Innovations in Ambulatory Stewardship

Ambulatory antibiotic use accounts for most of the global consumption of antibiotics leading to selection pressure, multidrug resistance, and significant healthcare costs (https://www.cdc.gov/antibiotic-use/community/pdfs/16_268900-A_CoreElementsOutpatient_508.pdf) The Centers for Disease Control and Prevention established the Core Elements of outpatient antimicrobial stewardship in 2016 as a framework to develop, expand, and evaluate ambulatory stewardship programs, which must address overuse in multiple settings (e.g., urgent care centers, adult and pediatric outpatient practices, dental practices, and retail clinics). As such, we present examples of innovative yet adaptable outpatient stewardship initiatives encompassing a variety of settings. We also address patterns of ambulatory antibiotic prescribing and novel stewardship initiatives implemented during the novel coronavirus disease 2019 (COVID-19) pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data non-stationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and outperform baselines. © 2022 IEEE.

Characteristics and Outcomes of Patients with SARS-CoV-2 Reinfections Requiring Treatment in a COVID-19 Ambulatory Treatment Program

Background. Montefiore Medical Center (MMC), Bronx, NY, established an ambulatory program to offer COVID-19 treatments (monoclonal antibodies [mAB] and oral antivirals) to patients with mild-moderate illness according to emergency use authorization criteria. Given multiple pandemic waves in the area, several patients have been reinfected and were treated twice. The objective of this analysis is to identify clinical characteristics and outcomes in patients with repeated COVID-19 infections and treatments. Methods. Electronic health records were reviewed to identify patients between December 1, 2020 and April 28, 2022 who received COVID-19 treatment on more than one occasion at MMC. Data collected included demographics, risk factors for progression to severe illness, name and date of COVID-19 treatments received, vaccination status, and clinical outcomes of 30-day emergency department (ED) presentation or hospital admission following each treatment. Results. Out of 3,042 total treated patients, 13 (0.4%) received multiple treatments with either mAB or oral antivirals for COVID-19 reinfection during the study period. Median age of reinfected patients requiring treatment was 50 years. Median days between first and second treatments was 298 days (range 91-468 days). The most common risk factor for progression to severe disease were cardiovascular disease (54%) and immunocompromised status (62%) (Table 1). Ten patients had received at least two doses of vaccine (77%) with Pfizer (54%) or Moderna (23%) vaccines prior to reinfection. No patients reported any adverse reactions to either treatment. Four patients presented to the ED or were hospitalized following treatment of reinfection, three of which were for COVID-related symptoms. Of these, two had two risk factors for progression and the third had been hospitalized previously for initial COVID-19 infection. Conclusion. Though COVID-19 reinfection has been described, especially during Omicron surges, there were relatively few reinfected patients requiring treatment in our cohort. Patients with risk factors for disease progression may also be at increased risk for reinfection, especially the immunocompromised.

Factors Predicting Bacterial Coinfection in Hospitalized COVID-19 Patients

Background. Despite multiple studies indicating a low prevalence of bacterial coinfection in coronavirus disease 2019 (COVID-19) patients, the majority of hospitalized COVID-19 patients receive one or more antibiotics. Patients with coinfection usually have multiple risk factors and poor clinical outcomes. Methods. A retrospective case control study was conducted comparing clinical characteristics and antimicrobial use in hospitalized adult COVID-19 patients with bacterial co-infections vs. randomly selected patients without co-infections (matched on month of admission). The study was conducted at three hospitals within the Montefiore Medical Center, Bronx, NY between March 1, 2020 and October 31, 2020. A multivariable logistic regression model was developed to assess the relationship of each predictor variable with coinfection status. Secondary outcomes included hospital mortality, antibiotic days of therapy (DOT), and C. difficile infection. Results. A total of 150 patients with coinfection and 150 patients without coinfection were included in the analysis. Table 1 summarized baseline characteristics and risk factors. The multivariable logistic regression model indicated that presence of a central line (OR=5.4, 95% CI: 2.7-11.1), prior antibiotic exposure within 30 days (OR=5.3, 95% CI: 2.8-10.0), prior ICU admission (OR=3.6, 95% CI: 1.7-7.6), steroid use (OR=2.7, 95% CI: 1.4-4.9), and any comorbid condition (OR=2.7, 95% CI: 1.4-5.2) were significantly associated with the development of coinfection (table 2). Mortality was higher in patients with coinfection (56% vs. 11%, p < 0.0001) (table 3). Average antibiotic DOT was 10.5 in coinfected patients compared to 4 in noncoinfected patients, (p < 0.0001). Forty-one percent of coinfected patients had a multidrug resistant organism isolated. C. difficile rate was higher in coinfected patients (4% vs. 0%, p=0.03). Conclusion. As the healthcare community contends with a 3rd year of COVID-19 pandemic, understanding risk factors most predictive of bacterial coinfection can guide empiric antimicrobial therapy and targeted stewardship interventions. Ideally, co-infection risk scores are developed which may be useful for future inpatient surges.

AMDetector: Detecting Large-Scale and Novel Android Malware Traffic with Meta-learning

In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-GAN plus : Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity - an intrinsic characteristic embedded in spatial data - poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. We also propose a spatial moderator to generalize learned space partitionings to new test regions. Experiment results on real-world datasets show that the spatial transformation and moderation framework can effectively capture flexibly-shaped heterogeneous footprints and substantially improve prediction performances.

Evaluation of Three COVID-19 Monoclonal Antibody Regimens in the Context of Rising B.1.526 Prevalence in New York City

Background. Monoclonal antibodies were given emergency use authorization (EUA) by the Food and Drug Administration for the treatment of high-risk, outpatient COVID-19 infection. In New York City (NYC), the emergence and rapid growth of the B.1.526 variant of concern (VOC) possessing the E484K mutation was first noted in February 2021. In-vitro studies subsequently confirmed attenuated monoclonal antibody neutralization against VOCs. At our institution, bamlanivimab (BAM) alone or with etesevimab (B/E) and casirivimab/imdevimab (C/I) were utilized at different phases of the pandemic. The objective of this study was to assess their comparative efficacies in a highly variant prevalent setting. Methods. This retrospective analysis was conducted at an urban hospital in the Bronx, NY and evaluated adult monoclonal antibody recipients from any of our infusion sites. Patients initially received BAM but given the high prevalence of variants, treatment was transitioned to first B/E and then C/I exclusively. We compared BAM versus combination therapy as well as B/E versus C/I individually. The primary outcome was all-cause hospital admission within 30 days post infusion. Results. From February 1 to March 7, 2021, 358 patients received BAM and from March 17 to May 9, 2021, 86 and 179 patients received B/E and C/I, respectively. Compared to any combination infusion, patients who received BAM were significantly older, more likely to possess ≥ 2 qualifying EUA criteria, and less likely to be vaccinated for COVID-19 prior to infusion (Table 1). Following B/E and C/I, 4.5% of patients were admitted versus 10.1% for BAM, p=0.011. There were no significant differences in admission between B/E and C/I recipients, p=0.485. After excluding fully vaccinated patients (n=14) and adjusting for age and ≥ 2 EUA criteria, combination therapy remained associated with decreased odds of hospitalization compared to BAM (odds ratio, 0.48;95% confidence interval, 0.24-0.94). Conclusion. Combination therapy may be associated with fewer hospital admissions following infusion, although there were no statistically significant differences between the individual combination infusions. We suggest similar studies be conducted by other sites to understand the clinical impact of local SARS-CoV-2 variants on antibody efficacy.

Behavioural interventions for micro-mobility adoption: Low-hanging fruits or hard nuts to crack?

This study explores the potential and challenges of applying behavioural interventions to promote micro-mobility adoption. Our online experiments with New York City residents showed that nudges and faming improved respondents’ willingness to adopt e-scooters significantly. Moreover, our experiments spanned over the pre-, during- and post- COVID-19 lockdown period in New York City. Findings from this natural experiment revealed that the effect of these behavioural interventions varied significantly during the pandemic, likely due to a heightened level of health consciousness and a new perspective regarding social interactions. Behavioural tools cannot be taken off-the-shelf and applied as a blanket policy. Individual and group characteristics have to be assessed to devise the pre-eminent behavioural interventions for a particular target audience. More experiments across a wide range of economic, social, cultural, and political settings are needed to guide the application of behavioural interventions in transportation studies. © 2021 Elsevier Ltd

Sharing food, vulnerability and intimacy in a global pandemic: The digital art of the Chinese diaspora in Europe

This article examines the digital artworks created by three Chinese diaspora artists based in Europe: Berlin-based queer filmmaker Fan Popo's short digital video Lerne Deutsch in meiner Kuche ('Learn German in my kitchen'), London-based performance artist Zeng Burong's performance Non-Taster and London-based writer David K. S. Tse's digital radio play The C Word. All three artworks were created in 2020 during the pandemic and all deal explicitly with the issues of anti-Asian racism and cross-cultural understanding. All these artworks also engage with issues of food and culinary practices. Through an analysis of the three artworks, I suggest that making digital art about food can serve as a creative and culturally sensitive strategy to engage with pandemic politics. Indeed, in an era of rising nationalism and international antagonism, diasporic Chinese artists have turned to seemingly mundane, apolitical and non-confrontational ways such as creating digital artworks about food to engage with the public about important social and political issues. This functions as a creative and culturally sensitive strategy to conduct social and political activism and to enhance cross-cultural understanding. It also showcases the political potential and social relevance of digital art for a pandemic and even a post-pandemic world.

Queer disidentification: Or how to cook chinese noodles in a global pandemic?

In this article, I offer a critical analysis of a video artwork titled Lerne Deutsch in meiner Küche (Learn German in My Kitchen) (2020), created by Berlin-based queer filmmaker Popo Fan. By focusing on Fan’s negotiation of racial, ethnic, and cultural identities in the video, I argue that Fan’s artwork offers a way to reimagine identities away from the identity politics that are widely circulated in the current pandemic discourse. My analysis draws on José Esteban Muñoz’s (1999) notion of ‘disidentification,’ which describes minority subjects’ complex processes of identification—in particular, instances of identifying partially, conditionally and contingently—with dominant identities, discourses and ideologies. In doing so, I unravel the intricate politics of identity in the current global pandemic and highlight the role of queer disidentification as an important critical intervention in the current political debate about the COVID-19 pandemic. © 2021 by the author(s).

Clinical characteristics of 77 COVID-19 cases imported from abroad

Objective: To investigate the clinical characteristics of coronavirus disease 2019 (COVID-19) imported from abroad in Pudong Hospital Affiliated to Fudan University so as to provide evidence for epidemic prevention and control of COVID-19. Methods: We extracted data regarding 77 patients with laboratory-confirmed COVID-19 in Pudong Hospital from Mar 14 to Jul 3, 2020.We then collected and analyzed their epidemic history, blood routine, C-reactive protein (CRP) and chest CT. Results: About 63.64%(49/77) of the confirmed cases showed characteristics of clustering onset, with migrant workers accounting for a large proportion (54.55%, 42/77), whom living in groups;followed by overseas students (25.97%, 20/77), most of whom lived in separate rooms.There were 54 males and 23 females, aged from 14 to 62 years old.Among them, 18.18%(14/77)were asymptomatic, 36.36% (28/77) had fever and 75.32%(58/77) had dry cough, sore throat, runny nose and other respiratory tract infection symptoms;and a few had chest tightness, fatigue, diarrhea and other symptoms.White blood cell counts were normal in 65 cases, decreased in 4 cases, increased in 8 cases;Lymphocyte absolute counts were all in the normal range and eosinophil absolute counts were decreased in 67 cases, and mononuclear cell absolute counts were increased in 35 cases.CRP was increased in 16 cases, and normal in 61 cases. CT scan of the chest revealed that 50 patients had different degrees of pulmonary abnormalities, and 27 had no abnormalities.There were 27 mild type cases, 50 common type cases, and no severe or critical type. Conclusion: COVID-19 imported from abroad has epidemiological history.Crowded living conditions are a risk factor for the increased incidence of COVID-19.Some of them were asymptomatic or mild type, and most of them were common type.Laboratory examination showed no obvious abnormalities, and chest CT imaging showed various manifestations.To avoid missed diagnosis, overall consideration of the epidemiological history, symptoms, laboratory examination and imaging is needed, so as to achieve early detection, early treatment and early control for imported COVID-19. © 2020, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.

COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks

The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality. © 2020 ACM.

An update on the status of COVID-19: a comprehensive review.

The last two decades have witnessed two large-scale pandemics caused by coronaviruses, including severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). At the end of 2019, another novel coronavirus, designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), hit Wuhan, a city in the center of China, and subsequently spread rapidly to the whole world. Latest reports revealed that more than 800 thousand people in over 200 countries are involved in the epidemic disease by SARS-CoV-2. Due to the high mortality rate and the lack of optimum therapeutics, it is crucial to understand the biological characteristics of the virus and its possible pathogenesis to respond to the SARS-CoV-2. Rapid diagnostics and effective therapeutics are also important interventions for the management of infection control. However, the rapid evolution of SARS-CoV-2 exerted tremendous challenges on its diagnostics and therapeutics. Therefore, there is an urgent need to summarize the existing research results to guide decision-making on the prioritization of resources for research and development. In this review, we focus on our current understanding of epidemiology, pathogenesis, diagnostics and therapeutics of coronavirus disease 2019 (COVID-19).