Global Animal Disease Outbreaks
Charlie Hobman (480363620) and Blaine Buchanan (480427704)
Acknowledgements
The data used in this study has been extracted from the Food and Agriculture Organization of the United Nations via Kaggle.
Information About This Dataset
This data has been downloaded from the EMPRES Global Animal Disease Information System. The data shows the when, where, and what of animal disease outbreaks from the last 2 years, including diseases such as African Swine Fever, Foot and Mouth Disease, and Avian Flu. The number of cases, deaths, potential spread of disease, and other related data are all included.
The EMPRES Global Animal Disease Information System, also known as EMPRES-i, is a web-based database of all noted animal disease outbreaks across the globe. This application has been designed and implemented by the Food and Agriculture Organization of the United Nations (the FAO).
About This Source
“The Food and Agriculture Organization (FAO) is a specialized agency of the United Nations that leads international efforts to defeat hunger. Our goal is to achieve food security for all and make sure that people have regular access to enough high-quality food to lead active, healthy lives. With over 194 member states, FAO works in over 130 countries worldwide. We believe that everyone can play a part in ending hunger.” (Food And Agriculture Organization of the United Nations, 2018). This quote demonstrates that the FAO is a sector of the United Nations who are dedicated, with significant knowledge and resources, to ending world hunger. They collect extensive data on food scarcity, climate, and disease outbreaks. As the FAO is a part of the UN, it is easily recognized as a trusted source of information. On their website, they provide free access to the data that they have collected from 245 countries, from 1961 to present day. This represents an enormous amount of data, and demonstrates their commitment to their cause.
Research Questions
This dataset has been analysed to answer the following research questions:
What is the most prevalent animal disease from the last two years?
What countries are most at risk for these types of diseases?
Are these diseases harmful to humans/ can these diseases be transferred to humans?
What other significant impacts can these diseases have? (Economic, agricultural)
What animal has posed the greatest risk for spread of disease/ severity of disease for humans?
What measures are being put in place to stop such diseases from spreading?
Stakeholders
According to the FAO, “…(EMPRES-i) is a web based application that has been designed by FAO’s Emergency Prevention System (EMPRES) to support veterinary services by facilitating the organization to national, regional, and global level disease data and information … Since 2004, the FAO of the UN has been at the forefront of the fight against highly pathogenic avian influenza (HPAI) - bird flu - in over 95 countries. FAO have mobilized over US$445 million to combat influenza and emerging disease threats through prevention, surveillance, and control…”
This data could potentially assist in both conservation efforts and pathogenic research, particularly benefiting the work of environmental scientists. This is mainly due to the extensive nature of the database that this dataset was extracted from - resulting in detailed information about various diseases across the globe being used to develop preventative measures and cures to stop, or at least hinder these outbreaks. This data could also be of use to General Practitioners and other medical professionals to help identify outbreaks of zoonotic disease when patients present with concerning symptoms after recently travelling overseas or to an area of great exposure. Furthermore, this data is of useful means of tracking and monitoring the numbers of confirmed cases, and allow the potential spread of disease to be estimated. Timely and reliable disease information enhances early warning and response to trans-boundary animal diseases (TADs), which, according to the FAO, are “highly contagious epidemic diseases that can spread rapidly, irrespective of national borders. They cause high rates of death and disease in animals, thereby having serious socio-economic and sometimes public health consequences while constituting a constant threat to the livelihoods of livestock farmers.” (Food And Agriculture Organization of the United Nations, 2018)
Domain Knowledge
“..there is growing concern about diseases that humans can acquire from animals (e.g., zoonoses). Zoonoses are over-represented among human diseases that are defined as emerging (Table ). Taylor et al. (2002) documented that 6% of all human pathogens are zoonotic. And of the 5 newly emerging pathogens in humans, 5% are listed as zoonotic (Cleveland et al., 00). From 6 to 006, eleven of the twelve global emerging diseases originated from animals (Gerberding, 004).”
- Recent Animal Disease Outbreaks and their Impact on Human Populations. Jeff B. Bender, William Hueston and Micheal Osterholm. University of Minnesota St. Paul, Minnesota
This data has been extracted from this global database in order to answer multiple relevant research questions as aforementioned. It must be noted that the original dataset included 17,009 different events across the globe. However, for the sake of this research and report, the dataset has been trimmed in order to contain only 500 entries.
load(file="Outbreak1.Rdata")The following data analyses the dimensions of the dataset. This result indicates that there are 21 variables included in this dataset. These variables are listed below.
dim(Outbreak1)## [1] 16267 21names(Outbreak1)## [1] "Id" "source" "latitude"
## [4] "longitude" "region" "country"
## [7] "admin1" "localityName" "localityQuality"
## [10] "observationDate" "reportingDate" "status"
## [13] "disease" "serotypes" "speciesDescription"
## [16] "sumAtRisk" "sumCases" "sumDeaths"
## [19] "sumDestroyed" "sumSlaughtered" "X__1"The following list defines the structure of the variables from the EMPRES dataset into two classifications: numerical and character vectors. It is visible here that whilst most of the variables are structured as character variables - there is still numerical data that it can easily be compared against in order to fully understand the level of outbreaks in each instance.
str(Outbreak1)## Classes 'tbl_df', 'tbl' and 'data.frame': 16267 obs. of 21 variables:
## $ Id : num 230399 230381 230333 230396 230371 ...
## $ source : chr "OIE" "OIE" "OIE" "OIE" ...
## $ latitude : num -27.9 54.8 -21.1 -26 49.2 ...
## $ longitude : num 30.8 73.4 30.2 28.3 17.7 ...
## $ region : chr "Africa" "Europe" "Africa" "Africa" ...
## $ country : chr "South Africa" "Russian Federation" "Zimbabwe" "South Africa" ...
## $ admin1 : chr "KwaZulu-Natal" "Omskaya Oblast" "Masvingo" "Gauteng" ...
## $ localityName : chr "HPAI_H5N8_2017_019" "Novaya Stanica" "Mwambe" "HPAI_H5N8_2017_020" ...
## $ localityQuality : chr "Exact" "Exact" "Exact" "Exact" ...
## $ observationDate : chr "2017-08-17" "2017-08-16" "2017-08-16" "2017-08-15" ...
## $ reportingDate : chr "2017-08-22" "2017-08-18" "2017-08-16" "2017-08-22" ...
## $ status : chr "Confirmed" "Confirmed" "Confirmed" "Confirmed" ...
## $ disease : chr "Influenza - Avian" "African swine fever" "Foot and mouth disease" "Influenza - Avian" ...
## $ serotypes : chr "H5N8 HPAI" NA NA "H5N8 HPAI" ...
## $ speciesDescription: chr "domestic, unspecified bird" "domestic, swine" "domestic, cattle" "wild, unspecified bird" ...
## $ sumAtRisk : num 248000 122 1283 NA NA ...
## $ sumCases : num 12 6 112 1 1 1 19 2 1600 5 ...
## $ sumDeaths : num 12 1 0 1 1 1 19 2 0 5 ...
## $ sumDestroyed : num 50000 0 NA 0 NA NA 0 0 4000 0 ...
## $ sumSlaughtered : num 0 0 7 0 NA NA 0 0 0 0 ...
## $ X__1 : num NA NA NA NA NA NA NA NA NA NA ...The following indicates the sum of all cases recorded in the population of the sample, followed by the sum of all deaths that had been recorded.
sum(Outbreak1$sumCases, na.rm = TRUE) ## [1] 4625522sum(Outbreak1$sumDeaths, na.rm = TRUE)## [1] 2681393If the sum of all deaths is subtracted by the sum of how many cases were recorded, the resulting number indicates how many animals survived the outbreak. This can be very indicative of the severity of the total outbreaks, however, further investigation into each individual disease would be more telling of the data.
sum(Outbreak1$sumCases, na.rm = TRUE) - sum(Outbreak1$sumDeaths, na.rm = TRUE)## [1] 1944129For all recorded outbreaks, there was a 58% total mortality rate.
The Data
Following is a small glimpse into the very extensive and thorough dataset.
head(Outbreak1)## Id source latitude longitude region country
## 1 230399 OIE -27.90000 30.80000 Africa South Africa
## 2 230381 OIE 54.83704 73.35416 Europe Russian Federation
## 3 230333 OIE -21.07774 30.21162 Africa Zimbabwe
## 4 230396 OIE -26.00000 28.30000 Africa South Africa
## 5 230371 OIE 49.23790 17.70020 Europe Czech Republic
## 6 230370 OIE 49.23730 17.70080 Europe Czech Republic
## admin1 localityName localityQuality observationDate
## 1 KwaZulu-Natal HPAI_H5N8_2017_019 Exact 2017-08-17
## 2 Omskaya Oblast Novaya Stanica Exact 2017-08-16
## 3 Masvingo Mwambe Exact 2017-08-16
## 4 Gauteng HPAI_H5N8_2017_020 Exact 2017-08-15
## 5 Jihomoravsky Hvozdn√° Exact 2017-08-15
## 6 Jihomoravsky Ostrata Exact 2017-08-15
## reportingDate status disease serotypes
## 1 2017-08-22 Confirmed Influenza - Avian H5N8 HPAI
## 2 2017-08-18 Confirmed African swine fever <NA>
## 3 2017-08-16 Confirmed Foot and mouth disease <NA>
## 4 2017-08-22 Confirmed Influenza - Avian H5N8 HPAI
## 5 2017-08-17 Confirmed African swine fever <NA>
## 6 2017-08-17 Confirmed African swine fever <NA>
## speciesDescription sumAtRisk sumCases sumDeaths sumDestroyed
## 1 domestic, unspecified bird 248000 12 12 50000
## 2 domestic, swine 122 6 1 0
## 3 domestic, cattle 1283 112 0 NA
## 4 wild, unspecified bird NA 1 1 0
## 5 wild, wild boar NA 1 1 NA
## 6 wild, wild boar NA 1 1 NA
## sumSlaughtered X__1
## 1 0 NA
## 2 0 NA
## 3 7 NA
## 4 0 NA
## 5 NA NA
## 6 NA NAtail(Outbreak1, n=3)## Id source latitude longitude region country
## 16265 207186 National authorities 23.40208 45.23404 Asia Saudi Arabia
## 16266 207182 National authorities 23.40208 45.23404 Asia Saudi Arabia
## 16267 207170 National authorities 23.40208 45.23404 Asia Saudi Arabia
## admin1 localityName localityQuality observationDate reportingDate
## 16265 Riyad Riyad Centroid Admin1 2015-08-17 2015-08-24
## 16266 Riyad Riyad Centroid Admin1 2015-08-17 2015-08-24
## 16267 Riyad Riyad Centroid Admin1 2015-08-17 2015-08-22
## status disease serotypes speciesDescription sumAtRisk sumCases
## 16265 Confirmed MERS-CoV <NA> <NA> NA NA
## 16266 Confirmed MERS-CoV <NA> <NA> NA NA
## 16267 Confirmed MERS-CoV <NA> <NA> NA NA
## sumDeaths sumDestroyed sumSlaughtered X__1
## 16265 NA NA NA 1
## 16266 NA NA NA NA
## 16267 NA NA NA NAlength(Outbreak1)## [1] 21What Is The Most Prevalent Animal Disease From The Last Two Years?
The following numerical table lists all of the recorded cases of animal disease outbreaks from the last two years. Furthermore, the data about the five most prevalent diseases were extracted and turned into a bar graph to further demonstrate the extent of these diseases. This data shows that Avian Flu was the most prevalent recorded disease, with 6218 recorded cases throughout the last two years. Avian Flu was followed by Bluetongue Disease(BTV), with almost half the amount of recorded Avian Flu cases - with 3384 recorded cases. BTV was followed by African Swine Fever Virus (ASFV) with 3091 cases, Lumpy Skin Disease with 2100 cases, and then Mers-CoV, which exhibited only 587 confirmed cases in the last two years.
table(Outbreak1$disease)##
## African horse sickness
## 12
## African swine fever
## 3091
## Anthrax
## 63
## Bluetongue
## 3384
## Bovine spongiform encephalopathy
## 6
## Bovine tuberculosis
## 2
## Brucellosis (Brucella abortus)
## 1
## Brucellosis (Brucella melitensis)
## 4
## Brucellosis (Brucella suis)
## 3
## Classical swine fever
## 12
## Contagious bovine pleuropneumonia
## 4
## Equine infectious anaemia
## 19
## Foot and mouth disease
## 252
## Influenza - Avian
## 6218
## Influenza - Equine
## 8
## Influenza - Swine
## 2
## Lumpy skin disease
## 2100
## MERS-CoV
## 587
## Newcastle disease
## 152
## Peste des petits ruminants
## 55
## Porcine reproductive and respiratory syndrome
## 7
## Rabies
## 33
## Rift Valley fever
## 51
## Sheep pox and goat pox
## 154
## West Nile Fever
## 47It must be emphasised that these figures have been collected over a period of two years.
Diseases<-c(3091, 3384, 6218, 2100, 587)
barplot(Diseases, names.arg=c("ASFV", "BTV", "Avian Flu", "LSDV", "MERS"), ylab="Cases", main="Most Prevalent Diseases", col="lightblue", las=2) 
Furthermore, the FAO also calculates how many organisms are at risk when a potential outbreak occurs. This is calculated by region, by the infectious nature of the disease, the number of organisms already infected, and many other factors. The following histogram is an accurate representation of the numerical data for the variable “Sum At Risk”, demonstrating how many individual animals are at risk of contracting the disease that has been input into the dataset for that particular instance.
hist(Outbreak1$sumAtRisk,col = "lightblue",main = "Histogram of At Risk Organisms At Time of Incident Reports", xlab="Organisms At Risk",breaks="FD",xlim=c(0,1400))
This histogram demonstrates that most incidence reports indicated that most outbreaks were of minimal risk. However, the larger sum of at risk organisms should not be ignored as whilst they are not as frequent, they can be devastating to fragile ecosystems.
Are These Diseases Harmful to Humans / Can They Be Transferred To Humans?
Zoonotic diseases are diseases that can transferred between animals and humans. The following data explores whether or not the five most common diseases could be transferred to humans:
Avian Flu: Although fatalities are rare, certain strains of Avian Flu can be transmitted to humans, especially the H5N1 and H7N9 strains (Australian Department of Health, 2018). According to the World Health Organization, most cases of Avian Flu in humans are caused by coming in direct contact with an infected animal - dead or alive. Furthermore, the WHO states that “controlling the disease in the animal source is critical to decrease risk to humans” (World Health Organization, 2018) which is a further testament to the importance of this dataset.
African Swine Fever: There is no evidence that supports the idea that ASF can be transferred to humans or is dangerous to human health. (QLD Department of Agriculture and Fisheries, 2017)
Bluetongue: Bluetongue is non-contagious to humans. However, this disease is related to African Horse Sickness, as it is spread by the same parasitic insects (midges) that transfer Bluetongue Disease. Mortality rates of African Horse Sickness can reach 90% in extreme epidemics.(QLD Department of Agriculture and Fisheries, 2017)
Lumpy Skin Disease: LSDV can not be contracted by humans and it mainly affects cattle.
MERS-CoV: MERS-CoV, or, Middle East Respiratory Syndrome, is contagious to humans, thought to be transferred to humans through camels, however, it is primarily a disease carried by Middle-Eastern Bats.
The following data visualisation, a scatter plot, shows the sum of cases vs the sum of deaths. There is a fit line added to the plot in order to show the linear pattern of this. It is shown that for most recorded cases of disease outbreaks, there were minimal deaths. However, a few of these outbreaks have resulted in significant loss of life.
plot(Outbreak1$sumCases, Outbreak1$sumDeaths, xlab= "Cases", ylab= "Deaths", main = "Scatter Plot of Cases vs Deaths", xlim=c(0,25000), ylim=c(0,25000))
abline(lm(Outbreak1$sumCases~Outbreak1$sumDeaths), col="red") #regression line (y~x)
We can test the correlation coefficient using Pearson’s product-moment correlation. This results in 0.71, which indicates fairly significant correlation between the two variables. It should be noted that a result of 1 would equal total positive liner correlation, and 0 would equal no linear correlation.
cor.test(Outbreak1$sumCases, Outbreak1$sumDeaths)##
## Pearson's product-moment correlation
##
## data: Outbreak1$sumCases and Outbreak1$sumDeaths
## t = 114.11, df = 13063, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6978461 0.7150237
## sample estimates:
## cor
## 0.706539The following plot outlines the correlation between the sum destroyed, and the sum of deaths. There are a few outliers, which represent the main disease outbreaks that devastated ecosystems, especially Avian Flu.
library(ggplot2)
p <- ggplot(Outbreak1, aes(sumDestroyed, sumDeaths))
p <- p + geom_point()
print(p)## Warning: Removed 4176 rows containing missing values (geom_point).
Furthermore, we can calculate the correlation efficient using Pearson’s product-moment correlation, with a result of 0.155, which indicates there to be very little correlation between these two variables.
cor.test(Outbreak1$sumDestroyed, Outbreak1$sumDeaths, na.rm=TRUE)##
## Pearson's product-moment correlation
##
## data: Outbreak1$sumDestroyed and Outbreak1$sumDeaths
## t = 17.251, df = 12089, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1375554 0.1723488
## sample estimates:
## cor
## 0.1550001What Countries Are Most At Risk For These Types of Diseases?
The following numerical summary documents the number of outbreaks per country as documented by the FAO. This summary is then followed by a graphical summary that makes the data more accessible. These summaries demonstrate that France is the most affected with 2719 outbreaks during the two year period, followed by China (1313 outbreaks), Albania (858 outbreaks), and Estonia (850 outbreaks).
table(Outbreak1$country)##
## Albania
## 858
## Algeria
## 6
## Angola
## 2
## Argentina
## 1
## Armenia
## 2
## Austria
## 37
## Bahrain
## 1
## Bangladesh
## 8
## Belgium
## 21
## Belize
## 4
## Bhutan
## 1
## Bosnia and Herzegovina
## 136
## Botswana
## 6
## Brazil
## 2
## Bulgaria
## 301
## Burkina Faso
## 2
## Burundi
## 3
## Côte d'Ivoire
## 18
## Cambodia
## 7
## Cameroon
## 24
## Canada
## 2
## Chile
## 3
## China
## 1313
## Colombia
## 8
## Croatia
## 80
## Cyprus
## 140
## Czech Republic
## 157
## Democratic Republic of the Congo
## 30
## Denmark
## 55
## Ecuador
## 7
## Egypt
## 409
## El Salvador
## 1
## Estonia
## 850
## Finland
## 18
## France
## 2719
## French Guiana
## 1
## Gambia
## 1
## Gaza Strip
## 5
## Georgia
## 7
## Germany
## 319
## Ghana
## 40
## Greece
## 255
## Guinea-Bissau
## 2
## Hong Kong, SAR
## 14
## Hungary
## 332
## India
## 45
## Indonesia
## 425
## Iran (Islamic Republic of)
## 29
## Iraq
## 17
## Ireland
## 11
## Israel
## 172
## Italy
## 486
## Japan
## 183
## Jordan
## 20
## Kazakhstan
## 13
## Kenya
## 2
## Kuwait
## 14
## Kyrgyzstan
## 1
## Lao People's Democratic Republic
## 2
## Latvia
## 625
## Lebanon
## 3
## Libya
## 2
## Lithuania
## 610
## Luxembourg
## 4
## Macau
## 7
## Malawi
## 2
## Malaysia
## 25
## Maldives
## 1
## Mali
## 3
## Mauritania
## 5
## Mauritius
## 8
## Mexico
## 30
## Moldova, Republic of
## 4
## Mongolia
## 145
## Montenegro
## 140
## Morocco
## 22
## Mozambique
## 3
## Myanmar
## 5
## Namibia
## 66
## Nepal
## 11
## Netherlands
## 69
## Niger
## 42
## Nigeria
## 324
## Oman
## 2
## Panama
## 3
## Philippines
## 1
## Poland
## 454
## Portugal
## 7
## Qatar
## 6
## Republic of Korea
## 508
## Romania
## 182
## Russian Federation
## 766
## Saudi Arabia
## 563
## Senegal
## 2
## Serbia
## 662
## Slovakia
## 77
## Slovenia
## 49
## South Africa
## 90
## Spain
## 15
## Swaziland
## 3
## Sweden
## 54
## Switzerland
## 90
## Syrian Arab Republic
## 2
## Taiwan (Province of China)
## 248
## Thailand
## 2
## The former Yugoslav Republic of Macedonia
## 192
## Togo
## 3
## Tunisia
## 30
## Turkey
## 7
## U.K. of Great Britain and Northern Ireland
## 35
## Uganda
## 11
## Ukraine
## 207
## United Arab Emirates
## 9
## United Republic of Tanzania
## 2
## United States of America
## 30
## Uruguay
## 3
## Viet Nam
## 74
## West Bank
## 6
## Zambia
## 11
## Zimbabwe
## 77barplot(table(Outbreak1$country), main = "Outbreaks by Country", ylab= "Outbreaks", las=2, xlim=c(0, 40))
What Other Significant Impacts Can These Diseases Have?
Avian Flu: Avian Flu can have disastrous agricultural and economic effects, as the disease can spread extremely quickly and cause sickness in humans and animals. Therefore, the diseased animals must be destroyed. The dataset we have extracted states that in the past two years, 665,000 chickens were destroyed in China alone to prevent spread of this disease.
African Swine Fever: ASFV can have huge effects on agriculture and therefore the economy, as it can decimate pig populations in a very short period of time. Notable examples of this are the 325,789 domestic animals destroyed in South Africa alone, in the prevention of the disease spreading.
Bluetongue Disease: BTV can have economic and agricultural effects, especially due to the fact that countries that have epidemics of Bluetongue Disease, are restricted in primary industry exportation.
Lumpy Skin Disease: Although the mortality rate for Lumpy Skin Disease is generally low, economic losses result from loss of condition, decreased milk production, abortions, infertility, and damaged hides. While no animals with LSDV need to be destroyed, the disease still impacts on production yield from farm animals.
MERS: This disease can cause acute respiratory illness, including fever, cough, and shortness of breath. Many of those affected have died.
The following histogram demonstrates the density of destroyed organisms. It shows that overall, not many organisms were recorded to be destroyed. The vast majority of destroyed animals were birds that had contracted Avian Flu, as the risk to humans and fragile ecosystems were too great. This is represented by the large bar in the histogram.
hist(Outbreak1$sumDestroyed,col = "lightblue",main = "Histogram of Destroyed Organisms", xlab="Destroyed Organisms",breaks="FD",xlim=c(0,30),ylim=c(0,0.8), freq=F)## Warning in hist.default(Outbreak1$sumDestroyed, col = "lightblue", main =
## "Histogram of Destroyed Organisms", : 'breaks = 2.12398e+06' is too large
## and set to 1e6
What Animal Has Posed The Greatest Risk For Spread of Disease/Severity of Disease For Humans?
The data has shown that birds pose the greatest risk for spread of disease due to the frequency and severity of Avian Flu. By looking at the quantitative variable “Sum at Risk”, it is easily demonstrated that Avian Flu is over-represented in the data.
summary(Outbreak1$sumAtRisk)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 11 87 7534 634 2000000 6799The following data shows the amount of organisms at risk throughout all of the outbreaks that occurred in the two year period that the FAO collected the data. This has been analysed to take into account the infectious properties of the disease, the region that it occurred, the number of organisms already affected and how many organisms, including humans, may have come into contact with infected animals.
sum(Outbreak1$sumAtRisk, na.rm = TRUE)## [1] 71330798This is an exponentially high estimate, and further demonstrates the importance of the FAO’s work on containing these outbreaks in order to protect humans and animals alike. If the sum at risk, 71,330,798 organisms had been infected - the effects on the environment, conservation, human and animal health, the economy and agricultural industries would be disastrous. This shows how this data can be useful to the aforementioned stakeholders.
What Measures Are Being Put In Place To Stop Such Diseases From Spreading?
One measure of prevention of the spread of disease is the FAO’s collection of data such as the dataset used in this study. This enables medical professionals, organisations and the general population to be aware of the presence, cause and effects of such diseases. An example of the prevention of disease outbreaks in Australia would be CSIRO’s Australian Animal Health Laboratory (AAHL). AAHL investigates cases of exotic diseases and implements controls in order to eradicate the disease or prevent them from spreading further.(CSIRO, 2018)
Conclusion
In conclusion, the Food and Agriculture Organization of the United Nations makes huge progress in reducing, eliminating, and identifying animal outbreaks across the globe, declaring the data publicly. This data is then used by countless of professionals world-wide to establish risk posed to humans, animals, the agriculture industry and the economy. However, as aforementioned, for all recorded outbreaks, there was a 58% mortality rate. Therefore, there is still room for improvement, and as scientific research makes advances over time, that percentage will drop.
Bibliography
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