Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study.

BACKGROUND: Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes. METHODS: In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation. FINDINGS: We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021. INTERPRETATION: By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems. FUNDING: Rockefeller Foundation.

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation.

Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

Bivariate GWAS reveals pleiotropic regions among feed efficiency and beef quality-related traits in Nelore cattle.

Feed-efficient cattle selection is among the most leading solutions to reduce cost for beef cattle production. However, technical difficulties in measuring feed efficiency traits had limited the application in livestock. Here, we performed a Bivariate Genome-Wide Association Study (Bi-GWAS) and presented candidate biological mechanisms underlying the association between feed efficiency and meat quality traits in a half-sibling design with 353 Nelore steers derived from 34 unrelated sires. A total of 13 Quantitative Trait Loci (QTL) were found explaining part of the phenotypic variations. An important transcription factor of adipogenesis in cattle, the TAL1 (rs133408775) gene located on BTA3 was associated with intramuscular fat and average daily gain (IMF-ADG), and a region located on BTA20, close to CD180 and MAST4 genes, both related to fat accumulation. We observed a low positive genetic correlation between IMF-ADG (r = 0.30 ± 0.0686), indicating that it may respond to selection in the same direction. Our findings contributed to clarifying the pleiotropic modulation of the complex traits, indicating new QTLs for bovine genetic improvement.

Cross-sectional study for COVID-19-related mortality predictors in a Brazilian state-wide landscape: the role of demographic factors, symptoms and comorbidities.

OBJECTIVE: The Brazilian state of Paraná has suffered from COVID-19 effects, understanding predictors of increased mortality in health system interventions prevent hospitalisation of patients. We selected the best models to evaluate the association of death with demographic characteristics, symptoms and comorbidities based on three levels of clinical severity for COVID-19: non-hospitalised, hospitalised non-ICU ward and ICU ward. DESIGN: Cross-sectional survey using binomial mixed models. SETTING: COVID-19-positive cases diagnosed by reverse transcription-PCR of municipalities located in Paraná State. PATIENTS: Cases of anonymous datasets of electronic medical records from 1 April 2020 to 31 December 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The best prediction factors were chosen based on criteria after a stepwise analysis using multicollinearity measure, lower Akaike information criterion and goodness-of-fit χ2 tests from univariate to multivariate contexts. RESULTS: Male sex was associated with increased mortality among non-hospitalised patients (OR 1.76, 95% CI 1.47 to 2.11) and non-ICU patients (OR 1.22, 95% CI 1.05 to 1.43) for symptoms and for comorbidities (OR 1.89, 95% CI 1.59 to 2.25, and OR 1.30, 95% CI 1.11 to 1.52, respectively). Higher mortality occurred in patients older than 35 years in non-hospitalised (for symptoms: OR 4.05, 95% CI 1.55 to 10.54; and for comorbidities: OR 3.00, 95% CI 1.24 to 7.27) and in hospitalised over 40 years (for symptoms: OR 2.72, 95% CI 1.08 to 6.87; and for comorbidities: OR 2.66, 95% CI 1.22 to 5.79). Dyspnoea was associated with increased mortality in non-hospitalised (OR 4.14, 95% CI 3.45 to 4.96), non-ICU (OR 2.41, 95% CI 2.04 to 2.84) and ICU (OR 1.38, 95% CI 1.10 to 1.72) patients. Neurological disorders (OR 2.16, 95% CI 1.35 to 3.46), neoplastic (OR 3.22, 95% CI 1.75 to 5.93) and kidney diseases (OR 2.13, 95% CI 1.36 to 3.35) showed the majority of increased mortality for ICU as well in the three levels of severity jointly with heart disease, diabetes and CPOD. CONCLUSIONS: These findings highlight the importance of the predictor's assessment for the implementation of public healthcare policy in response to the COVID-19 pandemic, mainly to understand how non-pharmaceutical measures could mitigate the virus impact over the population.