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Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia

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  • Diana Paola Lizarralde-Bejarano
  • Daniel Rojas-Díaz
  • Sair Arboleda-Sánchez
  • María Eugenia Puerta-Yepes

Abstract

Dengue disease is a major problem for public health surveillance entities in tropical and subtropical regions having a significant impact not only epidemiological but social and economical. There are many factors involved in the dengue transmission process. We can evaluate the importance of these factors through the formulation of mathematical models. However, the majority of the models presented in the literature tend to be overparameterized, with considerable uncertainty levels and excessively complex formulations. We aim to evaluate the structure, complexity, trustworthiness, and suitability of three models, for the transmission of dengue disease, through different strategies. To achieve this goal, we perform structural and practical identifiability, sensitivity and uncertainty analyses to these models. The results showed that the simplest model was the most appropriate and reliable when the only available information to fit them is the cumulative number of reported dengue cases in an endemic municipality of Colombia.

Suggested Citation

  • Diana Paola Lizarralde-Bejarano & Daniel Rojas-Díaz & Sair Arboleda-Sánchez & María Eugenia Puerta-Yepes, 2020. "Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-29, March.
    • Handle: RePEc:plo:pone00:0229668
    DOI: 10.1371/journal.pone.0229668
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    Cited by:

    1. Lam, Nicholas N. & Docherty, Paul D. & Murray, Rua, 2022. "Practical identifiability of parametrised models: A review of benefits and limitations of various approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 199(C), pages 202-216.
    2. Daniel Rojas-Diaz & Alexandra Catano-Lopez & Carlos M. Vélez & Santiago Ortiz & Henry Laniado, 2024. "Confidence sub-contour box: an alternative to traditional confidence intervals," Computational Statistics, Springer, vol. 39(5), pages 2821-2858, July.

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