ABSTRACT
This study aimed to build an machine learning based model to predict the COVID-19 severity and reveal risk factors related to COVID-19 severity based on laboratory testing and clinical data for 420 participants, using tree-based models such as XGBoost, LightGBM, random forest. We calculated the Odds Ratios (OR) to investigate whether the top-ranked features were statistically significant for severity classification, turning out that high sensitivity C-reactive protein (hs-CRP) was the most important feature for determining of COVID-19 severity and XGBoost model showed the highest performance in classifying COVID-19 severity and healthy controls with F1score (0.84) and AUC (0.87). We expect that our results are of considerable significance for early screening for diagnosing COVID-19 severity, which, in turn, assist in further retrospective research for uncommon infectious diseases. © 2023 IEEE.
ABSTRACT
Public bike systems provide the flexibility of bike-usage that users can rent and return a bike freely at any station. The convenience of the bike-travel system, however, may turn into a disadvantage of demand-supply imbalance in the bike inventory. The recent spread of the COVID-19 pandemic has changed the mobility demands due to the lockdowns which restrict the business operating hours and transit services. Therefore, investigating the impacts of the pandemic on the urban social life patterns with the bike usage is an important issue for the bike demand prediction and the improvement of the public bike system service.This research aims to investigate the correlation between the public bike demand and social environment factors during the pandemic applying a multivariate linear regression model to public bike usage data in Seoul, Korea. The results show some promising findings to further promote shared mobility services through policy and marketing strategies. It is noteworthy that the transport disruptions during the pandemic have made a spillover effect from taxi and public transit to bike as an alternative transport mode. The lockdown has restricted the range of activity and resulted in the decrease of the taxi demand, so the number of taxis. On the other hand, the correlations of the geography, meteorology, and date with the bike demand have shown consistency. Therefore, supply of extra bike facilities to improve the system service should be determined based on more accurate demand prediction considering lifecycle-related factors.
ABSTRACT
Background: Kidney graft recipients receiving immunosuppressive therapy may be at heightened risk for Covid-19 and adverse outcomes. We aimed to study how practice patterns and outcomes changed before and after the peak incidence of cases in New York City. Methods: We reviewed 68 consecutive adult kidney graft recipients from our center diagnosed with SARS-CoV-2 from March 13, 2020 to May 25, 2020. We compared outcomes of those treated from March 13 until the apex of infections on April 14 (Phase 1), and those treated from April 15th to May 25, 2020 (Phase 2). Results: Characteristics of both Phase 1 and Phase 2 patients are described in Table 1. Inflammatory markers were lower in the second phase as was patient mortality. Changes in management strategies between the two phases are highlighted in Figure 2. Graft loss occurred in 4 patients (6%) and there were 5 deaths (7%). Conclusions: Data from our study suggest that management strategies of immunosuppressed patients changed over the course of the Covid-19 Pandemic in New York City, including less use of hydroxychloroquine, and increased use of novel agents such as remdesivir. Additional data are needed to better understand if the decrease in patient mortality during the second phase is attributable to better management or lower inflammatory response in the setting of Covid-19 illness.