Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

The success of control programs for mosquito-borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity, and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-by-side comparisons of models using comparable parameterization. Here, we present a detailed comparison of two complex, spatially explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya, and Zika viruses. Both models describe the mosquito's biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings: A tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbed conditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivity to environmental conditions (temperature and rainfall) and trace differences to specific model assumptions. Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (direct or delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions. Copyright: © 2016 Legros et al.

Registro:

Documento: Artículo
Título:Comparison of two detailed models of Aedes aegypti population dynamics
Autor:Legros, M.; Otero, M.; Aznar, V.R.; Solari, H.; Gould, F.; Lloyd, A.L.
Filiación:Department of Entomology, North Carolina State University, Raleigh, NC 27695, United States
ETH Zörich, Institut for Integrative Biologie, Universitätstrasse 16, Zörich, 8092, Switzerland
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and IFIBA-CONICET, Buenos Aires, 1428, Argentina
Fogarty International Center, National Institutes of Health, Bethesda, MB 20892, United States
Department of Mathematics and Biomathematics Graduate Program, North Carolina State University, NC, Raleigh, 27695, United States
Palabras clave:Aedes aegypti; Model comparison; Mosquito-borne diseases; Population dynamics; Spatial model; Vector control
Año:2016
Volumen:7
Número:10
DOI: http://dx.doi.org/10.1002/ecs2.1515
Título revista:Ecosphere
Título revista abreviado:Ecosphere
ISSN:21508925
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_21508925_v7_n10_p_Legros

Referencias:

  • Achee, N.L., Gould, F., Perkins, T.A., Reiner, R.C., Jr., Morrison, A.C., Ritchie, S.A., Gubler, D.J., Scott, T.W., (2015) A Critical Assessment of Vector Control for Dengue Prevention. PLoS Neglected Tropical Diseases, 9, p. e0003655
  • Arrivillaga, J., Barrera, R., Food as a limiting factor for (2004) Aedes Aegypti in Water-storage Containers. Journal of Vector Ecology, 29, pp. 11-20
  • Bar-Zeev, M., The effect of temperature on the growth rate and survival of the immature stages of (1958) Aedes Aegypti. Bulletin of Entomological Research, 49, pp. 157-163
  • Bhatt, S., The global distribution and burden of dengue (2013) Nature, 496, pp. 504-507
  • Burke, A.W., Peirce's theory of abduction (1946) Philosophy of Science, 13, pp. 301-306
  • Capeding, M.R., Clinical efficacy and safety of a novel tetravalent dengue vaccine in healthy children in Asia: A phase 3, randomised, observer-masked, placebo-controlled trial (2014) Lancet, 384, pp. 1358-1365
  • Craig, G.B., Mosquitoes: Female monogamy induced by male accessory gland substance (1967) Science, 156, pp. 1499-1501
  • Dye, C., Models for the population dynamics of the yellow fever mosquito (1984) Aedes Aegypti. Journal of Animal Ecology, 53, pp. 247-268
  • Ekpereonne, E., Lenhart, A., Smith, L., Horstick, O., Effectiveness of periodomestic space spraying with insecticide on dengue transmission; Systematic review (2010) Tropical Medicine and International Health, 15, pp. 619-631
  • Ellis, A.M., Garcia, A., Focks, D.A., Morrison, A.C., Scott, T.W., Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control (2011) American Journal of Tropical Medicine and Hygiene, 85, pp. 257-264
  • Feller, W., On the integro-differential equations of purely discontinuous Markoff processes (1940) Transactions of the, 48, pp. 488-515. , American Mathematical Society
  • Focks, D.A., Haile, D.C., Daniels, E., Mount, G.A., Aedes aegypti: Analysis of the literature and model Development (1993) Journal of Medical Entomology Dynamics life table model for, 30, pp. 1003-1018
  • Focks, D.A., Haile, D.C., Daniels, E., Mount, G.A., (1993) Aedes Aegypti: Simulation Results and Validation. Journal of Medical Entomology, 30, pp. 1019-1029. , b. Dynamic life table model for
  • Gilpin, M.E., McClelland, G.A.H., Systems analysis of the yellow fever mosquito (1979) Aedes Aegypti. Fortschritte der Zoologie, 25, pp. 355-388
  • Gubler, D.J., Dengue and dengue hemorrhagic fever (1998) Clinical Microbiology Review, 11, pp. 480-496
  • Gubler, D.J., Clark, G.G., (1995) Dengue/dengue hemorrhagic fever. The emergence of a global health problem. Emerging Infectious Diseases, 1, pp. 55-57
  • Hadinegoro, S.R., Efficacy and long-Term safety of a dengue vaccine in regions of endemic disease (2015) New England Journal of Medicine, 373, pp. 1195-1206
  • Horsfall, W.R., Mosquitoes: Their bionomics and relation to disease (1955) Ronald, New York, New York, USA
  • Legros, M., Magori, K., Morrison, A.C., Xu, C., Scott, T.W., Lloyd, A.L., Gould, F., Evaluation of location-specific predictions by a detailed simulation model of (2011) Aedes Aegypti Populations. PLoS ONE 6, p. e22701
  • Magori, K., Legros, M., Puente, M.E., Focks, D.A., Scott, T.W., Lloyd, A.L., Gould, F., Skeeter Buster: A stochastic, spatially explicit modeling tool for studying (2009) Aedes Aegypti Population Replacement and Population Suppression Strategies. PLoS Neglected Tropical Diseases 3, p. e508
  • McGraw, E.A., O'Neill, S.L., (2013) Beyond Insecticides: New Thinking on An Ancient Problem. Nature Reviews Microbiology, 11, pp. 181-193
  • Morrison, A.C., Temporal and geographic patterns of (2004) Aedes Aegypti (Diptera: Culicidae) Production in Iquitos, Peru. Journal of Medical Entomology, 41, pp. 1123-1142
  • Musso, D., Cao-Lormeau, V.M., Gubler, D.J., (2015) Zika virus: Following the path of dengue and chikungunya? Lancet 386, pp. 243-244
  • Naksathit, A.T., Scott, T.W., Effects of female size on fecundity and survivorship of (1998) Aedes Aegypti Fed only Human Blood Versus Human Blood Plus Sugar. Journal of the American Mosquito Control Association, 14, pp. 148-152
  • Otero, M., Schweigmann, N., Solari, H.G., A stochastic spatial dynamical model for (2008) Aedes Aegypti. Bulletin of Mathematical Biology, 70, pp. 1297-1325
  • Otero, M., Solari, H.G., Schweigmann, N., A stochastic population dynamic model for (2006) Aedes Aegypti: Formulation and Application to A City with Temperate Climate. Bulletin of Mathematical Biology, 68, pp. 1945-1974
  • Peirce, C.S., Collected papers (1931) The Murray Printing Company, Cambridge, Massachusetts, USA
  • Romeo Aznar, V., De, S., Majo, M.S., Francisco, D., Natiello, M.A., Solari, H.G., A model for the development of Aedes (Stegomyia) Aegypti As A Function of the Available Food (2015) Journal of Theoretical Biology, 365, pp. 311-324. , Fischer
  • Romeo Aznar, V., Otero, M., Sol De Majo, S.M., Solari, H.G., Modeling the complex hatching and development of Aedes Aegypti in Temperate Climates (2013) Ecological Modelling, 293, pp. 44-55. , Fischer
  • Rueda, L.M., Patel, K.J., Axtell, R.C., Stinner, R.E., Temperature-dependent development and survival rates of Culex quinquefasciatus and (1990) Aedes Aegypti (Diptera: Culicidae). Journal of Medical Entomology, 27, pp. 892-898
  • Sabchareon, A., Protective efficacy of the recombinant, live-Attenuated, CYD tetravalent dengue vaccine in Thai schoolchildren: A randomised, controlled phase 2b trial (2012) Lancet, 380, pp. 1559-1567
  • Sharpe, P.J.H., DeMichele, D.W., Reaction kinetics of poikilotherm development (1977) Journal of Theoretical Biology, 64, pp. 649-670
  • Sinkins, S.P., Gould, F., Gene drive systems for insect disease vectors (2006) Nature Reviews Genetics, 7, pp. 427-435
  • Southwood, T.R.E., Murdie, G., Yasuno, M., Tonn, R.J., Reader, P.M., Studies on the life budget of Aedes Aegypti in Wat Samphaya, Bangkok Thailand (1972) Bulletin of the World Health Organization, 46, pp. 211-226
  • Villar, L., Efficacy of a tetravalent dengue vaccine in children in Latin America (2015) New England Journal of Medicine, 372, pp. 113-123
  • Walsh, R.K., Aguilar, C.L., Facchinelli, L., Valerio, L., Ramsey, J.M., Scott, T.W., Lloyd, A.L., Gould, F., Assessing the impact of direct and delayed density dependence in natural larval populations of (2013) Aedes Aeygpti. American Journal of Tropical Medicine and Hygiene, 89, pp. 68-77
  • Webster, D.P., Farrar, J., Rowland-Jones, S., Progress towards a dengue vaccine (2009) Lancet Infectious Diseases, 9, pp. 678-687
  • (2009) Dengue guidelines for diagnosis, treatment, prevention and control. TDR, , World Health Organization, Geneva, Switzerland WHO
  • (2012) Dengue and severe dengue. Fact Sheet, p. 117. , World Health Organization Geneva Switzerland
  • Xu, C., Legros, M., Gould, F., Lloyd, A.L., Understanding uncertainties in model-based predictions of the population dynamics of (2010) Aedes Aegypti. PLoS Neglected Tropical Diseases 4, p. e830

Citas:

---------- APA ----------
Legros, M., Otero, M., Aznar, V.R., Solari, H., Gould, F. & Lloyd, A.L. (2016) . Comparison of two detailed models of Aedes aegypti population dynamics. Ecosphere, 7(10).
http://dx.doi.org/10.1002/ecs2.1515
---------- CHICAGO ----------
Legros, M., Otero, M., Aznar, V.R., Solari, H., Gould, F., Lloyd, A.L. "Comparison of two detailed models of Aedes aegypti population dynamics" . Ecosphere 7, no. 10 (2016).
http://dx.doi.org/10.1002/ecs2.1515
---------- MLA ----------
Legros, M., Otero, M., Aznar, V.R., Solari, H., Gould, F., Lloyd, A.L. "Comparison of two detailed models of Aedes aegypti population dynamics" . Ecosphere, vol. 7, no. 10, 2016.
http://dx.doi.org/10.1002/ecs2.1515
---------- VANCOUVER ----------
Legros, M., Otero, M., Aznar, V.R., Solari, H., Gould, F., Lloyd, A.L. Comparison of two detailed models of Aedes aegypti population dynamics. Ecosphere. 2016;7(10).
http://dx.doi.org/10.1002/ecs2.1515