The study of infectious disease outbreaks allows for sharpening our epidemiological skills and learning more about the retrospective control of past pathogens. This is of particular importance since it is not excluded that humanity will face similar outbreaks in the future, which means that learning from the collected experiences is critical for the survival of the human species. Therefore, the use of such an interactive simulation seems like a sound academic solution for learning.
This paper used The Queens Killer game to study an outbreak of a mysterious disease that affects patients’ respiratory and nervous systems (CDC, n.d.). As a general result, the disease in question was determined to be the West Nile virus, presumably from East Africa to New York City in 1999. The probable cause of this intercontinental transfer is tourism, which has facilitated the migration of vectors, which are mosquitoes.
To investigate this case, the steps of classical epidemiology were applied, in which the factors and aspects potentially contributing to the spread of infection are studied comprehensively. According to this methodology, none of the options should be ruled out until sufficient evidence is gathered that it is not applicable (Nursing research in action, 2019). At the same time, classical epidemiology is designed to use adequate protective measures, which was especially clear from the last question of the game that suggested spraying insecticides throughout the United States. On the other hand, one might think that data epidemiology is being used, where any judgments are made based only on actual data (Nursing research in action, 2019).
Thus, the simulation could not reliably point to any conclusions if the data did not support it. That said, specific epidemiological data that were reliable were the rate of spread, mortality, number of infections, linkage chart, and generalized opinion of relatives of patients. All of these data sets are useful for epidemiology but require critical reflection.
In a sense, it could be said that a logical inductive method of going from the particular to the general was used. Initial data on a few patients were gradually supplemented with facts about their interactions, blood tests, age, and behavior before symptoms appeared. All these data were elements of an overall picture that were eventually added together: in doing so, it became clear that the West Nile virus was the disease (WHO, 2017). Notably, making this diagnosis rejected the initial assumption of St. Louis encephalitis because, despite the commonality of symptoms and course of infection, laboratory diagnosis of the dead birds indicated a different serotype of the virus.
The general course of work consisted of five sequential epidemiological questions, gradually narrowing the scope of candidates for the pathogen. The first of these assessed the ability to infer from the available data: it would be wrong to make a generalization based only on the ages of two patients. The second question aimed to analyze data from patients and relatives when the number of patients increased in a pattern. In this question, it was necessary to use a histogram of response frequencies to make tentative judgments about the cause of infection. The third question first assumed the diagnosis based on the results of the past two prompts.
However, the initial diagnosis was incorrect because the test results from question four clarified a different cause, namely the West Nile virus. A histogram of the distribution of cases of infection allowed the effectiveness of preventive measures to be determined, and therefore the hypothesis of mosquito vectors was confirmed. The fifth concluding question recorded the current agenda and offered recommendations for New Yorkers to stay healthy. All of this combined was a valuable strategy for gaining new knowledge about former epidemics and testing one’s skills.
CDC. (n.d.). The Queens killer. Solve The Outbreak. Web.
Nursing research in action: A look at key epidemiology examples. (2019). Regis College. Web.
WHO. (2017). West Nile virus. WHO Fact Sheets. Web.