Detalhes bibliográficos
| Ano de defesa: |
2024 |
| Autor(a) principal: |
SILVA JUNIOR, Marcelo Henrique Sousa
 |
| Orientador(a): |
COSTA JUNIOR, Livio Martins
|
| Banca de defesa: |
COSTA JUNIOR, Livio Martins
,
LUZ, Hermes Ribeiro
,
TAVARES, Caio Pavão
,
SOUSA, Dauana Mesquita
|
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
|
| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS DA SAÚDE/CCBS
|
| Departamento: |
DEPARTAMENTO DE PATOLOGIA/CCBS
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
|
| Link de acesso: |
https://tedebc.ufma.br/jspui/handle/tede/5846
|
Resumo: |
Rhipicephalus (Boophilus) microplus is an ectoparasite of great importance for animal health. Abiotic factors, such as temperature, interfere in several life stages of this parasite. This may reflect on the number of annual generations of this tick in different regions of Brazil. Its high prevalence, in addition to the transmission of pathogens such as Babesia bovis, Babesia bigemina and Anaplasma marginale, compromises the production of meat, milk and leather. As a result, Brazil loses ~ 3.2 billion dollars/year, compromising the local economy. Given this problem, predicting the population biology of the tick in its non-parasitic phase by computational modeling is a promising alternative to aid in parasite control. The objective of this study was to develop a model to predict the number of annual generations of R. (B.) microplus in Brazil. By searching for data on the biology of the non-parasitic phase of the tick, a bibliographic review was carried out in the PubMed, Scielo and Google Scholar databases. Subsequently, prediction equations by third-degree polynomial nonlinear regression models were performed, with temperature data as the predictor variable and the biological parameters pre-laying period and egg incubation as the response variables, considering R2 ≥ 0.70 and p < 0.05, significant. The model's prediction capacity was evaluated by comparing it with biology data from different studies in the literature. Subsequently,Brazil was divided into 136 quadrants of equal size (resolution 2.5o x 2.5°) by the geostatisticalexponential method and the climate data (2020–2024) from a meteorological station in eachquadrant accessed through the AGRITEMPO database served as input for the model to predict the number of annual generations of the tick. Statistical robustness was assessed using the root mean square error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2). As a result, the mathematical prediction equations for the pre-laying period (R2 > 0.70) and incubation (R2 > 0.80) showed a negative correlation (r < 0) as a function of the climate variables and obtained RMSE = 2.67 and 11.80, respectively. Statistically, the model predictions differ from the real values by approximately 1.93 days (MAE) for the pre-laying period and 9.21 days (MAE) for egg incubation. Based on the prediction of the pre-laying and incubation period, associated with the larval maturation period and parasitic phase (22 days), the estimate of the number of annual generations for the regions of Brazil ranged from a minimum of 2.92 (South region) to a maximum of 6.18 generations (North region), showing the highest number of generations in equatorial regions, with higher temperatures. The Amazon biome had the highest number of generations among the biomes, with an average of 5.46 ± 0.29 annual generations. The model predictions differ from the actual values by approximately 0.378 generations (MAE), with R2 = 0.71. These prediction results help in the development of new criteria for strategic tick control, such as changes in the management system, since knowledge of the parasite population dynamics in pasture are limiting factors for identifying tick peaks and, consequently, efficient parasite control. |
