The basic reproductive ratio, R₀, is a key concept in epidemiology, representing the expected number of secondary infections produced by a single infected individual during their infectious period in a fully susceptible population. It serves as a threshold parameter to predict whether an infection will spread. R₀ is widely used in assessing the transmission dynamics of infectious diseases, including emerging diseases like SARS, avian influenza, and vector-borne diseases such as malaria and dengue. While R₀ is a fundamental concept, related parameters may not always accurately reflect its true value. This paper reviews common methods for calculating R₀ from deterministic models and estimating it from epidemiological data. It also discusses the recent use of R₀ in assessing the risk of emerging and endemic diseases. R₀ is calculated using various methods, including the survival function and the next generation operator, which involve determining the rate of new infections and the average time individuals remain infectious. The value of R₀ is crucial for public health initiatives, as it helps determine the effectiveness of control measures in reducing R₀ below one. Recent studies have used R₀ to estimate the transmission potential of diseases like SARS, influenza, and avian influenza, highlighting its importance in understanding and managing infectious diseases. However, the accuracy of R₀ estimates depends on the assumptions made in the models and the quality of the data used. Despite these challenges, R₀ remains a vital tool in epidemiology for predicting disease spread and evaluating control strategies.The basic reproductive ratio, R₀, is a key concept in epidemiology, representing the expected number of secondary infections produced by a single infected individual during their infectious period in a fully susceptible population. It serves as a threshold parameter to predict whether an infection will spread. R₀ is widely used in assessing the transmission dynamics of infectious diseases, including emerging diseases like SARS, avian influenza, and vector-borne diseases such as malaria and dengue. While R₀ is a fundamental concept, related parameters may not always accurately reflect its true value. This paper reviews common methods for calculating R₀ from deterministic models and estimating it from epidemiological data. It also discusses the recent use of R₀ in assessing the risk of emerging and endemic diseases. R₀ is calculated using various methods, including the survival function and the next generation operator, which involve determining the rate of new infections and the average time individuals remain infectious. The value of R₀ is crucial for public health initiatives, as it helps determine the effectiveness of control measures in reducing R₀ below one. Recent studies have used R₀ to estimate the transmission potential of diseases like SARS, influenza, and avian influenza, highlighting its importance in understanding and managing infectious diseases. However, the accuracy of R₀ estimates depends on the assumptions made in the models and the quality of the data used. Despite these challenges, R₀ remains a vital tool in epidemiology for predicting disease spread and evaluating control strategies.