1 Purpose

The aim of the below document is twofold. Firstly, I wanted to understand the resources that are available on the internet during the coronvirus pandemic; Secondly, I wanted to utilise an Rmarkdown file to create a webpage. I did not want to create a dashboard as there lots of these in existence.

Before jumping to the final section about the COVID 19 disease, I try to understand the terminology. I am not a mathematical epidemiologist.

In writing this document, I have noted any weblinks that I found to contain useful information. Occasionally, I have tried to add the key points from these resources.

Where possible, I have choosen bullet points, to draw attention to important information so that a reader can identify the key issues and facts quickly.

After reading the below I hope you wonder what else is possible with an Rmarkdown, and what further exploring and modeling could be done with the data.

1.1 What is not included

I have chosen not to include any modelling in this document, for reasoning see: https://www.bmj.com/content/369/bmj.m1328?ijkey=c669f0e99a57934795786d640b7d9afdd6620e10&keytype2=tf_ipsecsha

2 Flu vs Cold

2.1 Respiratory System

  • upper respiratory system:
    • nose,
    • throat,
    • sinuses,
    • Eustachian tubes,
    • trachea (windpipe),
    • larynx (voice box),
    • bronchial tubes
  • lower respiratory system:
    • airways
    • lungs

2.2 Common Cold

viral infection of the upper respiratory system

2.2.1 cause/virus

  • rhinovirus (40%)
    • picornavirus with 99 known serotypes.
  • human coronaviruses (10-15%)
  • influenza viruses (10-15%)
  • adenoviruses (5%)
  • human respiratory syncytial virus (orthopneumovirus)
  • enteroviruses other than rhinoviruses
  • human parainfluenza viruses
  • human metapneumovirus
  • .. (200 different viruses)

2.2.2 symptoms

  • (milder than flu)

2.3 InFLUenza

viral infection of the upper respiratory and/or lower respiratory system

2.3.1 cause/virus

  • influenza viruses
    • influenza A
    • influenza B
    • influenza C

2.3.2 symptoms

  • fever or feeling feverish/chills
  • cough (mucus/phlegm)
  • sore throat
  • runny or stuffy nose
  • muscle or body aches
  • headaches and fatigue (tiredness)

2.4 “Feed a cold, starve a fever”

  • popular belief - probably, medical myth.

2.4.1 Origin?

2.4.1.1 Middle Ages

  • Just TWO types of illnesses!

    • illness caused by low temperatures (e.g cold)
      • needed to be “fueled”
        • eating was recommended
    • illness caused by high temperatures (e.g fever)
      • needed to be cooled down
        • refraining from eating was thought to deprive the furnace of energy

2.4.1.2 Canterbury Tales by Chaucer

  • mistranslation
  • intended meaning: feeding a cold would “stave off” a fever

2.4.2 Support (very indirect)

  • Dutch research team conducted a very small, preliminary study in 2002

  • Nutritional status has a bona fide effect on the regulation of the immune response

    • Benefits of breaking fast
      • gamma interferon
        • cell-mediated immune response
          • killer T cells destroy any cells that have been invaded by pathogens
            • i.e. viruses
    • Benefits of fasting
      • interleukin-4
        • humoral immune response
          • B cells produce antibodies that attack pathogens lurking outside our cells
            • i.e. bacterial infections

3 Virus

3.1 What is a virus?

  • small particles of genetic material (either DNA or RNA)
    • surrounded by a protein coat (capsid)
    • some have a fatty “envelope” covering
  • incapable of reproducing on their own
  • depend on the organisms they infect (hosts) for their very survival

Pathogenic organisms are of five main types: viruses, bacteria, fungi, protozoa, and worms

3.2 What is contagiousness?

ability of a virus to be transmitted from one person (or host) to another

3.3 What is the incubation period?

time between exposure to a virus (or other pathogen) and the emergence of symptoms

4 Taxonomy of Coronavirus

taxonomic rank virus classification
Realm Riboviria
Kingdom Orthornavirae
Phylum Pisuviricota
Class Pisoniviricetes
Order Nidovirales
Family Coronaviridae
Subfamily Orthocoronavirinae

for more info

4.1 Coronaviruses

Coronaviruses are enveloped, positive-sense, single-stranded RNA viruses that include both human and zoonotic species

  • the virus particle exhibits a characteristic ‘corona’ (crown) of spike proteins around its lipid envelope.
  • HKU = Hong Kong Uni

5 SARS-CoV

5.1 2003

  • Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) was eradicated by:

    • intensive contact tracing

    • case isolation measures

No cases have been detected since 2004

5.2 2019

  • Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

    • initial/interim name: 2019 novel coronavirus (2019-nCoV)

5.2.1 COVID-19

SARS-CoV-2 causes coronavirus disease 2019 (COVID-19)

  • colloquially:
    • coronavirus
    • COVID
    • corona
    • SARS2

5.2.2 transmission

  • COVID-19 spreads through respiratory droplets, such as through coughing, sneezing, or speaking
  • occurs in community settings
  • NOT healthcare settings (in contrast to SARS-CoV-1)

6 COVID-19 Pandemic

6.1 Data sources

6.1.1 Johns Hopkins

Johns Hopkins University Center for Systems Science and Engineering

6.1.1.1 CSSE at JHU

6.1.7 Informative Blogs

Blog post about Responsibility of Visualisations of Coronavirus

Blog post about R0

6.2 Events

6.3 Cases Worldwide

6.3.1 Confirmed

Visualise via a line plot to gain a sense of scale and growth of the outbreak.

  • odd jump in mid February
  • rate of new cases slows down for a while
  • speeds up again in March

6.3.2 New (past 24 hours)

6.3.3 UK (past 6 weeks)

6.3.4 USA (past 6 weeks)

6.3.5 India (past 6 weeks)

6.3.6 Canada (past 6 weeks)

6.3.7 Kenya (past 6 weeks)

6.4 Total Cases

6.4.1 By country: Treemap

  • A Treemap displays hierarchical data as a set of nested rectangles.

    • Each group is represented by a rectangle, which area is proportional to its value.

6.4.1.1 confirmed

## # A tibble: 1 x 3
##   country        total_cases parents  
##   <chr>                <int> <chr>    
## 1 United Kingdom      300717 Confirmed

6.4.1.2 deaths

## # A tibble: 1 x 3
##   country        total_cases parents
##   <chr>                <int> <chr>  
## 1 United Kingdom       42238 Death

6.4.1.3 recovered

## # A tibble: 1 x 3
##   country        total_cases parents  
##   <chr>                <int> <chr>    
## 1 United Kingdom        1304 Recovered

6.5 China vs World

Early on in the outbreak, the COVID-19 cases were primarily centered in China.

6.6 Annotate for events

  • February: the majority of cases were in China.

  • 13th Feb: leap in China’s cases, due to a change in methodology

    • China accepted chest infection on CT scans as evidence of coronavirus (previously just those confirmed by a lab test result)
      • i.e. no longer have to wait for lab confirmation

  • 11th March: WHO declared a pandemic

  • 14th March: global outbreak: total number of cases outside China > cases inside China

6.7 China: trend line

  • 15th February: growth of cases in China slows down.

    • growth rate in China is slower than linear:

6.8 World: trend line

  • rest of the world is growing faster than linearly:
  • straight line does NOT fit well

6.9 World: trend line: logarithmic scale

  • much closer fit to the data
  • cases of COVID-19 in the rest of the world are growing at an exponential rate

6.10 China by Province

6.11 Countries hit hardest: Confirmed cases

6.11.1 as of March

country total_cases
US 141194
Italy 97689
Spain 80110
China 67801
Germany 62095
France 40174
Iran 38309
  • outbreak was first identified in China
  • only one country from East Asia (South Korea) in the above table
  • Four of the listed countries (France, Germany, Italy, and Spain) are in Europe and share borders

6.11.2 as of April

country total_cases
US 1042926
Spain 213024
Italy 203591
France 167605
United Kingdom 165221
Germany 161539
Turkey 117589

6.11.3 as of May

country total_cases
US 1778993
Brazil 498440
Russia 396575
United Kingdom 272826
Spain 239228
Italy 232664
France 185616

6.11.4 as of June

country total_cases
US 2163290
Brazil 955377
Russia 552549
India 366946
United Kingdom 299251
Spain 244683
Peru 240908

6.12 Total Cases for Top 10 hardest hit as of “Now”

6.13 Total Cases for Top 10 least affected as of “Now”