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The theme of the pandemic is one of the currently discussed topics today. Therefore, researchers spend much time and resources investigating this field and offering the most relevant findings to support populations and solve COVID-19 related problems. Applying quantitative methods for COVID-19 cases data analysis is a common approach in modern research. There are many examples of gathering and analyzing quantitative information, including financial reports, marketing reports, and demographical changes, via online and paper surveys, interviews, longitudinal studies, and systematic observations. Lessons from the COVID-19 pandemic are numerous, and quantitative methodology is a reliable solution to understand the algorithms of the virus growth.
Qualitative data includes any valuable numbers and forms of counts with the help of which a research question (or questions) can be answered within research. This type of methodology allows measuring different parameters within a particular field and relies on mathematical derivations from surveys, polls, and other available sources. A distinctive feature of the COVID-19 epidemics is its unpredictability and poor public awareness. A number of deadlocks and gaps currently exist, pushing individuals and professional organizations to implement policies at local, state, and federal levels (Silva et al., 2020). As soon as the decision to gather calculations, measurements, and counters is made, it is necessary to think about how to analyze this type of data, with the most relevant results to be achieved.
Regarding the spread of the disease and the inability to predict its growth and impact on people, researchers prefer to demonstrate different analytical methods for understanding the gathered information. One of the frequently used analytical methods is cross-tabulation, when information is given in a basic tabular form to see the differences, changes, or other measuring aspects. For example, Farhadi and Lahooti (2021) present several tables that contain the results of the statistical analysis of the countries and their application of COVID-19 data. The benefits of this method are simplicity and clarity of information. It does not take much time to find the required country and learn the results of the statistical tests.
Some researchers prefer to pay attention to trend (statistical) analysis or conjoint analysis to identify and analyze advanced metrics, feedback, and other variables. To contribute to studying the COVID-19 progress and solution, the susceptibleinfectiousrecovered (SIR) model is recommended (Silva et al., 2020). Although the challenge of this method is the impossibility of investigating many regions at the same time, the relationship between variables can be properly identified (Silva et al., 2020). It seems to be effective to conduct a comprehensive analysis on several epidemiological models, use a limited number of measurements (birth and death rates, for example), and analyze some interventions efficiency. The analysis of quantitative data about COVID-19 cases may consist of several stages, like network construction, modeling, and evaluation, to demonstrate how data changes at different periods (Silva et al., 2020). Unfortunately, the pandemic is characterized by many unclear points and gaps in knowledge, but quantitative data helps advance solutions and recommendations.
Using quantitative methods for COVID-19 cases data analysis has certain advantages and limitations. On the one hand, this approach offers reliable information in the form of numbers from generalized and anonymous sources collaborated distantly. On the other hand, people who are profoundly interested in the COVID-19 situation cannot develop feedback from statistics. They usually need more information, a description of the environment, or additional qualitative explanations. Thus, a quantitative methodology is not the only option for modern researchers to reveal the truth about the pandemic and its impact on the modern world.
References
Farhadi, N., & Lahooti, H. (2021). Are COVID-19 data reliable? A quantitative analysis of pandemic data from 182 countries. COVID, 1(1), 137-152. Web.
Silva, T. C., Anghinoni, L., & Zhao, L. (2020). Quantitative analysis of the effectiveness of public health measures on COVID-19 transmission. MedRxiv, Web.
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