The variant of concern (VOC) P.1 emerged in the Amazonas state (Brazil) and was sequenced for the 1st time on 6-Jan-2021 by the Japanese National Institute of Infectious Diseases. It contains a constellation of mutations, ten of them in the spike protein. Consequences of these mutations at the populational level have been poorly studied so far. From December-2020 to February-2021, Manaus was devastated by four times more cases compared to the previous peak (April-2020). Here, data from the national health surveillance of hospitalized individuals were analysed using a model-based approach to estimate P.1 parameters of transmissibility and reinfection by maximum likelihood. Sensitivity analysis was performed changing pathogenicity and the period analysed (including/excluding the health system collapse period). In all analysed cases, the new variant transmissibility was found to be about 2.5 times higher compared to the previous variant in Manaus. A low probability of reinfection by the new variant (6.4%) was estimated, even under initial high prevalence (68%) by the time P.1 emerged. Consequences of a higher transmissibility were already observed with VOC B.1.1.7 in the UK and Europe. Urgent measures must be taken to control the spread of P.1.

Background The SARS-CoV-2 variant of concern (VOC) P.1 (Gamma variant) emerged in the Amazonas State, Brazil, in November 2020. The epidemiological consequences of its mutations have not been widely studied, despite detection of P.1 in 36 countries, with local transmission in at least 5 countries. A range of mutations are seen in P.1, ten of them in the spike protein. It shares mutations with VOCs previously detected in the United Kingdom (B.1.1.7, Alpha variant) and South Africa (B.1.351, Beta variant). Methods We estimated the transmissibility and reinfection of P.1 using a model-based approach, fitting data from the national health surveillance of hospitalized individuals and frequency of the P.1 variant in Manaus from December-2020 to February-2021. Results Here we estimate that the new variant is about 2.6 times more transmissible (95% Confidence Interval: 2.4–2.8) than previous circulating variant(s). Manaus already had a high prevalence of individuals previously affected by the SARS-CoV-2 virus and our fitted model attributed 28% of Manaus cases in the period to reinfections by P.1, confirming the importance of reinfection by this variant. This value is in line with estimates from blood donors samples in Manaus city. Conclusions Our estimates rank P.1 as one of the most transmissible among the SARS-CoV-2 VOCs currently identified, and potentially as transmissible as the posteriorly detected VOC B.1.617.2 (Delta variant), posing a serious threat and requiring measures to control its global spread.

Background Cutaneous leishmaniasis (CL) is a vector-borne disease classified by the World Health Organization as one of the most neglected tropical diseases. Brazil has the highest incidence of CL in America and is one of the ten countries in the world with the highest number of cases. Understanding the spatiotemporal dynamics of CL is essential to provide guidelines for public health policies in Brazil. In the present study we used a spatial and temporal statistical approach to evaluate the dynamics of CL in Brazil. Methods We used data of cutaneous leishmaniasis cases provided by the Ministry of Health of Brazil from 2001 to 2017. We calculated incidence rates and used the Mann–Kendall trend test to evaluate the temporal trend of CL in each municipality. In addition, we used Kuldorff scan method to identify spatiotemporal clusters and emerging hotspots test to evaluate hotspot areas and their temporal trends. Results We found a general decrease in the number of CL cases in Brazil (from 15.3 to 8.4 cases per 100 000 habitants), although 3.2% of municipalities still have an increasing tendency of CL incidence and 72.5% showed no tendency at all. The scan analysis identified a primary cluster in northern and central regions and 21 secondary clusters located mainly in south and southeast regions. The emerging hotspots analysis detected a high spatial and temporal variability of hotspots inside the main cluster area, diminishing hotspots in eastern Amazon and permanent, emerging, and new hotspots in the states of Amapá and parts of Pará, Roraima, Acre and Mato Grosso. The central coast the state of Bahia is one of the most critical areas due to the detection of a cluster of the highest rank in a secondary cluster, and because it is the only area identified as an intensifying hotspot. Conclusions Using a combination of statistical methods we were able to detect areas of higher incidence of CL and understand how it changed over time. We suggest that these areas, especially those identified as permanent, new, emerging and intensifying hotspots, should be targeted for future research, surveillance, and implementation of vector control measures. Graphic abstract

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The head morphology and feeding habits of pairs of characin species (family Characidae) that coexist in four different coastal rainforest streams were analysed. Coexisting species differed in size, but were very similar in eco-morphological attributes. Gut analyses revealed differences in feeding preferences for each coexisting species, indicating resource partitioning. A pattern of organization in species pairs that was repeated in the four studied streams was noticed. The pattern consisted of one slightly larger species with a feeding preference for items of allochthonous origin and another smaller species with a preference for autochthonous items. The hypothesis that small morphological differences enable the current coexistence of those species pairs was proposed. Furthermore, the results show ecological equivalence among different species in the studied streams.

This article discusses the epidemic situation of Covid-19 in Brazil, in the face of the emergence of a new strain called P.1, which is more transmissible and may be associated with reinfection. Given the collapse of hospital care in Manaus in January 2021 and the results of three recent preprints, each that reports increased transmissibility of the P.1 variant, we propose some urgent measures. Genomic surveillance based on multi-step diagnostics, starting with RT-PCR type tests and up to sequencing, should be established. Efforts to identify reinfections associated with this variant and the update of its definition in protocols should be prioritized, and studies on the efficacy of currently available vaccines in Brazil concerning the new variant should be conducted. We also propose improving the Brazilian health surveillance system such that genomic surveillance is coordinated and thereby better able to respond to future emergencies in a more timely fashion. We call on the public agents involved in health surveillance to share data and information regarding the epidemic in a clear, fast and transparent way. Finally, we propose a greater engagement in inter-institutional cooperation of all those involved in the response and production of knowledge about the pandemic in our country.

Background: Previous studies have shown that COVID-19 In-Hospital Fatality Rate (IHFR) varies between regions and has been diminishing over time. It is believed that the continuous improvement in the treatment of patients, age group of hospitalized, and the availability of hospital resources might be affecting the temporal and regional variation of IHFR. In this study, we explored how the IHFR varied along time and among age groups and federative states in Brazil. In addition, we also assessed the relationship between hospital structure availability and peaks of IHFR. Methods: A retrospective analysis of all COVID-19 hospitalizations with confirmed outcomes in 21 states between March 01 and September 22, 2020 (N=345,281) was done. We fit GLM binomial models with additive and interaction effects between age groups, epidemiological weeks, and states. We also evaluated the association between the modeled peak of IHFR in each state and the variables of hospital structure using the Spearman rank correlation test. Results: We found that the temporal variation of the IHFR was heterogeneous among the states, and in general it followed the temporal trends in hospitalizations. In addition, the peak of IHFR was higher in states with a smaller number of doctors and intensivists, and in states in which a higher percentage of people relied on the Public Health System (SUS) for medical care. Conclusions: Our results suggest that the pressure over the healthcare system is affecting the temporal trends of IHFR in Brazil.

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