National Holidays and Social Mobility Behaviors
Alternatives for Forecasting Covid-19 Deaths in Brazil
Bibliographic Data
| ID | 15478778 |
|---|---|
| Authors | Dunfrey Pires Aragão (0000-0002-2401-6985, Universidade Federal do Rio Grande do Norte), Davi Henrique dos Santos (0000-0002-4286-235X, Universidade Federal do Rio Grande do Norte), Adriano Mondini (0000-0002-5557-9721, Universidade Estadual Paulista (Unesp)), Luiz Marcos Garcia Gonçalves (0000-0002-7735-5630, Universidade Federal do Rio Grande do Norte, corresponding author) |
| Year | 2021 |
| Volume | 18 |
| Issue | 21 |
| Pages | 11595-11595 |
| Publication date | 2021-11-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph182111595 |
| PMID | 34770108 |
| OpenAlex | W3211218433 |
| Language | EN |
| Citations received | 2 |
| References cited | 35 |
In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (R0, Re) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model
Coronavirus disease 2019 (COVID-19 · Deep learning · Disease · Econometrics · Principal component analysis · Random variate · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Statistics · Telecommunications · Transmission (telecommunications · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Mathematics · Medicine · Artificial Intelligence
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| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2024 (2) |
| Highly cited | No |