No abstract

No abstract

No abstract

No abstract

Chikungunya virus (CHIKV) was first reported in Brazil in 2014 and, after it spread countrywide, an outbreak of febrile illness with reports of arthralgia happened in the municipality of Xinguara, Pará, Brazil in 2017, indicating the virus’ circulation. Here, we aimed to investigate CHIKV in mosquito vectors collected during an active surveillance of virus isolation in cell culture by using molecular detection and viral genome sequencing. A total of 492 Aedes, Culex and Mansonia mosquitoes were collected and separated in 36 pools according to the species and sex, and 22.2% (8/36) were positive. CHIKV was indentified in pools of Ae. aegypti females (n = 5), an Ae. aegypti male (n = 1) and in Culex quinquefasciatus females (n = 2). However, as the mosquitoes’ whole bodies were macerated and used for detection, one cannot suggest the role of the latter in the viral transmission. Despite this, vector competence studies must be carried out in the different species to investigate long-term adaptations. Viral genome sequencing has characterized the East-Central-South-African (ECSA) genotype in all positive pools analyzed, corroborating previous reports for the Amazon region.

No abstract

No abstract

Schistosomiasis is a transmissible parasitic disease caused by the etiologic agent Schistosoma mansoni, whose intermediate hosts are snails of the genus Biomphalaria. The main goal of this paper is to estimate the prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derived from the state transportation network of roads and rivers. The spatial information was incorporated in two ways: by introducing new variables that carry spatial neighborhood information and by using spatial regression models. Climate, socioeconomic and environmental variables were also used as co-variables to build models and use them to estimate a risk map for the whole state of Minas Gerais. The results show that the models constructed from the spatial regression produced a better fit, providing smaller root mean square error (RMSE) values. When no spatial information was used, the RMSE for the whole state of Minas Gerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added to the model) and 8.5% (with the use of spatial regression). Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes were important in explaining the spread of disease and identified certain conditions that are favorable for disease development. The use of spatial regression for the network of roads and rivers produced meaningful results for health management procedures and directing activities, enabling better detection of disease risk areas.