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Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM 2.5 and PM 10 ) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values. [ABSTRACT FROM AUTHOR]

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM 2.5 and PM 10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM 2.5 and PM 10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM 2.5 and −51.61% for PM 10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM 2.5 and 100.00% for PM 10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM 2.5 and PM 10 , respectively. On the other hand, when comparing the concentrations of PM 2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM 10 , a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs ( x 2 = 1.204) and between the DL-2021 and DL-2019 pairs ( x 2 = 0.372), with a p < 0.000 for PM 2.5 , and the contrast between pairs of PM 10 (DL) showed a significant difference between all pairs with p < 0.01 . [ABSTRACT FROM AUTHOR]

This study aims to analyze the correlation between environmental factors and confirmed cases of COVID-19 pandemic in Victoria, Mexico. The analysis is performed at the micro-level, filtering only confirmed cases of COVID-19 that are located near air quality monitoring stations, within an approximate coverage of 2.5 km, in order to identify a possible specific association between PM 2.5 , PM 10 , carbon monoxide (CO), relative humidity, temperature, absolute humidity, and total confirmed cases of COVID-19. The results evidenced that the cases of COVID-19 were very strongly associated with CO concentration. Our results also suggested that particulate matter pollution (PM 2.5 and PM 10) exposure have a significant correlation for confirmed cases of COVID-19. Furthermore, we studied the changes in air quality during the COVID-19 outbreak by comparing the average concentration of the four weeks before lockdown (February 16 to March 14, 2020) and the following twelve weeks during the partial lockdown (March 15 to June 06, 2020), revealing a very significant decrease of pollutants. • The first study on the effects of environmental and meteorological factors on COVID-19 cases in Mexico. • A decrease of more than 44% was found in the concentration levels of PM 2.5 , PM 10 and CO during the lockdown period. • A strong positive correlation between confirmed COVID-19 cases with carbon monoxide. • PM 2.5 and PM 10 show a positive association with confirmed cases of COVID-19. • Temperature is negatively correlated to the spread of COVID-19. [ABSTRACT FROM AUTHOR]