Introduction

Regarding the threat of the new coronavirus infection (COVID-19), a state of emergency has been issued in Japan as well, calling for the danger of medical collapse. In particular, in response to the rapid increase in the number of infected people in urban areas, measures such as accepting mild and asymptomatic people to accommodation facilities have begun. In addition, as reported on TV, it has been pointed out that medical collapse has occurred in China, Italy, the United States, etc., and there are cases where sufficient treatment cannot be received due to paralysis of hospital functions. In this article, I would like to define ** medical collapse quantitatively ** based on the data published by Johns Hopkins University, while defining what medical collapse is.

What is medical collapse?

According to Wikipedia, the origin of medical collapse It seems that it is used to mean "a stable and continuous medical care provision system will not be established due to a decline in the morale of doctors, an increase in defensive medicine, deterioration of hospital management, etc.", but regarding COVID-19, " There is a shortage of medical staff and medical equipment, and it is difficult to treat the severely ill. " In other words, it seems that the meaning changes slightly depending on the context in which it is used. In the former sense, [this graph](https://ja.wikipedia.org/wiki/%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB:Oecd- Looking at doctorconsult2013.svg), it seems that Japan and South Korea stand out as countries with a large number of consultations compared to the number of doctors per population. This time, I would like to define the definition of medical collapse * related to * COVID-19 as follows.

Medical collapse is a phenomenon in which the mortality rate rises as a result of the tightness or depletion of human and physical medical resources due to an increase in the number of patients (number of people requiring treatment).

Quantify medical collapse

Data published by Johns Hopkins University aggregates daily cumulative infections, recovery, and deaths from around the world. I am. Based on this data, the following two indicators are calculated.

Mortality rate rD: Number of deaths on that day generated from the number of people requiring treatment (C) on the previous day
Recovery rate rR: Number of recoverers on that day generated from the number of people requiring treatment (C) on the previous day

Actually, there is a time lag from hospitalization to death, or from hospitalization to recovery, so it is necessary to take this into account when calculating, but since it does not include data on the time required for death or recovery per patient, it is simple. I decided to calculate. Although it is simple, the number of patients requiring treatment (C) seems to include patients with varying days from hospitalization, so it is considered to be an index to some extent. By the way, the number of people requiring treatment is calculated by the following formula.

Number of people requiring treatment (C) = Cumulative number of infected people (PS) --Cumulative number of recoverers (RS) --Cumulative number of deaths (DS)

Try to calculate with Python

This code is available on GitHub. It is saved in Jupyter Notebook format. (File name: 03_R0_estimation-WLD-RDR-01c.ipynb)

GitHub: okimebarun / 01_COVID19_analysis

code

I won't cover all of the code in the article because it will be long, but I will explain the key points in the calculation.

Johns Hopkins University data is aggregated by country and state, but since there is a mixture of country-specific and country-wide data, we are re-aggregating by country. ..

def getSumOfCountry01(df, country):
    df1 = df[df["Country/Region"] == country]
    # summarise for each country/region
    if df1['Province/State'].isnull().any():
        df2 = df1.fillna({'Province/State':'SUM'})
        df3 = df2[df2['Province/State'] == 'SUM']
    else:
        df3 = pd.DataFrame(df1.sum(axis=0)).T
        df3['Province/State'] = 'SUM'
        df3['Country/Region'] = df1.iloc[0,1]    
    return df3

This is the part that calculates the above mortality rate rD and recovery rate rR. The moving average is taken with a little consideration of the time lag.

def calcCRD(df, keys):
    #
    nth = keys['nth']
    tf = keys['tf']
    #
    nrow  = len(df)
    getV  = lambda s, tag: df.loc[s, tag] if s < nrow else np.NaN
    # tf [days]Moving average to the end
    getV2 = lambda s, tag: np.nanmean([getV(u, tag) for u in range(s,s + tf)])
    df.loc[0, 'C'] = 0.
    for t in range(1, nrow):
        df.loc[t, 'C'] = getV(t, 'PS') - getV(t, 'RS') - getV(t, 'DS')
        if df.loc[t-1, 'C'] > nth: #For measures against small denominator
            df.loc[t, 'rR'] = getV2(t, 'R') / df.loc[t-1, 'C']
            df.loc[t, 'rD'] = getV2(t, 'D') / df.loc[t-1, 'C']
        else:
            df.loc[t, 'rR'] = np.NaN
            df.loc[t, 'rD'] = np.NaN
    return df

Multiple files are combined into one data frame.

def CRDinWorldArea01(area):
    keys = { 'nth':100, 'tf': 3 }
    df1 = makeCalcFrame(100) #
    df2 = readCsvOfWorldArea_Confirmed(area)
    df3 = readCsvOfWorldArea_Recovered(area)
    df4 = readCsvOfWorldArea_Deaths(   area)
    df = df1
    df = mergeCalcFrame(df, df2)
    df = mergeCalcFrame(df, df3)
    df = mergeCalcFrame(df, df4)
    df = calcCRD(df, keys)
    return df

result of analysis

Now let's take a look at the calculation results. The calculation target is countries where the number of infected people exceeds 5,000 as of April 15.

Overall trend

First, plot the average recovery rate rR on the horizontal axis and the average mortality rate rD on the vertical axis. Mortality rates vary considerably from country to country. Countries with particularly high mortality rates include EU countries such as Spain, the United Kingdom, Italy, France and Belgium and Iran. Conversely, countries with low mortality rates include Australia, Israel, Norway, Chile, South Korea and Germany. The recovery rate also varies widely, but it is characteristic that Iran and Peru are high. China seems to have a high recovery rate because it has been a long time since the pandemic.

Comparison within G7

Let's make a comparison within the G7. First is the mortality rate. The country on the left has a relatively low mortality rate, and the country on the right has a relatively high mortality rate. In countries with relatively low mortality rates (left), mortality rates tend to decrease as the number of people requiring treatment increases. In particular, the United States has the highest number of infected people in the world, but it is suppressed to around 0.5%. In countries with relatively high mortality rates (right), ** mortality rates tend to increase as the number of people requiring treatment increases **. When the number of people requiring treatment exceeds 1000, the mortality rate has increased rapidly from around 1% to around 3% at maximum.

Let's look at the recovery rate. In countries with relatively low mortality rates (left), with the exception of the United States, recovery rates appear to increase when the number of people requiring treatment is relatively small. In Canada and Germany, the recovery rate is skyrocketing when the number of people requiring treatment reaches a certain level. In countries with relatively high mortality rates (right), ** the recovery rate tends to decrease as the number of people requiring treatment increases **.

Comparison of countries with high and low mortality rates worldwide

Let's look at a comparison of countries with high and low mortality rates globally. First is the mortality rate. The country on the left has a relatively low mortality rate, and the country on the right has a relatively high mortality rate. In countries with relatively low mortality (left), a flat shape of 0.5% or less can be seen. In countries with relatively high mortality rates (right), without exception, ** mortality rates tend to increase as the number of people requiring treatment increases **.

Let's look at the recovery rate. In countries with relatively low mortality rates (left), recovery rates have skyrocketed at a certain number of people requiring treatment. In countries with relatively high mortality rates (right), recovery rates tend to decline as the number of people requiring treatment increases.

BCG Japanese strain inoculated countries and neighboring countries

A paper on whether regular BCG vaccine vaccination may affect COVID-19 was published in Germany, the Netherlands, Australia, etc. The clinical trial has begun. So let's take a look at the impact of BCG, especially Japanese equities. First is the mortality rate. The left is the country inoculated with BCG Japanese strains, and the right is the neighboring country with non-BCG Japanese strains inoculated. Mortality is high in Iran, but mortality is relatively low in other non-BCG Japanese strain-inoculated countries.

Let's look at the recovery rate. In BCG Japanese strain inoculated countries, the number of people requiring treatment is less than 1000 and the recovery rate is high, but in non-BCG Japanese strain inoculated countries, the recovery rate does not increase unless the number of people requiring treatment increases to 1000 or more. .. In other words, ** BCG Japanese strain inoculation does not have the effect of lowering the mortality rate, but it may have the effect of increasing the recovery rate at an early stage **.

Consideration

From the above, the following trends were found from the data as the verification results regarding the collapse of medical care.

In countries with high mortality rates (Spain, UK, Italy, France, etc.), ** there was a tendency for medical collapse to increase as the number of people requiring treatment increased **. The effect is to increase the mortality rate from 1% to 3%.
In countries with low mortality rates (Germany, Austria, Chile, South Korea, etc.), ** the recovery rate tended to rise sharply when the number of people requiring treatment reached a certain number **.
In BCG Japanese strain-inoculated countries, the recovery rate tended to increase early when the number of patients requiring treatment was less than 1000. In other words, it may not become severe.

Furthermore ...

The fact that the mortality rate increases as the number of people requiring treatment increases means that there will be a significant difference in the total mortality rate.
Even within the same EU, the trends are quite different, for example in France and Germany. It suggests that health resources and policies have a significant impact on COVID-19 mortality.
There may be a strong link between not becoming severe and avoiding medical collapse. In other words, the theory is that the bed can be opened early because it does not become severe, and sufficient medical resources can be invested in the treatment of the severely ill, resulting in lower mortality.

Reference link

I referred to the following page.

[PYTHON] Let's test the medical collapse hypothesis of the new coronavirus