Modelling Contagious Diseases

Description

In this activity, students investigate and model the effectiveness of vaccines and vaccination programs and their role in maintaining herd immunity. They also investigate societal issues relating to vaccinations as well as the science of vaccines. This activity is suitable for year levels 9 to 12.

Recommended Understandings

Before beginning this learning activity, it is recommended that students be familiar with the following concepts:

Cellular and non-cellular pathogens as disease causing agents.

Acquiring immunity through natural and artificial means.

Learning Objectives

At the end of this learning activity, students will be able to:

define the terms: ‘contagious’ , ‘vaccine’ , ‘epidemic ’ , ‘pandemic’ and ‘herd immunity’.

explain the transmission of contagious disease through a population.

apply knowledge of vaccination programs to their role in maintaining herd immunity.

model how vaccination rates (percentage of population vaccinated) effect the spread of disease through a population.

evaluate the strengths and weaknesses of mathematical models in predicting real life epidemics and pandemics.

critically evaluate claims for and against the effectiveness of vaccination programs.

Introduction

Contagious diseases are diseases which can be transmitted from one person to another by various methods. They are typically caused by parasites, fungi and microorganisms such as viruses and bacteria.

Contagious diseases can be detrimental to a population if allowed to spread uncontrollably. When an infectious disease spreads over a large geographic area (or worldwide) it is usually referred to as a pandemic. When an infectious disease spreads in a more local area, it is referred to as an epidemic.

Increasing the proportion of immune individuals within a population can prevent the spread of contagious disease. Immunity can be naturally acquired by direct exposure and recovery from a disease, or it can be artificially acquired through vaccination. Vaccines contain fragments from the disease causing agent, in an attenuated or inactivated state, which promotes an immune response in the vaccine recipient. 

In populations with a large number of immune individuals, the spread of contagious disease is disrupted as there are fewer susceptible hosts to contract and transmit the disease.  This provides an indirect form of protection for non immune individuals within the population, as there is a smaller probability that they will come into contact with an infectious individual. This form of indirect protection is called herd immunity. 

The minimum proportion of the population required to achieve herd immunity varies for different diseases, depending on how easily the disease is transmitted, the contagious period and the effectiveness of the vaccine in achieving immunity in the recipient. As a general rule, herd immunity is achieved when immunity rates are above 95% (0.95). 

Once a certain threshold of immunity has been reached within a population, herd immunity can reduce the infection rates of a disease to zero. When this is achieved worldwide, the disease is declared eradicated. Smallpox is an example of a contagious disease which was declared as eradicated in 1980 following the successful implementation of a vaccination program. 

Contagious diseases caused by emerging pathogens are more likely to cause pandemics. This is because humans are unlikely to have been exposed to the disease and therefore have not developed immunity to the pathogen. It is also very unlikely that a vaccine exists. With the explosion of global travel over the past 50 years, the spread of contagious diseases is now a particularly concerning issue that governments need to address. Preventing epidemics and pandemics require knowledge about the disease, where it comes from, how quickly it spreads, how deadly the disease is and effective implementation of immunisation programs. 


Figure 1. Spread of global pandemic. Source: Wolfram 

Mathematical models are important tools used in predicting disease spread in epidemics and pandemics by government agencies and health officials worldwide. They provide important information about the rate of disease spread and predict whether pandemic levels will be reached. They are used to determine the effectiveness of interventions, such as vaccination, in preventing the spread of a disease.

Simulation: Modelling Disease Spread

The spread of infectious disease through a population can be modelled mathematically. The S-I-R model is one of the simplest infectious disease models. It was initially developed in the early 20th century to explain the rise and fall of infected patients during the plague and cholera outbreaks in London. 

S-I-R stands for the three states: S - Susceptible, I - Infected, R - Recovered. 

- Susceptible describes individuals at risk of contracting the disease.
- Infected described individuals who have contracted the disease.
- Recovered described individuals who have overcome the disease and can no longer be infected, or individuals who have died. 


Figure 2. S-I-R Model. Source: Wolfram

Diseases like measles, mumps, rubella, and polio are referred to as ‘S-I-R diseases’. 

The model below illustrates the concept of herd immunity. The model allows variables to be manipulated to determine the minimum proportion of vaccinated individuals needed to prevent an epidemic from occurring.

When using this model, the following can be varied:
- Initial number of infected individuals in the population.
- Disease duration
- Contact number (how many individuals interacted with a contagious person in a day)
- Percentage of the population vaccinated
- Vaccination effectiveness. 
 
 The model shows the change in the proportions over time of:
 - Susceptible people (represented by black)
 - Contagious people (represented by red) 
 - Recovered people (represented by green)
 - People who have been successfully immunized using the vaccine (represented by blue) 
 
Data Representation:
The data is represented both graphically and by a visual representation of the population- by a display of 200 dots at three different times (time 0, 25 days and 50 days). 

Preset buttons:
There are preset buttons that represent a very simplified account of actual epidemic outbreaks which occurred in the United States. When you click these buttons, you can see what the necessary vaccination rate is to prevent an epidemic from occurring in the United States. 

Simulation 1. Modelling Disease Spread. Source: Wolfram.

Manipulate[
DynamicModule[{s0, r0, a,b, sol, s, i, r, t,m, minV, diseaseState,x,y, error}, 
error="";
x=v*eff;
y=1;
s0=1-i0-x;
r0=0;
a=1/d;
b=c/d;
If[i0+x>1.0,MessageDialog[ Text@StringJoin["i0 + ", "(proportion vaccinated)*(vaccine effectiveness) cannot exceed 1. Will automatically reset i0 to ", ToString[1-x-0.000000000001]] ]];
If[i0+x>1.0, i0=1-x-0.000000000001 ];
Which[c*s0>1, diseaseState= Text@"epidemic", c*s0 ==1, diseaseState  = Text@"endemic", c*s0<1, diseaseState = Text@"no epidemic"];
minV=(1-i0-(1/c))*(1/eff);

If[minV>1.0, minV= "not possible", (1-i0-(1/c))*(1/eff)];
sol= NDSolve [
{s'[t] == -b*s[t]*i[t], 
i'[t] == b*s[t]*i[t] - a*i[t], 
r'[t] == a*i[t],
s[0]==s0, i[0]==i0, r[0]==r0}, {s,i,r}, {t, 0, tmax}];
m=Plot[{s[t]/.sol,i[t]/.sol,r[t]/.sol}, 
   {t, 0, tmax}, PlotStyle->{Black, Red, Darker[Green]}, 
    PlotRange-> Automatic, PlotLegends->{Style["S",Italic],Style["I",Italic],Style["R",Italic]},
    FrameLabel->{Style["time (in days)", Medium], Style["proportion of population", Medium]},
            Frame->{{True, False}, {True, False}}];
Grid[{{initialPop[Abs[s0], i0, r0, x],
midPop[Abs[s[tmax/2]/.sol [[1]]], Abs[i[tmax/2]/.sol[[1]]],r[tmax/2]/.sol[[1]], x]},{Text@Row[{"at ",Style["t",Italic]," = 0"}], Text@Row[{"at ",Style["t",Italic]," = ", N[tmax/2], " days"}]},
{finalAgents[Abs[s[tmax]/.sol [[1]]], Abs[i[tmax]/.sol[[1]]],r[tmax]/.sol[[1]], x],m},{Text@Row[{"at ",Style["t",Italic]," = ", tmax, " days"}], ""},{"", ""},{Text@"minimum proportion of vaccinated" , diseaseState}, {Text@StringJoin["individuals needed: ",   ToString[minV]], ""}, {error}}]
],
"i0: % infected at beginning",
{{i0, 0.01, ""}, 0.001,1, 0.01, ImageSize->Tiny, Appearance->"Labeled"}, 
"",
"proportion vaccinated",
{{v, 0.11, ""}, 0,1.0, 0.01, ImageSize->Tiny, Appearance->"Labeled"},
"",
"vaccine effectiveness",
{{eff, 1.0, ""}, 0.9,1.0, 0.01, ImageSize->Tiny, Appearance->"Labeled"},
"",
"contact number",
{{c,10, ""},2,40,0.5, ImageSize->Tiny, Appearance->"Labeled"},
"",
"contagious period (in days)",
{{d,7, ""},2, 30, 1, ImageSize->Tiny, Appearance->"Labeled"}, 
"",
"time (in days)",
{{tmax,50, ""}, 10, 100, 5, ImageSize->Tiny, Appearance->"Labeled"},
"",
Button["CA measles",i0=0.01;v=0.90;c=18;d=8;eff=1; tmax=50,ImageSize->Medium],
Button["CA whooping cough",i0=0.01;v=0.83;c=17;d=21; eff=1; tmax=50,ImageSize->Medium],
Button["NYC Hong Kong flu",i0=0.01;v=0;c=6;d=3; tmax=15,ImageSize->Medium],
Button["reset",i0=0.01;v=0.11;c=10;d=7;eff=1.0,ImageSize->Medium],
"",
"",
Style["key: ", FontSize->12],
Style[Row[{"Susceptible ",Style["S",Italic]}], FontColor->Black, Bold,FontSize->12],
Style[Row[{"Infected ",Style["I",Italic]}], FontColor->Red,Bold, FontSize->12],
Style["Immunised", FontColor->Blue,Bold, FontSize->12],
Style[Row[{"Recovered ",Style["R",Italic]}], FontColor->Darker[Green], Bold,FontSize->12],
Initialization:> (
initialPop[a_, b_, c_, d_]:=Show[
Table[Graphics[{RandomChoice[{a,b,c,d}->{Black,Red,Green,Blue}],Disk[{i,j},.3]}],{i,1,20},{j,1,20}]];
midPop[a2_, b2_, c2_, d2_]:=Show[Table[Graphics[{RandomChoice[{a2,b2,c2,d2}->{Black,Red,Green,Blue}],Disk[{i,j},.3]}],{i,1,20},{j,1,20}]];
finalAgents[a1_, b1_, c1_, d1_]:=Show[Table[Graphics[{RandomChoice[{a1,b1,c1,d1}->{Black,Red,Green,Blue}],Disk[{i,j},.3]}],{i,1,20},{j,1,20}]]),
ControlPlacement->Left, SynchronousUpdating->False, SynchronousInitialization->False
]

Part 1 - Modelling the effectiveness of vaccination and herd immunity

Background Information

Pertussis – A SAEFVIC Case Study* Department of Health Newsletter: Issue 74 April 2015
A three week-old previously healthy term baby girl was taken to the local GP with a one-week history of coughing and post-tussive vomiting. Numerous episodes of central cyanosis and associated lethargy and respiratory distress had occurred after the coughing fits. Whilst at the GP she had a further episode of respiratory distress post coughing that required facial oxygen and tactile stimulation to recover. Triple zero was called and she was appropriately transferred to hospital. The day after her admission to hospital she experienced a cardiac arrest secondary to hypoxia and apnea following an extensive coughing episode. She was incubated and ventilated for a period of nine days and with a total hospital stay of 14 days.
      Pertussis (whooping cough) is a respiratory infection caused by the bacterium Bordetella pertussis with an average incubation period of seven to 20 days. B. pertussis is highly infectious and most serious in infants under the age of six months. The maximum risk of infection and severe morbidity is before infants are old enough to have received at least two vaccine doses. Pertussis is spread by airborne respiratory droplets when an infected person sneezes, coughs or comes into direct contact with infected secretions; with adults being the main reservoir (approximately 50 per cent of cases) of infection because of their waning immunity acquired from vaccination or infection. 
      Pertussis still remains highly prevalent in Australia and is the least well controlled of all vaccine-preventable diseases. In Victoria, the Department of Health over the period of January 2014–January 2015 recorded 4,606 cases, a 56 per cent increase in reports compared to the same period in 2013. A 38% increase in cases in infants less than six months of age (55 cases in 2013 and 76 cases in 2014).
      This case highlights the severity of pertussis in infants too young to be vaccinated. Without paediatric intensive care support this child may have died or had severe neurological morbidity Maternal vaccination in the third trimester is one strategy that can minimise the risk of severe pertussis in young infants too young to have commenced their routine vaccines. 

Read more about whooping cough on the BetterHealth Channel here  

The following question relate to Simulation 1 above:

Question 1 
Click on the “CA whooping cough” button and using the data generated from the model, state the number of people who are susceptible, infected and have recovered from California whooping cough epidemic when the vaccination rate is 0.83 when t = 50 days.

Question 2 
Predict what will happen to the spread of whooping cough in the population if immunisation rates are increased above 0.83.

Answer:

Question 3  
To explore how vaccination rates impact the spread of disease, alter the proportion of vaccinated individuals in the CA whooping cough model. Observe how the number of infected individuals changes over time 

Complete the data table below, using the same time point, e.g. t=10, for each proportion vaccinated . All other variables will need to be kept constant to ensure it is a fair test.

Question 4
To graph the results, fill the command below with the data from the table above and push ‘Shift and Enter’ at the same time, to evaluate the cell.  
vacc={{0,y},{0.1,y},{0.2,y},{0.3,y},{0.4,y},{0.5,y},{0.6,y},{0.7,y},{0.8,y},{0.9,y},{1,y}}
ListLinePlot[vacc]

Question 5 
Using the data generated in the graph, explain the relationship between the vaccination proportion and the number of infected individuals with whooping cough.

Answer:

Question 6
For the above simulation, state the dependent variable.

Answer:

Question 7
For the above simulation, state the independent variable.

Answer:

Question 8
For the above simulation, state the control variables.

Answer:

Question 9
Write an appropriate title for the graph generated above.

Answer:

Question 10
Write an appropriate label for the X-axis of the graph.

Answer:

Question 11
Write an appropriate label for the Y-axis of the graph.

Answer:

Question 12
The minimum proportion of vaccinated individuals needed for herd immunity of Whooping Cough is 0.93. 
Click on the NYC Hong Kong flu button and record the minimum proportion of vaccinated individuals required for herd immunity.

Answer:

Question 13
Explain herd immunity.

Answer:

Question 14
In herd immunity explain why the minimum proportion of vaccinated individuals differ for different diseases?

Answer:

Question 15
Maternal vaccination can minimise risk of severe pertussis infection in young infants before their routine vaccines. Explain how maternal immunity may protect the infant.

Answer:

Extension
A  new  infectious  disease  has  been  discovered  in  outback  Queensland, with no vaccine currently available.  Dr Kylie Carville is the  epidemiologist  (a  person  who  is  an  expert  on  the  incidence,  distribution,  and  possible  control  of  diseases)  who  has  been  placed  in  charge  of  the  research  group  to  research  the  disease  and  the  possible  health  implications.

Question 16
What information concerning the disease does Dr Carville require?

Answer:

Question 17
What would be Dr Carville’s first recommendations to the health authorities in Queensland to limit the spread of the disease?

Question 18
What other recommendations might Dr Carville make?

Part 2 - Vaccination and society

Background
In recent times, the media has presented vaccination as a debatable issue. Sometimes emotions rather than scientific facts have been used to sway the general public. Read the following newspaper articles by clicking on the hyperlinks and answer questions 1, 2, & 3 below.

“Melbourne’s inner city child vaccination rates dangerously low” 
The Age

“Sydney postcode has Australia’s worst vaccination rate for five year old children”
 The Sydney Morning Herald

“Byron Shire’s dangerous anti-vax sentiment is catching”
The Daily Telegraph

Question 1
Compare the vaccination rates for measles in the suburbs of Eltham and Mullumbimby. Hint

Make sure all three articles are read carefully

Answer:

Question 2 
Suggest reasons why the Victorian State Government initiatives are targeting communities with new arrivals and refugees.

Answer:

Question 3
What role does peer pressure, community expectations and education play in the vaccination rates of specific communities?

Answer:

Question 4
Identify one incorrect statement that is used by anti-vaccination lobby groups. Explain why this statement is scientifically incorrect.

Answer:

The National Immunisation Program is a series of free vaccinations given in Australia following a specific immunisation schedule. Answer the questions below by visiting the Australian Immunisation Program Schedule here.

Question 5
What injections are given at 6 months of age?

Answer:

Question 6
What injections are included in the Schools Program?

Answer:

MyHealthyCommunities is an Australian Government website which gives information on immunisation rates in communities all over Australia. This website allows users to compare their local area with other areas. 

Visit the MyHealthyCommunities website to explore data for child immunisation rates in your postcode. In the ‘Immunisation interactive data table’ ensure you select all ‘Data Periods’ from 2011-12 to 2016-17 and that ‘Geography’ is set to ‘Postcode’, as shown below.



Type your postcode into the box above the table that is titled:  ‘Search table by postcode, state or territory’, then click ‘Search’. Click on the hyperlink for your postcode from the first column in the table. To view the data as line graphs, click on the ‘Time series’ tab (as shown below for 3124).

MyHealthyCommunities website

Question 7
What trends in the data can you identify for your postcode?

Question 8
Compare the child immunisation rates for your postcode to those of postcode 2481.

Answer:

Question 9
Suggest reasons why postcode 2481 has the immunisation rates it does.

Answer:

Herd immunity is very important for the health of communities. Watch the video explaining herd immunity.

Video: Herd Immunity

Question 10
Discuss two ways herd immunity is beneficial for the health of a community.

Answer:

The video referred to the Victorian State Government’s ‘No Jab, No Play’ legislation. Information about this legislation can be found at the following link:

No Jab No Play Legislation

Question 11
Describe some biological reasons for individuals having a ‘medical exemption’ under the ‘No Jab, No Play’ legislation.

Answer:

Part 3a - The Science of Vaccines

Immunisation rates for children
 
In this section, students will analyse and represent data collected from the Australian Government, showing the percentage of children aged 1, 2 and 5 years who were fully immunised from 2011–12  to 2016–17. Students will also investigate how vaccination rates differ across different regions of Australia and within the Aboriginal and Torres Strait Islander communities. 

Terminology: vaccination versus immunisation
Vaccination means having a vaccine – that is actually getting the injection, or nasal spray or oral vaccine. Immunisation means both receiving a vaccine and then becoming immune to a disease. In a very small number of cases, a person may be vaccinated however not become immune to the disease. As this is rare, both terms are often used interchangeably across the academic literature and in media reporting.
 
The information below has been collected from the Australian Government, Department of Health. Refer to this information when answering Questions 1 to 3 below.

Children aged 1 year 
Fully immunised at 1 year means that a child has received three doses of diphtheria, tetanus and whooping cough (dTpa) vaccine, three doses of polio vaccine, three doses of hepatitis B vaccine, two or three doses of Haemophilus influenzae type B vaccine, and three doses of the pneumococcal conjugate vaccine. 
It is assumed that all previous vaccinations were received.

Children aged 2 years
Fully immunised at 2 years means that a child has received three doses of diphtheria, tetanus and whooping cough (dTpa) vaccine, three doses of polio vaccine, three doses of hepatitis B vaccine, three or four doses of Haemophilus influenzae type B vaccine, two doses of measles, mumps and rubella vaccine, one dose of varicella vaccine and one dose of meningococcal C vaccine.
It is assumed that all previous vaccinations were received.

Children aged 5 years
Fully immunised at 5 years means that a child has received four doses of diphtheria, tetanus and whooping cough (dTpa) vaccine, four doses of polio vaccine, and two doses of measles, mumps and rubella vaccine.
It is assumed that all previous vaccinations were received.

Question 1
 The dTpa vaccine immunises against three diseases: diphtheria, tetanus and whooping cough. Its is often referred to as a ‘triple antigen’ vaccine. Define the term ‘antigen’.

Answer:

Question 2
Using the internet or other sources, complete the table below to summarise these three diseases.  Hint

Look at websites for drug companies such as Glaxo Smith Kline or the website for Australian Institute of Health and Welfare.

Answer:

({{Disease, Pathogen type (bacterial / viral), Scientific name, Mode of transmission, Disease Symptoms}, {
Diptheria
, 
, □, □, □}, {
Tetanus, 

, □, □, □}, {
Whooping cough, 

, □, □, □}})

Question 3
A child is considered fully immunised against diphtheria, tetanus and whooping cough if they have received 3 doses of the dTpa (DTPa) vaccine at 2 years of age. For a child to be considered fully immunised at 5 years of age, they must receive a 4th dose of the dTpa vaccine. Explain why this fourth dose is needed to maintain immunity against these diseases.

Answer:

Use the product information sheet below to answer question 4 relating to the dTpa vaccine.

©GlaxoSmithKline group of companies. 
Sourced from: ‘Boostrix®  product information’ from GlakoSmithKline Australia, accessed 3 December 2018

Question 4a
List the components of the dTpa vaccine.

Answer:

Question 4b
List the amount (in milligrams) of aluminium contained within the dTpa vaccine.

Answer:

Question 4c
A mother claims that her child contracted whooping cough from the dTpa vaccine. Use the information provided about the BOOSTRIX vaccine to evaluate this claim.

Answer:

Question 5
A concerned parent is unsure whether to vaccinate their child after reading a Facebook article about the dangers of aluminium contained in vaccines. Read the information below before responding to the parent’s questions.

Aluminium, an adjuvant
Many vaccines contain aluminium salts such as aluminium hydroxide, aluminium phosphate or potassium aluminium sulphate. They act as adjuvants, strengthening and lengthening the immune response to the vaccine. Aluminium salts appear to slow down the release of the active ingredient from the vaccine once it is injected, and stimulate the immune system to respond to the vaccine. They also absorb protein well, and stop the proteins in the vaccine sticking to the walls of a container during storage.

Aluminium is the most common metal in the earth’s crust and we are exposed to it all the time. It reacts with other elements to form aluminium salts, and small amounts of these are found naturally in almost all foods and drinking water, as well as in breast milk and in formula milk for babies. Aluminium salts are used as food additives (for example in bread and cakes) and in drugs such as antacids, and aluminium is widely used in food packaging.

Aluminium is not used by the body. Any aluminium absorbed from food or other sources is gradually eliminated through the kidneys. Babies are born with aluminium already present in their bodies, probably from the mother’s blood. Over time, small amounts of the aluminium from food, drink and other sources do accumulate in the body, but this is not believed to pose a significant risk to health (see for example this UK research from 2004 ). The view of most experts is that there is currently no convincing evidence that exposure to everyday levels of aluminium in any form increases the risks of Alzheimer’s disease, genetic damage or cancer.

The amount of aluminium present in vaccines is small - less than 2 milligrams of the salts, and less than a milligram of actual aluminium. In the UK, the highest dose of aluminium that babies receive in one go from vaccines is just under 1.5 milligrams (from the 6-in-1, PCV and MenB vaccines at 8 weeks and 16 weeks). A study from 2011  modelled the impact of aluminium from diet and vaccines in infants, and concluded that the total amount of aluminium absorbed from both sources was likely to be less than the weekly safe intake level. A study from 2002  drew similar conclusions. A study published in March 2018  took samples of blood and hair from 85 babies and measured their levels of aluminium. These levels varied considerably, but researchers did not find any correlation between aluminium levels in blood or hair and the estimated amount of aluminium that the babies had received from vaccines.

Vaccines that contain aluminium are associated with more redness and hardness at the injection site than other vaccines. Rarely, aluminium adjuvants may cause small itchy lumps (granulomas) to form at the injection site. A 2014 Swedish study  found that this happened in a small number of children (fewer than 1 in 100) after vaccination with the 5-in-1 vaccine (Infanrix) and pneumococcal vaccine (Prevenar). Granulomas are not dangerous but can be irritating and last for months or even years. The study found that children with granulomas often developed an aluminium contact allergy. However, most children recovered from their symptoms.

Sourced from:  'Aluminium' from Vaccine Knowledge Project (University  of Oxford); accessed 3 December 2018

Parent question A
 ‘Why is aluminium used in vaccines?’

Answer:

Parent question B
‘Will the aluminium in the dTpa vaccine be harmful for my child?’ Use evidence from the text above to explain.

Parent question C 
‘Would it be safer not to vaccinate against these diseases?’

Answer:

Part 3b - Graphing the spread of disease
 
The National Immunisation Program has been successful in increasing the vaccination rates across Australia. To view the vaccination rates from 2005 to 2017 in each state of Australia, place the cursor in the yellow coloured cell below after “Percentage Vaccinated by State for One Year Olds “]”. Push ‘Shift and Enter’. The data will be evaluated and a line graph will appear.

Sourced from:  ‘Historical coverage data tables for all children’ from Australian Government, Department of Health; accessed 3 December 2018

act={{2005.`,93.92`},{2006.`,91.35`},{2007.`,93.36`},{2008.`,93.63`},{2009.`,93.99`},{2010.`,93.26`},{2011.`,93.24`},{2012.`,92.83`},{2013.`,92.97`},{2014.`,92.91`},{2015.`,93.76`},{2016.`,94.87`},{2017.`,94.84`}}
nsw={{2005.`,90.64`},{2006.`,90.68`},{2007.`,91.52`},{2008.`,91.44`},{2009.`,92.1`},{2010.`,91.43`},{2011.`,91.16`},{2012.`,91.4`},{2013.`,90.02`},{2014.`,90.11`},{2015.`,92.2`},{2016.`,93.31`},{2017.`,93.87`}}
vic={{2005.`,91.65`},{2006.`,91.27`},{2007.`,91.72`},{2008.`,91.76`},{2009.`,92.18`},{2010.`,92.02`},{2011.`,92.39`},{2012.`,92.35`},{2013.`,91.04`},{2014.`,90.83`},{2015.`,92.18`},{2016.`,93.35`},{2017.`,94.25`}}
qld={{2005.`,90.86`},{2006.`,90.38`},{2007.`,91.`},{2008.`,90.87`},{2009.`,91.49`},{2010.`,91.71`},{2011.`,91.6`},{2012.`,91.86`},{2013.`,91.55`},{2014.`,91.53`},{2015.`,92.42`},{2016.`,93.89`},{2017.`,94.11`}}
sa={{2005.`,91.15`},{2006.`,90.82`},{2007.`,90.93`},{2008.`,91.14`},{2009.`,91.52`},{2010.`,91.26`},{2011.`,91.83`},{2012.`,92.01`},{2013.`,90.6`},{2014.`,90.38`},{2015.`,92.41`},{2016.`,93.36`},{2017.`,94.25`}}
wa={{2005.`,89.27`},{2006.`,89.52`},{2007.`,89.28`},{2008.`,89.75`},{2009.`,89.4`},{2010.`,89.82`},{2011.`,89.78`},{2012.`,90.08`},{2013.`,90.16`},{2014.`,90.67`},{2015.`,92.09`},{2016.`,92.75`},{2017.`,93.38`}}
tas={{2005.`,92.39`},{2006.`,93.22`},{2007.`,92.32`},{2008.`,91.84`},{2009.`,91.95`},{2010.`,92.17`},{2011.`,91.56`},{2012.`,92.81`},{2013.`,91.05`},{2014.`,89.79`},{2015.`,92.38`},{2016.`,93.56`},{2017.`,94.26`}}
nt={{2005.`,91.48`},{2006.`,91.23`},{2007.`,90.8`},{2008.`,90.41`},{2009.`,90.03`},{2010.`,89.98`},{2011.`,90.75`},{2012.`,92.48`},{2013.`,91.22`},{2014.`,89.63`},{2015.`,92.63`},{2016.`,92.98`},{2017.`,93.51`}}
labels={2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017}
ListLinePlot[{act,nsw,vic,qld,sa,wa,tas,nt},AxesLabel->{"year","% vaccinated"},AxesLabel->Placed[labels,{{1,0},{0.9,1}},Rotate[#,(2/7)Pi]&],PlotLegends->{"ACT","NSW","VIC","QLD","SA","WA","TAS","NT"},PlotLabel->"Percentage Vaccinated by State for One Year Olds"]

Question 1
According to the line graph above, what states in Australia has the highest and lowest vaccination rate as of 2017?

Question 2
Compare the maps below, to the line graph generated above. Is the data represented on these figures consistent?

Sourced from : ‘Explore the data, downloadable maps’ from MyHealthyCommunities; accessed 3 December 2018

Answer:

Question 3
Why is it good practise to cross reference multiple sources of data before drawing conclusions?

Answer:

Question 4
Suggest some potential explanations for any inconsistencies observed in the data.

Answer:

Examine the graphs below. Graph 1 shows the percentage of one year olds worldwide who have received vaccinations from 1980 to 2015. Graph 2 shows the number of deaths caused by vaccine-preventable diseases worldwide from 1990 - 2016.

Sourced from: ‘Vaccination’ from Our World in Data; accessed 3 December 2018

Graph 1                                                                                                                                                                                                    Graph 2

Question 5
Is there a correlation between vaccine coverage and deaths caused by vaccine-preventable diseases? Explain.

Answer:

Summary

Vaccination has been the most successful medical procedure ever introduced. It has saved more lives than any other intervention. The risk of serious adverse reactions has remained extraordinarily low for all modern vaccines. (source) Despite this, there is an increasing trend of individuals choosing not to vaccinate due to misunderstanding of data and scientific evidence. The development of technology has enabled these misconceptions to spread at a faster rate than ever before.

A world wide survey was carried out by researchers from the Vaccine Confidence Project to investigate the level of confidence people have in vaccines. 
Percentage of respondents from each country replying “Strongly disagree” or “Tend to disagree” with the survey question “Overall I think vaccines are safe”.



Sourced from: ‘Vaccination’ from Our World in Data; accessed 3 December 2018

“The world map shows that in most countries the huge majority considers vaccines to be safe. But in some countries – several former Soviet countries, France, and Japan – a substantial share of the population disagrees. In France 41% of the population does not consider vaccinations safe. In other countries the share that is of the same opinion is smaller than 10% (in Bangladesh only 0.2%).”

Question 6
Reflect on the information presented in this learning activity. Write a short summary statement, based on factual information, to counter argue the statement ‘Vaccines are unsafe’.

Answer:

Further information

To read more about vaccine preventable diseases and their impact in Australia, including the number of cases reported, hospitalisations and deaths visit the Australian Institute of Health and Welfare website click here

To read more about the facts of how immunisation saves lives click here

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