Epidemiology: Parametes Affecting Influenza


Epidemiology Assignment


Part 1

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Influenza is an upper respiratory infection commonly known as the flu. It is caused by either influenza virus A or B and can cause “fever, headache, muscle pain, runny nose, sore throat, extreme tiredness and cough” (Facts, 2019). It is quite dangerous for young children, elders or people with already weakened immune systems (Facts, 2019). There are many ways to prevent the flu from spreading; getting vaccinated and being cleanly. Being cleanly means washing your hands frequently, coughing and sneezing into your elbow and staying home when very sick (Facts, 2019). Getting vaccinated is a needle that makes the body produce the antibodies needed to fight influenza. It takes approximately 14 days to be in affect after administration. There are different strains of the flu so the most likely starin to occur is predicted and that is the vaccine that made (Key).


Part 2

  1. Vaccines


Hypothesis:

An altered infectious period of the virus will affect the effectiveness of the vaccine.


Null Hypothesis:

There will be no difference in the effectiveness of the vaccine if the infectious period of the virus is changed.


Prediction:

The changing infectious period will be inversely proportional to the effectiveness of the vaccine.


Experimental design:

The infectious period of the virus is altered, it is increased by 5 days. First the Netlogo simulation will be run without alterations as a control then the parameter will be changed and the simulation will be run again.


Results:

The results from Table 1 show that an increased infection period of 5 days, the attack rate went from 67.9% to 79.20%. Of the people who received the vaccine in the control, 47.08% of them were protected. This was decreased to 47.47% when the infectious period was lengthened.


Conclusion:

Based on the results we can conclude that an altered infectious period of influenza will not have an effect on the effectiveness of the vaccine. The longer the virus is contagious the more people could be affected but the vaccine would still function the same, how many people are unaffected would still be determined by the effectiveness of the vaccine to begin with and the amount of the population that got it. Therefore, the null hypothesis cannot be rejected.


Table 1. Effects of the infectious period of a virus on vaccines effectiveness.


Parameter

Control

Average infection period increased by 5

Attack Rate/ %
67.90 79.20

Maximum Number of Infections
228 533

Time of Maximum Number of Infectious/ days
59.75 57.25

Population Mean of Average Daily Contacts
11.62 11.61

Susceptible
321 208

Recovered
679 792

Received Vaccine
480 415

Protected by Vaccine
226 197
  1. Isolation


Hypothesis:

The alteration of the daily isolation chance will inversely affect the transmission of the virus.


Null Hypothesis:

Changing the daily isolation chance will make no difference in the way the virus is transmitted.


Prediction:

An increase in isolation will decrease the amount of people affected and  time it takes for the population to recover.


Experimental design:

The original Netlogo simulation will be run to get a control. Next, the daily isolation chance will be increased to 40 and the simulation will be run again. The results will be analyzed and compared.


Results:

The data from Table 2 show that when the isolation chance is increased to 40  that the attack rate will decrease to 0.30% from 58.7%. Similarly, the number of infection decrease from 136 to 3.


Conclusion:

The results show that a change in isolation duration will inversely affect the transmission of influenza. If the isolation is increased then the amount of people that contract the virus decreases.Iit can be assumed that vise versa is the same, if the daily chance of isolation is decreased then the attack rate and amount of people infected will increase. Therefore, the null hypothesis can be rejected.


Table 2. Effects of increased isolation on influenza.


Parameter

Control

Daily Isolation Chance Increased

Attack Rate/ %
58.70 0.30

Maximum Number of Infections
136 3

Time of Maximum Number of Infectious/ days
26 2.25

Population Mean of Average Daily Contacts
11.66 11.78

Population Mean of Average Daily Not Isolated Contacts
11.29 11.75

Susceptible
407 997

Recovered
593 3


Part 3

Elders have weaker immune systems so practicing all control strategy would be most effective and they would also have to be more precautious than with younger populations (Facts, 2019). For somewhere like a nursing home I would recommend isolation as a more important control strategy. Vaccines are important and effective but they are made and distributed based on prediction for that season (Key) so if the vaccine does not include a specific strain that occurs then all the patients in the nursing home would be at risk. Isolation for people not in a nursing home isolation is not very practical as it is hard to stop day to day life and stay home by yourself. In a nursing home the ill could be kept separate from the rest of the population quite easily because of the nurses caring for them, they have the time to do so and lack the higher demands of someone living independently. Based on the simulations on Netlogo, isolation had a much more drastic difference in how many people were affected and the attack rate of the virus compared to the vaccine alone. The data shows that it is much more difficult to contract the virus with very limited exposure to it which more be more effective for those with immune systems potentially too weak to fight off the virus.


Works Cited

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