Moreover, they also show probable high risk areas in the future epidemics. These models are based on previous trends of incidents and climate factors. Thus they allow for better prediction of disease incidents or outcomes.
This is the continued article of the interpretations for variable returns to scale (VRS) from the last article. However, this article is about the summary of peers and the rest of the analysis conducted for VRS-DEA (Data envelopment analysis).
Epidemics of infectious diseases arise due to spread of the disease across individuals. It spreads within a geographical region over a period of time. When they spread at global level, it is referred to as pandemic. An infectious disease originates at a particular point.
Variable returns to scale (VRS) is a type of frontier scale used in data envelopment analysis (DEA). It helps to estimate efficiencies whether an increase or decrease in input or outputs does not result in a proportional change in the outputs or inputs respectively (Cooper, Seiford, & Zhu, 2011).
Epidemiology of infectious diseases often require geographical information. Both spatial and temporal factors of a population can affect disease spread.
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.
Descriptive epidemiology refers to analyzing the existing trends of a disease epidemic. The study is conducted with respect to time, place and persons (Aschengrau and Seage, 2013).
Forecasting models help detect future epidemics using related factors like environment, vector density or socioeconomic factors. In this article, the role of forecasting models in epidemiology is explained.