ABSTRACT
Visceral leishmaniasis (VL) is a neglected disease of public and animal health importance. With the urbanization of the disease, there is evidence of a temporal correlation between the occurrence of human (HVL) and canine (CVL) visceral leishmaniasis, usually with cases in dogs preceding those in humans. In this context, the objective of this study was to develop a time series model suitable for canine-human transmission of Leishmania infantum. Monthly cases of HVL and CVL from 2006 to 2018 in Belo Horizonte, Minas Gerais, Brazil, were evaluated, and monthly health indicators were calculated for HVL and CVL, i.e., incidence coefficient (HVL_IC) and proportion of seropositive dogs (PSD), respectively. The temporal relationship was evaluated using an autoregressive integrated moving average with exogenous variable (ARIMAX) model for two different periods (January 2006-August 2013 and September 2013-December 2018). During the 13 years studied, 1115 new cases of HVL and 103,358 dogs seropositive for CVL were recorded. HVL_IC and PSD exhibited decreasing trends throughout the first study period (January 2006-August 2013). According to the ARIMAX model adjusted for this period, there was a temporal relationship between HVL_IC and PSD, with HVL_IC being influenced by HVL_IC for the last two and five months and by PSD for the third previous month. For the second study period (September 2013-December 2018), it was not possible to fit an ARIMAX model. This study highlights the improvements made by VL surveillance since 2006 in Belo Horizonte and contributes to a better understanding of the epidemiology of the disease by public health policy-makers, doctors and veterinarians involved in the prevention and control of zoonoses.
Subject(s)
Dog Diseases , Leishmaniasis, Visceral , Humans , Animals , Dogs , Leishmaniasis, Visceral/epidemiology , Leishmaniasis, Visceral/veterinary , Brazil/epidemiology , Dog Diseases/prevention & control , Zoonoses , IncidenceABSTRACT
Objetivo: detectar a presença de agrupamentos espaço-temporais dos casos de dengue em Três Corações, Minas Gerais, Brasil, utilizando informações da localização e do tempo de cada ocorrência e a série histórica da precipitação pluviométrica do período de estudo. Métodos: o método Kernel foi utilizado para estimar a intensidade dos casos, enquanto a função K espaço-temporal e o método de varredura foram utilizados para detectar o padrão e identificar agrupamentos, respectivamente. Resultados: a partir dos 2.818 casos observados, verificou-se que a maior parte desses ocorreu no final dos períodos chuvosos. Também foi detectada a presença de agrupamentos de casos, principalmente na Região Central da cidade. Uma razão para a formação de agrupamentos pode ser devido à maior densidade populacional das regiões afetadas. Conclusão: os resultados mostram que indivíduos que moram em regiões densamente povoadas são mais propensos a contrair dengue. Os métodos estatísticos utilizados permitiram caracterizar a distribuição espaço-temporal dos casos de dengue e também podem ser utilizados para analisar outras doenças endêmicas ou pandêmicas, o que pode contribuir para as políticas de prevenção e combate à proliferação dessas doenças.(AU)
Objective: detect the presence of space-time clusters of dengue cases in Três Corações, Minas Gerais, Brazil, using information on the location and time of each occurrence and the historical series of rainfall in the study period. Methods: the Kernel method was used to estimate the intensity of the cases, while the space-time K-function and the scan method were used to detect the pattern and identify clusters, respectively. Results: from the 2,818 observed cases, it was found that most of these occurred in the end of rainy periods. The presence of clusters of cases was also detected, mainly, in the central region of the city. One reason for the formation of clusters may be due to the higher population density of the affected regions. Conclusion: the results show that individuals who live in densely populated regions are more likely to get dengue. The statistical methods used allowed to characterize the spatio-temporal distribution of dengue cases and, they can also be used to analyze other endemic or pandemic diseases, which can contribute to policies to prevent and combat the proliferation of these diseases.(AU)
