"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

PurposeThis study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.Design/methodology/approachA panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.FindingsThis study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.Practical implicationsThe COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.Originality/valueTo the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.

Dynamic Changes of ORF1ab and N Gene Ct Values in COVID-19 Omicron Inpatients of Different Age Groups - Beijing Municipality, China, November-December 2022.

Introduction: In November 2021, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant was identified as the variant of concern and has since spread globally, replacing other cocirculating variants. To better understand the dynamic changes in viral load over time and the natural history of the virus infection, we analyzed the expression of the open reading frames 1ab (ORF1ab) and nucleocapsid (N) genes in patients infected with Omicron. Methods: We included patients initially admitted to the hospital for SARS-CoV-2 infection between November 5 and December 25, 2022. We collected daily oropharyngeal swabs for quantitative reverse transcriptase-polymerase chain reaction tests using commercial kits. We depicted the cycle threshold (Ct) values for amplification of ORF1ab and N genes from individual patients in age-specific groups in a time series. Results: A total of 480 inpatients were included in the study, with a median age of 59 years (interquartile range, 42 to 78; range, 16 to 106). In the <45-year-old age group, the Ct values for ORF1ab and N gene amplification remained below 35 for 9.0 and 11.5 days, respectively. In the ≥80-year-old age group, the Ct values for ORF1ab and N genes stayed below 35 for 11.5 and 15.0 days, respectively, which was the longest among all age groups. The Ct values for N gene amplification took longer to rise above 35 than those for ORF1ab gene amplification. Conclusion: The time to test negative varied among different age groups, with viral nucleic acid shedding taking longer in older age groups compared to younger age groups. As a result, the time to resolution of Omicron infection increased with increasing age.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

PurposeThis study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities. Design/methodology/approachA panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak. FindingsThis study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels. Practical implicationsThe COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision. Originality/valueTo the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.

3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Challenges of COPD Patients during the COVID-19 Pandemic.

Coronavirus disease 2019 (COVID-19) is a severe systemic infection that is a major threat to healthcare systems worldwide. According to studies, chronic obstructive pulmonary disease (COPD) patients with COVID-19 usually have a high risk of developing severe symptoms and fatality, but limited research has addressed the poor condition of COPD patients during the pandemic. This review focuses on the underlying risk factors including innate immune dysfunction, angiotensin converting enzyme 2 (ACE2) expression, smoking status, precocious differentiation of T lymphocytes and immunosenescence in COPD patients which might account for their poor outcomes during the COVID-19 crisis. Furthermore, we highlight the role of aging of the immune system, which may be the culprit of COVID-19. In brief, we list the challenges of COPD patients in this national pandemic, aiming to provide immune-related considerations to support critical processes in COPD patients during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and inspire immune therapy for these patients.

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.

OBJECTIVE: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. METHODS: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( OR) and 95% confidence interval (95% CI) of the associations between comorbidities (cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of COVID-19. RESULTS: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. CONCLUSION: Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.