RESUMO: Objetivo: Descrever, de forma retrospectiva, os casos graves de pacientes hospitalizados e os óbitos relacionados à epidemia de COVID-19 no estado de São Paulo, desde a data do primeiro registro, com início de sintomas em 10 de fevereiro de 2020 até registros disponíveis em 20 de maio de 2021. Métodos: Trata-se de um estudo descritivo realizado por meio da base de dados do Sistema de Vigilância Epidemiológica da Gripe. Foram calculadas as taxas de incidência, mortalidade e incidência acumulada no período, estratificadas por faixa etária e agrupadas de acordo com cada Departamento Regional de Saúde. Os casos graves foram geocodificados para a análise de seu espalhamento pelo estado e foi calculado o R efetivo, que estima o potencial de propagação de um vírus em uma população. Resultados: Houve aumento significativo dos casos graves e óbitos registrados no período de um ano, e as taxas de incidência e mortalidade foram heterogêneas no estado. Os períodos mais críticos em relação à incidência de casos graves ocorreram entre maio e julho de 2020 e entre março e abril de 2021. Os Departamentos Regionais de Saúde de São José do Rio Preto, Grande São Paulo e Araçatuba concentraram as maiores taxas de incidência e mortalidade. Os casos graves e óbitos foram mais frequentes nos homens e na população acima de 60 anos, e as principais condições de risco relacionadas aos óbitos foram cardiopatia (59%) e diabetes (42,8%). Conclusões: Espera-se que esses resultados ofereçam embasamento e possam contribuir para uma ação de controle mais eficiente da COVID-19, além de permitir o entendimento histórico de sua evolução no estado.

The Middle Paranapanema River region of São Paulo, Brazil is home to significant diversity of Biomphalaria species and is very vulnerable to health and environmental impacts such as schistosomiasis. This study updates freshwater malacological surveys for ecosystems in one portion of the Middle Paranapanema River Basin, with emphasis on the genus Biomphalaria. Snails were collected from 114 distinct bodies of water between 2015 and 2018. Biomphalaria specimens were identified according to morphological and molecular characteristics, while animals in other genera (Drepanotrema, Lymnaea, Melanoides, Physa and Pomacea) were identified solely according to shell characteristics. A geographic information system was used to update intermediate host colonization sites and consequently assist in identifying probable hotspots for intermediate hosts of schistosomiasis. The sequences of the COI gene relating to the DNA barcode stretch were tested for similarity against sequences found in GenBank, for monophyly through Maximum Likelihood phylogenetic inference, and analyzed in ABDG, bPTP and GMYC for the delimitation of putative species. Of the 10,722 snails collected, 86.7% were in the Planorbidae family (75.5% Biomphalaria and 11.2% Drepanotrema) and 13.3% were other non-Planorbidae species (Lymnaea, Melanoides, Physa and Pomacea). The taxonomic COI reference sequences in the NCBI nucleotide database used for DNA sequence comparison, and phylogenetic analysis used to test the monophyly of the groups, resulted in more reliable taxonomic units than delimitation of the COI sequences in MOTUs using statistical taxonomic models. Analysis of the species distribution shows that B. glabrata and B. tenagophila are heterogeneously distributed in the study area. B. glabrata colonizes only five water bodies, in the study area, most of them in Ourinhos, while B. tenagophila predominates in water bodies in Ipaussu. Contrasting with this, B. straminea, B. occidentalis and B. peregrina are evenly distributed throughout the study area.

DNA barcoding and morphological characters were used to identify adult snails belonging to the genus Biomphalaria from 17 municipalities in the state of São Paulo, Brazil. The DNA barcode analysis also included twenty-nine sequences retrieved from GenBank. The final data set of 104 sequences of the mitochondrial cytochrome oxidase I (COI) gene was analyzed for K2P intraspecific and interspecific divergences, through tree-reconstruction methods (Neighbor-Joining, Maximum Likelihood and Bayesian inference), and by applying different models (ABGD, bPTP, GMYC) to partition the sequences according to the pattern of genetic variation. Twenty-seven morphological parameters of internal organs were used to identify specimens. The molecular taxonomy of Biomphalaria agreed with the morphological identification of specimens from the same collection locality. DNA barcoding may therefore be a useful supporting tool for identifying Biomphalaria snails in areas at risk for schistosomiasis.

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Introduction: The Middle Paranapanema watershed is known for the transmission of schistosomiasis, and there have been autochthonous cases since 1952. This study aimed to describe this disease in space and time and evaluate its current importance as a public health problem. Methods: Thematic maps showing the risk areas for transmission of schistosomiasis, using scan statistics, and flow maps were created in the period 1978-2016. Incidence was calculated, and the existence of spatial dependence between autochthonous and imported cases was evaluated using Ripley's K12-function. Species of snails were identified in high-risk clusters. Results: A total of 1,511 autochthonous cases were reported in eight of the 25 municipalities in the study area, of which 92.8% occurred in Ourinhos. A total of 2,189 imported cases were reported (27% in Ourinhos and 20% in Assis), mainly originating in the states of Paraná and Minas Gerais. Clusters of autochthonous and imported cases with higher risk were identified in Ourinhos, Assis and Ipaussu. However, over the years, the cases began to occur in low density in Ourinhos and no longer in other municipalities in the region. The cluster detected in the period 2007-2016 in Ourinhos still has risk for the transmission of schistosomiasis. K12-function analysis indicated positive spatial dependence between autochthonous and imported cases. Conclusions: The study showed that, currently, schistosomiasis as a public health problem in Middle Paranapanema is restricted to Ourinhos. This fact may be related to the presence of Biomphalaria glabrata at a specific point and low coverage of basic sanitation.

Abstract: Morphological and molecular identifications were carried out for Biomphalaria occidentalis, Biomphalaria oligoza, Biomphalaria peregrina, Biomphalaria schrammi, Biomphalaria straminea and Biomphalaria tenagophila collected from 55 sites located along the upper basin of Tietê River in the Southeast Region of Brazil. Morphological analysis considered aspects of the shell, mantle, excretory organs and reproductive system. Molecular data included 122 sequences of Cytochrome C Oxidase I gene (COI). Our results showed that some shell characters, as well as other characters related to the mantle and the reproductive system, are fundamental for the identification of the six Biomphalaria species included in this study. The use of DNA barcoding together with morphological taxonomy generated more reliable results, proving to be a very useful approach, even for malacological surveillance services.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis—a debilitating parasitic disease of poverty affecting over 200 million people across Africa, Asia, and South America—is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. parasites released from freshwater snails, the parasite’s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata,B. tenagophilaandB. straminea). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails’ ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

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