

I am declaring a public health emergency of international concern over the global outbreak of #2019nCoV, not because of what is happening in #China, but because of what is happening in other countries.

Not (yet) a pandemic call

The WHO International Health Regulations Emergency Committee is not calling 2019-nCov a pandemic.[1] Formally it is a ‘public health emergency of international concern’ (PHEIC).

The aim is to assist countries that are less able to prevent entry and spread of the illness. For example, at this point in time there are no confirmed cases in African nations. The Bill and Melinda Gates Foundation move to split their support between China and African Union nations was likely done with the same thinking in mind.

As this is being written, around 12,000 cases are reported, with all but about 1% of these in China. Almost all cases outside China are from people who have travelled in China or come from there.

Over the last few days a small number of secondary infections outside of China have been reported (in Germany, Japan , Vietnam and the US; 8 cases in all[2 – now a few more]). This, along with other issues, is what has prompted revising the status of the outbreak.

China has put in place stunning steps to try shut down the outbreak within their country. In closing down entire cities, suspending out-going tour groups and so on, they are also dramatically reducing the impact on other countries.

The revision in status is, in many ways, a call for a more formal recognition of the ‘all together now’ that is already happening in some spaces, notably (for me) the research community.

January 31th was the day Ghebreyesus announced the first ever World Neglected Tropical Diseases Day. While we’re all talking about 2019-nCov, let’s not forget all the ‘small-but-surprisingly large’ diseases in tropical countries that often don’t get the attention they deserve.

A word about reading fast science

This outbreak features some very fast science output. It’s useful for specialists and the teams trying to tackle the outbreak.

Online I see a lot of people making statements based on one or two statistical figures or things said in research studies in very definite ways that aren’t sound. This is not helpful, even to themselves.

A lot of this science is from preprints or ‘letters to the editor’-type publications. Preprints are essentially drafts of what would be sent to a journal for criticism. They haven’t been checked by any scientists outside of the research team. No peer review before being released. And even if that had be done, the true peer review is what happens in the following weeks or months. Formal peer review is limited, with more subtle things are caught later.

You have to look to the criticism, not (just) the initial claims. As an extreme example there a preprint claiming a relationship between the 2019-nCov virus and HIV. This was immediately shown to be a poor claim by computational biologists (my field). More on that later, perhaps, but the point for now is that those writing about this (including journalists) using these fast publications must look to the criticism.

If you were to look at critiques by scientific colleagues—I have—you’d see every one of these publications are being critiqued.

Every one.

None of them are definitive in the way that some rushed reporting is implying.

A debated issue in scicomm

Use of preprints in reporting is a debated issue in science communication. In the hands of a specialist writer with background in the wider field, it might come out well. Tackled by non-specialists, I’d be wary (and a bit nervous). Too often we are seeing what is basically a gloss or essay that takes the claims literally, with little or no critique.

(I’ve often argued the value of scientist-writers for niche topical issues, or just in-depth science writing in general. This outbreak is giving plenty of example of both better and poor coverage.)

Arguing a case

Yesterday I saw leading, well-established infectious disease reporter saying a rushed letter to the editor ‘showed proof’. I’d like to think just a slip—Twitter posts tend to feature that, and I make my share of them!—but I still cringed at the word ‘proof’.

Initial findings in science do not ‘prove’ things, they argue a case. (Especially so for biology.)

Unless you’re very familiar with the science, you’re very unlikely to know how to read the claims or the argument.

The best advice then is to not to. It may be bugging you, but you won’t help yourself or anyone else by using poorly-founded ideas.

In the final sections of this article I’ve offered a few better sources of information. Even at these sites, please don’t over-read them.

Details matter in science

Calling it ‘snake flu’ was like that. Aside from that it’s not a flu, for specialists it was very unlikely to be from snakes from the get-go. (I spent years making the type of tables those calculations relied, and I immediately was skeptical. I thought to cover this earlier, but since then many other scientists have pointed out other issues with the claim and it seems to have dropped from wider attention.)

Many things in science prove to be quite subtle. All of this get caught out by this, scientists too. Some of the testing, for example, doesn’t measure how infectious a person is, nor now affected they are, but what RNA load they are carrying. It’s a subtle difference, but it can affect what conclusions you draw from the testing. (It’s one aspect of looking at the so-called ‘asymptomatic’ cases.)

Journalists are using these fast scientific preprints to write stories. The coronavirus outbreak is a hot topic and these preprints are open for anyone to read.

I’m not seeing enough critique applied. Some are rushing out without asking any other scientists if the claims have problems.

I’m scientist-writer. I have to work very hard to try present even ‘soft’ glosses of findings accurately. And I end up dropping things that I can’t say confidently.

This rushed reporting, and repeated claims online, are going to cause a lot of ‘correcting’. It’s something those of us who work in science communication dislike. It’s really hard to put right an incorrect idea once people have latched on to it.

Missing data

No, not some conspiracy about hiding data. The Chinese teams been sharing what they have.

One problem is that faced with an epidemic those at the medical frontline face are very understandably focused on seriously ill patients. A consequence is that there is not much data on milder cases. The shortfall in the count of milder cases may mean in time that some statistics will change. For example, the case fatality rate would fall: the same number of deaths would be compared against a larger number of mild cases.

(A longer account of this issue can be found at Stat News. In particular, they note that, “The Chinese are currently only testing people who are sick enough to seek medical care because they have pneumonia — a criterion that automatically excludes anyone on the mild end of the disease spectrum.” If true, this will have a strong effect on statistics.)

There will no doubt be other data limited in various ways. One example might be the observation that there was a reduction in the increase in number of cases recently. Those familiar with on-hands work during epidemics have said this is likely due to insufficient testing kits rather than an actual reduction in cases. Taking things just on face value is generally a bad idea!

What scientists are tackling

Scientists are looking at lots of different aspects of this outbreak. Let’s look very briefly at what they’re tackling, and why.

(These are glosses. I’m not critiquing what is being reported as here it is what they are tackling that is important.)


I’ve covered R0 in a previous piece. The estimated values range from 1.4 to 3.8, with different methods giving broadly the same result. All estimates have ‘typical case’ values of 2–3 or so. Call it 2.5, or slightly less. (Some new calculations have come in since I wrote that article that offer slightly lower estimates, but the broad statement is still true.)

R0 is not a fixed thing, and it’s not the last word on how ‘nasty’ an outbreak might be. An R0 greater than one indicates it is likely to continue to spread. Beyond that on it’s own it doesn’t give the full story.

It’s perhaps best thought of as a measure of the pattern in the data measured, given activities going on then and that environment. It reflects the situation during a time—the earlier phase of the outbreak—and a place—Wuhan. It will be different in other settings, as it is affected by a lot of things, things we can change. Those are things we change to slow down transmission of the illness.

Mixed into this is that current estimates of the number of mild infections are probably low (see Missing data, above). When this is taken into account estimates for R0 may rise (more cases, but milder) while the case fatality rate will fall (same number of deaths from a larger of number of cases).

The claims circulating online of R0 being 14 or 3.8 are not what the research shows, and are best ignored. There are also claims that it is the most infectious illness or similar online. A quick look at R0 for common illness shows that isn’t right![3] –

  • Measles 12–18
  • Diphtheria 6–7
  • Smallpox 5–7
  • Polio 5–7
  • Rubella 5–7
  • Mumps 4–7
  • Pertussis 5.5

Incubation periods

Knowing when a person is likely to be infectious is really useful!

You’ll read claims that the incubation period is ‘up to 14 days’. This has been recommended as a quarantine period, and certainly looks to reflect the longest time patients can be infectious.

BUT: if you were to plot what days a patient was infectious, you can get some idea of what is typical.

Just using the one study as an example, they find: “we estimate the mean incubation period to be 5.8 (4.6 – 7.9, 95% CI) days, ranging from 1.3 to 11.3 days (2.5th to 97.5th percentile)” (Other studies have slightly different estimates.)

One big limitation of this estimate is that it is based on just 34 cases, so it is a very initial stab at what a typical incubation period will be.

With only 34 cases, the 14 day incubation case must be one individual. This person might prove particularly exceptional in time, and might also reflect underlying illness that prolonged their infection period. Then again, it might be that patients with particular illnesses have extended infectiousness.

You can see in the illustration that this range of days of infectiousness is very similar to what is seen for other coronaviruses.

Asymptomatic infections

There is a lot of talk about reports of asymptomatic people spreading the disease. Several virologists have spoken out saying that if asymptomatic people have a contribution, it will be rare. As always in research, time will tell.

Speaking to the Washington Post, Anthony Fauci, director of NIAID at NIH is quoted as saying,

“Even if there some asymptomatic transmission, in all the history of respiratory-borne viruses of any type, asymptomatic transmission has never been the driver of outbreaks,” Anthony Fauci, director of NIAID at NIH, said. “The driver of outbreaks is always a symptomatic person.”

One key point is just what do these accounts mean by ‘asymptomatic’? (I’ve even seen a newspaper article saying both that the person had a mild cough, and that the were asymptomatic. It can’t be both! You could say that their only symptoms where a mild cough.)

Infectious diseases physician and scientist, Isaac Bogoch, writes offering answers to questions on this issue (reformatted for clarity) –

What if they had a cough and now it’s gone? Then they were symptomatic and now are asymptomatic.

Can these people continue to shed virus after symptoms resolve? Yes.

A lot of virus? Meh.

For how long? Unclear, but sometimes for a few days as in other respiratory infections.

Can asymptomatic people have evidence of this novel coronavirus? It appears there are some cases of this.

Do asymptomatic people significantly contribute to driving epidemics? No.

Could it still be possible for truly asymptomatic to transmit infection? Yes but likely rare.

It’s perhaps a reminder of the next for care in reporting what is seen in preprints or informal comments by scientists.

Mortality and case fatality rates

We all want to know how dangerous an illness might be!

A few newspapers are giving some descriptions of this, but describing it as a mortality rate, when what they quote is actually a case fatality rate (CFR). Terminology is a hassle, and mortality rate is simpler, but I’m seeing a few people going off, finding actual mortality rates for other illnesses, then getting upset.

2019-nCov has a temporary, estimated case fatality rate of approximately 3%.

The case fatality rate is the rate that diagnosed cases die. What percentage of diagnosed cases didn’t make it?

The mortality rate is the rate out of (say) 100,000 people in the whole population, infected or not.

CFRs are dependent on a lot of things, including the treatment. As doctors and researchers learn how to better treat the patients, the CFR falls. It can depend on delay until treatment, use of particular treatments (say, anti-viral agents) or not, and so on.

I’m reading a general comment by specialists that CFRs tend to be over-estimated early on in epidemics. That should be factored-in too.

A 3% case fatality rate is much lower than CFRs for SARS, MERS, Nipah and other pathogens that have garnered wide media attention. This statistic, too, is not the final word! It’s a measure of how things currently are (or, more accurately, were), in China.

This excellent blog post by Maimuna Majumder, a faculty member in the Computational Health Informatic Program in Boston Children’s Hospital and Harvard Medical School has more on these statistics.

Ages affected

Is everyone affected? So far it seems that serious 2019-nCov cases are mostly older people, especially men. Infections in milder cases seem to cover most age groups.

For example, an earlier study in Lancet of 99 patients with 2019-nCoV reports a mean age of 55·5 years of seriously affected patients, that most patients were men, and that about a half of the patients had underlying chronic conditions. These patients ranged over 21–82, with only 10% being younger than 40. 32% percent of patients were female.

Treatments and vaccines

As well as understanding the outbreak, scientists are racing to find treatments, and to develop vaccines.

There are several groups aiming to develop a vaccine for 2019-nCov, including a consortium. CEPI is supporting 3 efforts. While it might be possible to develop a potential vaccine relatively quickly, it still has to be adopted to scaling up in a commercial settings, and to be tested. Testing vaccines in particular usually takes a long time.

One approach to try make vaccines faster is synthetic DNA vaccines (a topic I may return to, as I had planned to write about this before this outbreak took off). Others are basing their work on existing vaccines for SARS and MERS.

Likewise, there are efforts to see if existing drugs can be used as anti-virals, and there are trials of some of these in Wuhan. Several research preprints suggest candidate drugs.

The source of the virus

We all like to know where things come from! It also helps to understand the disease, and how it arose.

The source of the virus is still unknown.[4] It is not snakes.

That bats are somewhere in the backstory of this disease isn’t surprising. Bats are also hosts for SARS, MERS and many other viruses. What researchers are expecting, though, is that there is an intermittent or temporary host in the same way that civet cats likely played a role in SARS.

(I may write separately about this.)

Cases outside of China

There is a table of these on Wikipedia (there may be another at the WHO website).

Recent additions include the United Arab Emirates and Finland. In all there are at least 160 cases in at least 27 countries outside of China.

(I note that with the exception of the Gulf States, there are no African cases to date. A Bill and Melinda Gates Foundation grant was split between between China and African Union nations. The thinking will be similar to that which prompted the PHEIC call.)

What to call it

It’s got a terrible name hasn’t it? 2019-nCov is meant to be a temporary name.

One recent aspect of naming infectious viruses has been a move away from naming viruses after the place they were first identified, to avoid stigmatising the location or the people that live there.

Rightly or wrongly the name of the city, Wuhan, is firmly associated with this virus in the media, and no doubt the general public. I, like others, would encourage people to call it 2019-nCov in the meantime.

Readers are free to offer their ideas for a name in the comments!

Silly and scare-mongering claims

There is a disturbing amount of nonsense and wild claims going around. To be fair, that may partly because I looked to see if there is! When you check these things, it’s always rather disturbing…

My best suggestion is to use better sources. I’ve listed a few in the next two sections. While I could try counter the worse claims, these take time, and invariably there’s a new claim to counter in a matter of hours. I think my time is better spent bring the real story.

Some sources of general information

My choices of sources may lean on the geeky side—I am a scientist, after all—but there are articles for non-scientists here too! (Readers: please feel free to suggest sites in the comments.)

The WHO has a page dedicated to the 2019-nCov outbreak. On it there are information about how to protect yourselfsituation reportstechnical guidance and travel advice. There’s a section Myth busters under Public advice.

The American Society for Microbiology (ASM) has a resource page for 2019-nCov that includes updates, interviews with experts, and links to recently published coronavirus papers.

Wikipedia has a fairly comprehensive page on the outbreak. (Bear in mind that Wikipedia information will sometimes, to be polite, get a little ahead of itself: it’s best treated as a starting point for verification.[3])

University of Otago has a page with update information.

The Guardian newspaper has a dedicated page for the outbreak, including rolling live updates like this one about evaucations. (Other broadsheets will have similar pages; I mention this one partly because I am familiar with it, and partly as it is accessible.)

Better Twitter streams

Science twitter is very busy on this outbreak! My suggestions are not meant to be a ‘best of’, but a few Twitter streams that, in my experience, are good sources of accurate information on infection and virology-related topics. The lean to the geeky side.

As well as individual accounts there are Twitter lists, for example this list from Ellie Murray where you can read just the Tweets from selected coronavirus experts.

Laurie Garrett – Award-winning author of several books, perhaps best known for The Coming Plague, a huge book covering emerging infectious diseases.

Marion Koopmans – virology; public health microbiology; global pathogen surveillance; preparedness and response; syndromic diagnostics, human animal interface.

Tara C Smith – Professor, infectious disease epidemiologist, writer, and more including antibiotic resistance, zoonotic disease, sci-comm, and zombies. (Yes, zombies.)

Ian MacKay – virologist. He’s ‘been called into help out with outbreak analysis and horizon scanning’, so he’ll be very busy but is still (!) occasionally dropping a few posts. (You have wonder how he does it.)

Helen Branswell – Senior writer on infectious diseases and global health for Stat News, who I’ve found are generally a good source of medical & science stories.

FluTrackers – A non-profit that tracks infectious disease worldwide. Despite the name, this is for more than “just” flu.

There are many others who I am less familiar with, or whose material may be too geeky for most. (Heck many of those I have named are.)

Others at Sciblogs

There’s also us at Sciblogs! Helen Petousis-Harris is a vaccinologist writing Diplomatic Immunity, and Siouxsie Wiles is a microbiologist writing Infectious Thoughts. (Yes, very punny.) Others may offer content later.

(I’m computational biologist. Some might think I think in terms of ‘computer stuff’ but in practice most of my research-related reading is in genetics, and my undergraduate is a microbiology major [a near double major with computer science]. I’ve also spent a fair amount of time over the last year learning about zoonoses.)

I’ve since written about R0, and more recently about the unconfirmed claim pangolins may be a source of the virus. There’s also an article listing all that has been written about the outbreak at Sciblogs.

Other articles in Code for life

The coronavirus outbreak: what is R0? There are a few misunderstandings about the coronavirus outbreak from Wuhan getting around. This piece gives a short explanation of one of them: what is R0, and what does it mean.

For new parents or parents-to-be facing vaccine opinions

Rubella, not a benign disease if experienced during early pregnancy

Autism revisited: genetics, environment, not vaccines

Genome-edited babies – what’s the worry?


Temperature-induced hearing loss 

1000 of these now (Includes links to lots and lots to read!)


I’ve been following this since sometime in December when it was a “pneumonia of unknown cause”.

My little effort has limitations! Aside from the sheer amount of material, I’m also hampered by lack of access to some material. For some websites, I’ve expired their limits of numbers of pages per months I can visit. For example, I can no longer access News articles from Science. There’s also that I do not have institutional access to scientific journals.

1. The WHO also did not call for restrictions on travel to China.

2. Taiwan also has reported secondary cases; the WHO reports their counts under China.

3. Any Wikipedians here? The table of R0’s on the basic reproduction number page has been edited up from 2–5. It’s now 3–5. This is what one preprint reports, but that report is higher than what others find. I also find this a nice example of why people should be cautious about information on Wikipedia!

4. I’ll admit I would love to put some time on this. Many years ago I applied for funding to develop a method for identifying host-pathogen interactions using computational biology. (The application was rejected with no feedback.) That was intended for bacterial pathogens, but the same thinking might be re-applied to 2019-nCov.

Featured image

Description: “Citizens of Wuhan lining up outside a drugstore to buy masks.”

Source: China News Service/中国新闻网, Creative Commons Attribution 3.0 Unported license. Obtained from Wikimedia.

0 Responses to “The 2019-nCov coronavirus outbreak: all together now”

  • This tweet from FunctionalViromics adds another angle to the science rush –

    The “snake paper,” the “fish paper,” the paper that called 2019-nCoV an IAV, the protein docking study that predicted low h2h transmissibility, and now the HIV paper show how the science community has become clouded in its rush to make the next “discovery” #KeepCalmAndThinkFirst

    It’s a fair point.

    (You always see things just after you press “send”!)

  • Very nice blog. I have a university library account, so if there are specific articles you’d like to get, send me an email.

    I’m a computational geneticist myself, so not fully familiar with the epidemiology. How do CFR estimates depend on the time lag between diagnosis and death? For example, today’s news quotes about 1E4 diagnosed cases and 259 deaths. However, the vast majority of the diagnosed cases are fairly new during an exponential growth period, while deaths will lag behind.

  • I’m not sure if this was present earlier as I download my copies directly into reference management software, but I am now seeing this message on the top of 2019-nCov-related preprints in bioRxiv on their website –

    bioRxiv is receiving many new papers on coronavirus 2019-nCoV. A reminder: these are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information.

  • I found this and your previous article about R numbers really helpful as a general member of the public. I forwarded this article to a friend who is a manager at the airport to help her to help her staff be better prepared and more informed. Thank you.

  • An update to several points:

    Just to add to my remark yesterday about bioRxiv adding a new message about use of the preprints (see above), it seems this was added ~13 hours ago so it is new (As reported in ). It is, apparently on all bioRxiv preprints, not just the 2019-nCov ones.

    (On a related note, on Twitter I suggested watermarking the ‘preview’ “screen shots” that Twitter/Facebook show for links for ‘popular’ controversial or retracted articles; I may write on this later. It’s not a perfect idea—as if there is ever one!—but it’s interesting to think about.)

    I have received an email from the researchers whose work estimated R0 to be (roughly) in the range 3-5 (see Footnote 3). They tell me that their estimate is now revised to be same general range that other researchers see, roughly 2-3. I’ll put a more detailed explanation in the comments of my earlier post about R0 later ( ).

    The Wikipedia ‘basic reproduction rate’ page has now been updated (see Footnote 3). It now gives R0 for 2019-nCov as 1.4–3.9, which is more accurate (most researchers point at 2-3, generally speaking, as that covers the bulk of cases).

    I may later edit the mortality section with a simple (and simplistic!) comparison that if a population mortality of 3% were applied to just the Wuhan city over just the time so far (2+something months) you’d estimate over 300,000 deaths. (3% of ~11 million). That clearly is much greater than the actual number of deaths (a good thing!). Thing is, if people were to go looking for mortality rates for other things, the 3% (per annum) would give them the wrong idea in a bad way. (It does depend on people going out and looking, though, but with Google and whatnot……) Of course, the 3% actually refers to a percentage of diagnosed cases, not a percentage of everyone.