Case Study (4)

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BIO 209 Medical Microbiology Case study 4 Assignment BIO 209 Medical Microbiology Case Study Assignment 4 Case 1: Dangerous Waters There are bacteria adapted to survive in every kind of habitat. There are marine bacteria and freshwater bacteria —and even some that thrive only in brackish waters, which are part marine and part fresh. Lately, bacteria that live in brackish waters have been causing serious wound infections, and even some deaths, in humans. In 2006, a mortgage broker in his 30s fell into a harbor in Hawaii 6 days after a sewage pipe failure had allowed 48 million gallons of sewage to flow into the harbor water. The man was admitted to the hospital, and within days he died from massive organ failure caused by septicemia with Vibrio vulnificus . You may be familiar with Vibrio cholerae, the diarrhea agent. Its relative, V. vulnificus, tends to invade breaks in the skin and then proliferate in the bloodstream, often causing massive infections necessitating amputation, and sometimes even leading to death. a. Once the man was admitted to the hospital, what steps would you take to determine what was making him ill? Upon the patient's arrival at the hospital, to determine the patient with Vibrio Vulnificus bacterium, a blood culture should be requested immediately after admitting him to the hospital. A blood culture would indicate a patient with unstable vital signs, severe skin, sepsis, and massive organ failure. on the other hand, a blood culture helps the doctor figure out the cause of the affections or understand if the patient has an infection within the bloodstream. That may affect the entire human body creating discomfort. b. If you were a public official in charge of Hawaii’s recreational waters, how would you determine when the waters were safe again? If I were appointed to ensure the safety of Hawaii's recreational water, is to determine the possible threat the water might bring to the public. By sending off a term professional in the study of Microbiology to test the levels of a bacterium in the harbor water where the experts would count the number of Vibrio Vulnificus in the area. After observing bacteria levels and guaranteeing that the water is bacteria-free, I would announce if the water is safe. www. anualrevies.com Case 2: Internet Searches used for Epidemic Tracking Internet users have grown accustomed to using Google to search for all kinds of information: the definition of a word, the latest news about a celebrity, or the possible cause of an ache or

BIO 209 Medical Microbiology Case study 4 Assignment pain. Now it turns out that Google may be one of the more powerful tools for predicting disease outbreaks—some say even more powerful than the best tools of the Centers for Disease Control and Prevention (CDC). In 2008 Google launched Google Flu Trends, an application that compiles aggregated data from keyword searches for clinical terms, such as thermometer, chest congestion, muscle aches , or flu symptoms . Google reports the data on a website, which then provides an early warning system for the locations of new flu outbreaks. Because the data are collected from searches performed each day, trends in flu symptoms become apparent much more quickly than when they are based on data reported during office visits or in lab reports from physicians around the country. When the CDC compared actual cases over the course of a year with Google’s findings, the data from the two sources matched. a. Is there any possible downside to this approach to data collection? Yes, there is a possibility of a downside to this approach to data collection; because the google result might be an unrepresentative sample of the flue break and non-behavioral data. In addition, when acquiring data from google, the length of study could be limited, and data collected from google might lack data follow-up. b. How might data collected in this way not be representative of a particular population? Data collected in this way from google may not be representative of a particular population because the sampling may be biased. In statistic bias, a sample is serene where few members of a certain population have a lower or higher sampling likelihood than others. Due to this, it may not be representative of a particular population. www.health.gov

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