Machine learning in predicting severe acute respiratory infection outbreaks
Bibliographic Data
| ID | 5166470 |
|---|---|
| Authors | Amauri Duarte Da Silva (0000-0001-6395-458X, Universidade Federal de Ciências da Saúde de Porto Alegre), Marcelo Ferreira Da Costa Gomes (0000-0003-4693-5402, Fundação Oswaldo Cruz), Tatiana S Gregianini (0000-0002-9912-9060, Secretaria de Saúde do Estado do Rio Grande do Sul, Brasil), Leticia Garay Martins (0000-0002-5614-6952, Secretaria de Saúde do Estado do Rio Grande do Sul, Brasil), Ana Beatriz Gorini Da Veiga (0000-0003-1462-5506, Universidade Federal de Ciências da Saúde de Porto Alegre) |
| Year | 2024 |
| Volume | 40 |
| Issue | 1 |
| Pages | e00122823-e00122823 |
| Publication date | 2024-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cadernos de Saude Publica (JOURNAL) |
| Journal identifiers | ISSN: 0102-311X • E-ISSN: 1678-4464 |
| Publisher | FapUNIFESP (SciELO) (PUBLISHER) |
| DOI | 10.1590/0102-311xen122823 |
| PMID | 38198384 |
| OpenAlex | W4390658935 |
| SCIELO_PID | S0102-311X2024000105005 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series
Artificial neural network · Attendance · Geography · Incidence (geometry) · Machine learning · Outbreak · Pathology · Predictive modelling · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Emergency Medicine · Mathematics · Medicine
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |