Multivariate data driven prediction of COVID-19 dynamics: Towards new results with temperature, humidity and air quality data.
Environ Res
; 204(Pt D): 112348, 2022 03.
Article
in En
| MEDLINE
| ID: mdl-34767822
ABSTRACT
Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Air Pollution
/
COVID-19
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
America do sul
/
Brasil
Language:
En
Journal:
Environ Res
Year:
2022
Document type:
Article
Affiliation country:
Brazil