Irakli Loladze, PhD; Mathematical Biologist and Data Scientist.

A ghost is haunting the world — the ghost of SARS-CoV-2. With a corona on its head, it marches through the countries, suffocating thousands of humans daily. With every passing hour, it has been getting hungrier. It consumes not only lives but the entire economies. The world order, as we know it, may be on the brink of collapse.

It is insane that in this highly interconnected world, we do not have a central repository for all the past and ongoing treatments for COVID-19.

But here is a paradoxical and shocking truth.The humanity has a weapon to kill the ghost but fails to use it. The weapon is nearby, readily available. The only obstacle is connecting a few obvious dots in a way we have never connected them for a pandemic. Connecting the dots is feasible and doable. We are capable and have all the tools to do so.

Image courtesy of the Centers for Disease Control and Prevention (CDC)

What are the dots?

I’m writing this hastily to get it out ASAP, so there could be some errors. As a mathematician and practicing scientist, I strive for accuracy and will correct technicalities/errors later. But I believe the gist of what’s below is correct. Here are the seven dots:

1)The current pandemic is not a 1918-type pandemic. This is not an influenza for which no cure exists. This is a variant of SARS coronavirus, formally called SARS-CoV-2.

2) SARS is potentially treatable. We (the world) have identified promising drugs following the previous two outbreaks of deadlier coronaviruses, namely SARS-CoV and MERS-CoV. Examples of mechanisms of action and potential treatments for SARS and MERS can be found in Thiel et al (2003), Cinatl et al (2003), de Wilde et al (2013), de Wilde et al (2014), and De Clercq et al (2014). Broad spectrum antivirals, including those acting on SARS-CoV-2 have been proposed (Sheehan et al 2020, Li & De Clercq 2020)

3) To identify a cure for the new coronavirus, SARS-CoV-2, we need treatment data. WE NEED DATA.

4) How does the world get relevant data? Sadly, only via clinical trials, mostly via randomized controlled clinical trials. These trials are safe but … excruciatingly slow. For today, they are DEADLY slow. The ongoing clinical trials for COVID-19 are expected to wrap by… the end of this year! END OF THIS YEAR! Imagine a person drowning — this is our world today — and we saying let’s wait for a few month and then rescue her. If a study fails, it takes months for the world to know about the failure. In the times of a pandemic, this protocol becomes deadly — other patients may given a similar non-effective treatment by desperate physicians while the authors of the failed study prepare a manuscript. Absurdity, deadly absurdity! But that’s what the world is doing right now.

5) Here where it really gets really interesting. If you look at the crust of the Scientific Method, you will see that there is nothing that says “Clinical trials are the only way to get data for finding cures.” This is simply a habit the modern world developed. Reliable, safe, and SLOW. The methodology of clinical trials was developed relatively recently — in the second half of the last century. Not only it became the gold standard but the only standard for identifying and approving cures.

6) It gets worse. The reliance on clinical trials created an incredibly high, and I would say snobbish, yes snobbish, barrier for accepting relevant treatment data. Any treatment data coming from sources other than trials are considered “low quality” and they are routinely discarded, swept under the rug, hidden or just hoarded locally by physicians. However, there is no fundamental reason for discarding low quality data. Every data point, irrespective of its origin, carries a kernel of information. By collecting enough of those kernels and analyzing them, we will find a cure. And find it fast!

7) New non-clinical data on treatments for COVID-19 are generated by physicians daily, who try various existing drugs off-label. However, the vast majority of the data are never compiled in any central place. In fact, such a central place does not exist! A tiny percent of the data eventually, maddeningly slowly, percolates to the world via peer-reviewed papers and preprints. In short, the bulk of non-clinical-trial data, thousands of treatment data points generated daily, are lost to the world because they do not rise to the accepted standard of randomized clinical trials. They are “low quality data” the world shuns upon.

Case example: On March 8, Margaret Novins, a 53-year old New Jersey nurse, fell ill and had fevers. Her condition deteriorated rapidly. On March 19, she tested positive for SARS-CoV-2. She was administered hydroxychloroquine and felt better the next day. Within three days, the fever was gone. The only reason I know about this treatment is because a reporter wrote up her case. Data from thousands of cases like this are lost, never added to any repository. Even if Margaret’s overtaxed physician finds time and desire to report her case, writing a preprint takes weeks. The barrier for data entry is too high for physicians and nurses exhausted by the tsunami of incoming patients. And since these data are not from a randomised clinical trial, there is even less motivation to report them. However, it would take the same physician, or her her assistant, only three minutes, may be even less, to enter Margaret’s anonymized data as a distinct row into a centralized spreadsheet such as the COVID-19 Treatment Worldsheet -in Vivo sheet I created, a snapshot of which is below.

Another physician, Dr. Zelenko, added zinc sulfate to the cocktail hydroxychloroquine and azithromycin cocktail claiming that he cured all the 500 patients treated with the cocktail. However, these patients were not tested for the virus, yet the physician sent a letter to the White House providing only a summary of the results. Such information is incomplete and potentially misleading. Instead, having 500 rows in the spreadsheet in a standardized format such as the age, gender, dosage, symptoms, outcomes and other information, when combined with treatment data from other locations, will be much more informative. The problem is that all these physicians do not have any central repository to place the results. No authority encourages them to post their results because the results do not rise to the occasion of clinical trials.

How to connect the dots?

The world — stop being such a data snob! Lower the barrier for data acceptance. Embrace all the relevant datasets. Do not discriminate by the size, origin, or quality. The data can come from any kind of trials, doctors, nurses, researchers, lab technicians, individuals, peer-reviewed manuscripts, preprints, news articles, anecdotal evidence, and other sources. Let’s gather all the treatment data. Worry about their quality? We can always, in real-time, sift and filter the data by its quality. This is what Data Scientists do. But let’s first collect them in one place!

It is insane that in this highly interconnected world, we do not have a central datasheet for all the past and ongoing treatments of COVID-19.

What I suggest here is neither original nor new. Centralized databases, updated in real-time, exist for various human endeavors, ranging from weather to stock markets. However, when it comes to treating a pandemic the world has no such centralized database. For the number infected and dead, we do have a centralized source, e.g. Wolrdometer, updated in real-time. But we have nothing like that for treatments, neither for in vivo nor for in vitro.

And let’s not get confused by ClinicalTrials.gov — a database of privately and publicly funded clinical studies. It contains info about trials but has no compiled sheet on treatment outcomes for COVID-19. What data scientists and physicians need is a single spreadsheet with uniform format, where every row represents a single patient/treatment. Such a database does not exist as paradoxical as it may sound.

Instead, we have disorganized, scattered around the world journals in various languages that raise high barriers, both in terms of effort and time, for adding new data. And while the pandemic is consuming the world, we still insist on keeping the same high barriers for accepting new data.

The world slowly, criminally slowly, is awaking to the need for lowering the barrier for accepting treatment data. A few days ago, the WHO started an alternative trial, a distributed trial, called Solidarity. It lowered the barrier for data acceptance but this is not enough. It only collects data for four treatments. Most of the real-world treatment data generated by doctors and hospitals are still not compiled anywhere, the data — for all the effective purposes — are lost daily.

So here is the Manifesto:

Create a central, updated in real-time, repository. It can be just a spreadsheet editable by anyone, similar to a Wikipedia page. For starters, I created one and started placing the data. Anybody can add new columns as needed. The only constraint is that each column contains only the same type of data, e.g patient age, gender, drug, dosage and so on. Add new treatment data to the repository as soon as they are available. No waiting for preprints, for trials to finish, or peer-reviews to be completed (these can be done later). No data hoarding. The already existing data from preprints and papers can be added as well. The data can be analyzed by anyone in real-time. The high quality data can be easily separated from the low quality via identifiers in the “treatment type” column. Each individual row, even if it is only partially filled, and irrespective of its origin, brings new kernels of information. Statistical, data science and AI techniques will be applied to identify promising treatments. Both the collection and the analysis of the data will be distributed over the world. The real-time feedback from the dataset will be used to adjust dosages, combine drugs for more effective drug cocktails. Guided by the coordinated data flow, a cure will be found in a matter of weeks.

If the above steps had been implemented in December of the last year, we would already have had a cure. The world never connected the dots, but instead stayed disorganized and disjointed, selectively letting to trickle the data to a few journals in disparate languages.

It’s about time to connect the dots to identify a cure!