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Human society always wants a safe environment from pollution and infectious diseases, such as COVID-19, etc. To control COVID-19, we have started the big effort for the discovery of a vaccination of COVID-19. Several biological problems have the aspects of symmetry, and this theory has many applications in explaining the dynamics of biological models. In this research article, we developed the stochastic COVID-19 mathematical model, along with the inclusion of a vaccination term, and studied the dynamics of the disease through the theory of symmetric dynamics and ergodic stationary distribution. The basic reproduction number is evaluated using the equilibrium points of the proposed model. For well-posedness, we also test the given problem for the existence and uniqueness of a non-negative solution. The necessary conditions for eradicating the disease are also analyzed along with the stationary distribution of the proposed model. For the verification of the obtained result, simulations of the model are performed. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]