COVID-19 data configuration and statistical analysis

CORONAVIRUS REPORT
Ansh Jain

Author’s Note
During a shutdown, the things that mark our days—going to school, engaging in sports,
watching a movie with friends—vanish and time takes on a flat, seamless quality. Without
some self-imposed structure, it’s easy to feel a little untethered. A friend recently posted on
social media: “For those who have lost track, today is Blursday the Fortyteenth of Maprilay.”

Giving shape to time is especially important now, when the future is so shapeless. We do
not know whether the virus will continue to rage for weeks or months or, god help us, on
and off for years. We do not know when we will feel safe again. And so many of us, minus
those who are gifted at compartmentalization or denial, remain largely captive to fear. We
may stay this way if we do not create at least the illusion of movement in our lives, our long
days spent with ourselves or families.
__________________________________________________________________________________

LINK TO EVERY COUNTRY TOTAL DEATH AND TOTAL CASE

Prelude
In December 2019, a novel coronavirus was isolated, after a cluster of patients in
Wuhan, China were diagnosed with pneumonia of unknown cause. This new isolate
was named ‘SARS-CoV-2’ and is the cause of the disease COVID-19. The virus has
led to an ongoing outbreak and an unprecedented international health crisis. The
number of infected people is rapidly increasing globally and most probably is a vast
underestimation of the real number of patients worldwide, as infected people are
contagious even when minimally symptomatic or asymptomatic. The spread of the
disease has presented an extreme challenge to the international community, and
policy-makers from different countries have each chosen different strategies,
depending on the local spread of the virus, healthcare-system resources, economic
and political factors, public adherence, and their perception of the situation.

1.0 Introduction
The following report aims to identify the primary factors influencing the spread of
Covid-19. To do this, we analyzed the rate of spread in MEDCs and LEDCs,
countries differing significantly in development. MEDCs, being more economically
developed, tend to have superior healthcare, higher life expectancy, and generally
better infrastructure, contrasting with LEDCs. This report aims to understand
whether the characteristics of MEDCs and LEDCs can significantly impact the rate of
spread of Covid-19, as well as more obscure factors that could have a greater
impact than previously thought. In this report we will be examining 3 different
MEDCs and LEDCs to develop a clear conclusion on whether we believe a country's
development correlates to the rate of spread of Covid-19.

The 3 MEDCs which will be explored:

CHINA ITALY JAPAN

The 3 LEDCs which will be explored:

ISRAEL (CONTROVERSIAL) INDIA INDONESIA

2.0 Hypothesis for Factors Impacting Spread
Before diving into the details from different countries, we hypothesized what
apparent factors could significantly contribute to the disparity in case and death
rates across the world.
Firstly, medical experts have confirmed the risk the virus poses to different
age groups. For this coronavirus, SARS-CoV-2, the elderly are more susceptible to
the dangers of the virus, and are more likely to become critically ill or to die when
compared to the youth demographic. When looking at data from China and other
MEDCs, we concluded that people between the ages of 40 and 49 have an
estimated CFR (Case Fatality Rate) of about 0.4%; for those 80 and older, it’s 13.4%.
This gulf of survivability is already playing out in some countries with older
populations, such as Italy.
Additionally, Covid-19 has been demonstrably deadlier for those with
existing health conditions, including lung disease (often caused by smoking),
cardiovascular disease, severe obesity, diabetes, kidney failure, and liver disease. So
countries — or regions — with less healthy populations might also be seeing big
differences in the rates at which people are dying from the illness.
Beyond the varying of the impacts of the illness itself, there are lots of
variables on how numbers are being gathered and recorded. Perhaps the biggest
factor here is testing. When experts calculate a basic fatality rate, it can be as simple
as dividing the number of deaths by the number of confirmed cases (although - and
we’ll get to this later - it really shouldn't be).
Since the international spread of the novel coronavirus, countries have varied
widely in their ability and willingness to roll out testing. So that means the
denominator (the number of cases) can be closer or further from an accurate count
of how many people actually have the virus. The larger the percentage of a
population that has been tested, the more complete picture we will get of the
virus’s actual fatality rate there.

The other issue with the poor testing rates is sampling bias. Tests that are
available are usually saved for the sickest and riskiest cases. This pushes the fatality
rate higher than it actually is because the testing is more likely to omit mild or
asymptomatic cases and instead overrepresents those who are more likely to die. It
also means that it presents the total cases as much less than it is, and gives off the
picture that the rate of spread will be slower. So, as testing becomes more
widespread in various countries, their fatality rates will drop. This particular issue is
something that pervades Japan’s response to the pandemic - something you’ll read
about in our analysis below.

3.0 Data and Analysis
The first stage of this process was to consider what data from the sheet we
received would be relevant to our study. The purpose of this data would be to allow
us to see any major trends to then evaluate. As our paper is focused on the rate of
spread of the disease in MEDCs and LEDCs, the cumulative cases would be
integral. The deaths per day, while not directly related to the spread of Covid-19,
would provide valuable insight contributing to the importance and relevance of our
analysis.
To begin the evaluation process, we initially modeled the data on a graphing
software for ease of visualization. This raised the subsequent consideration of an
appropriate function to represent the data points.

3.1 Function Selection
To determine the ideal function to represent the data as well as fully
understand the nature of the spread we experimented with a number of
expressions. The previews are from the data of Italy.

1. Linear Function

2. Quadratic Function

3. Cubic Function

4. Quartic Function

5. Sine Function

6. Cosine Function

7. Tangent Function

8. Exponential Function

9. Root Function

10. Logarithmic Function

11. Absolute Value Function

12. Verhulst Function

After modeling the 12 different functions on DESMOS (an online modelling
tool), we compared the accuracy of each function using the R2
value. A common
trend appeared to be that as the power of the function and the amount of variables
increased, the R2
value approached 1. However, the Verhulst function, created to
model population increase, stood out due not only to its high average R2
value, but
it’s prediction of a plateau.

3.2 Data Analysis for Total Cases and Total Deaths
MEDCS: Italy Japan China
LEDCS: Israel India Indonesia

Graph of Total Amount of Recorded Cases for the 3 MEDCs and 3 LEDCs
(x: Days, y: Cases)

(The line for JAPAN is covered by INDIA)

Graph of Total Amount of recorded Deaths for the 3 MEDCS and 3 LEDCS
(x: Days, y: Deaths)

From comparing the graph of total cases and total deaths we are able to see
how the quality of healthcare differs for the MEDCs and LEDCs. For example,
despite CHINA having way greater total cases than INDONESIA, we can see that
their total number of deaths are similar. Another example would be JAPAN and
ISRAEL. Despite Japan having a predicted trend of increasing total cases, its
predicted total deaths is close to that of Israel's. From these 2 pieces of evidence
we are able to infer that the healthcare provided in MEDCs is better than LEDCs.

3.3 Total Amount of Cases Tested SOURCE
To further look at the rate of spread between the countries chosen we
decided to look at an external source to compare the ratio between a country's total
test to the percentage tested positive. If the ratio of positive test to total test is high
it would also connect to the rate of spread. We were not able to get our hands on
the data of total tests in CHINA as the country isn’t open to sharing their statistics.

Country DATE TOTAL
TESTED
TESTED
POSITIVE
PERCENTAGE of
TESTED POSITIVE to
TOTAL TESTS
PERCENTAGE
INCREASE IN
TOTAL TESTS
PERCENTAGE
INCREASE IN
POSITIVE TESTS
RATIO OF
PERCENTAGE
INCREASES
(POSITIVE:TOTAL)
Italy Apr 6, 2020 721732 128948 17.9%
Italy May 1, 2020 2053425 205463 10.0% 184.5%   59.3%   0.3214
Japan Apr 6, 2020 46172 3654 7.91%
Japan May 1, 2020 174150 14281 8.20%   277.2% 290.8%   1.049
Israel Apr 6, 2020 124828 8430 6.75%
Israel May 1, 2020 385922 15946 4.13%   209.2%   89.2% 0.426
India Apr 6, 2020 101068 4067 4.02%
India May 1, 2020 902654 35043 3.88%   793.1% 761.6%   0.960
Indonesia Apr 6, 2020 11460 2273 19,83%
Indonesia May 1, 2020 76538 10118 13.22%   567.9%   345.1%   0.608

A very distinct trend which we notice from just looking at this data table is
that the 2 major LEDCs (INDIA and INDONESIA) have had the biggest increase in
total number of tested cases. We can infer that with an increased amount of tests, it
also means that there is an increase in the rate of spread discovered from the trend
in total cases. Despite INDIA having a 793.1% increase in total tests, there is also a
761.6% increase in total positive cases, hence showing that the percentage of total
tested positive to percentage of total tested on April 6th can now be applied to a
bigger audience, hence showing a larger spread of virus. The ratio of percentage
increase between total positive cases to total tested cases is useful as it shows how
well a country has contained the virus. If the ratio of percentage increase is 1 it
means that the virus continues to spread at the previous rate however (in this case

April 6th). If the value is below 1 that means the virus is spreading slower than
before and vice versa.

Another distinct piece of data is the ratio of percentage increase between
total positive cases and total cases for JAPAN. Japan is the only country with a ratio
higher than 1, showing how the amount of total positive cases are increasing in the
total amount of cases tested, corroborating with the predicted exponentially
increasing spread of virus for JAPAN from the Verhulst graph on the total number of
cases shown in section 3.2.

JAPAN and INDIA, the 2 countries with the closest to 1 ratio of percentage
increase between total positive cases and total cases are also the 2 plotted lines
which show a continuous exponential increase in the graph in section 3.2. Whereas
the ITALY, ISRAEL and INDONESIA (countries with low ratio of percentage increase)
are the plots which show a plateau in total cases as there is a decrease in rate of
spread of the virus.

To further analyze the data we decided to plot the total tested cases (x-axis)
against the total positive cases (y-axis) over a period of around 2 month in a
quadratic function to see if we could notice a trend. We used a quadratic equation
as it allows us to see the point of inflexion (point at which percentage of positive
tests out of total cases go down)

From this graph we can see the correlation to the ratio of percentage
increase between the total positive cases and total tested cases as the only plot
which is drastically bigger than the rest is JAPAN; the country's ratio of percentage
increases higher than 1. This tells us that according to the statistics given, Japan's
cases will continue to surge whereas the spread of virus will begin to slow down for
the other countries.

We also notice how the plot for INDIA is considerably more stretched out
than most of the other countries. And the x-value for the point of inflexion is
considerably higher than the rest. This piece of data once again reflects back to its
high ratio of percentage between total positive cases and total cases. This means
that the amount of positive cases are able to keep up with the increases in total
tested cases showing the continuation of the spread of the virus. This may also
mean that the countries’ social distancing measures and other regulations are not
working as well as the other countries.

4.0 Identification and Evaluation of Factors

4.1 Population Density SOURCE
Understanding that Covid-19 is a virus which is able to be droplet transmitted and
social distancing has been implemented in various nations to reduce the spread of
the virus. We decided to look into how the different population densities affected
the number of total cases for the 3 MEDCs and 3 LEDCs. Through gathering data
on the number of bodies across an area of 1km^2, we decided to divide the total
cases on each day for the 6 countries by their corresponding population density so
that each country would have a population density of 1 person per km^2. This
change in data will show how by increasing the space between each individual
within each country, some of the disparities between the total cases in different
countries will fade.

Population Density for the 3 MEDCs:
ITALY= 200/km^2 JAPAN= 333/km^2 CHINA= 145/km^2
Population Density for the 3 LEDCs:
ISRAEL= 416/km^2 INDIA= 414/km^2 INDONESIA= 141/km^2

As you can see from the data presented above. After taking into account the
population density, the large disparities between some countries have disappeared
such as between CHINA and ITALY. Originally ITALY’s Total Cases was 150% of
CHINA’s Total Cases however after taking into account that Italy has 55 more
people in a km^2 area of land in terms of Population Density, we can see that the
gap between the 2 countries has narrowed. This demonstrates how population
density does have an affect towards the spread of the virus. As closer the proximity
between the individuals in a confined space, the more likely it is for the virus to
spread. Hence by changing the ratios of people per km^2 to 1 person per km^2,
we shall see a decrease in transmission as larger the distance between 2 people, the
less likely for transmission.

GRAPH BEFORE CONSIDERATION OF POPULATION DENSITY :

GRAPH AFTER CONSIDERATION OF POPULATION DENSITY :

Above shows a comparison of the plotted graphs before and after
consideration of population density. The disparity between ITALY’s line and
CHINA’s line isn't as large anymore. This illustrates how due to the high population
density in Italy, the spread of the virus was faster than China. However if we made
the population density controlled between the 2 countries, the total number of
cases begin to get closer to each other.

Another example of which population density played a role was the
comparison between INDONESIA and ISRAEL. From the original data presented
above and the original graph prior to consideration of population density, there is
an obvious difference between the Total Cases with ISRAEL almost having 4 times
the amount of which INDONESIA’s. This difference in total cases may appear to be
significant initially however ISRAEL does have a population density which is 3 times
of which INDONESIA’s. Hence after dividing the two countries total cases by their
corresponding Population Density to create a controlled comparison, the disparity
once again narrows.

Through using these 2 comparisons between CHINA and ITALY,
INDONESIA and ISRAEL. It can be observed that population density does play a
role in the spread of the Covid-19 virus.

4.2 Development in Terms Wealth and Poverty Percentage (SOURCE)
A major factor which separates MEDCs from LEDCs is a country's development in
terms of wealth. To investigate the effects of this factor we looked at the
percentage of people living below $3.20 daily. We then took into account this
percentage with our original total amount of cases for the 6 countries. Wealth is a
relevant factor as it directly correlates to sanitation and accessibility to clean
resources. With high poverty rates, there are more people who are vulnerable to
catching the Covid-19 virus.

Percentage of people living below $3.20 for the 3 MEDCs:
ITALY= 4.5% JAPAN= 0.7% CHINA= 1.5%
Percentage of people living below $3.20 for the 3 LEDCs:
ISRAEL= 0.9% INDIA= 81.2% INDONESIA= 28.8%

Immediately after seeing the percentages above, we noticed an outlier with
ISRAEL having very low poverty rates. The reason for this is because in terms of
wealth ISRAEL is indeed developed however in other factors it is still considered
developing. For instance it is a country which is highly dependent on imports
meaning it is not self sufficient in terms of resources. Besides this anomaly, we can
observe a distinct difference in terms of the poverty percentage between the
MEDCs and LEDCs.

In order to see whether this external factor has an effect on the rate of
Covid-19 spread we decided to plot a graph of the first 20 days after each country
hit more than 10 cases. And see whether we could spot a trend between the rates
of increase and the poverty rate through using the percentage of individuals living
under $3.20

INITIAL 20 DAYS AFTER TOTAL CORONAVIRUS COUNT HITS 10:

The graph of the initial 20 days after a total of 10 cases is very different to the
graph of all the recorded cases. The predicted plots of the different countries for
the initial 20 days are a lot closer together as most of the countries have yet to take
their own measures to prevent the spread of the virus hence we do not see any
disparities.

We know that ISRAEL, INDONESIA and INDIA all fall under the LEDCs. From
the graph above we can see that the 3 countries are of the 4 which have the fastest
initial increase in total cases with ITALY being an outlier

INITIAL 20 DAYS AFTER TOTAL CORONAVIRUS COUNT HITS 10 TAKEN INTO
ACCOUNT WEALTH FACTORS:

To further look at how the wealth factor affected the rate at which the
Covid-19 virus spread. I multiplied the values of the total cases 20 days after each
country had surpassed 10 cases by the percentage of people living under $3.20 to
zoom in on the minority group.

From this graph we can see how the 2 countries (INDIA and INDONESIA)
with high poverty rates have the fastest initial spread of the Covid-19 virus with
ITALY once again being an outlier. There is a very visible difference between the
gaps in the graph after taking into account the wealth factor and prior to. We can
see that the 2 countries with high poverty rates stand out a lot more from the rest
compared to the initial graph. This shows how countries with high poverty rates are
more susceptible to initial spread of the virus.

From the zoomed in version of the graph we can actually see that the
predicted initial rate of increase for the 2 countries with the highest percentage of
poverty are increasing the fastest even surpassing ITALY; the outlier. We also see
that ISRAEL and JAPAN’s lines are almost parallel to the x-axis, showing an almost 0
increase in cases. This nicely connects to its percentage of people living under
$3.20 daily. As these 2 countries both have a percentage of under 1%.

4.3 Demographics Age

JAPAN ITALY

CHINA INDIA

ISRAEL INDONESIA

A factor that oftentimes - especially in the countries we’ve chosen - separate
MEDCs and LEDCs is age demographic. As shown in visuals above, ITALY and
JAPAN have about a quarter of their population above the age of 65. In the case of

JAPAN 28.1% of its population is at 65 and above, and in the case of ITALY 23% of
its population is at 65 and above.
The rapid spread of COVID-19 has revealed the need to understand how
population dynamics interact with pandemics. Population aging is currently more
pronounced in wealthier countries, which, mercifully, may lessen the impact of this
pandemic in lower-income countries with weaker health systems but younger age
structures. Poor general health status and co infections such as HIV and tuberculosis
will increase the danger of COVID-19 in these countries, along with
intergenerational proximity and challenges to physical distancing. Anybody can get
sick in this pandemic. But different people have different risks of getting severe
symptoms that require hospitalization or intensive care - and the chance of dying
vary widely across age groups.
ITALY currently has the highest fatality rate for Covid-19 of any country with a
major outbreak. The deluge of fatal COVID-19 cases in ITALY was unexpected,
given the affected region’s health and wealth. This could be attributed to its ageing
population. Research by experts already suggests that the novel coronavirus is
considerably more fatal with each passing decade of life, and our data analysis
shows that those 65 and older have a 3.5% chance of dying if they got the
coronavirus; those 80 and older face a 20.23% percent chance. ITALY is also
characterized by extensive intergenerational contacts, supported by a high degree
of residential proximity between adult children and parents. Even when
intergenerational families do not coreside, daily contacts are frequent. Many Italians
prefer to live close to extended family, with over half of the population in the
northern regions commuting. Intergenerational interactions, coincidence, and
commuting may have accelerated the outbreak in Italy through social networks that
increased the proximity of elderly to initial cases.
JAPAN, a country with more than 35.8 million people 65 or older,has data
that does not fit the above analysis. Although its demographic leans towards an
ageing population even more than ITALY, it’s COVID-19 related deaths and total
cases have been relatively constant, displaying a linear like trend. This anomaly as
mentioned earlier could be the result of a poor testing on the government's part.

5.0 Limitations of Predictions
While this paper does comprehensively evaluate the factors potentially
impacting the spread of Covid-19 across the world, it is important to assess the
accuracy of the data procured.
Firstly, when it comes to modelling the data, the functions tested, while
representative of the data procured so far, aren’t likely to apply accurately to the
future. Countries have different levels of lockdown and different agendas as to when
different services become available which aren’t factored into the models and
predictions.
Secondly, the data itself might not prove to be entirely accurate. For one, the
reported death rate might not be representative of the total deaths arising as a
result of Covid-19. Due to the inadequate testing we analysed earlier, there remains
doubt that a number of deaths may have gone unreported. While this doesn’t
directly impact our analysis as we focused primarily on the positive cases of
Covid-19, an accurate picture of the impact it has had on different countries would
have opened up further opportunities for evaluation.
On this topic, the majority of predictions made in the evaluations were based
on data from the past which, while it shows the country’s effectiveness at tackling
the virus, can’t be verified as representative of the future. Case in point: when
predicting the amount of people who are going to test positive for coronavirus over
the next few months, we used a quadratic graph. The models in this scenario were
likely not completely accurate with the existing data and, as a result, not accurate
enough to predict the future.
Lastly, even when the data is adjusted mathematically to justify the impact of
a specific factor, it isn’t possible to ensure that it impacts the rate of spread
proportionally. For example, when adjusting for population density, it is likely that it
affects the spread of the coronavirus to a degree, but after that point, doesn’t
strongly impact the number of cases. Additionally, these predictions don’t take into
account the other identified factors. For example, in a densely populated area, the
coronavirus is more likely to spread quickly, but there might be more deaths due to
less access to healthcare in less populated areas.

6.0 Conclusion
At an overall level, our report allowed us to identify the factors that contributed most
significantly to the spread of Covid-19. While the predictions we made from these factors
may not have been entirely accurate, they provide us with a detailed understanding of why
some countries appeared to be more affected by the virus than others. In terms of practical
application, it would be useful to predict the countries which are likely to be more affected
in the near future, as well as in future pandemics.

COVID-19 data configuration and statistical analysis

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