www.covid19analytics.com.br (discontinued)
Short-term forecasting tool for COVID-19 cases and deaths in Brazil
Intending to create a tool to support the management of the health crisis by the responsible authorities and inform the public, a group of professors from PUC-Rio built a model to predict the number of cases and deaths of COVID-19 in Brazil up to two weeks ahead (Medeiros, Marcelo et al., 2022). The founding professors, who are associated with different departments (Electrical Engineering, Industrial Engineering, and Economics), have quantitative articles in several areas published in top international journals and extensive experience in the construction and practical implementation of predictive models for different sectors through its laboratories.
The model, driven only by data, estimates a relationship between Brazil and countries affected by the pandemic in a previous period. The fact that Brazil is “lagging” in relation to these countries allows, based on this estimated relationship, the evolution of the number of cases and deaths in the short term to be projected. This model is complementary to epidemiological models, which are difficult to quantitatively discipline because it is a new disease whose scientific community still has little knowledge.
The proposed model is re-estimated daily based on new data from the Brazilian trajectory and from other countries, automatically selected based on their adherence to the trajectory and predictive capacity. So far, the model has only been tested for official data reported by the Ministry of Health (https://covid.saude.gov.br), and relevant testing and social distancing indicators still need to be considered.
The model predicts the evolution of the total number of reported cases, the number of new cases, and the growth rate. The same prediction method is also applied to deaths and could be applied to specific cases of critically ill hospital networks. In addition to point predictions based on means, the model also informs a 95% confidence interval, which takes into account the risk arising from the prediction error.