Abstract
When mathematical models of infectious diseases are used to inform health policy, an important first step is often to calibrate a model to disease surveillance data for a specific setting (or multiple settings). It is increasingly common to also perform sensitivity analyses to demonstrate the robustness, or lack thereof, of the modeling results. Doing so requires the modeler to find multiple parameter sets for which the model produces behavior that is consistent with the surveillance data. While frequently overlooked, the calibration process is nontrivial at best and can be inefficient, poorly communicated and a major hurdle to the overall reproducibility of modeling results. In this work, we describe a general approach to calibrating infectious disease models to surveillance data. The technique is able to match surveillance data to high accuracy in a very efficient manner as it is based on the Newton-Raphson method for solving nonlinear systems. To demonstrate its robustness, we use the calibration technique on multiple models for the interacting dynamics of HIV and HSV-2.
Highlights
In order to take a mathematical model of infectious disease from a theoretical construct to a practical tool for informing health policy in a specific setting, an important initial step is model calibration
The terms “model calibration”, “model fitting” and “model parameterization” all describe the process of estimating values for the parameters used within a mathematical model so that its output is relevant to the situation of interest
In this work we present an exact approach to calibrating infectious disease models to surveillance data that aims to streamline the process while increasing efficiency and reducing reliance on computational methods
Summary
In order to take a mathematical model of infectious disease from a theoretical construct to a practical tool for informing health policy in a specific setting, an important initial step is model calibration. The terms “model calibration”, “model fitting” and “model parameterization” all describe the process of estimating values for the parameters used within a mathematical model so that its output is relevant to the situation of interest. In this work we present an exact approach to calibrating infectious disease models to surveillance data that aims to streamline the process while increasing efficiency and reducing reliance on computational methods. Mathematical modeling, calibration, model-fitting, HIV, HSV-2, surveillance data. Once a set of values has been specified for nonfitting parameters, values of the fitting variables are found so that the mathematical model exactly matches the given calibration conditions.
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