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Monitoring influenza-like illness through syndromic surveillance could be an important strategy in the COVID-19 emergence scenario. The study aims to implement syndromic surveillance for children aged 6-11 years in COVID-19 sentinel schools in Catalonia. Data collection was made by self-applied survey to collect daily health status and symptoms. We proceed logistic mixed models and a Latent Class Analysis to investigate associations with syndromes and school absence. Were enrolled 135 students (2163 person-days) that filled 1536 surveys and 60 participants reported illness (29.52 by 100 person/day) and registered 189 absence events, 62 of them (32.8%) related to health reasons. Subgroups of influenza-like illness were founded such as a significantly and positively association with school absences. The findings of this study can be applied to the detection of health events, and association with school absences, offering an opportunity for quick action, or simply for monitoring and understanding the students’ health situation.ARTICLE SUMMARY LINEThis study confirms the relevance of syndromic surveillance in students from 6 to 11 years of age as a strategy to timely detect events that can cause school absence, either to support public health actions by applying analytical models that improve their potential in providing systematized information, or to monitor and understand the health situation of students, thus offering an opportunity for rapid action.

This cross-sectional observational study that describes the epidemiological data of the first year of the COVID-19 pandemic in the Mato Grosso do Sul State, aimed to demonstrate the differences between indigenous and non-indigenous populations, characterize confirmed cases of COVID-19 according to risk factors related to ethnicity, comorbidities and their evolution and to verify the challenges in facing the disease in Brazil. SIVEP-Gripe and E-SUS-VE, a nationwide surveillance database in Brazil, from March 2020 to March 2021 in Mato Grosso do Sul state, were used to compare survivors and non-survivors from indigenous and non-indigenous populations and the epidemiological incidence curves of these populations. A total of 176,478, including 5,299 indigenous people, were confirmed. Among the indigenous population, 52.5% (confidence interval [CI] 51.2-53.9) were women, 38% (CI 36.7-39.4) were 20-39 years old, 56.7% were diagnosed by rapid antibody tests, 12.3% (CI 95%:11.5-13.2) had at least one comorbidity, and 5.3% (CI 95%:4.7–5.9) were hospitalized. In the non-indigenous patients, 56.8% were confirmed using RT-PCR, 4.4% (CI 95%:4.3-4.5) had at least one comorbidity, and 8.0% (CI 95%:7.9-8.2) were hospitalized. The majority of non-survivors were ≥60 years old (65.1% indigenous vs. 74.1% non-indigenous). The mortality in indigenous people was more than three times higher (11% vs. 2.9%). Indigenous people had a lower proportion of RT-PCR diagnoses; deaths were more frequent in younger patients and were less likely to be admitted to hospital. Mass vaccination may have controlled the incidence and mortality associated with COVID-19 in this population during the period of increased viral circulation.

INTRODUCTION: The coronavirus disease (COVD-19) outbreak has overburdened the surveillance of severe acute respiratory infections (SARIs), including the laboratory network. This study was aimed at correcting the absence of laboratory results of reported SARI deaths. METHODS: The imputation method was applied for SARI deaths without laboratory information using clinico-epidemiological characteristics. RESULTS: Of 84,449 SARI deaths, 51% were confirmed with COVID-19 while 3% with other viral respiratory diseases. After the imputation method, 95% of deaths were reclassified as COVID-19 while 5% as other viral respiratory diseases. CONCLUSIONS: The imputation method was a useful and robust solution (sensitivity and positive predictive value of 98%) for missing values through clinical & epidemiological characteristics.

The first case of COVID-19 was detected in Brazil on 25 February 2020. We report and contextualize epidemiological, demographic and clinical findings for COVID-19 cases during the first 3 months of the epidemic. By 31 May 2020, 514,200 COVID-19 cases, including 29,314 deaths, had been reported in 75.3% (4,196 of 5,570) of municipalities across all five administrative regions of Brazil. The R0 value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4–5.5), with a higher median but overlapping credible intervals compared with some other seriously affected countries. A positive association between higher per-capita income and COVID-19 diagnosis was identified. Furthermore, the severe acute respiratory infection cases with unknown aetiology were associated with lower per-capita income. Co-circulation of six respiratory viruses was detected but at very low levels. These findings provide a comprehensive description of the ongoing COVID-19 epidemic in Brazil and may help to guide subsequent measures to control virus transmission.

BACKGROUND: In the absence of vaccines and effective pharmacological interventions to reduce with the COVID-19 transmission, social distancing measures have been implemented to mitigate the impact on healthcare systems and secure time to prepare the public health response. METHODS: We assessed the relationship between mobility data collected by mobile phone and time-dependent reproduction number R(t) using severe acute respiratory illness cases reported by 102 Brazilian cities with COVID-19 confirmed cases until April 15, grouped by demographic density (low, intermediate and high). FINDINGS: The mean social distancing index from February 1 to April 15 was 43.6% (27.2% to 63.7%), and no significant difference observed comparing the groups of demographic density (p-value = 0.809). The social distancing index measure obtained from mobility data was able to predict future values of R(t) in all groups of demographic density. Furthermore, using SARI cases, cross-correlation analyses showed that isolation was highly correlated with R(t) (ccf

BackgroundCOVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system.MethodsWe analyzed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who traveled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation.ResultsWe described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset, including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41% - 96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51% – 97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%).ConclusionWe obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modeling epidemiological trends.

Introducción: las tecnologías móviles por su fácil uso y gran difusión se han convertido en una herramienta importante en salud pública, permitiendo identificar brotes de manera temprana como complemento de la vigilancia tradicional. El objetivo fue analizar los datos de una aplicación móvil para detectar rápidamente enfermedades de interés en salud pública relacionadas con la visita del Papa Francisco a Colombia. Métodos: se utilizó vigilancia participativa por medio de la aplicación móvil Guardianes de la Salud, implementada para Colombia en dispositivos Android, adaptada del desarrollo de Proepi y Ministerio de Salud de Brasil para los Juegos Olímpicos de Rio de Janeiro 2014. Se interrogó sobre 20 síntomas que permitían detectar síndromes respiratorios, febriles, gastrointestinales, exantémicos e ictéricos, relacionados con eventos de interés en salud pública. Se preguntaba por antecedentes de viaje y contacto con otros enfermos. Se monitorearon los datos durante los días de visita del Papa al país y hasta siete días después. Resultados: se recibieron 936 reportes, 60,2% (283) fueron de Bogotá, 23,0% (215) del departamento del Meta y 22,0% (206) de Antioquia, se recibieron también reportes de otros países como Brasil (21), Estados Unidos, Panamá (3), Chile (2) y Costa Rica (1). El 55,1% (516) fueron mujeres, el promedio de edad fue 39 años (D.E. 12,3). El 8,5% (80) reportaron su estado de salud mal, siendo el principal síntoma dolor de cabeza en el 41,3% (33), seguido de malestar 40,0% (32) y congestión nasal 38,8% (31). El 53,7% (43) de los reportes estuvieron asociados a alguno de los síndromes priorizados, siendo el principal el febril en 22 casos y gastrointestinal en 11. Conclusión: no se detectaron conglomerados de casos de importancia en salud pública. La herramienta permitió involucrar al público general en la notificación de su estado de salud. Es necesario promover el uso de esta aplicación ya que medida que aumenta el número de usuarios, también aumenta la sensibilidad que brinda el monitoreo ampliado de la vigilancia participativa.