Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

Roshan Wathore; Samyak Rawlekar; Saima Anjum; Ankit Gupta; Hemant Bherwani; Nitin Labhasetwar; Rakesh Kumar

doi:10.1016/j.gr.2022.03.014

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

Gondwana Res. 2023 Feb:114:69-77. doi: 10.1016/j.gr.2022.03.014. Epub 2022 Apr 8.

Authors

Roshan Wathore^{1

2}, Samyak Rawlekar³, Saima Anjum¹, Ankit Gupta^{1

2}, Hemant Bherwani^{1

2}, Nitin Labhasetwar^{1

2}, Rakesh Kumar^{2

4}

Affiliations

¹ CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, Maharashtra, India.
² Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India.
³ Indian Institute of Technology (IIT) Dharwad, Dharwad 580 011, Karnataka, India.
⁴ Council of Scientific and Industrial Research (CSIR), Anusandhan Bhawan, 2 Rafi Ahmed Kidwai Marg, New Delhi 110001, India.

Abstract

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Keywords: COVID-19; Deep Learning; LSTM. Multivariate time series forecasting; SARS-CoV-2.