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PubMed: 34767822 DOI: 10.1016/j.envres.2021.112348
Multivariate data driven prediction of COVID-19 dynamics: Towards new results with temperature, humidity and air quality data.
Environ Res 204(Pt D):112348
Authors: Dunfrey P Aragão, Emerson V Oliveira, Arthur A Bezerra, Davi H Dos Santos, Andouglas G da Silva Junior, Igor G Pereira, Prisco Piscitelli, Alessandro Miani, Cosimo Distante, Jordan S Cuno, Aura Conci, Luiz M G Gonçalves
Affiliations
- Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, Campus Universitário, 59.078-970, Natal, 3000, Brazil.
- Instituto Federal do Rio Grande do Norte, R. Raimundo Firmino de Oliveira, 400, 59.628-330, Mossoró, Brazil.
- Italian Society of Environmental Medicine, SIMA, Milan, Italy.
- Department of Environmental Science and Policy, University of Milan, Milan, Italy.
- Institute of Applied Sciences and Intelligent Systems, via Monteroni sn, 73100, Lecce, Italy.
- Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/n, São Domingos, 24.210-346, Niteroi, Brazil.
- Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, Campus Universitário, 59.078-970, Natal, 3000, Brazil.
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.
MeSH
Work, Climate, Temperature, Humidity, Air Pollution, Covid-19
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- Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States. Sci Rep, 2022
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries. Intell Syst Appl, 2022
- Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China. Int J Environ Res Public Health, 2023
- In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning. Int J Environ Res Public Health, 2022