Meningitis Detection Algorithm

Meningococcal meningitis outbreaks have been rare in the United States since the 1990’s due to mass vaccination that occurred. However, sporadic events do occur, but swift intervention has prevented the development of an outbreak. The disease is in the notifiable illnesses list in all states across the United States. Syndromic surveillance algorithms can be used to prevent possible outbreaks before they occur (Abat, Chaudet, Rolain, Colson, & Raoult, 2016). This paper will discuss a simple algorithm based on several of the disease’s indicators, including the estimation of the strengths and limitations of the given algorithm.

The meningococcal meningitis outbreaks are common in the ‘meningitis belt of Africa’ that consists of over 25 countries in the continent (Goodman, Masuet-Aumatell, Halbert, & Zuckerman, 2014). Therefore, the first indicator involves travel logs to Africa. People who travel to the regions, especially in the dry condition, are likely to get infected. The algorithm, first, will isolate the people who have traveled to Africa in the dry season.

The second indicator involves living conditions. Meningococcal meningitis spreads in crowded living conditions, like in college dorms, military barracks or slums. The majority of the recent outbreaks have occurred in university hostels (Goodman, Masuet-Aumatell, Halbert, & Zuckerman, 2014). The algorithm will review person’s living conditions also in terms of travel logs. Therefore, if a person lives in a crowded place and there has been someone who recently traveled to Africa among the crowd, then the algorithm will classify them as possibly infected.

The last indicator is the purchase of pain-relieving medication. Early stages of meningitis are characterized by headaches, muscle aches, and stiff necks (Ferraro et al., 2014). A person highlighted from the earlier stages of the algorithm, buying pain medication, is likely to have meningitis. However, other tropical diseases like malaria and dengue fever may have similar symptoms that require pain medication (Andersson et al., 2013). Therefore, it is vital that the algorithm should isolate people that have traveled to Africa only during the dry season when other tropical fevers are at a low point.

As for the pros and cons of the algorithm, it can detect any possible meningitis infection at the early stages of the disease. The program can also easily utilize any related available data due to the simplicity of the indicators. However, the program may detect false positives, as meningitis symptoms are similar to several tropical infections. The algorithm also requires accessing travel logs that may need special permission to access due to their sensitivity.

In conclusion, the algorithm will utilize state travel records, hospital records and social sites like Facebook or Twitter to track the potential patients’ movements and symptoms. The algorithm will be prone to false positives due to the similarity of symptoms. Despite the limitations of the algorithm, with refined programming, it can save lives by preventing meningitis outbreaks.

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