A Temperature Conditioned Markov Chain Model for Predicting the Dynamics of Mosquito Vectors of Disease
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Título: | A Temperature Conditioned Markov Chain Model for Predicting the Dynamics of Mosquito Vectors of Disease |
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Autor/es: | Damos, Petros | Dorrestijn, Jesse | Thomidis, Thomas | Tuells, José | Caballero, Pablo |
Grupo/s de investigación o GITE: | Salud Comunitaria (SALUD) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Enfermería Comunitaria, Medicina Preventiva y Salud Pública e Historia de la Ciencia |
Palabras clave: | Culex sp. | Decision making | Mosquitos | Public health | Stochastic process | West Nile virus |
Área/s de conocimiento: | Enfermería |
Fecha de publicación: | 13-ago-2021 |
Editor: | MDPI |
Cita bibliográfica: | Damos PT, Dorrestijn J, Thomidis T, Tuells J, Caballero P. A Temperature Conditioned Markov Chain Model for Predicting the Dynamics of Mosquito Vectors of Disease. Insects. 2021; 12(8):725. https://doi.org/10.3390/insects12080725 |
Resumen: | Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs. |
URI: | http://hdl.handle.net/10045/117228 |
ISSN: | 2075-4450 |
DOI: | 10.3390/insects12080725 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/insects12080725 |
Aparece en las colecciones: | INV - SALUD - Artículos de Revistas |
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