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The Effect of Seasonal Weather Variation on the Dynamics of the Plague Disease

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  • Rigobert C. Ngeleja
  • Livingstone S. Luboobi
  • Yaw Nkansah-Gyekye

Abstract

Plague is a historic disease which is also known to be the most devastating disease that ever occurred in human history, caused by gram-negative bacteria known as Yersinia pestis . The disease is mostly affected by variations of weather conditions as it disturbs the normal behavior of main plague disease transmission agents, namely, human beings, rodents, fleas, and pathogens, in the environment. This in turn changes the way they interact with each other and ultimately leads to a periodic transmission of plague disease. In this paper, we formulate a periodic epidemic model system by incorporating seasonal transmission rate in order to study the effect of seasonal weather variation on the dynamics of plague disease. We compute the basic reproduction number of a proposed model. We then use numerical simulation to illustrate the effect of different weather dependent parameters on the basic reproduction number. We are able to deduce that infection rate, progression rates from primary forms of plague disease to more severe forms of plague disease, and the infectious flea abundance affect, to a large extent, the number of bubonic, septicemic, and pneumonic plague infective agents. We recommend that it is more reasonable to consider these factors that have been shown to have a significant effect on for effective control strategies.

Suggested Citation

  • Rigobert C. Ngeleja & Livingstone S. Luboobi & Yaw Nkansah-Gyekye, 2017. "The Effect of Seasonal Weather Variation on the Dynamics of the Plague Disease," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2017, pages 1-25, August.
  • Handle: RePEc:hin:jijmms:5058085
    DOI: 10.1155/2017/5058085
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    Cited by:

    1. Emmanuel Escobar-Avalos & Martín A. Rodríguez-Licea & Horacio Rostro-González & Allan G. Soriano-Sánchez & Francisco J. Pérez-Pinal, 2021. "A Comparison of Integrated Filtering and Prediction Methods for Smart Grids," Energies, MDPI, vol. 14(7), pages 1-16, April.

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