Insights into the relationship between weather parameters and COVID-19 outbreak in Lombardy, Italy

Social and economic variables explain COVID-19 diffusion in European regions

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

Seasonal correlation of meteorological parameters and PM2.5 with the COVID-19 confirmed cases and deaths in Baghdad, Iraq

The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020-2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60-69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.

Assessment of Meteorological Variables and Air Pollution Affecting COVID-19 Cases in Urban Agglomerations: Evidence from China

The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.

Investigating the roles of meteorological factors in COVID-19 transmission in Northern Italy

The novel COVID-19 is a highly invasive, pathogenic, and transmittable disease that has stressed the health care sector and hampered global development. Information of other viral respiratory diseases indicates that COVID-19 transmission could be affected by varying weather conditions; however, the impact of meteorological factors on the COVID-19 death counts remains unexplored. By investigating the impact of meteorological factors (absolute humidity, relative humidity, and temperature), this study will contribute both theoretically and practically to the concerned domain of pandemic management to be better prepared to control the spread of the disease. For this study, data is collected from 23 February to 31 March 2020 for Milan, Northern Italy, one of the badly hit regions by COVID-19. The generalized additive model (GAM) is applied, and a nonlinear relationship is examined with penalized spline methods. A sensitivity analysis is conducted for the verification of model results. The results reveal that temperature, relative humidity, and absolute humidity have a significant but negative relationship with the COVID-19 mortality rate. Therefore, it is possible to postulate that cool and dry environmental conditions promote virus transmission, leading to an increase in COVID-19 death counts. The results may facilitate health care policymakers in developing and implementing effective control measures in a timely and efficient way.

Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization

The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modelling (1,1) is a popular approach used to construct a predictive model with a small-sized dataset.​ In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied to create short-term estimates of the total number of confirmed COVID-19 cases for three countries, Germany, Turkey, and the USA. A rolling mechanism that updates data in equal dimensions was applied to improve the forecasting accuracy of the models. The PSO algorithm was used to optimize the Grey Modelling parameters (1,1) to provide more robust and efficient solutions with minimum errors. To compare the accuracy of the predictive models, a nonlinear autoregressive neural network (NARNN) was also developed. According to the analysis results, Grey Rolling Modelling (1,1) optimized by PSO algorithm performs better than the classical Grey Modelling (1,1), Grey Rolling Modelling (1,1), and NARNN models for predicting the total number of confirmed COVID-19 cases. The present study can provide an important basis for countries to allocate health resources and formulate epidemic prevention policies effectively.