Disease in Vaccinated Populations

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

Results

This lab reports 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 rates 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

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

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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Disease in Vaccinated Populations

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