China has closed down its last coronavirus hospital. Not enough new cases to support them.

Doctors in India have been successful in treating Coronavirus. Combination of drugs used: Lopinavir, Retonovir, Oseltamivir along with Chlorphenamine. They are going to suggest same medicine, globally.

Researchers of the Erasmus Medical Center claim to have found an antibody against coronavirus.

A 103-year-old Chinese grandmother has made a full recovery from COVID-19 after being treated for 6 days in Wuhan, China.

Apple reopens all 42 china stores,

Cleveland Clinic developed a COVID-19 test that gives results in hours, not days.

Good news from South Korea, where the number of new cases is declining.

Italy is hit hard, experts say, only because they have the oldest population in Europe.

Scientists in Israel likely to announce the development of a coronavirus vaccine.

3 Maryland coronavirus patients fully recovered; able to return to everyday life.

A network of Canadian scientists are making excellent progress in Covid-19 research.

A San Diego biotech company is developing a Covid-19 vaccine in collaboration with Duke University and National University of Singapore.

Tulsa County’s first positive COVID-19 case has recovered. This individual has had two negative tests, which is the indicator of recovery.

A coronavirus tracker put up by Johns Hopkins University of Medicine shows that more than 73,000 people worldwide have recovered from Covid-19 so far.

Plasma from newly recovered patients from Covid-19 can treat others infected by Covid-19.

So it’s not ALL bad news. Let’s care for each other and stay focused on the safety of the most vunerable.

(Borrowed from my amazing friend and kick ass genius Dr Qusai Hammouri and Yousra Y. Fazili).

And this number is a lot higher in reality, since most cases don’t need hospitalization, and since “recovered” in this case means that they have tested negative in two consecutive tests.

Right. Natural antibodies! I received human antibodies against rabies once (so-called “passive immunization”), so it’s definitely a kind of treatment that is in standard practice. Although expensive, and there are logistical challenges here. But the good news is, governments don’t care about money much these days.

That’s new cases per day in Italy, and they seem to have stabilized. Means, the growth is now linear and not exponential. Means, Chinese-inspired lockdown measures work in Western democracies where they are necessarily enforced less and more reliant on citizen participation. They may not work to bring the numbers back to zero, but they work to flatten the curve, preventing (much of) the feared excess mortality from lack of treatment options. I think we can expect a similar development in much of Europe in about a week to 10 days. Italy is simply that much ahead with their containment measures.

I was too optimistic with my above estimation re. the trajectory of the COVID-19 epidemic in Italy. But anyway, by now it’s apparent that the growth rate is no longer exponential, which means, the interventions have an effect.

Then today I thought, can we predict the total number of cases or deaths in a country then? I look around, and the best model I can find is in a completely under-appreciated paper on a pre-print server: “Predicting the ultimate outcome of the COVID-19 outbreak in Italy”. Its first predictions from March 19 about the Italy situation were not accurate, but of course a model becomes more accurate the more data it can rely on. So I expect its predictions will be better now.

(Note, you select the country in the bottom-right using the little circles in front of country names.) There might be ways to create an even better model, of course. But this one (based on a logistic growth model) is already much, much better than just looking at a graph and visually fitting a curve – which I used to do so far. It’s like the weather forecast but for a pandemic. Logistic growth is simply what happens to an exponential function that runs out of fuel (means, R<1 as they say, the goal of social distancing and lockdown: everyone infected infects less than one more person).

So for everyone struggling with the uncertainty of how long this first wave of the pandemic will go on and how many people will die, I warmly recommend this tool. It even has a line that says: “Estimated number of days until it’s over: …”. For Italy, the prediction from 2020-03-26 looks like this:

Best information about how to deal with the virus in the US comes from Dr. Anthony Fauci, infections disease expert, and in my country the no-bullshit voice of sanity, science, and practical, patriotically factual advice on the current pandemic.

I read the paper, freshened up on logistics curves, but do not understand the logic completely unless it’s by approximation. The clever idea is this: think about a number of cases (or deaths, which is better because the data are more reliable) no longer grow, as a function of time. Then solve for time, and you get the day when deaths stop. Ok, but how do you know how many deaths are “needed” for this to happen?

The approximation seems to be the following: at some point the growth rate of deaths (as a function of the number of deaths) follows a downward linear trend. If you can estimate the trend (including constant term), then you can look at where it hits the X axis. That is the total number of deaths that will occur. The problem with this is that the growth rate of a logistic does not follow a linear trend, exactly. You can employ statistical approximations, of course, and in the paper you see that the one the author employs fits the data well, but I am not sure what the underlying mathematical reasoning is – my calculus is rusty enough that it would take me too long to derive and analyze a formal expression for the average daily rate of growth of deaths over the last two days, as a function of the cumulative number of deaths (which is what the author estimates). I covered a couple of whiteboards, but could not find an intuitive expression.