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Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

Dengue fever is among the most significant public health concerns in Brazil. To date, the highest number of Dengue notifications in the Americas has been reported in Brazil, with cases accounting for a total number of 3,418,796 reported cases as of mid-December 2022. Furthermore, the northeastern region of Brazil registered the second-highest incidence of Dengue fever in 2022. Due to the alarming epidemiological scenario, in this study, we used a combination of portable whole-genome sequencing, phylodynamic, and epidemiological analyses to reveal a novel DENV-1 genotype V clade and the persistence of DENV-2 genotype III in the region. We further report the presence of non-synonymous mutations associated with non-structural domains, especially the NS2A (non-structural protein 2A), as well as describe synonymous mutations in envelope and membrane proteins, distributed differently between clades. However, the absence of clinical data at the time of collection and notification, as well as the impossibility of monitoring patients in order to observe worsening or death, restricts our possibility of correlating mutational findings with possible clinical prognoses. Together, these results reinforce the crucial role of genomic surveillance to follow the evolution of circulating DENV strains and understand their spread across the region through inter-regional importation events, likely mediated by human mobility, and also the possible impacts on public health and outbreak management.

Tracking the spread of SARS-CoV-2 variants of concern is crucial to inform public health efforts and control the ongoing pandemic. Here, we report genetic evidence for circulation of the P.1 variant in Northeast Brazil. We advocate for increased active surveillance to ensure adequate control of this variant throughout the country.

[This corrects the article DOI: 10.1371/journal.pntd.0009591.].

During these past years, several studies have provided serological evidence regarding the circulation of West Nile virus (WNV) in Brazil. Despite some reports, much is still unknown regarding the genomic diversity and transmission dynamics of this virus in the country. Recently, genomic monitoring activities in horses revealed the circulation of WNV in several Brazilian regions. These findings on the paucity of genomic data reinforce the need for prompt investigation of WNV infection in horses, which may precede human cases of encephalitis in Brazil. Thus, in this study, we retrospectively screened 54 suspicious WNV samples collected between 2017 and 2020 from the spinal cord and brain of horses with encephalitis and generated three new WNV genomes from the Ceará and Bahia states, located in the northeastern region of Brazil. The Bayesian reconstruction revealed that at least two independent introduction events occurred in Brazil. The first introduction event appears to be likely related to the North American outbreak, and was estimated to have occurred in March 2013.The second introduction event appears to have occurred in September 2017 and appears to be likely related to the South American outbreak. Together, our results reinforce the importance of increasing the priority of WNV genomic monitoring in equines with encephalitis in order to track the dispersion of this emerging pathogen through the country.

The emergence of chikungunya virus (CHIKV) has raised serious concerns due to the virus' rapid dissemination into new geographic areas and the clinical features associated with infection. To better understand CHIKV dynamics in Rio de Janeiro, we generated 11 near-complete genomes by means of real-time portable nanopore sequencing of virus isolates obtained directly from clinical samples. To better understand CHIKV dynamics in Rio de Janeiro, we generated 11 near-complete genomes by means of real-time portable nanopore sequencing of virus isolates obtained directly from clinical samples. Our phylogenetic reconstructions indicated the circulation of the East-Central-South-African (ECSA) lineage in Rio de Janeiro. Time-measured phylogenetic analysis combined with CHIKV notified case numbers revealed the ECSA lineage was introduced in Rio de Janeiro around June 2015 (95% Bayesian credible interval: May to July 2015) indicating the virus was circulating unnoticed for 5 months before the first reports of CHIKV autochthonous transmissions in Rio de Janeiro, in November 2015. These findings reinforce that continued genomic surveillance strategies are needed to assist in the monitoring and understanding of arbovirus epidemics, which might help to attenuate public health impact of infectious diseases.

The chikungunya East/Central/South/Africa virus lineage (CHIKV-ECSA) was first detected in Brazil in the municipality of Feira de Santana (FS) by mid 2014. Following that, a large number of CHIKV cases have been notified in FS, which is the second-most populous city in Bahia state, northeastern Brazil, and plays an important role on the spread to other Brazilian states due to climate conditions and the abundance of competent vectors. To better understand CHIKV dynamics in Bahia state, we generated 5 complete genome sequences from a local outbreak raised in Serraria Brasil, a neighbourhood in FS, by next-generation sequencing using Illumina approach. Phylogenetic reconstructions revealed that the new FS genomes belongs to the ECSA genotype and falls within a single strongly supported monophyletic clade that includes other older CHIKV sequences from the same location, suggesting the persistence of the virus during distinct epidemic seasons. We also performed minor variants analysis and found a small number of SNPs per sample (b_29L and e_45SR = 16 SNPs, c_29SR = 29 and d_45PL and f_45FL = 21 SNPs). Out of the 93 SNPs found, 71 are synonymous, 21 are non-synonymous and one generated a stop codon. Although those mutations are not related to the increase of virus replication and/or infectivity, some SNPs were found in non-structural proteins which may have an effect on viral evasion from the mammal immunological system. These findings reinforce the needing of further studies on those variants and of continued genomic surveillance strategies to track viral adaptations and to monitor CHIKV epidemics for improved public health control.

RT-PCR testing data provides opportunities to explore regional and individual determinants of test positivity and surveillance infrastructure. Using Generalized Additive Models, we explored 222,515 tests of a random sample of individuals with COVID-19 compatible symptoms in the Brazilian state of Bahia during 2020. We found that age and male gender were the most significant determinants of test positivity. There was evidence of an unequal impact among socio-demographic strata, with higher positivity among those living in areas with low education levels during the first epidemic wave, followed by those living in areas with higher education levels in the second wave. Our estimated probability of testing positive after symptom onset corroborates previous reports that the probability decreases with time, more than halving by about two weeks and converging to zero by three weeks. Test positivity rates generally followed state-level reported cases, and while a single laboratory performed ~90% of tests covering ~99% of the state’s area, test turn-around time generally remained below four days. This testing effort is a testimony to the Bahian surveillance capacity during public health emergencies, as previously witnessed during the recent Zika and Yellow Fever outbreaks.

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression). This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in the PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration. Competing Interest Statement The authors have declared no competing interest.

ABSTRACT The Northeast region of Brazil registered the second highest incidence proportion of chikungunya fever in 2019. In that year an outbreak consisting of patients presented with febrile disease associated with joint pain were reported by the public primary health care service in the city of Natal, Rio Grande do Norte state, in March 2019. At first, the aetiological agent of the disease was undetermined. Since much is still unknown about chikungunya virus (CHIKV) genomic diversity and evolutionary history in this northeasternmost state, we used a combination of portable whole genome sequencing, molecular clock, and epidemiological analyses that revealed the re-introduction of the CHIKV East-Central-South-African (ECSA) lineage into Rio Grande do Norte. We estimated CHIKV ECSA lineage was first introduced into Rio Grande do Norte in early June 2014, while the 2019 outbreak clade diverged around April 2018 during a period of increased chikungunya incidence in the Southeast region, which might have acted as a source of virus dispersion towards the Northeast region. Together, these results confirm the ECSA lineage continues to spread across the country through interregional importation events likely mediated by human mobility. HIGHLIGHTS CHIKV ECSA lineage introduction into Rio Grande do Norte state, Northeast Brazil, was estimated to early June 2014 At least two CHIKV importation events occurred in Rio Grande do Norte state, Brazil The 2019 chikungunya outbreak in Rio Grande do Norte was likely caused by a second event of CHIKV introduction imported from Rio de Janeiro state. Competing Interest Statement The authors have declared no competing interest.