SARS-CoV2 is increasingly recognised as an important, worldwide public health concern. The number of confirmed cases and casualties of SARS-CoV2, at a global scale, has significantly increased. These have drawn the attention of researchers and policy-makers to devise measures against the rapid development of the diseases by providing guidance and options for a better healthcare delivery needed to address the prevailing and future challenges (Eker 2020). It has been postulated that the peak time of the transmission of these diseases is most peculiar to a specific season and weather conditions, for instance, the peak of SARS-CoV1 occurred during the spring while MERS-CoV was transmitted in the warm climate during spring and summer seasons (Caspi et al. 2020). In contrast, the transmission of influenza shows seasonality in the regions with a temperate climate where the peak of infections happens during winter (Bukhari and Jameel 2020; Sajadi et al. 2020; Rasul 2021).
The spread of COVID-19 could be influenced by weather and climatic variables and human demographic variables such as population density, culture, and country measures against the disease (Caspi et al. 2020). At the global scale, association between per country COVID-19 cases and principal climatic variables were examined by previous studies (Rasul 2021). The research reported a significant weak inverse relationship between R0 of COVID-19 cases and wind speed. At the local scale, some research were carried out to evaluate the association between COVID-19 disease and climatic variables. For instance, in Iran, a study reported that COVID-19 infection is high in cities with low degrees of wind speed, humidity, and solar radiation (Ahmadi et al. 2020). In China, linear regression was used to assess the impact of temperature in the cities. Results indicated that high temperatures and high humidity significantly reduced the spread of COVID-19 in 100 cities (Wang et al. 2020). A tropical case study, in Rio de Janeiro, Brazil, concludes that the spread of COVID-19 could be suppressed by a high solar radiation (Rosario et al. 2020). In Jakarta, Indonesia, results display COVID-19 pandemic is weakly positively correlated with average temperature (Tosepu et al. 2020).
To date, only weak evidence is provided by peer reviewed published papers confirming that SARS-CoV-2 is more transferrable under lower air temperatures and low absolute humidity. In addition, the reported relationships between SARS-CoV-2 and air pollution, UV, and wind speed are ambiguous. There is, therefore, the need for the researchers to critically investigate the influence of environmental and weather on the virus and the disease. This means that the impact of weather and climate variables on COVID-19 infection is not only localized but also at experimental stages.
One another question is whether socio-economic and socio-demographic variables of the people may affect the transmission of COVID-19 infection. The influence of these variables on the spread of COVID-19 infection is still not well understood. The example of these variables are population size, population density, the degree of urbanization and poverty (Smit et al. 2020). Despite the fact that different results were reported in the literature about the influence of the environmental conditions on the COVID-19, it is still quite imperative to study the influence of the daily weather and sociodemographic elements on the virus in Iraq (at the local scale). In addition, having more observations and dense time series data regarding the new Coronavirus can reveal pattern that may not have been explored in the past, perhaps by using strict hypotheses and the development of analytical methods and statistical models(Smit et al. 2020). The use of GWR for evaluating the relationships between environmental and human factors and COVID-19 has been not been adequately researched.
In this study, we aimed to investigate whether COVID-19 detection is related to weather elements and sociodemographic indicators in Iraqi cities, which is important to comprehensively understand the factors that contribute to rapid spread of the disease at a given spatial scale. Specifically, we evaluated the relationship between weather and climate elements and sociodemographic variables and COVID-19 using GWR and Multiple Linear Regression (MLR). A recent study by Wu and Zhang (Wu and Zhang 2021), who explore the spatial-temporal varying impacts on cummulative case in Texas using GWR, emphasized that there is a lack of country level research on COVID-19 GWR modeling. The findings should make an important contribution to policy makers to enable them to prepare measures and strategize against this disease.