| Full text | |
| Author(s): |
Andrade Silva, Joao Flavio
;
Izbicki, Rafael
;
Bastos, Leonardo S.
;
Soares, Guilherme P.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | DEVELOPMENTS IN STATISTICAL MODELLING, IWSM 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Abstract | |
We introduce a novel methodology for estimating the distribution of viruses in Severe Acute Respiratory Syndrome (SARS) patients in Brazil, addressing significant challenges for data in that country, such as data delays and the absence of negative test results. By employing a probabilistic classifier, our approach offers precise, adaptable estimates across various demographic characteristics and regions of the country without the need for predefined groups. Comparative analyses demonstrate the effectiveness of the model. This methodology significantly contributes to public health by enhancing disease monitoring and supporting targeted prevention strategies. (AU) | |
| FAPESP's process: | 23/07068-1 - Statistical machine learning: toward better uncertainty quantification |
| Grantee: | Rafael Izbicki |
| Support Opportunities: | Regular Research Grants |