A Comprehensive Review of Statistical Methods in Microbiology and epidemiology: A Data-Driven Approach

Author(s)	Dr.C.Madhavi
Country	India
Abstract	The rapid growth of computational power and big data analytics has transformed the landscape of statistical methods in microbiology and epidemiology. Advanced techniques such as machine learning, Bayesian inference, and network modeling are now being used alongside traditional statistical approaches. These methods offer unprecedented precision in identifying microbial patterns, tracking infectious diseases, and predicting outbreaks. This review highlights recent advances in statistical tools used in microbiology and epidemiology, particularly in analyzing antibiotic resistance, viral mutation rates, and epidemiological forecasting models for global pandemics such as COVID-19. The integration of novel statistical methodologies has improved our understanding of complex microbial interactions, resistance evolution, and the dynamics of infectious disease spread, enabling more informed public health interventions.
Keywords	Statistical methods, microbiology, epidemiology, microbial patterns, viral mutation rates. antibiotic resistance.
Field	Biology
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-20
Cite This	A Comprehensive Review of Statistical Methods in Microbiology and epidemiology: A Data-Driven Approach - Dr.C.Madhavi - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29054
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.29054
Short DOI	https://doi.org/g8np76

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International Journal For Multidisciplinary Research