China Pakistan Economic Corridor Digital Transformation.

The China-Pakistan Economic Corridor (CPEC) vision and mission are to improve the people's living standards of Pakistan and China through bilateral investments, trade, cultural exchanges, and economic activities. To achieve this envisioned dream, Pakistan established the China-Pakistan Economic Corridor Authority (CPECA) to further its completion, but Covid-19 slowed it down. This situation compelled the digitalization of CPEC. This article reviews the best practices and success stories of various digitalization and e-governance programs and, in this light, advises the implementation of the Ajman Digital Governance (ADG) model as a theoretical framework for CPEC digitalization. This article concludes that the Pakistani government needs to transform CPEC digitalization by setting up the CPEC Digitalization and Transformation Center (DTC) at the CPECA office to attract more investors and businesses.

Environmental quality, climate indicators, and COVID-19 pandemic: insights from top 10 most affected states of the USA.

In the current context of the COVID-19 pandemic, researchers are working with health professionals to inform governments on how to formulate health strategies. In this study, we examine the correlation between environmental and climate indicators and COVID-19 outbreak in the top 10 most affected states of the USA. In doing so, PM2.5, temperature, humidity, environmental quality index, and rainfall are included as crucial meteorological and environmental factors. Kendall and Spearman rank correlation coefficients, quantile regression, and log-linear negative binominal analysis are employed as an estimation strategy. The empirical estimates conclude that temperature, humidity, environmental quality index, PM2.5, and rainfall are significant factors related to the COVID-19 pandemic in the top 10 most affected states of the USA. The empirical findings of the current study would serve as key policy input to mitigate the rapid spread of COVID-19 across the USA.

Association of Inflammatory Markers with Mortality in COVID-19 Infection.

OBJECTIVE: To evaluate the association of serum ferritin, lactate dehydrogenase, and C-reactive protein at admission with in-hospital mortality in COVID-19 infection; and to determine best predictive cut-offs. STUDY DESIGN: Cross-sectional study. PLACE AND DURATION OF STUDY: Department of Medicine, Combined Military Hospital, Peshawar Cantt; Pakistan from March to June 2020. METHODOLOGY: Admitted patients with SARS-CoV-2 detectable by polymerase chain reaction (PCR) were included. Patients with suggestive radiological findings but negative PCR for SARS-CoV-2, those with incomplete data or those leaving against medical advice were excluded. Serum C-reactive protein, ferritin and LDH levels were tested on admission. SARS-CoV-2 viral load was checked on nasopharyngeal samples. Disease severity was assessed using World Health Organization guidelines. RESULTS: There were 238 patients, aged 41.18 ± 16.74 years. Disease was mild in 157 (65.97%), moderate in 36 (15.13%), and severe in 45 (18.91%) cases. Twenty-two (9.24%) patients died in the hospital. Serum C-reactive protein, ferritin and lactate dehydrogenase levels were elevated in 122 (51.26%), 83 (34.87%) and 184 (77.31%) patients, respectively; more frequently amongst patients with moderate/severe disease or mortality. Areas under receiver operating characteristic curves  and 95% confidence intervals for serum C-reactive protein, ferritin and LDH were 0.909 (0.854-0.964), 0.915 (0.835-0.995) and 0.863 (0.785-0.942), respectively. C-reactive protein ≥45.5 mg/L had sensitivity 86.36% and specificity 88.89%; serum ferritin ≥723 ng/ml had sensitivity 95.45% and specificity 86.57%, and lactate dehydrogenase ≥428.5 U/L had sensitivity 90.91% and specificity 80.56% for predicting mortality. CONCLUSION: Levels of the three inflammatory markers at admission can predict mortality in COVID-19 infection. Key Words: Coronavirus, Inflammation, Mortality, Outcome, Pakistan.

Investigating the Effects of Meteorological Parameters on COVID-19: Case Study of New Jersey, United States.

This research aims to explore the correlation between meteorological parameters and COVID-19 pandemic in New Jersey, United States. The authors employ extensive correlation analysis including Pearson correlation, Spearman correlation, Kendall's rank correlation and auto regressive distributed lag (ARDL) to check the effects of meteorological parameters on the COVID new cases of New Jersey. In doing so, PM 2.5, air quality index, temperature (°C), humidity (%), health security index, human development index, and population density are considered as crucial meteorological and non-meteorological factors. This research work used the maximum available data of all variables from 1st March to 7th July 2020. Among the weather indicators, temperature (°C) was found to have a negative correlation, while humidity and air quality highlighted a positive correlation with daily new cases of COVID-19 in New Jersey. The empirical findings illustrated that there is a strong positive association of lagged humidity, air quality, PM 2.5, and previous infections with daily new cases. Similarly, the ARDL findings suggest that air quality, humidity and infections have lagged effects with the COVID-19 spread across New Jersey. The empirical conclusions of this research might serve as a key input to mitigate the rapid spread of COVID-19 across the United States.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain.

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.