Comparison of clinical, laboratory and radiological features in confirmed and unconfirmed COVID-19 patients.

Background: We aimed to compare the clinical, laboratory and radiological findings of confirmed COVID-19 and unconfirmed patients. Methods: This was a single-center, retrospective study. Results: Overall, 620 patients (338 confirmed COVID-19 and 282 unconfirmed) were included. Confirmed COVID-19 patients had higher percentages of close contact with a confirmed or probable case. In univariate analysis, the presence of myalgia and dyspnea, decreased leukocyte, neutrophil and platelet counts were best predictors for SARS-CoV-2 RT-PCR positivity. Multivariate analyses revealed that only platelet count was an independent predictor for SARS-CoV-2 RT-PCR positivity. Conclusion: Routine complete blood count may be helpful for distinguishing COVID-19 from other respiratory illnesses at an early stage, while PCR testing is unique for the diagnosis of COVID-19.

National Early Warning Score 2 and laboratory predictors correlate with clinical deterioration in hospitalized patients with COVID-19.

Aim: We aimed to determine the prognostic values of the National Early Warning Score 2 (NEWS2) and laboratory parameters during the first week of COVID-19. Materials & methods: All adult patients who were hospitalized for confirmed COVID-19 between 11 March and 11 May 2020 were retrospectively included. Results: Overall, 611 patients were included. Our results showed that NEWS2, procalcitonin, neutrophil/lymphocyte ratio and albumin at D0, D3, D5 and D7 were the best predictors for clinical deterioration defined as a composite of ICU admission during hospitalization or in-hospital death. Procalcitonin had the highest odds ratio for clinical deterioration on all days. Conclusion: This study provides a list of several laboratory parameters correlated with NEWS2 and potential predictors for clinical deterioration in patients with COVID-19.

Lay abstract The COVID-19 pandemic is a grueling problem worldwide. There is a lack of knowledge about the predictive value of National Early Warning Score 2 (NEWS2) for severe COVID-19 illness. We analyzed the prognostic value of NEWS2 and laboratory parameters during the clinical course of COVID-19. This study provides a list of several laboratory parameters correlated with NEWS2 and potential predictors for intensive care unit admission during hospitalization or in-hospital death.

Predictors of Intensive Care Unit Admission or Mortality in Patients with Coronavirus Disease 2019 Pneumonia in Istanbul, Turkey.

We aimed to determine the predictors of intensive care unit (ICU) admission or death in patients with coronavirus disease 2019 (COVID-19) pneumonia. This retrospective, single-center study included patients aged ≥18 years who were diagnosed with COVID-19 pneumonia (laboratory and radiologically confirmed) between March 9 and April 8, 2020. The composite endpoint was ICU admission or in-hospital mortality. Univariate and multivariate logistic regression analyses were performed to evaluate the factors associated with the composite endpoint. A total of 336 patients with COVID-19 pneumonia were evaluated. The median age was 54 years (interquartile range: 21), and 187 (55.7%) were men. Fifty-one (15.2%) patients were admitted to the ICU. In-hospital mortality occurred in 33 patients (9.8%). In the univariate analysis, 17 parameters were associated with the composite endpoint, and procalcitonin had the highest odds ratio (odds ratio [OR] = 36.568, confidence interval [CI] = 5.145-259.915). Our results revealed that body temperature (OR = 1.489, CI = 1.023-2.167, P = 0.037), peripheral capillary oxygen saturation (SpO2) (OR = 0.835, CI = 0.773-0.901, P < 0.001), and consolidation (> 25%) on chest computed tomography (OR = 3.170, CI = 1.218-8.252, P = 0.018) at admission were independent predictors. As a result, increased body temperature, decreased SpO2, a high level of procalcitonin, and degree of consolidation on chest computed tomography may predict a poor prognosis and have utility in the management of patients.

Modelling the volatility of international visitor arrivals to New Zealand.

International visitor arrivals are considered to be a major source of foreign exchange, tourism-related employment and other tourism-related activities. This study used SARIMAX/(E)GARCH volatility models to forecast visitor arrivals by air transport to New Zealand from its eight key tourist source markets (Australia, Canada, China, Japan, South Korea, Germany, the United Kingdom (UK) and the United States (US)) and control macroeconomic factors together with global and regional structural changes. The empirical models reveal that the macroeconomic factors contributed at various levels to different markets, and the models we provided made accurate and reliable forecasts for visitor arrivals by air transport from all studied markets. The results from the markets for Germany, Japan, South Korea and the UK showed that significantly negative tourism demand shocks increased the volatility of tourism arrivals, more than positive tourism demand shocks of equal magnitude. Accordingly, the findings of this study will allow policy-makers in the New Zealand tourism sector and other stakeholders (e.g. airline management) to better understand the impacts on the volatility of visitor arrivals to New Zealand.

Operational efficiency of Asia-Pacific airports.

Airports are important drivers of economic development and thus under tremendous pressure from emerging competitors. However, few studies have analysed the operational efficiency of Asia-Pacific airports. This study therefore evaluated the operational efficiency of 21 Asia-Pacific airports between 2002 and 2011. A two-stage method was used: Data Envelopment Analysis (DEA) to assess airport efficiency, followed by the second-stage regression analysis to identify the key determinants of airport efficiency. The first-stage DEA results indicated that Adelaide, Beijing, Brisbane, Hong Kong, Melbourne, and Shenzhen are the efficient airports. The second-stage regression analysis suggested that percentage of international passengers handled by an airport, airport hinterland population size, dominant airline(s) of an airport when entering global airline strategic alliance, and an increase in GDP per capita are significant in explaining variations in airport efficiency.