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Statistics play a major role in epidemiology. Numerous populations of interests in epidemiology require articulate analysis and interpretation using statistics. For instance, vital conclusions in epidemiology can only be made after carrying out statistical analysis on a given database of raw data. Uncertainty is accounted for by the art and science of manipulating statistics (Fendyur, 2011).
Measured observations are contained in epidemiological datasets. The latter also show the degree of exposure to infections and occurrence level of diseases. Due to limited resources that analyze trends in epidemiology in a given area, preparing a statistical sample using knowledge in statistics can be instrumental towards understanding any given dataset in epidemiology (Ziegel, 2005). For instance, the relationship between smoking and cancer outbreak can be best understood when statistical tools are applied.
Within a given population, the link between human behavioral patterns and occurrence of certain diseases can be established using random samples of raw data gathered from the field (Fendyur, 2011). Epidemiologists can easily determine whether the qualitative reference linked to a certain disease is accurate when they employ statistics in the analysis. In most instances, statistics reveal the relationship between the occurrence of diseases and human behavior even if the link is marginal.
Public health science is drawn from the key tenets of epidemiology. As a result, appropriate research methodologies can be used to gather epidemiological data from the field when statistics is embraced in the entire process. Needless to say, disease aspects such as causes, symptoms, risk factors and vulnerability may be comprehended through the application of statistics (Ziegel, 2005). Initially, epidemiology largely dealt with the public health risk of disease outbreaks. In fact, this medical field restricted itself to outbreaks due to lack of analytical tools in statistics. As it stands now, statistics has enabled broad and diversified study of diseases. The discipline no longer roots its objectives on outbreaks alone. Injuries, stroke, cancer and cardio-vascular diseases are also addressed in epidemiology courtesy of statistics.
The onset, development and spread of diseases are also some of the key areas of epidemiology that are aided by knowledge in statistics. The behavioral tendencies of a given type of sickness can be precisely predicted using statistics. A case in point is the development and progress of flu (Fendyur, 2011). Biostatistics can forecast its outbreak, symptoms and mortality rates. In other words, the specific season when a population can contract it may be vividly highlighted by statistics.
It can also be recalled that statistics has been used for several decades in developing necessary vaccines. Through research and development, statistics offers the best platform for researching the most effective measures that may be applied in curbing the spread of diseases. Biostatistics led to the successful invention of polio vaccine during the 1950s. This scientific practice has been replicated over the decades to control some of the most deadly infections in human history (Ziegel, 2005). In any case, vaccines provide the most reliable prevention measure against common infections such as those transferred through water and air.
Apart from vaccines, terminal illnesses such as cancer, hypertension and diabetes have been brought under control through biostatistics in epidemiology. New and effective drugs are continually being formulated to treat and manage terminal conditions (Fendyur, 2011). The latter would not be possible in the absence of statistical knowledge. In the event that ordinary therapies fail to work, scientists are usually left with biostatistics as the most viable option.
References
Fendyur, A. (2011). Applications of operations Research/Statistics in infection outbreak management. The International Business & Economics Research Journal, 10(2), 131-143.
Ziegel, E. R. (2005). Statistics for Epidemiology/Multivariate methods in epidemiology. Technometrics, 47(1), 107.
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