Abstract
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.
Highlights
A viral infection named COVID-19 was initially discovered in mid-December 2019 in Wuhan city of China [1], which spread across the whole world, and eventually WHO declared it as a pandemic [2]
Statistical performance of time series prediction models was measured in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE)
Testing data was evaluated using MSE, RMSE, and MAPE, while training data was validated through time series prediction models Autoregressive Integrated Moving Average (ARIMA), linear regression, SVR, Multilayered Perceptron (MLP), Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRU), and MAPE
Summary
A viral infection named COVID-19 was initially discovered in mid-December 2019 in Wuhan city of China [1], which spread across the whole world, and eventually WHO declared it as a pandemic [2]. Albeit there is a cure, the main focus is to curb the spread through national blockades and quarantine measures [5] Such a high daily number of cases warrants an immediate plan of action to control it effectively and its need to prepare for future outbreaks in Pakistan and other nations. To illustrate the nature of SARSCoV-2 and to forecast its transmission, there is a dire need to explore its effect on weather In this regard, the systematic approach of our study includes the following:. (a) Using existing data to predict the number of actual COVID-19 affected cases and the total number of deaths in upcoming months with or without weather data in Pakistan. It is time to understand the relationship between weather variables and the epidemic spreading of COVID-19 in Pakistan
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