Syndromic Detection of Upper Respiratory Infections in Primary Healthcare as a Candidate for COVID-19 Early Warning in Brazil: A Retrospective Ecological Study

Oliveira, Vinícius de Araújo; Sironi, Alberto; Florentino, Pilar Tavares Veras; Marcílio, Izabel; Cerqueira‐Silva, Thiago; Flores-Ortiz, Renzo; Machado, Tales Mota; Penna, Gerson O.; Barreto, Marcos Ennes; Barral‐Netto, Manoel

doi:10.2139/ssrn.4364869

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By issuing anticipatory warnings to health authorities, the system aims to provide early and timely alerts, as severe cases are not expected to be reported until days or weeks after an increase in mild cases seeking PHC assistance [ 1 , 10 ]. A proof-of-concept evaluating the early-warning capability performance of the system was conducted in Brazil, totaling over 589 million patient visits to PHC facilities between 2017-2020 in 97% of Brazilian municipalities [ 11 ]. Of these visits, 4.24% were associated with upper respiratory infections, and results showed that rises in patient consultation in PHC encounters for respiratory disease were able to anticipate increases of severe acute respiratory syndromes, with geographical variabilities [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…A proof-of-concept evaluating the early-warning capability performance of the system was conducted in Brazil, totaling over 589 million patient visits to PHC facilities between 2017-2020 in 97% of Brazilian municipalities [ 11 ]. Of these visits, 4.24% were associated with upper respiratory infections, and results showed that rises in patient consultation in PHC encounters for respiratory disease were able to anticipate increases of severe acute respiratory syndromes, with geographical variabilities [ 11 ]. In a retrospective analysis conducted with data from Bahia, a state with 14.8 million inhabitants in the Northeast region, PHC syndromic monitoring was able to detect COVID-19 entry in 20 out of 21 regions of the state at least 1 week before the rise in COVID-19 cases, showing feasibility for use of the system [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential

Ramos,

Marcilio,

Bento

et al. 2024

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential

Ramos,

Marcilio,

Bento

et al. 2024

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

show abstract

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential (Preprint)

Ramos,

Marcilio,

Bento

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

UNSTRUCTURED Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

show abstract

Early detection of respiratory disease outbreaks through primary healthcare data

Cerqueira-Silva,

Marcilio,

de Araújo Oliveira

et al. 2023

J Glob Health

View full text Add to dashboard Cite

Syndromic Detection of Upper Respiratory Infections in Primary Healthcare as a Candidate for COVID-19 Early Warning in Brazil: A Retrospective Ecological Study

Cited by 3 publications

References 0 publications

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential (Preprint)

Early detection of respiratory disease outbreaks through primary healthcare data

Contact Info

Product

Resources

About