Comparison with other pandemics

In this chapter we will compare the COVID-19 pandemic with some of the pandemics before.

Scientists have been researching previous pandemics (even before this one started) in order to understand what has worked before. Looking at previous pandemics can help us understand the current pandemic from different points of view. Comparing the current and previous viruses that were the root cause, transmission and incubation specifics as well as the social factors, such as human behaviour and reaction to different measures, such as social distancing and masks.

The code for creating the dataset that contains the data for all pandemics, as well as the visualizations can be found in the Jupyter Notebook titled Comparison with other pandemics.

3.2.1 Overview of previous/ongoing pandemics

MERS: Middle East respiratory syndrome is a viral respiratory infection and just like COVID-19 it is caused by coronavirus, albeit a different one. The first case occurred in June 2012 in Jeddah, Saudi Arabia.[45][46]

SARS: Severe acute respiratory syndrome broke out in 2002 and no case has been reported since 2004. It was caused by the first identified strain of the SARS coronavirus SARSr-CoV.

In 2019 another strain of SARSr-CoV was identified and it is the cause of COVID-19.[46]

ZIKA: Zika virus, which is spread by mosquitoes, caused an epidemic that lasted from 2015 to 2016. Zika can spread from a pregnant woman to the baby and cause different birth defects.[47][48][49]

HIV/AIDS: Human immunodeficiency virus infection and acquired immunodeficiency syn-drome is a spectrum of conditions caused by the human immunodeficiency virus. The person at first may not even know they have been infected, but as a result their immune system will continue to weaken unless they start taking medications. At the moment there is no vaccine, but the antiretroviral treatment can slow the course of the disease. It was first recognized in 1981 and to this day it is still considered to be a pandemic.[50]

The Spanish flu: The Spanish flu, also known as the 1918 flu pandemic, was caused by the H1N1 influenza A virus. It caused a pandemic that lasted from February 1918 to April 1920.[51]

Ebola: Ebola is a viral hemorrhagic fever that was first identified in 1976, but it has had multiple outbreaks since then, last one in July 2019 in Congo. The largest outbreak to this day was in West Africa from 2013 to 2016.[52][53]

H1N1: The 2009 swine flu pandemic was an influenza pandemic that lasted about 19 months, from January 2009 to August 2010. Just like the 1918 flu pandemic it was caused by the H1N1 influenza virus.

Nipah: Nipah virus has caused numerous disease outbreaks in South and Southeast Asia.[54]

H3N2: Influenza A virus subtype H3N2 (A/H3N2) is a subtype of viruses that causes influenza (also known as the seasonal flu).[55]

Cholera: Cholera is an infection of the small intestine caused by some strains of the bac-terium Vibrio cholerae. In the last 200 years there have been seven cholera pandemics along with numerous outbreaks. The first pandemic originated in India in 1817. The sixth cholera pandemic that lasted from 1899 to 1923 is considered to be the biggest one.[56]

3.2.2 Comparison

Using data from various cited sources we have created a simple dataset that consists of total number of cases, total number of deaths, case fatality rate, reproductive rate number, number of countries to which the pandemic had spread and the year the first case was reported. This dataset can be seen below and ‘-1’ indicates missing data.

Table 3.1: Overview of previous/ongoing pandemics

Pandemic Cases Deaths CFR R0 Countries Reported COVID-19 61800000 1450000 2.5 2.1 214 2019

MERS 2519 86 35.0 2.7 27 2012

SARS 8098 774 11.0 2.7 29 2002

ZIKA 711381 18 8.3 3.0 87 2015

HIV/AIDS 65000000 25000000 -1 -1 214 1981 Spanish flu 500000000 60000000 5.0 -1 214 1918

EBOLA 28646 11323 50.0 2.0 10 1976

H1N1 491382 18449 0.03 1.75 214 2009

NIPHA 19 17 -1 -1 1 2018

H3N2 -1 2000000 -1 1.8 214 1968

6th Cholera -1 1500000 -1 -1 214 1899

H2N2 1100000 1100000 0.67 -1 214 1957

Case Fatality Rate

Case fatality rate is the proportion of people who die from a specified disease among all individuals diagnosed with the disease over a certain period of time.[57]

While this is a statistic that is most commonly used, especially when someone wants to estimate how “dangerous” a pandemic may be, it is not ideal. A better one and a precise statistic would be the infection fatality rate. That is the number we get when we divide the number of people who died from the disease with the total number of cases. But this is almost impossible, because we cannot know the total number of cases. It would require knowing for every single person whether they have had the disease or not. And it is especially difficult for COVID-19, even in modern times, due to the fact that a lot of people will have the disease and have no symptoms. There are two main reasons why this statistic needs to be interpreted carefully. This is how CFR is calculated.

CF R= N umberof deathsf romdisease N umberof diagnosedcasesof disease

As explained above, this statistic greatly depends on the denominator. If there is not enough testing to catch all the mild cases or cases with no symptoms then it can make it appear as if the CFR is higher. Since countries have different approaches to testing it is difficult to compare countries using CFR.

The second one of them is that due to the fact that while it shows one thing, which is the percentage of people that die from the infection it does not include any other severe outcomes in its numbers. Unfortunately, there is currently no such statistic that would include all of the people that while have not died have had severe long lasting symptoms. Such a statistic could further help the general public understand the seriousness of this pandemic.

Figure 3.1: Graph showing CFR values for different pandemics.

R0

R0 tells you the average number of people who will contract a contagious disease from one person with that disease.

In order to calculate the reproduction rate scientists have to work backwards. They look at all of the infections up until that point in time and conclude what the current reproduction rate is. But this number is not fixed for the entire duration of the pandemic. It can change as our behaviour changes, as our immunity develops or the time progresses. Government restrictions, social distancing and isolation can help bring this number down. While reproduction rate matters and it helps us understand and predict how many people will potentially get infected in the upcoming period it is not the most important statistic. The second statistic we will look at is also very important.

Figure 3.2: Graph showing R0 values for different pandemics.