Posted: August 4th, 2022

The Multiple Data Analysis to Prevent the Cases of Epidemics

The Multiple Data Analysis to Prevent the Cases of Epidemics
Epidemics are diseases that spread fast within a large population. They affect not only people’s health but also the economy; they have harmful effects on the economy. Since ages, the world has experienced several epidemics; they include Covid-19, Spanish flu, and Ebola outbreak in the Democratic Republic of Congo.
Since epidemics spread fast, there is a need for immediate interventions to curtail spreading. Epidemiologists conduct both quantitative and qualitative research to make informed decisions. There are various sources of data, such as laboratory tests, mortality statistics, and population surveys. Epidemiologists should have not only expertise in research but also biology.
Data analysis is an essential aspect during outbreaks. It enables the application of evidence-based interventions, such as retrieval of contaminated food or isolation of suspected cases. Since a single factor does not cause diseases, multiple factors count during analysis. Multiple data is analyzed using various frameworks; they include the multivariable and multivariate approach, regression model, and Bayesian network structure. These frameworks help develop an inference in data analysis.
The regression model seeks to understand the relationship between the potential causes of diseases. A multivariate and multivariable approach establishes the relationship between variables causing an infection. That includes various attributes in an individual and the risk of infection. The Bayesian network structure is a graphical tool used to put the variables together and determine the relationship between them.
Finding the relationship between independent variables and connecting them to form one dependable variable is not easy. It helps epidemiologists develop inferences used in disease control. The epidemiologists should work collaboratively with the legislators and other professionals in restraining the disease. Various computational advances have made data analysis easier. The implementation of electronic health records and other technologies make it easy to access big data. Due to advancements in technology, data is readily available. However, determining the relevant information that will guide the right conclusion is a challenging task. That is the reason why data analysis is essential.
During epidemics, available data informs temporal forecasts. It is essential to analyze data as it unveils. In 2001, during the foot and mouth outbreak, the United Kingdom used the sequential Monte Carlo algorithm to analyze data in real-time. It greatly assisted in reducing its spread. It is essential to analyze one factor at a time before including other elements using the regression model. Multiple data analysis informs disease prevention and control. Epidemiologists should use it effectively to make suitable inferences.
References
Béguin, Cédric, and Beat Hulliger. “Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 167.2 (2004): 275-294.
Draper, Norman R., and Harry Smith. Applied regression analysis. Vol. 326. John Wiley & Sons, 1998.

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