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Bayesian compartmental models and associated reproductive numbers for an infection with multiple transmission modes

Author

Listed:
  • Marie V. Ozanne
  • Grant D. Brown
  • Angela J. Toepp
  • Breanna M. Scorza
  • Jacob J. Oleson
  • Mary E. Wilson
  • Christine A. Petersen

Abstract

Zoonotic visceral leishmaniasis (ZVL) is a serious neglected tropical disease that is endemic in 98 countries. ZVL is primarily transmitted via a sand fly vector. In the United States, it is enzootic in some canine populations; it is transmitted from infectious mother to pup transplacentally, and vector‐borne transmission is absent. This absence affords a unique opportunity to study (1) vertical transmission dynamics in dogs and (2) the importance of vertical transmission in maintaining an infectious reservoir in the presence of a vector. In this paper, we present Bayesian compartmental models and reproductive number formulations to examine (1) and (2), providing a mechanism to plan and evaluate interventions in regions where both transmission modes are present. First, we propose an individual‐level susceptible, infectious, removed (SIR) model to study the effect of maternal infection status during pregnancy on pup infection progression. We provide evidence that pups born to diagnostically positive mothers during pregnancy are more likely to become diagnostically positive both earlier in life, and at some point during their lifetime, than those born to diagnostically negative mothers. Second, we propose a population‐level SIR model to study the impact of a vertically maintained reservoir on propagating infection in a naive canine population through emergent vector transmission using simulation studies. We also present reproductive numbers to quantify contributions of vertically infected and vector‐infected dogs to maintaining infection in the population. We show that a vertically maintained canine reservoir can propagate infection in a theoretical naive population in the presence of a vector.

Suggested Citation

  • Marie V. Ozanne & Grant D. Brown & Angela J. Toepp & Breanna M. Scorza & Jacob J. Oleson & Mary E. Wilson & Christine A. Petersen, 2020. "Bayesian compartmental models and associated reproductive numbers for an infection with multiple transmission modes," Biometrics, The International Biometric Society, vol. 76(3), pages 711-721, September.
  • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:711-721
    DOI: 10.1111/biom.13192
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    References listed on IDEAS

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    1. Abdulaziz Y A Mukhtar & Justin B Munyakazi & Rachid Ouifki & Allan E Clark, 2018. "Modelling the effect of bednet coverage on malaria transmission in South Sudan," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-22, June.
    2. Phenyo E. Lekone & Bärbel F. Finkenstädt, 2006. "Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study," Biometrics, The International Biometric Society, vol. 62(4), pages 1170-1177, December.
    3. Grant D. Brown & Jacob J. Oleson & Aaron T. Porter, 2016. "An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks," Biometrics, The International Biometric Society, vol. 72(2), pages 335-343, June.
    4. N. A. Samat & D. F. Percy, 2012. "Vector-borne infectious disease mapping with stochastic difference equations: an analysis of dengue disease in Malaysia," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2029-2046, June.
    5. Marie V. Ozanne & Grant D. Brown & Jacob J. Oleson & Iraci D. Lima & Jose W. Queiroz & Selma M. B. Jeronimo & Christine A. Petersen & Mary E. Wilson, 2019. "Bayesian compartmental model for an infectious disease with dynamic states of infection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(6), pages 1043-1065, April.
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