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Disease in Vaccinated Populations

This academic work is a laboratory report summarizing the results of a practical simulation of the epidemiological dynamics of a simulated population. Thus, the report’s central tasks are to summarize the data obtained, answer the questions posed, and analyze the results critically.

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Results

This lab report’s main results were obtained using the Lab Disease online simulation tool to quantitatively predict the incidence dynamics in the simulated population for a selected infectious disease. KOLD disease was investigated for samples of three sizes to determine the pattern of respiratory disease prevalence in patients as a function of regional population density. The simulation data are shown in the two parts of Table 1. It is noteworthy that each simulation run yielded unequal results because it is difficult to expect identity in the case of clinical trials. The spread of a contagious disease is individual and depends on many factors, including sample size and homogeneity, innate genetic resistance, and vaccination.

As can be seen, as the sample population increased in size, the incidence of the disease naturally increased. The number of infected people was calculated as the total number of sick days divided by the average duration of illness (equation [1]), and the infection rate as the proportion of infected people from the total number of vulnerable group (equation [2]). Thus, the CR infection rate’s dependence on the population size is practically linear, as shown in Figure 1. Consequently, for too low a density, the need for strict clinical restrictions is less than for high densities, where quarantine and universal vaccination are required. For low densities, these steps are selective vaccination, control, and tracking of individual cases.

Table 1: Disease Simulation

Step 1 Population Number Starting Number of Contagious People Sick Days Reported Contagious Contagion Rate (/100%)
Prediction 600 3 1000 200 0.33
Simulation Run 1 600 3 1661 332 0.55
Simulation Run 2 600 3 1133 227 0.38
Simulation Run 3 600 3 1497 299 0.50
Step 2 Population Number Population Density Starting Number of Contagious People Sick Days Reported Contagious Contagion Rate
(/100%)
Prediction 1 (low) 200 low 3 750 150 3.75
Simulation Run 1 200 low 3 32 6 0.03
Simulation Run 2 200 low 3 12 2 0.01
Simulation Run 3 200 low 3 42 8 0.04
Prediction 2 (high) 900 high 3 1900 380 0.42
Simulation Run 1 900 high 3 4287 857 0.95
Simulation Run 2 900 high 3 4407 881 0.98
Simulation Run 3 900 high 3 4327 865 0.96

Folmura

Linear regression of mean CR values as a function of population size
Fig. 1. Linear regression of mean CR values as a function of population size

The second important result in this laboratory report was to find a relationship between the vaccinated population and the total number of infections and deaths. The working hypothesis was that a large number of vaccinated individuals in the sample leads to an expectedly low mortality outcome (Disease in Vaccinated Populations). Table 2 summarizes the simulation data for Impfluenza, the average duration of which was equated to three days according to the tool settings.

Table 2: Vaccine Simulation

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Step 1 Population Number Population Density Starting Number of Contagious People Sick Days Reported Contagious Dead
for 50% vaccinated
Prediction 1 600 medium 3 700 233 35
Simulation Run 1 600 medium 3 123 41 2
Simulation Run 2 600 medium 3 105 35 1
Simulation Run 3 600 medium 3 42 14 0
for 90% vaccinated
Simulation Run 4 600 medium 3 9 3 0
Simulation Run 5 600 medium 3 12 4 0
Simulation Run 6 600 medium 3 6 2 0

As shown in Table 2, as the proportion of vaccinated people increased to 90%, a significant reduction in the infections and deaths cases in the simulated sample was observed. Thus, the average number of infections for the first scenario was 30 (equation [3]), whereas when the proportion increased to 90%, this parameter was numerically equal to three ([4]). In a comparative analysis, the vaccine for the medium density sample reduced the number of infections by 90% ([5]). It was also measured that when the 75% vaccinated mark was reached, the CR per capita did not exceed 0.1.

Formula

Conclusion

In conclusion, it should be noted that the study of epidemiological situations is a statistical task aimed at the numerical analysis of individual scenarios. The report showed that the higher the population density, the higher the infection rate among people due to residence proximity and the frequency of social contacts. It was also demonstrated that increasing the proportion of people who are vaccinated significantly reduces infection rates. This laboratory work is a useful material for studying the theory of epidemiology and predicting COVID-19 scenarios. Since this viral disease is highly contagious, reducing social contact, distancing, and universal vaccination should be an effective weapon to suppress the pandemic.

Works Cited

“Disease in Vaccinated Populations.” University of Oxford, 2020, Web.

Lab Disease. The Habitable Planet, n.d., 2021. Web.

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StudyCorgi. (2022, August 15). Disease in Vaccinated Populations. Retrieved from https://studycorgi.com/disease-in-vaccinated-populations/

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StudyCorgi. (2022, August 15). Disease in Vaccinated Populations. https://studycorgi.com/disease-in-vaccinated-populations/

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StudyCorgi. "Disease in Vaccinated Populations." August 15, 2022. https://studycorgi.com/disease-in-vaccinated-populations/.

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StudyCorgi. 2022. "Disease in Vaccinated Populations." August 15, 2022. https://studycorgi.com/disease-in-vaccinated-populations/.

References

StudyCorgi. (2022) 'Disease in Vaccinated Populations'. 15 August.

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