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OBJECTIVE: This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis. METHODS: We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases. RESULTS: Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living. CONCLUSION: Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.

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ABSTRACT Objective: This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis. Methods: We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases. Results: Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living. Conclusion: Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.

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Infectious diseases significantly contribute to global morbidity and mortality, highlighting the critical need for robust disease surveillance systems. The rapid and accurate identification of infection hotspots is crucial for effective disease control and eliminating vector reservoirs. Traditional methods, reliant on patient-reported data, are vague, slow, and non-integrative, presenting substantial barriers to fully understanding the underlying causes of infection transmission. The widespread usage of smartphones presents a unique opportunity to access, analyze, and monitor digital data. Particularly, location data can offer potential insights into infectious disease dynamics, which has remained largely unexplored. Firstly, the present study leverages location history data from smartphones of malaria patients in Manaus, Amazonas region, to pinpoint mosquito-breeding sites. Upon quantifying the location data, the primary transmission hotspots were identified to be concentrated on the outskirts of the city of Manaus. Additionally, the quantification and hotspot validation confirmed that newly visited locations during the exposure period were potential sources of infection transmission. Secondly, the current study also employs a novel digital contact investigation method for a human-to-human transmission infection such as tuberculosis to measure the exposure risk between the active index cases and their close contacts. The digital contact investigation revealed varied exposure durations between the recruited paired index and close contact participants based on the outcome of close contact. To summarize, the present study determines distinct mobility patterns associated with both these infectious diseases, potentially aiding in drafting targeted public health strategies and policies for digital epidemiological surveillance

As doenças infecciosas são um dos principais contribuintes para a morbidade e a mortalidade globais, enfatizando a necessidade crítica de sistemas robustos de vigilância de doenças. A identificação rápida e precisa dos pontos críticos de infecção é fundamental para o controle eficaz de doenças e a eliminação de reservatórios de vetores. Os métodos tradicionais, que dependem de dados relatados por pacientes, são vagos, lentos e não integrativos, apresentando barreiras significativas para a compreensão total das causas subjacentes da transmissão de infecções. O uso generalizado de dispositivos móveis apresenta uma oportunidade única de acessar, analisar e monitorar dados digitais. Especialmente, dados de localização podem oferecer informações úteis sobre a dinâmica de doenças infecciosas, que permanecem em grande parte inexploradas. Primeiramente, o presente estudo utiliza dados de histórico de localização de smartphones de pacientes com malária em Manaus, na região do Amazonas, para identificar locais de reprodução de mosquitos. Ao quantificar os dados de localização, identificaram-se os principais pontos de transmissão concentrados nos arredores da cidade de Manaus. Além do mais, a quantificação e a validação em campo confirmaram que os locais recém-visitados durante o período de exposição eram potenciais fontes de transmissão da infecção. Em segundo lugar, o estudo atual também emprega um inovador método de investigação digital de contato para uma infecção por transmissão de humano para humano, como a tuberculose, a fim de medir o risco por exposição entre os casos índice ativos e seus contatos próximos. A investigação digital de contato revelou períodos de exposição variados entre os participantes recrutados em pares de casos índice e contatos próximos, com base no resultado do contato próximo. Em resumo, o presente estudo identifica padrões distintos de mobilidade associados a ambas essas doenças infecciosas, auxiliando potencialmente na elaboração de estratégias e políticas de saúde pública direcionadas para a vigilância epidemiológica digital

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Background: Public health research frequently requires the integration of information from different data sources. However, errors in the records and the high computational costs involved make linking large administrative databases using record linkage (RL) methodologies a major challenge. Methods: We present Tucuxi-BLAST, a versatile tool for probabilistic RL that utilizes a DNA-encoded approach to encrypt, analyze and link massive administrative databases. Tucuxi-BLAST encodes the identification records into DNA. BLASTn algorithm is then used to align the sequences between databases. We tested and benchmarked on a simulated database containing records for 300 million individuals and also on four large administrative databases containing real data on Brazilian patients. Results: Our method was able to overcome misspellings and typographical errors in administrative databases. In processing the RL of the largest simulated dataset (200k records), the state-of-the-art method took 5 days and 7 h to perform the RL, while Tucuxi-BLAST only took 23 h. When compared with five existing RL tools applied to a gold-standard dataset from real health-related databases, Tucuxi-BLAST had the highest accuracy and speed. By repurposing genomic tools, Tucuxi-BLAST can improve data-driven medical research and provide a fast and accurate way to link individual information across several administrative databases.

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The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/ . OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

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BACKGROUND & AIMS: Obesity is associated with low grade systemic inflammation and insulin resistance. Although metabolic and immunological changes may contribute to the increased risk for COVID-19 mortality in obese, little is known about the impact of obesity in the lungs of patients with COVID-19. METHODS: We analyzed gene expression profiles of autopsy lungs of a cohort of 14 COVID-19 patients and 4 control individuals. Patients were divided into 3 groups according to their comorbidities: hypertension, type 2 diabetes (T2D) and obesity. We then identified the molecular alterations associated with these comorbidities in COVID-19 patients. RESULTS: Patients with only hypertension showed higher levels of inflammatory genes and B-cell related genes when compared to those with T2D and obesity. However, the levels of IFN-gamma, IL22, and CD274 (a ligand that binds to receptor PD1) were higher in COVID-19 patients with T2D and obesity. Several metabolic- and immune-associated genes such as G6PD, LCK and IL10 were significantly induced in the lungs of the obese group. CONCLUSION: Our findings suggest that SARS-CoV-2 infection in the lungs may exacerbate the immune response and chronic condition in obese COVID-19 patients.

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BACKGROUND: The progression and severity of COVID-19 vary significantly in the population. While the hallmarks of SARS-CoV-2 and severe COVID-19 within routine laboratory parameters are emerging, the impact of sex and age on these profiles is still unknown. METHODS: A multidimensional analysis was performed involving millions of records of laboratory parameters and diagnostic tests for 178 887 individuals from Brazil, of whom 33 266 tested positive for SARS-CoV-2. Analyzed data included those relating to complete blood cell count, electrolytes, metabolites, arterial blood gases, enzymes, hormones, cancer biomarkers, and others. FINDINGS: COVID-19 induced similar alterations in laboratory parameters in males and females. CRP and ferritin were increased, especially in older men with COVID-19, whereas abnormal liver function tests were common across several age groups, except for young women. Low peripheral blood basophils and eosinophils were more common in the elderly with COVID-19. Both male and female COVID-19 patients admitted to intensive care units displayed alterations in the coagulation system, and higher values for neutrophils, CRP, and lactate dehydrogenase. CONCLUSIONS: Our study uncovered the laboratory profiles of a large cohort of COVID-19 patients, which formed the basis of discrepancies influenced by aging and biological sex. These profiles directly linked COVID-19 disease presentation to an intricate interplay between sex, age, and immune activation.

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The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/. OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

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