Development of a generic and open source Incidence-Prevalence-Mortality model for the assessment of chronic disease epidemiology

&lt;p&gt;It is often a problem for researchers when the epidemiology of a disease is unreliable or altogether lacking. This thesis was created in order to make an R package called DisModR that accounts for this problem. In particular, the package was made for applications on chronic diseases.&lt;/p&gt; &lt;p&gt;The compartmental model used was taken from previous iterations of a software called DisMod. This model gives the logical relationship between di erent epidemiological parameters of a disease. In particular, inputs on disease incidence, remission, and case fatality were needed to determine the disease-speci c mortality and prevalence. However, an iterative optimization procedure called the Nelder-Mead method was employed in order to accept more inputs. This was done through the fminsearch() function of the neldermead package. Likewise, in order to make the values of the input variables vary continuously as a function of age, cubic smoothing splines were incorporated in DisModR.&lt;/p&gt; &lt;p&gt;The DisMod() function in DisModR allows the user to compute the di erent epidemiological parameters of a disease. It accepts data on three of the following ve inputs: incidence, remission, case fatality, mortality, and prevalence. Likewise, it allows users to manipulate options on the cubic smoothing splines and the tolerance limits of the optimization procedure. Using the breast cancer data set of the Netherlands, the properties of the DisMod() function was shown. Additional outputs on case fatality, prevalence, and relative risk mortality were produced using only inputs on incidence, remission, and mortality. In addition, the function also produced various objects that contained lists of&lt;br&gt; input and output values, as well as their corresponding graphs.&lt;/p&gt; &lt;p&gt;There are several uses to DisModR. The first, which is perhaps the most obvious, is that it provides values for epidemiological parameters, which are otherwise unavailable such as mortality and prevalence of particular diseases. The second is that it allows users to compare its results with existing data. This is to check if the present data is consistent with the logical relationship of the other input variables. The third gives researchers the opportunity to work in the R environment, which is a unique feature of this package compared to other versions of DisMod. Furthermore, users are advised that they should not only rely on the R package alone, but they must combine the results with expert&lt;br&gt; knowledge of the epidemiology of the disease.&lt;/p&gt;</p