Background Cutaneous leishmaniasis (CL) is an important public health problem in Brazil and in several tropical regions of the world. In the Americas, Brazil is the country with the highest number of registered cases. In Brazil, the state of Minas Gerais has the highest number of cases in the southeastern region. In the present study, we used spatial analysis in the State of Minas Gerais to identify municipalities of priority during a nine-year period (2007–2015), which might be used to guide surveillance and control measures. Methods An ecological study with spatial analysis of autochthonous cases of CL was performed in the state of Minas Gerais between 2007 and 2015. We calculated incidence rates, used Empirical Bayesian smoothing for each municipality, and divided the analyses into three-year intervals. In order to analyze the existence of spatial autocorrelation, and to define priority areas, Moran’s Global Index and Local Indicators of Spatial Association (LISA) were used. Results The mean incidence rate for the entire state was 6.1/100,000 inhabitants. For Minas Gerais, analysis of CL cases over time revealed a successive increase of indicated mesoregions with high priority municipalities. Eight of the designated mesoregions contained municipalities classified as high priority areas in any of the three evaluated trienniums, and four mesoregions had high priority municipalities throughout the entire investigation. Conclusions Within the southeastern region of Brazil, Minas Gerais State stands out, with highest CL incidence rates. Using spatial analysis, we identified an increasing numbers of cases in the municipalities classified as high priority areas in different mesoregions of the state. This information might be of value to direct surveillance and control measures against CL and to understand the dynamics of the expansion of CL in Minas Gerais. Similar approaches might be used to map CL in other regions throughout Brazil, or in any other country, where national notification and control programs exist.

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Intestinal schistosomiasis, caused by the parasitic trematode Schistosoma mansoni, is a chronic disease and the prolonged and continuous exposure to S. mansoni antigens results in a deviation of the host's immune response. For diagnosis, the Kato-Katz (KK) method is recommended, however, this method showed low accuracy in areas of low endemicity. This study aimed to characterize the cytokine and chemokine profile of individuals with an extremely low parasite load (<4 eggs per gram of feces), e.g., individuals who were detected by alternative parasitological methods, such as the saline gradient and/or Helmintex®. In order to search for immunological markers for infection, the immunological profile in serum samples of these individuals was then compared with patients detected with the KK method and with a higher parasite load and with individuals repetitively negative by extensive stool exams. The study was conducted in Northern Minas Gerais in a rural area of the Municipality of Januária. Serum samples of a total of 139 parasitologically well-characterized individuals were assessed for the following immunological markers by commercially available immunoassays: TNF-α, IL-1β, IL-6, IL-17A, IL-5, IL-10, IL-13, IL-33, IL-27, CCL3, CCL5, CXCL10, CCL11, and CCL17. As a result, there were no significant differences in concentrations or frequencies for immunological markers between egg-negative individuals or individuals with ultra-low (<4 epg) or low (4–99 epg) parasite loads. However, we found significant correlations between egg counts and eosinophil counts and between egg counts and IL-1β or TNF-α concentrations. The most striking alterations were found in individuals with the highest parasite load (≥100 epg). They had significantly higher TNF-α concentrations in serum when compared with individuals with a low parasite load (4–99 epg) and CCL17 concentrations were significantly elevated when compared with egg-negative individuals. Radar diagrams of frequencies for cytokine and chemokine responders in each infection group confirmed a distinct profile only in the infection group with highest parasite loads (≥100 epg).

COVID-19 is an infectious disease caused by the recently discovered coronavirus SARS-Cov-2. The disease became pandemic affecting many countries globally, including Brazil. Considering the expansion process and particularities during the initial stages of the epidemic, we aimed to analyze the spatial and spatiotemporal patterns of COVID-19 occurrence and to identify priority risk areas in Minas Gerais State, Southeast Brazil. An ecological study was performed considering all data from human cases of COVID-19 confirmed from the epidemiological week (EW) 11 (March 08, 2020) to EW 26 (June 27, 2020). Crude and smoothed incidence rates were used to analyze the distribution of disease patterns based on global and local indicators of spatial association and space-time risk assessment. Positive spatial autocorrelation and spatial dependence were found. Our results suggest that the metropolitan region of the State capital Belo Horizonte (MRBH) and Vale do Rio Doce mesoregions, as major epidemic foci in the beginning of the expansion process, have had important influence on the dispersion of SARS-CoV-2 in Minas Gerais State. Triangulo Mineiro/Alto Paranaiba region presented the highest risk of infection. In addition, six statistically significant spatiotemporal clusters were identified in the State, three at high risk and three at low risk. Our findings contribute to a greater understanding of the space-time disease dynamic and discuss strategies for identification of priority areas for COVID-19 surveillance and control.

Introduction: Due to recent outbreaks of Dengue and Chikungunya and an absence of effective monitoring of the mosquito Aedes spp. in the municipality of São Raimundo das Mangabeiras, State of Maranhão, we aimed to demonstrate the potential of ovitraps used together with mathematical models and geotechnology to improve control of this mosquito. Methodology: From January to December of 2017, ovitraps were set up in five different neighborhoods (Centro, Vila Cardoso, Nazaré, São José e São Francisco). Positivity indices were calculated for each ovitraps, besides the egg density and average number of eggs. Some of the eggs were used for species identification. Mathematical models of correlation and logistic regression were used to evaluate the influence of abiotic factors on egg distribution during each month. Spatial analysis was carried out using georeferencing. Results: A total of 4,453 eggs were counted, with A. aegypti and A. albopictus present in each month and neighborhood. The mathematical models show that rainfall can result in a significant increase in the number of eggs. Entomological calculation indicates that there is a high risk of dissemination of arboviruses in the area. Spatially, it was possible to indicate sites with the largest number of collected eggs, which may facilitate future interventions. Conclusions: As such, ovitraps have proven to be an effective and low cost method for the monitoring of Aedes spp., and that its use may help in arboviruses prevention campaigns.

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