Since its detection in China in 2019, coronavirus, has been subjected to intense studies in many aspects to limit its transmission or suppressing it. Recently, research work was conducted to investigate the effect of metrological condition (the quality of air) and metrological data, (i.e. humidity, ambient temperature, solar radiation) on COVID-19 transmission around the world.
Huang et al. (2020) conducted a research work on the effect of ambient environment on the spread of SARS-CoV-2 and found that, the concentration increases with ambient environment. They suggested that this increase imply that the transmission of coronavirus may increase in large cities in mid-latitudes in Autumn season 2020.
Xie and Zhu (2020) used a generalized additive model to investigate how the active cases of COVID-19 is influenced by the mean ambient temperature in 122 cities in China, they concluded that COVID-19 active cases increase linearly with ambient temperature with a threshold of 3°C. They also concluded that there is no evidence supporting the fact that the positive cases will decline in warmer conditions.
Bashir et al. (2020) analyzed the relation of COVID-19 cases with metrological data in New York including average temperature, minimum temperature, maximum temperature, rainfall, average humidity, wind speed, and air quality. They concluded that, the minimum and maximum temperatures together with air quality are significantly related to the number of COVID-19 positive cases.
Méndez-Arriaga (2020) used Statistical analysis to investigate the association between the daily local COVID-19 positive cases and both climate characteristics and the regional meteorological data in 31 states and the capital city Mexico from February 29 to March 31, 2020. He concluded that there is no concrete association between temperature and positive cases, while precipitation is positively associated to positive cases.
Lian et al. (2020) conducted a study on the effect of lockdown caused by COVID-19 on air quality in Wuhan, china. They found that most common pollutants concentration were significantly reduced during the lockdown, with the average monthly air quality index was 59.7, which is 33.9% less than that before the lockdown.
Zambrano-Monserrate et al. (2020) Studied the effects of COVID-19 on the environment in China, USA, Italy, and Spain. They stated that contingency measures are related with clean environment including good air quality, noise reduction and clean beaches. However, they concluded that there is a reduction in recycling and increase in waste that leads to contamination of water and land.
Collivignarelli (2020) Studied the effect of the lockdown on air quality in Milan Metropolitan / Italy. He concluded that the lockdown caused a significant reduction of pollutants concentration (PM10, PM2.5, BC, benzene, CO, and NOx) which is mainly due to the reduced vehicular traffic. While, O3 demonstrated a major increase, possibly, due to the minor NO concentration. Furthermore, He found that the lockdown led to a major drop in the concentration of SO2 in Milan city.
Pirouz et al, (2020) investigated the effect of climate on active cases of COVID-19 using (multivariate linear regression (MLR) in an attempt to propose a prediction model. They considered including the parameters of relative humidity, daily average temperature, and wind speed, with some urban parameters such as population density. Their analysis showed a positive effect of the proposed model on the confirmed cases and they concluded that Their findings may be applied by considering several variables that exhibit the exact delay to new confirmed cases of COVID-19.
Abdelhafez et al. (2021), performed a study to correlate the COVID-19 active cases with metrological components to include: relative humidity (%), the average daily temperature (0C), maximum ambient temperature (0C), pressure (kPa) wind speed (m/s), and average daily solar radiation (W/m2). In their work, they used the Spearman correlation test for data analysis. They concluded that the most effective weather parameter on the active cases of COVID-19 is the average daily solar radiation in the initial wave transmission, while all other tests for other parameters failed. Furthermore, in the second wave transmission, the maximum temperature was found to be the most effective weather parameter. Finally, using a global sensitivity analysis using Sobol analysis it was found that the daily solar radiation has a high rate of active cases that support the virus's survival in both wave transmissions.
In this work, Artificial neural network (ANN) and multiple linear regression are used to predict COVID-19 active cases in the three cities in Jordan by using climate indicators as an input. Also, sensitivity analysis between variables (climate indicators) will be conducted using Sobol method.