• Descriptive Statistics in Epidemiology
• Modeling Epidemics: Interaction-based Approach
• Modeling Epidemics: Synthetic Information Environments Approach
• Working with large data sets
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## Descriptive Statistics in Epidemiology

Descriptive statistics are used in epidemiology to identify and describe how diseases and determinants are distributed. It provides useful tools that organize and analyze data that defines the frequency of disease variation among populations. As a result, we can say that descriptive statistics in epidemiology can be used to generate hypotheses from etiologic research. For example, standard survey instruments (which are tools of descriptive statistics) are often used to measure health conditions such as cancer, diabetes, and heart failure.

## Modeling Epidemics: Interaction-based Approach

Mathematical epidemiology stresses differential equation models that are rate-based. The interaction-based approach uses various criteria to divide the population into sub-groups. These criteria can be the state of diseases or demographic characteristics. Differential equation models are then used to define the dynamics of the disease across the created groups. However, this method is limited and fraught with several technical difficulties. It is near impossible to get a detailed and accurate human social contact network using simple measurements.

## Modeling Epidemics: Synthetic Information Environments Approach

This approach is made up of four components, the statistical model of the synthetic population, social contact network activity-based model, disease progression models, and models that will be used to represent and evaluate individual adaptation, public policies, and interventions. A synthetic population is the group of interest. To generate this population, we have to create a group of individual agents by integrating the census data with other geographic or demographic data. Next, we will generate a comprehensive schedule for each agent in the population being studied. This is usually done using machine learning methods and time-use surveys. The third step is to use a probabilistic transition system to represent the within-host model and endow each individual. Lastly, we analyze how effective the intervention strategies used are.

## Working with large data sets

In epidemiology, scientists work with a mammoth of data that arises from medical records from patients registered with various conditions and cohorts compiled with a definitive objective. As you already know, epidemiology is constrained to a sample of individuals from a population of the study. Since it is impractical to study the entire population, samples from the population are often used to make statistical inferences. Statistical methods such as statistical significance tests and confidence intervals can be used to assess the observations and estimates made.