On the misuse of case studies: a case study

Our organization is committed to equality, diversity and inclusiveness. For example, Dr Clara Madeup benefitted from our ‘return to work’ programme that permitted her to come back to work after an extended 2 years maternity break. Clara is now a tenure track associate professor leading in the field of biotechnology.

How many Claras and Johns showcase success stories across our industry? More often than ever, we need to submit case studies during assessment processes, so much so that it is not unlikely to receive negative feedback if we describe our actions and outcomes carefully but without illustrating case studies.

Which is the likelihood that an organization does not have good case studies to showcase? And how likely is it that an organization decided to illustrate a failure in a case study? How representative success stories are of an organization, particularly organizations that are based on high staff turnover and competition? In fact, a few handpicked case studies can conceal otherwise worrisome statistics available within a document right alongside nice case studies.

Of course, the exclusive use of positive case studies in our websites, the brochures we use to describe how great we are, or at least we want to be, is absolutely obvious and legitimate.

I have seen case studies related to negative events within my organization only in two cases. First, introductory courses for health&safety that often provides plenty of examples of incidents with few cases discussed in detail. They are very informative because in the utter boredom of a long H&S course they actually tell you the story of not what can go wrong but what did go wrong in a lab like yours, maybe next door. Second, I had volunteered for a course designed to inform how to help victims of rape and sexual harassment. Instead of dwelling on how good our organization is, we went deep in describing which problems we have to deal with, how problematic communication can be, and how both academic and justice systems can easily fail victims. Very different situations but the illustration of what CAN and what DOES go wrong was absolutely instructive and helped focus on what we should do to prevent incidents.

During management meetings, we usually discuss what we can improve. Obviously, we do not speak about positive things only, quite the contrary. However, we do this often through rather unevocative statistics and get excited when we see progress compared to the past, or we are better than other organizations in the same area. I wished, however, organizations would focus more on the investigation of negative case studies during management meetings, of course, anonymized and taking any necessary precautions or even with the consent of colleagues involved, so that we could understand more deeply the consequences of our failures and identify better strategies to eliminate or mitigate our shortcomings.

I think we should bring a bit of the scientific method we experimentalists are so accustomed to deal with. We often learn a lot from experiments that fail for no apparent reason, and we showcase our failures to colleagues to get help and to teach less experienced how to identify solutions.

I am not really sure about how often ‘negative’ case studies are used in academic management to inform executive decisions in the broader community. In my experience not enough, probably, because the ‘negative’ case studies we should analyze are often just simply buried, swiped under the carpet, a topic for more specialist discussions reserved for those that make issues disappear.

I hope organizations will adopt more the use of ‘negative’ case studies as a tool to improve and fully understand the suffering of those who find themself in challenging situations. And I hope we are asked to produce case studies to illustrate success stories and good practices less frequently during an assessment, reserving these to public brochures.

Against (online) abuse

English football has announced a three days boycott of social media to raise awareness against online abuse. I am no footballer and I even do not follow football but I follow Formula One, and thanks to Lewis Hamilton engagement against racism I got aware of this initiative. Sport should be all about coming together in a joyful way and transforming the instinct to compete and fight into a game based on fairness and respect. However, far too often sport – and football in particular being so popular – makes itself a vector of abuse, online or in person, verbal or physical.

Abuse is part of our society and – like all of the human shortcomings – it will be never fully eradicated. However, abuse should still rise indignity from all us, either if online or not. Sadly, influential people have contributed to normalize online abuse, attitudes that are then percolating back into the streets. Admittingly, every person might have a different sensitivity and personal judgment about what ‘abuse’ is beyond the strict legal definition. This should not be, however, used as a free pass even just to be unkind, certainly not to be, well… you guessed… abusive.

Then, as someone active on social media, despite not being an athlete for the past quarter of a century, I’ll turn my social media off until Monday night in solidarity with this initiative and any victim of abuse.

A brief journey to India, and into models of carcinogenesis

In early 2016, I was asked if I wished to speak at the discussion meeting “Conflict and Competition in Cellular Populations” in Bangalore, India organized by Dr Sandeep Krishna and Dr Sunil Laxman (NCBS). The title sounded so intriguing that I accepted without even checking the actual topic of the meeting. Then an adventure begun, that now concluded (did it?) in 2021 with a small paper entitled “Cooperation of partially transformed clones: an invisible force behind the early stages of carcinogenesis” published in the journal of the Royal Society, Open Science (10.1098/rsos.201532). Let me tell you the story of this journey that, perhaps, might inspire you to adventure outside of your field.

For brevity, I’ll skip the details about the actual trip. It was of course exciting to experience a culture I am often exposed but I never lived. The food, the people, the contrasts of India, a small glimpse into a complex galaxy of humanity. My short trip to India started with a sleep-deprived-me trying to explain to the border police that the conference Conflict and Competition in Cellular Populations, nicknamed CCCP, which poster was written in pseudo-Cyrillic, was not a political conference (I would have needed a different visa in that case!) and concluded back in Cambridge a week later with a slightly embellished bedtime story for my 3 years old daughter about the animals I saw in the park that hosts NCBS, a story that I am still telling now and then to her.

But of course, here I focus on the science. The conference hosted a good number of great speakers (referring to others) on the topic of ecology (er, yes, the title made sense). Suddenly it dawned on me I was ‘a bit’ off-topic. However, I loved talk after talk learning a bit about ecology, including its mathematical foundations. I really enjoyed the meeting, so much so I could not stop thinking about its relevance for my work that back then was focused on non-genetic heterogeneity in cell decisions, carcinogenesis and the DNA damage response.

The study of cancer as an ecological problem is not new, of course. Something very specific started to bug me though, something I could not find literature about. We know that different clones of cancer cells cooperate and compete in tumours but what happens during the very early steps of carcinogenesis? I was queuing to board the airplane when I succeeded to download the paper “Evolution of cooperation among tumor cells” published ten years earlier by Axelrod and colleagues in PNAS. It was a nice in-flight read, but the flight from Bangalore to London is long and I started to obsess about a very simple mathematical fact.

For a moment, let’s imagine you dream of establishing a business but you need £1M to start it. However, you are a bit of an odd person and decide to do it only if you win the lottery which jackpot is £500k. You clearly make strange decisions but I am not here to judge… the oddest thing is, however, that you bet on winning the lottery not just once but twice. Then you have an idea You agree with your village of similarly odd-minded people that if anyone wins the lottery, you will pool the money together to invest in this start-up. This is still an unlikely strategy, and certainly one that has a tiny probability to succeed, but it is definitely more likely to work out than waiting to win the jackpot twice alone.

Back to carcinogenesis. Every day, each cell has a certain probability to mutate because of exposure to radiation, chemicals or simply the chance of errors of biochemical machineries. Mutation after mutation in the right genes, a cell might grow into cancer. A very unlikely series of events that, however, with trillion of cells in our bodies, over one’s lifespan is likely to happen. We know that certain mutations occurs in cells that eventually lead to cancer. We know that one cell wins the macabre lottery of disease multiple times before leading to cancer. We then know that many cells will get mutations within an otherwise healthy tissue.

We usually consider that all these other mutant cells will either accrue neutral mutations (i.e., mutations that will not change the fitness of the cell, nor confer a cancer phenotype), or deleterious mutations that will be purged by tumour suppressive mechanisms. However, cells within a tissue communicate and mutations occurs also in genes responsible of cell-to-cell communication. In my recent work I propose a ‘toy model’ with which I explore the possibility that the gene- and cell- centric mutational process should be reconsidered in the context of an overall tissue where cell-to-cell communication might reshape the early steps of carcinogenesis. I am not the first one doing so, but I try to emphasize with simple modelling how the mutational process should be seen in the context of a collective of cells rather than in a gene- or cell- centric fashion.

What did I learn beyond what I have written in the paper (i.e. in addition to the science)?

First I had really fun, something that over time does not happen with every paper, even those more important ones where we invest major resources in. I even had fun during the revision process. As many of us experience, I often got half of the referees very supportive of my work and half rather dismissive. But those very supportive have been often extraordinary kind and helpful, either defining the manuscript ‘a refreshing read different from what I usually read in this field‘ (earlier submission in a different journal) to ‘the models presented here make the point in a clear and dramatic manner‘. The last referee of the last submission now published was particularly helpful. Not only they critically review the manuscript but also invested time to describe a discrete time Markov chain model that I could have integrated in the manuscript. This suggestion permitted me to learn a bit of maths I did not practise before, and to improve the work… this is what refereeing should be.

Second, alongside the enthusiasm of adventuring in a rather different field from my already eclectic research interests, I also felt the pain of being an outsider; a pain I feel often but that it was made sharper by the fact I was a single author. This was really a ‘pet project’. I got convinced to shape my notes in a manuscript only after I attended a seminar by Prof. Allan Balmain in 2018 related to the Nat Cell Biol article “Multicolour lineage tracing reveals clonal dynamics of squamous carcinoma evolution from initiation to metastasis“. It was a great talk and somehow relevant to the notes I had written since my trip to India. I decided to try to publish my ideas after reading the commentary by Prof. Kornelia Polyak and Prof. Michalina Janiszewska where they state: “One possible explanation is that there is a cooperative interaction between the streak and bulk tumour cell populations; an intriguing hypothesis that warrants further investigation but was not tested by Reeves et al.5. The streak pattern observed by Reeves et al. is reminiscent of the streaks generated by non-mutualistic budding yeast analysed by Muller et al.13.” Eventually, I am not sure the work I had put in this manuscript was worth the pain.

Then, do I advise others to adventure so wildly in other territories? As I have written before, it is rarely rewarding career-wise and never easy. But, once in a while, let’s just follow the passion and enthusiasm for something new, with no regrets. Any adventure comes with some pain but the fun of exploring, eventually, makes the experience worth living overall.

I wish that this small new paper can really provoke some thoughts, or inspire some young scientist to adventure… perhaps not too much and not alone as exploring comes with its perils.

Changing of the Guard

I more excited than other times for a talk I will deliver next week. When invited, I read the list of speakers and I noticed so many names of people whose science I follow very closely. This time something is different though. I read their papers since I am a student, papers they published perhaps when they were students or young postdocs, in fact many of them are my generation. I grew with their science as if they were well-established academics as I never paid attention to affiliations or titles. Some of those I had recognised early in my career disappeared from the field or academia, others are fully established by now. This made me thing about my attitude towards generational change… a great contradiction of thoughts.

Missing the Old Guard. Several scientists I really respect have retired or are about to. I have been privileged to meet so many, particularly in the area of biochemical/biophysical imaging. Scientists who contributed so much, inspiring figures who shaped contemporary science, often without hype or even recognition in the broader community. Wait, am I missing the Old Guard? This feeling contrast so much with another one. In time, old ideas become an obstacle to progress and a generational change is desirable. You might indeed know the popular concept that ‘science advance one funeral at a time’. I do think there is an element of truth in it. So, why do I have such profound contradiction in my feelings?

Loving the New Guard. I am active in the area of biophotonics since an undergrad student, and having swapped discipline a few times, it is simpler for me to use microscopy as an example. The super-resolution revolution has been inspirational although I have observed it from the outside. In a few years, a new generation of stars begun to shine and a constellation of younger scientists who broke with the past was born alongside. Also in biochemical imaging I see great changes, the consolidation of certain ideas that once were considered heresy or simply very very niche. And yes, this get me rather excited. Wait, do I really love the New Guard? I see so much I do not like in science, and this is not just something imposed or inherited by previous generations. There are so many colleagues* with whom I might disagree about science and often on how Academia should be run. Disagreement is ok but sometime this is a much more profound divide.

OK, I got it wrong. Today, I have suddenly realised how wrong I was in interpreting my own feelings about generational change in Academia. While the majority of us would agree that generational change is necessary to avoid science stagnating, perhaps we do not really understand why**.

I love challenging established ideas on the basis of logic and experiment, I love discussing alternative interpretations that are not mainstream (but still scientific!), I love risk-taking in science (not in life although sometimes it is difficult to keep them separate), I love intellectual change (not so much change in my everyday life). Generational change might help the things I like to emerge but old generations do not have exclusivity in being dogmatic or risk-adverse, indeed those I admire are not. The issue is that too often also the younger generations accept dogmas (not just critically incorporating established theories and models in their thinking), they would guard an old ‘truth’ no matter what. But when they lose their authority of reference because of generational change, somehow their confidence or power is weakened, leaving space for positive change.

Hence, I now realise I am merely recognising a new generation of scientists with whom I might share a vision and I am excited that new people now replace those who retire for whom I had the same affinity and respect. Generational divides are much less important than an open attitude to change.

So, perhaps, I do not like guards in science at all because in science the fewer cages or palaces we have the better it is.

And after this lucubration, I will thoroughly enjoy my next talk in any case 🙂


* I use the term colleague very loosely to refer scientists in related fields.

** I just had a glance to this paper by Azoulay et al., interesting concepts

COVID | data analysis (new CAAT 4.3 release)

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow the advice of your national authorities and health system.


I have just published a new release of CAAT, Matlab code to analyse Johns Hopkins dataset on the SARS-CoV-2 pandemics. The usual caveat is that data is likely to be underestimates. Underestimation does not occur only because of lack of transparency but most of the times because of differences in definitions of COVID-related fatalities and efficiency of reporting systems across countries. For example, using the excess mortality statistics we know that about 30-50% under reporting is rather physiological because of deaths occurring outside the hospital settings or because people might die positive to COVID but not for COVID. This also accounted to a significant adjustment of statistics we have noticed in the past in Hubei. However, changes over time within a country might be more reliable and, therefore, there is still something to learn from this data.

I started analyzing data when the UK government decided to drop the policies set up for containment of SARS-CoV-2. Now that data has been extensively discussed in the UK, I curate CAAT only for others that would like to explore the data. However, at this release it is worth mentioning just the main observation. While the first acute phase of the pandemics is subsiding in Europe and Northern America, it is now flaring in South America at worrying pace.

Relative fatalities in the population at risk for each country. This is a new visualization I provide. Both bar graphs are ordered according to the values shown to the right, i.e. the fraction of the population at risk who has already died. At the right, the weekly rate of fatalities during the current week and the preceding two weeks. Those countries that, so far, has experienced high casualties are showing a significant reduction of weekly fatalities showing the positive effects of policies aimed to contain the virus.

Commenting out line 362 ‘d_ord = p_ord;’, you can order the weekly rate of fatalities according to the last week. This might reveal which are the countries at most risk now. Several South American countries are topping this list. In Brazil, Ecuador and Peru, about 0.5% of the population at risk died during the last week and, sadly is several countries this rate is accelerating.
Let’s also focus on some good news. This is one of the several plots hard coded in CAAT, comparing Italy, UK, Germany, Denmark and Sweden. As it is well established by now, new fatalities are dropping significantly and lock-down measures are gradually abandoned in favor of social distancing measures. The individual outbreaks started at different times and at different rates. Comparisons between countries are therefore difficult but the effectiveness of policies within countries can be evaluated. If you were interested in different graphs or comments, let me know but I will not elaborate more on this at this stage.
This graph is a bit of a mess but I present it for completeness. In North America lock-down measures are having a clear effect. In South America we see alarming increases. As I do not follow South American politics and specific news, I can’t draw conclusions, but it is evident that – assuming no change in reporting occurred, Argentina has slowed down the outbreak but not yet put it under control.

Although I have explained this before, I should probably clarify how I evaluate the population at risk. To estimate how many people might die in each country in the (unrealistic) scenario where everyone would get ill, I used the age-dependent fatality rates published in The Lancet by Ferguson’s group and multiplied these values with the demographics of each country as reported by the UN.

Reviewer 3 | A semi-serious discussion

I guess that if you landed here, you know what I am referring to, but let me clarify the subject of this post for the benefit of the youngest scientists. During peer-review, we get good and bad feedback, either deserved or not. We can then respond and revise our work. However, it is not rare to get the reports from the mythological reviewer 3 (also known by a different number), one that dismisses your hard work in ways that you will find particularly unfair and difficult – if possible at all – to rebut. There are various flavours of Reviewer 3, but common traits – not necessarily all present in an individual report – might be the condescending tone, vague unreferenced criticisms, the request of impossible experiments, a deep misunderstanding of the manuscript, accusations of various type. The important aspect of referee 3 is that, generally, responding to their critique is either factually impossible or would not improve the quality of your work. Of course, there is a lot of subjective interpretation here, and some of referee 3’s suggestions might be proper, or some report that at first glance are good (negative but well done) might be written by reviewer 3 in incognito. In any case, most scientists agree that Reviewer 3 does exist and, some of us asked for an independent assessment of a controversial refereeing report, might even know the identity of some of them, however ever-shifting they are.


Why Reviewer 3? Well, this is very anecdotal and indeed people might do the same ‘joke’ but changing the numbering. The argumentation I am going to elaborate on (I beg you, Reviewer 3, if you are reading, please remember this is not a completely serious discussion), does not depend on precise numbers, certainly not by the cardinality of the referee. In any profession, there are very skilled and bad professionals; this applies also to the academic world, of course. However, referee 3 does not have to be particularly bad scientifically, they might be the smartest of all, but for the scope of the refereeing process, referee 3 is doing this particular job and at that particular time, particularly badly, perhaps for lack of time, hubris, a particular emotional state, ignorance or for a genuine misunderstanding: it does not matter. It exists. Then, let’s take the anecdotal report of Referee 3, for a moment, at face value.


Let’s say that each time an editor nominates a reviewer, it is like the toss of an unfair coin (i.e., the probability of heads is different from the probability of tails) – either we get a Referee 3 or we do not. The probability to get at least one referee 3, is then the complement of the probability to not get one at all, i.e. p1=1-(1-p0)^n, with n the number of referees nominated by an editor. Which is the probability p0 for referee to be referee 3?

There might be some data out there, but as data is relatively unimportant both to make my point and to reviewer 3, I will assume that as Reviewer 3 is often called Reviewer… THREE, it is a frequent occurrence to observe one out of three referees been, well you got it, referee three. Then, after ‘careful consideration’, I assumed that one out of three is the most frequent occurrence.  The mode of a binomial distribution is floor((n+1)p0)=1 or ceil((n+1)p0-1=1. We can thus infer that between 1/4 to 1/2 of all referees could provide a Reviewer3-like  response. Hence, which is the probability to get at least one Referee 3 for your submission? Well, although a rare occurrence, if the editor asks the opinion of just one expert (perhaps as a preliminary inquiry) this probability is somewhere between 1/4 and 1/2, of course, identical to p0. For two referees, we will get a 43-75% probability and for three (the most common case), almost a 60-90% probability. Therefore, getting a Referee 3 report might be a rather obvious outcome of the peer-review process.

Now, let’s do another outrageous assumption. Let’s assume that also the editor, when handling a manuscript, could make the same mistakes as a referee 3 and that the journal has a very high bar for a manuscript to be accepted, i.e. any substantial negative feedback causes a rejection. In this case, the probability that the Referee 3 syndrome might negatively affect your submission is between 70-95%. Unrealistic? Maybe.


Now that several weeks passed, the referees’ reports are back in the hand of the editor. This is a very complex stage where so many objective and subjective factors might change how referee 3 is handled.

One possible outcome is that you get two Referee 3s… a rare outcome… isn’t it? If three referees have sent reports in, the probability to get at least two Referee 3s is actually between 15-50% Let’s say that – on average – a quarter of papers could be rejected because of Referee 3s, as if you get at least two of them any editor would, legitimately, dismiss the idea that those are ‘bad’ referees.

Let’s assume now you got just one referee 3 report. Again, with no intention to be accurate, these are the possible outcomes I can think about:

  1. The Editor considers Referee 3’s points valid and the paper is rejected. Unexperienced authors will give up this submission at this stage, address any valid point raised during the refereeing and move to a second journal. Keep in mind now, that at the next journal, you will get the same probability of getting a Referee 3. However, if Referees one and two were positive with a few criticisms that could be addressed with new data, the experienced author would appeal. Until recently, I did not realize that Editors are quite open to this option assuming they find the manuscript interesting and that you get only a single problematic referee. Unfortunately, journals have mechanisms to discourage this path. However, if you can disregard emotions and humbly reassess your work on the basis of the Referees’ critique and you still find that the main issue is a Referee 3, engage – positively – the Editor. In most cases, you will find nice people trying to help out.
  2. The Editor considers Referee 3’s points invalid and in one way or another, you will be allowed to address only the solid scientific point of Referee 3. It is very rare this will be written to you explicitly. I still find difficult to handle this situation. In most cases, this is the more likely situation you will get published even with a Referee 3 in the cohorts of referees. My suggestion is to speak with a senior colleague to decide how to proceed, or again to engage in a polite and proactive way the Editor.
  3. The Editor considers Referee 3’s points invalid and asks for the opinion of Referee 4. This is the most sympathetic and proactive response that an Editor can have. However, this is also a situation that does not protect you from Referee 3, as the shapeshifting nature of Referee 3 might make them reappear with a differently numbered T-shirt. You will have between 25-50% to get another Referee 3 and being rejected not on merit. On a positive side, you might have up to 75% probability to replace a Referee 3 with a more objective peer.


Which is the point of this post? As I stated in the title, this is not a serious and quantitative analysis of peer-review. But I wished to address with outrageous simplifications a basic issue. Does the attitude of Referee 3 play an important role in peer-review? There are several reports showing how peer-review, despite its importance and the several mechanisms to establish a formal and objective process, give rise to a high degree of randomness in the outcome. Here, I just wished to point out that the probability to get a random and unfair report might be high. I leave to others the study of how high this value really is. However, while very experienced Editors and Authors might know how to handle the situation, there are two issues that concern me:

  1. We are accustomed to harsh criticism. Often, a solid scientific debate is confused with been tough, and assertiveness is confused with freedom to not be polite. Who manage peer-review, academic or professional Editors, or managers in funding agencies, might consider this the natural and obvious rules of the game. Being a scientist has become something of a high-pressure job and it seems everyone has to accept this. Most of us are good and well-intentioned people, but the gears of this heavy machinery that is science are difficult to change, at least while the machine is in action.
  2. The authors, or grant applicants, should have a very balanced approach. On the one hand, they should always make an effort to learn from criticism, even unfair criticism. This is a bit tricky with Referee 3. However, we always have to dissect Referee 3 to identify any useful critique. This is the trivial advise, trivial as it should be obvious. There is something more about this, that if you are a younger scientist with no proper mentoring, you might not know. Referee 3s can have a huge psychological impact on you. I’ve seen this happening to group leaders, and I have experienced this on my own.

*** UPDATE ***

After the publication of this blog-post, Reviewer 3 contacted me privately with the following message.

  1. The assumptions the author does are all wrong and WordPress should not have allowed the submission of this article
  2. The conclusions of the authors are clearly impossible as they conflict with a large body of literature
  3. The authors do not cite any literature, but particularly the papers I published in 1965 that clearly and unequivocally demonstrate the opposite finding or the same findings.
  4. The article is written in English, Latin would be the preferred choice for this field
  5. Even if the authors could address these shortcomings with a major revision, this article should not be even posted on LinkedIn
  6. Moreover, the article is poorly written, for instance, for instance ‘my own’ is not Korektly PhrammatiKalleee

*** UPDATE 2 ***

Hi Donald,

yes, that is sarcasm… not, you know…

Take care,


Managing risk in the lab at the times of coronavirus

In the UK, we are waiting for good news to reopen our laboratories. Well, not ‘waiting’ but getting ready. It might be in two weeks or two months but we have to be ready because if we will be ‘back to normal’, we will have new outbreaks. In science, we are lucky as we are already trained to manage risk. However, most scientists in the UK have a conflictual relationship with health and safety procedures that are often perceived (probably rightly) as overly bureaucratic and can drive people away from good practice. I am lucky as I deal with wonderful people both locally at the Cancer Unit and centrally at University on the regard to safety, at least in those areas I have responsibilities.

In my opinion, this is the moment to restructure how we handle safety. On the one hand, formal risk assessment is very important to identify the source of hazards. It is easy to imagine we can work safely but there are some topics that are very tricky. For example, we are discussing how to deal with fire doors… we can’t keep them wedged open because of fire safety but it would be better to not close them to avoid touching surfaces unnecessarily. There are perhaps solutions that avoid any risk, for example providing hand sanitizers on either side of the door or reverting previous rules and enforce the use of gloves in any area of the labs, or install automatic (fire) doors as soon as possible. What about air conditioning? We need it in a modern building with sensitive equipment but should we do any change? Are they safe? What about cell culture? The other day I joked about infected cell cultures in CL1 laboratories? Wait… it was a joke but then – out of curiosity – I realized that coronaviruses, this included, can propagate in several mammalian cell lines (they express ACE2 and most of them are not killed by the virus). Is this an issue? I assume it is not as it is unlikely we contaminate cultures (we work in aseptic conditions) and cells do not generate aerosols we can breathe. But I wished to mention this just to make a point: it is worth thinking deeply about how work will be when we return in the laboratories to identify possible issues, without paranoia and without panicking, but proactively and scientifically.

However, paperwork never protected us. It helps to identify issues and to protect us legally. There is a set of rules that have been gradually abandoned in favour of PPEs and engineering measures to manage risk and I believe we have to retrain people using those rules. It will be impossible to make the world 100% safe from coronavirus, certainly in the short term. We can, however, manage risk by changing behaviours to make it negligible but we need to be prepared and everyone has to comply.

Let me do two examples, not specific to viral work. Even just twenty years ago, for some of us laser safety was just removing any reflecting surfaces from your body and the environment (no rings, badges, other jewellery, no wall mirrors in an optics lab etc) and changing your behaviour: never align your eyes with the likely direction of the laser. This meant, for example, that a researcher would mature the instinct of turning the head always away from the optical table when picking something from the floor. Those were the times when accidents would still happen at a certain frequency because good laboratory practises without PPE relies on a person never do a mistake. PPEs should protect us from our mistakes but once you wear protective gear, once you feel shielded from the hazards, behaviour will change back to normal.

Another example is tissue culture. It is a fair amount of years I do not do TC work in person but, sometimes, when I get a coffee at the Hutch canteen and I pay, I pass on top of my mug and my brain signal me not to do it. Under hood, we avoid to pass over open flasks to minimize the risk of contamination (of the cultures). Again, some of us might have worked perfectly aseptically and safely with no PPE in the past.

I DO NOT advise to drop PPEs or risk assessments, do not misunderstand. The only point I want to make is that changes in behaviour such as social distancing and enhanced hand hygiene will be very important, more important than anything else to come back to work safely. We need to be careful in retraining ourselves. Again, without paranoia or panic. Other than doing ridiculous elbow bumps to replace shaking hands, a smile and a greeting will do. Giving way to people to maintain distance in close environments, planning how to move around cramped laboratories, how to reach instrumentation, when and how to clean hands or use PPE, but also very practical and trivial things such as the use of toilets or where and when to have a lunch or a break, how to reach the workplace might be more challenging. Challenging – not impossible, at least in most cases.

I have been very supportive of lockdowns. Among other things, this period is permitting us to exercise social distancing and train on how to handle materials we buy or we get delivered at home. This is valuable time if and only if you are actually using this opportunity to actively prepare for a life with COVID. We all hope that this virus will burn itself out soon. However, at the moment it seems unlikely and therefore the keyword is one: preparedness.

Do not do the mistakes that several people in leadership have done. They were not prepared to manage this pandemic despite they knew it would happen soon or late. They were not prepared to instruct us for timely societal changes. Are they now really prepared for the next phase, i.e. the management of life with SARS-CoV-2 endemic? I hope in more clarity and transparency. However, to be fair, it would have been difficult not to do mistakes.

If you did not do this already, brainstorm with your team and communicate to your managers what you might want to plan. I also advise having clear and shared rules. As safety will be based quite significantly on behavioural changes, conflicts at the workplace are also likely. Although we are all feeling closer to each other and more helpful, there are always the zealots and the neglectful. Those that are worried about anything and those that are worried about nothing. We need to reassure the former and letting them perhaps working only off the lab (whenever possible) if they cannot handle the situation. We should dialogue with the latter to explain we have to abide by a set of shared rules and, if they do not comply, we should get them off the lab. And in any case, help categories at risk and colleagues that might struggle with mental health.

If you did not do this already, it is time to prepare. Not business as usual but with a new norm as soon as the government will permit us to resume work until this virus will be defeated or at least tamed. For the same reason that most of us are staying at home, helping keyworkers to do their job, we’ll soon be called back to work. Not just for ourselves but again for those amazing people who have kept working in difficult situations in the streets, hospitals, care homes, shops. In fact, it is our duty to share the burden of a society that cannot remain on the shoulders of only a fraction of us. However, we shall do this not in irresponsible ways, but with absolute preparedness. This applies to governments and public institutions but it does apply also to each of us.

Covid (v3) – data visualizations

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow the advice of your national authorities and health system.

Let me publish this short update on the COVID pandemic to share some of the most interesting visualizations I found online and the last update of Matlab code for those of you who would like to play with the data. For some of us, keeping an eye on the data is a way to diffuse anxiety about the pandemic and it is in this spirit I keep posting, now and then, comments on COVID. If data, trajectories, comments on the pandemics are stressing you and you are experiencing a COVID overload, perhaps you would like to read Prof. Aisha Ahmad’s article – particularly if you work in higher education. And… not read this far less useful post of mine.

First, NextStrain is a tool that permits to monitor the spread of various infectious diseases, including CoV-2019 (thanks to Quentin to share the link). You can appreciate how the virus is mutating and how different clusters/mutations are geographically spread. I should note that, to my knowledge, there is no evidence for differences in the severity of the disease depending on mutations identified so far. These differences are currently used just as simple fingerprints, or ‘paternity tests’ to identify the evolution and the spread of the virus. I avoid interpretations of this data but you can find reports on NextStrain. I assume that these maps are biased significantly according to the amount of sequencing that each country is doing. You might appreciate the central role of, of course, China, but also of the UK as a hub of transmission. This might be because of London, an important aviation hub, and the high numbers of sequencing happening in the UK, but neglecting the number of samples, if the timing/trajectory of the spread will be confirmed in due time, it will be very interesting to understand the implications.

Before showing a bit of new Matlab code I worked on in the little spare time I had lately, I would like also to introduce the work that inspired my new analysis. There is this very nice tool by Aatish Bhatia and a great introductory YouTube clip by the same author.

Like most non-expert people, I am interested in understanding the efficacy of public health measures taken in different countries. As confirmed cases are rather unreliable, and the likely cause of the big differences in mortality rates across countries (see previous posts), I currently focus only on reported fatalities. It should be noted that this data is more reliable, but again it depends on the reporting criteria of each individual country and these might change. Hence, the true impact on public health will be clear only retrospectively in a year or so, but for the time being, we can appreciate trends to get an impression if containment measures are working or not.

First, I would like to start with the raw data. Here I present groups of selected countries based on my own interest but with the Matlab code available on GitHub you can generate the same graphs for the groups of countries of your choice using the Johns Hopkins CSSE dataset. To analyse just the raw data Bahtia’s tool is easier to use, but I show here the results to introduce my next analyses. Please note that the data is averaged over a sliding window of three days and we usually get data with 1 day of delay. Therefore, the curves are representative of the situation as it was 2-4 days ago.

These graphs are difficult to interpret as countries not only are experiencing different types of epidemics (for example, more or less localised) but do have also rather different population sizes. However, these trends allow us to understand – broadly speaking – if the policies enacted by individual governments are providing the expected results.

You might notice that I present two entries for China: one for China overall, and another just for the Hubei province. Here, the two graphs are almost identical because the Chinese outbreak started in Wuhan, within the Hubei province, and there caused many more fatalities. The black line is just shown as a reference to guide the eye. When the traces significantly deviate from this line, eventually tracing a horizontal line, confirmed deaths stop increasing exponentially. In that phase, the epidemic is still causing fatalities but it becomes more manageable and predictable. Most importantly, for those countries in lockdown, it is the clear sign that the strategy is effective. For those countries that are not in lockdown, for example, the Republic of Korea (aka South Korea), such trend imply that the epidemics is not resolved but controlled to strike a balance between socioeconomic sustainability and control of the epidemics.

The second gallery is a collection of the very same plots but normalized to the total population of each country. You might appreciate here why I report data for both Hubei alone and mainland China overall. I heard – amazingly even from virologists – that the data from China is not correct because China cannot possibly have so few fatalities being such a large country. At this stage, we can’t trust data as fully reliable from any country, but it should be clear that the epidemics was initially localized in Wuhan, Hubei. There it went out of control but in the rest of China a combination of the lockdown and tracking of patients made possible to avoid an uncontrolled epidemics. This is why Hubei, with its 60M inhabitants, should be considered as a reference and not all mainland China and why I am reporting the two curves.

Normalizing fatality by population size, we can now appreciate how some countries are in much similar state at the moment in Europe with, of course, plenty of exceptions. Another note on the reliability of data. All of us, even the non-specialists like me, have learnt how ‘confirmed cases’ of covid are a rather unreliable indicator because of different capacities in each country to test, particularly in different stages of the epidemics. Reported fatalities are a more robust indicator. However, different countries might adopt different methodologies to report ‘confirmed’ cases. Some countries make a distinction between people who died with COVID and people who died of COVID, but others do not. Some countries are faster than others in reporting and deaths outside hospitals – anywhere – are likely to be counted with a significant delay, or sometimes not reported at all because of lack of testing. Therefore, keep in mind that data from any country is not rock solid, and we will discover the real impact of COVID only at a later stage.

Most importantly, this is not a race between countries. Each of these numbers is a human life cut short and a complex network of relations broken. Therefore, my comments are provided as a means to try to understand what is happening from the standpoint of the layman I am in this context, with the deepest respect of what ‘fatalities’ means.

This noted, I wished to present the last gallery. Different countries have different demographics and the risks, as we know, of COVID are age-dependent. Therefore, I used the age-dependent mortalities inferred from mainland China (Hubei excluded – this represents a best-case scenario) to provide a rough estimate of the population at risk in each country. For example, once adjusted for the different demographics, an average fatality rate in China would amount to about ~1% but ~2% in Italy and 1.7% in the UK. Notably, the mortality rate in Hubei was higher (~4%) and using this value for Italy we would expect ~9% mortality. However, many have noted how these values are unrealistic and heavily depends on testing capacity, that is constrained during phases when a health system is overwhelmed. Most studies estimate fatality rates around 0.5% to 1%. If 1% is the real mortality rate, to evaluate the population at risk in Hubei, we can simply take 1% of the ~60M inhabitant as the population at risk. For all other countries, this proportion is made considering the different number of inhabitants in different age bands.

The take-home message is that the actions that governments decided to take are having the desired effects. While I might disagree with one policy or another, I thus invite people to follow the guidance provided from each country. However, it will be interesting to keep comparing the Netherlands and Sweden with other European countries, and China with other countries in South East Asia as, by choice of necessity, different strategies have been employed. Data from the UK and Sweden (and others) is rather noisy, with some temporal variation that might depend on the way data is reported. Therefore, it is to early to tell how the situation is developing in these two countries, but over the next week, a picture will be rather clear. We have now to watch out for the US and also the many countries that I did not study so far but that will play an equally important role in the evolution of this disease.

Personally, I had supported a fast initial response. While trying to shrink the epidemics, I hope that countries now will cooperate to share resources to save as many lives as possible. At the same time, I hope that countries will also cooperate in rebooting the world economy and productivity as soon as possible. We should not rush to not waste all the work done but we should have clear plans to remerge from lockdowns.

We are still adapting to this new reality. However, while supporting our societies in passing through this public health issue, as soon as we’ll see those trajectories dropping (or before if you can), we will have to quench the pandemics of hate that might break out between countries. We can do that only by resisting the populist trends we had been already experiencing, dark energies that might be getting stronger than ever.

I hope, instead, that we will feel closer to each other. In our streets, in our nations, with our neihboring countries but also with those far far away who suffered or will suffer like any of us. Together, we can build a brighter future. Against each other, more and more lifes will be lost.

Corona virus – data mining (v2)

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow advice of your national authorities and health system.

NOTE: For a more comprehensive blog post, you might be interested in Tomas Pueyo’s website. A good discussion about mortality rates can be found at CEBM website.

The trends

I have run a comparison between selected countries. I use Hubei (the Chinese province where Wuhan is located, in red) and Italy (in green) as references. The top plot in each figure shows confirmed cases and the middle plot shows covid-related deaths. Check the appendix for a discussion on the bottom plots (mortality rates). The JH data starts with Hubei on the day that the province went into lockdown. I will often comment on the comparison between the UK and Italy as I am a British-Italian dual-national and I started to follow the data to understand how to adapt to the situation.

Day 1 in these plots is relative to the start of Hubei tracking, which coincides with the day of Wuhan lockdown (red vertical line). The vertical green lines represent two key moments of the Italian response. The first is the local lockdown of towns where outbreaks in Northern Italy occurred, the second is the national lockdown. The vertical blue lines are key moments of the British response. The first one marks the day when the PM announced abandoning the containment strategy to pursue ‘herd immunity’, the second line marks the day when a UK nation-wide lockdown was implemented.

The comparison between Hubei, Italy and the UK is interesting as these are regions with similar total populations (~60M). Imported cases are identified at a similar time in both UK and Italy, but the two countries then follow quite a different trajectory. Until day 35, the UK seems capable to track and isolate cases, after which local outbreaks are evident from the start of a steep exponential (linear in this log-scale) growth. Instead, Italy experienced a sudden (apparent) outbreak at day 30. The difference between Italy and UK at this stage is that Italy shut flights from China (contrary to WHO indications) and kept testing only people with a declared travel history from China. This caused the coronavirus to spread undetected, particularly in the hospitals that did not trigger emergency procedures timely. It seems likely that the coronavirus was imported from Germany by people returning from China through Germany. However, it seems also likely that the spread was boosted by contacts between Germans and Italians, between strong productive regions, completely bypassing the Italian monitoring strategies, too focused on China. From the trends of detected cases in Italy, one could extrapolate that the local epidemic starts immediately and undetected as soon as the first imported cases are identified. The good British monitoring of the epidemic gives the UK two weeks of breathing time for preparing to the epidemics.

In my previous post, I mentioned about the puzzle of Germany exhibiting low mortality and some reports about Germany counting covid-related deaths differently from other countries. Differences in counting are probably real but I am now convinced that too much emphasis on this point is unadvisable. My impression is that there is a lot of talking about this to spread fake news aimed to construe a conspiracy theory about some countries hiding covid-cases to protect their economies. Personally, I wished someone would make clarity on how deaths are counted, but I am keen to interpret – at this point in time – data as more realistic that some people might consider. Probably, Germany did simply a better job than the UK and Italy and delayed the local epidemic of three weeks compared to the latter.

We can browse the plots of different western European countries and notice similar trends, with different delays caused by the probability to import cases and the capability of each country to track and contain imported cases. Amongst the countries I check, Spain is the outlier, with a steep rise of covid-related cases that has been broadly described in the media and not yet fully explained.

In South East Asia (I checked China, Singapore, South Korea and Japan), the trends are very different from Europe. We can argue that different political and societal systems permitted a better response. In general, we can state that they responded fast and proactively. You might notice second waves of infections. Japan, Singapore and South Korea did not enforce national lockdowns but are containing the epidemics with careful tracking and strong mitigation measures. Somehow it is what the UK wanted to do. However, the UK, in my opinion, acted too late and when they announced changing from containment to mitigation plans, it was too late to adopt a balanced strategy between public health and economy (the South East Asian model) and too late to fully contain the disease with low casualties. Therefore, western European countries will likely follow the Chinese trend, full containment followed – in due time – with the South East Asian response unless a cure or vaccine will be ready early.

Singapore is the outlier, however. We should consider that Singapore has only 10% of the inhabitant in Italy and Hubei (Japan twice, South Korea similar), it is, in fact, a high-density population city-state. The death rate is very low… I wonder how the situation will evolve and how the data is collected. Back to Europe, we should keep an eye on Sweden as – if I understood correctly – the Swidish government did what the UK wanted to do, i.e. mild mitigation measures.

Let’s conclude this overview with USA. We should keep in mind that in any country, the statistics are the sum of multiple local outbreaks. USA has >300M inhabitant split in 52 states. For the time being, I just check the overall trends. And the trends are simply alarming. Of course, I neglected many countries but you can freely use/adapt my Matlab code on GitHub to draw your own conclusions.

Next…. let’s synchronize the curves.

Contrary to my previous blog post, I am now synchronizing the curves on the death statistics, as they are more realistic, while confirmed covid-cases heavily depend on the capability of health systems to run tests across the non-hospitalized population. I used an arbitrary number of deaths, 40, sufficiently large to provide a robust synchronization, but sufficiently low to precede containment actions of governments that would, of course, change the shape of the curves. Most South-East Asia countries are not shown as they are containing the epidemics at the moment. With this data, now you can appreciate that the epidemic across European countries is very similar with the caveat that any type of synchronization of data is wrong, certainly if not done with proper models and study of local outbreaks. However, the general conclusions might not change.

Rolling back to the UK, Italy and Hubei, you can see that the British government announced the abandoning of the containment phase, exactly when Hubei did the opposite (in relative time). After initial errors, Italy acted fast but had to play a chase game with the outbreak. Their strategy was not working and Italy had to enforce a nation-wide lockdown that started about 10 days later in comparison to Hubei. A nation-wide lockdown might not have been necessary, but the lack of compliance of the general population, some doctors insisting that coronavirus was not worse than the flu and some political parties trying to score points rather than supporting a common strategy eventually required a national lockdown that now is finally having the desired effects. We can be still concerned, however, about possible second-waves of the epidemics also in the short terms because of waves of inner migration from North to South, people running away from the epicentre of the Italian outbreak, although the current lockdown might be able to quench new outbreaks. We can just wait and see for now. However, that delay and initial lack of cohesion will result in several thousand preventable deaths.

In the UK, the first stage of the epidemics is still unfolding. It is interesting to observe that about a week after the British government decided not to take action to reach ‘herd immunity’, the increase in deaths deviated from the original trend, slowing down. On the ground, we have noticed how a large majority of people, seeing what was happening in Italy, took action independently from government advise. Sport organizations cancelled events, Universities started to close, people increasingly worked from home and several families started to withdraw children from schools. In all effect, the British population started social distancing measures ahead of the Government that, eventually triggered a national lockdown approximately at the same (relative) time than Italy.

One issue that in Italy has been publicized but not in the UK is what I referred to as internal migration. The reason is simple. Paradoxically, in both countries this happened because people did not follow advice. In Italy, the Government asked not to travel from the afflicted regions. People did not follow advise and when the government was preparing the regional lockdown, an opposition party leaked the measure to the press. People panicked and travelled to their holiday homes and University students went back to their family, many travelling from North to South. In the UK, the Government did not ask to close University, but Universities did close and students were invited to go back to their families. This advice was sensible as we do not want to have students trapped in student accommodations. However, this happened without control and without a good sense of the status of the local epidemics. Therefore, while not broadly reported by the media because of how this happened, this large movement of people might contribute to the future dynamics of the UK epidemics, and possibly also in other countries.

It is just my opinion, not a scientific fact for now, that the UK wasted an incredible amount of time. While the NHS and the Government might have done everything right initially, all those efforts had been squandered. What I argue here is that no strategy is a good strategy in these circumstances. Coronavirus will kill a lot of people and cause damage to the economy. Adapting a strategy is also important, new information might require a new strategy. However, contrary to the story depicted by the Government, there was no change of science about the epidemics. A U-turn in policy can be costly as it might result in none of the positive outcomes desired with either one or the other strategy.

The only hope is that, those countries that were able to delay the onset of local epidemics compared to Italy and Iran, either by chance (low exposure) or by good management, were able to scale-up contingency plans and availability of specialist ICU beds to draw the final numbers of deaths as low as possible. Why this was not arranged at the onset of the epidemics is something we will have to analyse in the future.

Soon, we will speak a lot about USA. China has four times the population of USA. China was able to contain the disease (for now) and to avoid widespread diffusion across all China. Outbreaks after outbreaks, the trends from USA are accelerating and already overshooting both Hubei and China overall, with no sign of slowing down of the epidemics.

Concluding remarks

In the next sections, I describe the methods I used and I provide a discussion about mortality rates. However, I wished to conclude my post here stating that this might be the last time I post graphs, as by now there is so much data around and from people with a background in epidemiology. However critical I might be of certain political decisions, I would like to be very clear on the following. The individual risk to people is comparatively low. The large majority of the population has to fear more the socioeconomic repercussions of the pandemic rather than the disease in itself. The socioeconomic impacts of nationwide lock-downs will impact people’s health. Mental health will deteriorate. Health systems will be weakened and therefore more people will suffer even if not infected. Governments all over the world are trying to guess what is best to do. My advice is to follow guidance from your national medical and governmental authorities. It might seem I am contradicting myself. I am not. I simply acknowledge there is no good solution to the problem. However, governments should speak the truth and clarify why they take certain decisions, at least in democracies. Governments should also work together not against each other. Some of us were fearing a big war was brewing after the 2008 financial crisis but we were not expecting a pandemic. Now that we got the pandemic, I hope we do not get both, but that this situation is bringing all peoples of all nations together. At the moment there are both good and bad signs. In Italy, we have a saying: “La speranza e’ l’ultima a morire”. Hope is last to die. And we hope, we hope for more rainbows at the windows and fewer clouds at the horizon.

Take care, my friends.

The data | The data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering is available on GitHub.  To adjust mortality rates by local demographics, I have downloaded the population pyramid data from www.populationpyramid.net. There are several estimates for age-dependent mortality. I was able to find only the following pre-print for mortality in Hubei compared to the rest of China. The dataset analysed was small, let me know if you find something better.

The software | In spare time, I prepared a bit of Matlab code that can import the JH data and does just two simple things: compare trends between different countries and compare age-adjusted moralities. The code is available on GitHub. Keep in mind, sorry to repeat, this is just for the curious geeks.

Appendix – Mortality rates.

Let’s now discuss, briefly, mortality rates. This is a very complicated issue, not just for the non-experts like me. We will have better estimates only much later. At the moment, there is no evidence about the existence of multiple strains of CoV-SARS-2 exhibiting different aggressiveness. There are of course different strains, as viruses do constantly mutate, but the idea that some country is more affected than others because of different strains – at the moment- seems to be just a way to justify their own shortcomings. Of course, we will understand this later.

Let’s consider Washington state (not shown here) that resulted in a very high mortality. This was the result of very little testing of the general population, and a spike of deaths of elderly people arising from outbreaks in retirement homes. New York is on the opposite scale as it was a more ‘standard’ outbreak. We noticed mortality rates going from very high to very low and bouncing back. These are all artefacts of sampling.

I re-propose here some graphs comparing demographics. First, the age-adjusted mortality. The blue bar in China at ~1% is the mortality rate in China outside Hubei. This, in my opinion, is a good measure and backed up by epidemiological studies. WHO is setting this number at ~3.5% because at the moment they are just dividing confirmed deaths by confirmed cases, a number likely to be overestimated.

The blue bars for other countries are adjusted to the different demographics of individual regions. Italy has an older population compared to most other countries and, therefore, higher mortality is to be expected. There is some report suggesting that also men are more likely to die compared to women. Therefore, in the middle graph, I compare the demographics of Italy, UK and Germany to China. The red bars in the first graph are the mortality rates inferred from Hubei, thus in a situation where the health system is overwhelmed. As you can see, in Italy we can expect apparent mortality rates of almost 10%. Indeed, at the moment this value has been exceeded. However, here the keyword is ‘apparent’.

Therefore, while I might be still discussing COVID in this blog, I will probably stop speaking about mortality rates. Eventually, these numbers could be very low. Perhaps, we might have ‘just’ 0.5% of the population at risk of death, maybe 1% in countries with older demographics. However, keep in mind that like the WHO has always said, the issue is the overwhelming of the health systems. UK, Hubei and Italy have around 60M inhabitants. This means that a ‘do-nothing’ policy would result in 300-600k deaths in each country (40M world-wide). Or half of that if some sort of herd immunity would protect us. To put this in perspective, ~500-600k people die in the UK every year (60M world-wide).

Coronavirus – data mining

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow advice of your national authorities and health system.
NOTE. This post was updated on 15/3.

The data | The data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering is available on GitHub.  To adjust mortality rates by local demographics, I have downloaded the population pyramid data from www.populationpyramid.net. There are several estimates for age-dependent mortality. I was able to find only the following pre-print for mortality in Hubei compared to the rest of China. The dataset analysed was small, let me know if you find something better.

The software | In spare time, I prepared a bit of Matlab code that can import the JH data and does just two simple things: compare trends between different countries and compare age-adjusted moralities. The code is available on GitHub. Keep in mind, sorry to repeat, this is just for the curious geeks.


I summarize a few countries I checked. As I am Anglo-Italian, and with Italy and UK having adopted very different strategies to fight conoravirus, I developed this code to check trends between UK, Italy and Hubei. It is interesting that these three territories have similar population sizes but, until now, experienced the epidemics in different ways. Hubei got off-guard because it is the origin of the epidemics. Italy, together with South Korea and Iran got off-guard because they thought the coronavirus was somehow under control. UK might have done fewer mistakes so far and controlled the spread of the virus better and it has decided not to further contain the epidemics against WHO advice. Let’s see.

I synched the curved to a number of confirmed cases equal to 400. By chance, this is about the number of cases since we have data from Hubei, the day when Hubei went into lockdown, and a similar number when UK decided not to contain the virus.

Italy, just before and just after introduced first a local lockdown and then a national lockdown. Italy and Hubei seem to be on a similar trajectory of confirmed cases. For the UK is too early to say. We should keep in mind that confirmed cases depend on the methodologies of testing. Hubei’s and Italy’s health systems got overwhelmed, therefore it is possible at a certain point might have struggled to test the general population. The UK has decided to stop screening the general populations. Therefore, the reported deaths might be more realistic as numbers. At the time of writing, the JH dataset is one day behind, but we know that the UK is now in line with the other curves, and Italy is overshooting Hubei’s trajectory. Mortality rates are heavily affected by the reporting of confirmed cases. We will know the actual mortality rates only after epidemiologist will be able to do their statistical work retrospectively. More on this at the end of this post.

What about other countries? South Korea is interesting as they did not go into lock down but they also had a major outbreak. They were able to contain it by tracking those infected.

Assuming that Korea counted all covid-related deaths, their strategy was rewarded with a successful containment and lower deaths than other regions. Spain seems to be the EU country that will struggle next, let’ see…

Unfortunately, it seems that Spain is on the same trajectory compared to Italy and Hubei. But remember, Hubei succeed to contain the outbreak, which gives hopes. This and the experience in Korea is why WHO is still recommending to attempt containing the virus.

The same is true for France.

What about Germany?

For confirmed cases, Germany looks like on a similar trajectory. However, unless I did a mistake, the mortality rate seems very low. There are reports on the news that Germany considers as covid-related deaths only those patients who did not have other important related pathologies.

This, of course, would bias completely the curves we presented, but the situation in Germany might be not different form other countries. We’ll understand this in the future. Now a few comments on mortality rates. Initially, many of us were puzzled by the differences in mortalities between countries. There are several factors that influence these statistics: i) confirmed cases are underestimated in different ways in different countries because of testing capacity or policy. ii) covid-related deaths seem to be accounted similarly in many countries, except for Germany. iii) different countries have different demographics and iv) when a health system is strained both mortality might increase and confirmed cases decrease. All this considered, I just thought to give a reference for demographic adjustments.

I used mortality figures in Hubei and rest of China as to estimate the worst and best case scenario for an overwhelmed and a coping health system. The red and blue curves are these values adjusted by demographic differences in each country.

Then it seems that the high mortality in Italy is just demographics. Pay attention that these are cumulative statistics and, therefore, even if the situation improves massively like in Hubei, the mortality remains high because historically it was high. Thus, so far it looks like that only in Hubei and Italy the outbreaks arrived to the point to fully overwhelm the health systems. However, check the drift of the Italian curve, that is what might (hopefully not) happen in other countries that are on similar trajectory.

Keep in mind, I am no expert. I think however that there are two possibilities that explain this, and probably they both coexist. First, when ICU is overwhelmed, we rescue fewer people. Second, when a country is overwhelmed, there might be also fewer testing. So, plenty of limitations in this data (mortality rate data are no great, I am no expert, and several factors might explain the trends)… but at least there is some pattern that might indicate what is happening.

To conclude. Every country can still do what Hubei did. Not my work, but WHO’s. We need to protect the most vulnerable waiting for the vaccines and drugs that WILL come. Take care and find ways to keep positive and help people around you!