All articles by Priya Chetty

How to perform and apply Monte Carlo simulation?

Monte Carlo simulation is an extension of statistical analysis where simulated data is produced. This method uses repeated sampling techniques to generate simulated data. For instance, a regression model analyzes the effect of independent variables X1 and X2 on dependent variable Y. Read more »

Setting the ‘Time variable’ for time series analysis in STATA

Time series analysis works on all structures of data. It comprises of methods to extract meaningful statistics and characteristics of data. Time series test is applicable on datasets arranged periodically (yearly, quarterly, weekly or daily). This article explains how to set the ‘Time variable’ to perform time series analysis in STATA. Read more »

Spatial modeling in disease epidemic studies

Epidemiology of infectious diseases often require geographical information. Both spatial and temporal factors of a population can affect disease spread. Spatial factors refer to the geographical or topological factors associated with a disease. Temporal factors mean time-bound progress of a disease in a population. These factors influence the virulence of a disease, patterns of prevalence and also future incidents. Read more »

Common pipeline for statistical analysis in epidemiological studies

Previous articles discussed the need for statistical analysis and modeling of epidemiological studies in public health studies. Analysis in epidemiological studies typically requires descriptive and analytical methods. Descriptive analysis of epidemiological data includes hypotheses development. This is based on variability of disease outcome rates with demographic variables. On the other hand, analytical epidemiology determines cause or mode of disease epidemic outbreak. Read more »

Statistical tests in descriptive and analytical epidemiology

In the previous article, the importance of statistical analysis in epidemiological studies was established. Statistical analysis can contribute towards strategic planning of public health strategies. Consequently, this analysis is mainly done through mathematical and statistical techniques. Both offer unique advantages and purposes. Read more »

Time series and forecasting models in disease epidemiology

Time series analysis refers to analysis of observations that are time-dependent. Therefore, observations from an event are dependent upon the time at which it took place. Time intervals can be minutes, hours, days, months or years. Observing the trends of these events over a long period enables identifying hidden relationships. Moreover, future trends can be predicted using this analysis. Read more »

Systematic review of forecasting models in disease epidemiology

In the previous article, the role and advantages of using forecasting models in disease epidemiology was discussed. Forecasting models are important tools assisting public health decision making. They help predict future disease trends, incidents and possible risks in a population or community. Read more »

Significance of statistical analysis in epidemiological studies

Infectious diseases continue to pose significant threat to humans and animals. Stringent disease control policies and advancement in vaccines have not eradicated them. Therefore, in 2015 alone, 10 top deadly diseases were responsible for killing 30 million people. Among these diseases, communicable diseases like lower respiratory infections, diarrhea, tuberculosis and HIV were the major culprits (World Health Organization, 2017). Read more »

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