Using inverse problem methods with surveillance data in pneumococcal vaccination

  • Authors:

  • Karyn L. Sutton;H. T. Banks;Carlos Castillo-Chavez

  • Affiliations:

  • Center for Research in Scientific Computation, & Center for Quantitative Studies in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, United States and Department of Mathematic ...;Center for Research in Scientific Computation, & Center for Quantitative Studies in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, United States;Department of Mathematics & Statistics, Arizona State University, Tempe, AZ 85287-1804, United States and Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe, ...

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2010

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Abstract

The design and evaluation of epidemiological control strategies is central to public health policy. While inverse problem methods are routinely used in many applications, this remains an area in which their use is relatively rare, although their potential impact is great. We describe methods particularly relevant to epidemiological modeling at the population level. These methods are then applied to the study of pneumococcal vaccination strategies as a relevant example which poses many challenges common to other infectious diseases. We demonstrate that relevant yet typically unknown parameters may be estimated, and show that a calibrated model may used to assess implemented vaccine policies through the estimation of parameters if vaccine history is recorded along with infection and colonization information. Finally, we show how one might determine an appropriate level of refinement or aggregation in the age-structured model given age-stratified observations. These results illustrate ways in which the collection and analysis of surveillance data can be improved using inverse problem methods.