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A two-patch epidemic model with nonlinear reinfection

  • CALVO, JUAN G. [1] ; HERNÁNDEZ, ALBERTO [1] ; PORTER, MASON A. [2] ; SANCHEZ, FABIO
    1. [1] Universidad de Costa Rica

      Universidad de Costa Rica

      Hospital, Costa Rica

    2. [2] University of California Los Angeles

      University of California Los Angeles

      Estados Unidos

  • Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 27, Nº. 1, 2020 (Ejemplar dedicado a: Revista de Matemática: Teoría y Aplicaciones), págs. 23-48
  • Idioma: inglés
  • DOI: 10.15517/rmta.v27i1.39946
  • Títulos paralelos:
    • Un modelo epidémico de dos poblaciones con reinfección no lineal
  • Enlaces
  • Resumen
    • español

      La propagación de enfermedades infecciosas y su impacto en individuos juega un gran rol en la dinámica de enfermedades, y es importante incorporar heterogeneidad en la población en los esfuerzos por estudiar enfermedades. De manera simplística pero ilustrativa, se examinan interacciones entre una población urbana y una rural en la dinámica de la propagación de una enfermedad. Utilizando un sistema compartimental de dinámicas entre susceptibles–infectados–susceptibles (SIS) con cierto nivel de inmunidad, se formula un modelo que permite reinfecciones no lineales. Se investiga los efectos de movimiento de poblaciones en un escenario simple: un caso con dos poblaciones, que permite modelar movimiento entre un área urbana y otra rural. Con el fin de estudiar la dinámica del sistema, se calcula el número básico reproductivo para cada comunidad (rural y urbana). Se calculan también puntos de equilibrio, la estabilidad local del estado libre de enfermedad, y se identifican condiciones para la existencia de estados de equilibrio endémicos. Del análisis y experimentos computacionales, se ilustra que el movimiento en la población juega un rol importante en la dinámica del sistema. En algunos casos, puede ser beneficioso, pues incrementa la región de estabilidad del punto de equilibrio del estado libre de infección.

    • English

      The propagation of infectious diseases and its impact on individuals play a major role in disease dynamics, and it is important to incorporate population heterogeneity into efforts to study diseases. As a simplistic but illustrative example, we examine interactions between urban and rural populations on the dynamics of disease spreading. Using a compartmental framework of susceptible–infected susceptible (SIS) dynamics with some level of immunity, we formulate a model that allows non linear reinfection. We investigate the effects of population movement in a simple scenario: a case with two patches, which allows us to model population movement between urban and rural areas. To study the dynamics of the system, we compute a basic reproduction number for each population (urban and rural). We also compute steady states, determine the local stability of the disease-free steady state, and identify conditions for the existence of endemic steady states. From our analysis and computational experiments, we illustrate that population movement plays an important role in disease dynamics. In some cases, it can be rather beneficial, as it can enlarge the region of stability of a disease-free steady state.

  • Referencias bibliográficas
    • [1] L. Alvarado, Costa Rica once again under malaria alert, The Costa Rica Star, 2018. Available at https://news.co.cr/ costa-rica-once-again-under-malaria-alert/73681,...
    • [2] D. Bichara, C. Castillo-Chavez, Vector-borne diseases models with residence times — A Lagrangian perspective, Math. Biosci. 281(2016),...
    • [3] F. Brauer, C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, 2nd edition, Springer-Verlag, Providence RI,...
    • [4] Center for Disease Control and Prevention, Severe acute respiratory system (SARS), 2019. Available at https://www.cdc.gov/sars/index....
    • [5] Center for Disease Control and Prevention, Ebola (Ebola virus disease), 2019. Available at https://www.cdc.gov/vhf/ebola/ history/2014-2016-outbreak/index.html
    • [6] Center for Disease Control and Prevention, Measles (Rubeola), 2019. Available at https://www.cdc.gov/measles/index.html
    • [7] G. Chowell, P.W. Fenimore, M.A. Castillo-Garsow C. Castillo-Chavez, SARS outbreaks in Ontario, Hong Kong and Singapore: The role of diagnosis...
    • [8] M.P. Coffee, G.P. Garnett, M. Mlilo, H.A.C.M. Voeten, S. Chandiwana, S. Gregson, Patterns of movement and risk of HIV Infection in rural...
    • [9] J.M. Crutcher, S.L. Hoffman, Malaria, in: S. Baron (Ed.) Medical Microbiology, 4th edition, University of Texas Medical Branch at Galveston,...
    • [10] R. DeVore, A. Ron, Approximation of functions, Proc. Sympos. Appl. Math.36(1986), 34–56.
    • [11] Z. Feng, J. X. Velasco-Hernández, Competitive exclusion in a vector–host model for the dengue fever, J. Math. Biol. 35(1997), no. 5,...
    • [12] Z. Feng, C. Castillo-Chavez, A. F. Capurro, A model for tuberculosis with exogenous reinfection, Theor. Popul. Biol. 57(2000), no. 3,...
    • [13] H. Frankowska, The Poincaré–Miranda theorem and viability condition, J. Math. Anal. Appl. 463(2018), no. 2, 832–837. doi: 10.1016/j.jmaa....
    • [14] J. Gjorgjieva, K. Smith, G. Chowell, F. Sanchez, J. Snyder, C. CastilloChavez, The role of vaccination in the control of SARS, Math....
    • [15] J.R. Glynn, J. Murray, A. Bester, G. Nelson, S. Shearer, P. Sonnenberg, Effects of duration of HIV infection and secondary tuberculosis...
    • [16] J.L. Grun, W.P. Weidanz, Antibody-independent immunity to reinfection malaria in B-cell-deficientmice, Infect. and Immun. 41(1983),no.3,1197–...
    • [17] W. Kulpa, The Poincaré–Miranda theorem, Amer. Math. Month. 104(1997), no. 6, 545–550. doi: 10.2307/2975081
    • [18] S.Lee,C.Castillo-Chavez,The role of residence times in two-patch dengue transmission dynamics and optimal strategies, J. Theor. Biol....
    • [19] C.A. Manore, K.S. Hickmann, S. Xu, H.J. Wearing, J.M. Hyman, Comparing dengue and chikungunya emergence and endemic transmission in A....
    • [20] P. Martens, L. Hall, Malaria on the move: Human population movement and malaria transmission, Emerg. Infect. Diseas.6(2000), no. 2, 103–109....
    • [21] D. Murillo, S. Holechek, A. Murillo, F. Sanchez, C. Castillo-Chavez, Vertical transmission in a two-strain model of dengue fever, Lett....
    • [22] R. Pastor-Satorras, C. Castellano, P. van Mieghem, A. Vespignani, Epidemic processes in complex networks, Rev. Mod. Phys. 87(2015), no....
    • [23] F. Sanchez, M. Engman, L.C. Harrington, C. Castillo-Chavez, Models for dengue transmission and control, in: A.B. Gumel, C. Castillo-Chavez,...
    • [24] F. Sanchez, X. Wang, C. Castillo-Chavez, D. Gorman, P.J. Gruenewald, Drinking as an epidemic — A simple mathematical model with recovery...
    • [25] F. Sanchez, D. Murillo, C. Castillo-Chavez, Change in host behavior and its impact on the transmission dynamics of dengue, in: R.P. Mondaini...
    • [26] F. Sanchez, J. G. Calvo, E. Segura, Z. Feng, A partial differential equation model with age-structure and nonlinear recidivism: Conditions...
    • [27] B. Song, M. Castillo-Garsow, K.R. Rios-Soto, M. Mejran, L. Henso, C. Castillo-Chavez, Raves, clubs and ecstasy: The impact of peer pressure,...
    • [28] B. Song, W. Du, J. Lou, Different types of backward bifurcations due to density-dependent treatments, Math. Biosci. Eng. 10(2013), no....
    • [29] K. Szyma´nska-De¸bowska, On a generalization of the Miranda Theorem and its application to boundary value problems, J. Diff. Equ. 258(2015),...
    • [30] A.J. Treno, P.J. Gruenewald, L.G. Remer, F. Johnson, E.A. LaScala, Examining multi-level relationships between bars, hostility and aggression:...
    • [31] M.Turzánski, The Bolzano–Poicaré–Mirandatheorem—Discreteversion, Topol. Appl. 159(2012), no. 13, 3130–3135. doi: 10.1016/j.topol. 2012.05.026

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