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1.
2nd ACM Conference on Information Technology for Social Good, GoodIT 2022 ; : 125-131, 2022.
Article in English | Scopus | ID: covidwho-2053346

ABSTRACT

We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. © 2022 Owner/Author.

4.
American Journal of Respiratory and Critical Care Medicine ; 203(9):2, 2021.
Article in English | Web of Science | ID: covidwho-1406933
5.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277647

ABSTRACT

Introduction: Pregnant patients with a severe form of COVID-19 are at increased risk of maternal and fetal complications. Nitric Oxide (NO) gas is a selective pulmonary vasodilator currently approved to treat newborns with pulmonary hypertension. Inhaled NO has been safely used in patients with severe pneumonia and for cases of pregnant patients with pulmonary hypertension. The antimicrobial effect of NO has been confirmed against bacteria and viruses. In vitro study demonstrated a dose-dependent effect of NO against SARS-CoV-1 and 2. We hypothesize that breathing NO at 160-200 ppm twice daily for 30 minutes in spontaneously breathing pregnant patients might provide a safe and effective treatment for COVID-19. Methods: We retrospectively reviewed the data of 6 pregnant patients hospitalized for COVID-19 treated with inhaled NO. Nitric Oxide was delivered at 160- 200 ppm for 30 minutes twice daily until resolution of respiratory symptoms or negative RT-PCR for SARS-CoV- 2. Demographic and clinical data were collected to assess cardiopulmonary function and safety during the treatment. For safety, we focused on the values of blood methemoglobin (MetHb) and inhaled Nitrogen Dioxide (NO2). Data regarding newborn delivery and health, and 28 days outcomes of mothers and babies were collected. Results: Six pregnant patients were admitted with a severe (2 patients) or critical (4 patients) form of COVID-19 and received inhaled NO therapy between April and June 2020. Two pregnant patients were in the second trimester, while 4 were in the third trimester. A total of 39 treatments were administered. No adverse events were reported relating to NO administration. MetHb peaked at 2.5% (1.95%-3%, safety limit =5%) and inhaled NO2 remained below the safety limit of 2ppm. The patients remained hemodynamically stable;cardiac ultrasound performed in three patients did not detect any rebound pulmonary hypertension after NO interruption. Oxygen saturation improved in hypoxemic patients after the initiation of NO (Figure-1). All patients experienced a reduction in respiratory rate (by a median 4.5breaths/min after NO initiation). Three patients delivered a total of 4 babies (negative for SARS-CoV-2) while the other 3 remained pregnant after hospitalization (gestational age 22-26-33weeks) with normal follow-ups. Five mothers out of 6 tested negative for COVID-19 28 days after hospitalization. Conclusion: Nitric oxide gas at 160-200 ppm was safely administered to pregnant patients with severe-critical COVID-19, improved oxygenation and reduced respiratory rate in all 6 patients. The clinical effectiveness shown suggests inhaled high dose NO as a therapeutic novel therapy for COVID-19 in pregnancy.

6.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277580

ABSTRACT

RationaleAcute respiratory distress syndrome (ARDS) secondary to Coronavirus Disease-2019 (COVID-19) is characterized by substantial heterogeneity in clinical, biochemical, and physiological characteristics. However, the pathophysiology of severe COVID-19 infection is poorly understood. Among “classical' ARDS cohorts, previous studies established two predominant biological phenotypes - patients with and without evidence of a hyperinflammatory response - with important prognostic and therapeutic implications. The phenotypic profile of COVID-19 associated ARDS remains unknown. Methods We used latent class modeling via a multivariate mixture model to identify phenotypes from clinical and biochemical data collected from 263 patients admitted to Massachusetts General Hospital intensive care unit with COVID-19-associated ARDS between March 13 and August 2, 2020. Classdefining variables included demographic features, respiratory parameters, hematologic and inflammatory biomarkers, and markers of end-organ function. Interleukin-6 (IL-6) and fibrinogen levels, which were available for n = 53 and n = 189 patients, respectively, were incorporated post-hoc. Results We identified two distinct latent classes representing 74.4% (Class 1, n = 193) and 26.6% (Class 2, n = 70) of the cohort, respectively. Posterior probability of class assignment was high (median 98.2%, IQR [98.0%, 100%]). To understand each class's distinguishing biological features, we compared the standardized mean of the continuous class-defining variables (Fig. 1A). The minority phenotype (class 2, n = 70, 26.6%) demonstrated increased markers of vascular dysregulation, with mild relative hyper-inflammation and dramatically increased markers of end-organ dysfunction (e.g., creatinine, troponin). There was little distinction according to respiratory parameters. The class 2 phenotype was characterized by significantly decreased fibrinogen and increased IL- 6 compared to Class 1 (Fig. 1B), even though these variables were not used in the statistical inference. Furthermore, the 28-day mortality among the class 2 phenotype was more than double that of the class 1 phenotype (40.0% vs. 23.3%, OR 2.3, 95% CI [1.3, 4.1]). Conclusion We identified distinct phenotypic profiles in COVID-19 associated ARDS, with little variation according to respiratory physiology but with important variation according to systemic and extra-pulmonary markers. Phenotypic identity was highly associated with shortterm mortality risk. The class 2 phenotype exhibited prominent signatures of vascular dysregulation, suggesting that vascular dysfunction may play an important role in the clinical progression of severe COVID-19-related disease.

7.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277559

ABSTRACT

Rationale High dose nitric oxide (NO) produces broad antimicrobial activity and several clinical trials are now testing the efficacy of NO inhalation on patients infected by SARS CoV-2. Nitrogen dioxide (NO2) is formed by the reaction between NO and oxygen, and when combined with water in the airways, NO2, forms nitric acid, leading to a caustic burn of the airways. In this study, we designed and developed a breathing system capable of safely delivering high concentrations of NO. Methods We developed a gas delivery system that utilizes standard respiratory circuit connectors, a reservoir bag, and a scavenging chamber containing calcium hydroxide (Ca(OH)2) (Fig. 1). The system was tested using a bench testing lung and a mechanical ventilator. The NO concentration was measured by a NO analyzer connected to the inspiratory limb via a sampling line. NO2 levels were simultaneously evaluated by the Cavity Attenuated Phase Shift (CAPS) NO2 monitor using the same sampling port. To assess the efficacy of the Ca(OH)2 scavenger in reducing the inspiratory levels of NO2, we used a range of target NO concentrations (50, 150, and 250 ppm), two different levels of FiO2 (0.21 and 0.40) and measured NO2 levels with and without the scavenger. We administered high-dose NO with our system to healthy adult volunteers as part of a trial conducted at MGH. Each administration lasted for 15 minutes. Peripheral oxygen saturation (SpO2) and methemoglobin (MetHb) were continuously monitored with a pulse co-oximeter. Results Using our delivery system, we were able to reach NO concentration up 250 ppm with an Inspired oxygen fraction (FiO2) between 0.21 and 0.4. The scavenger reduced the inhaled NO2 concentration to an average of 0.9 ppm (CI -1.58, -0.22;p=0.01). At 150 ppm of inhaled NO, the NO2 concentration was maintained below 1.2 ppm with FiO2 from 0.21 to 0.40. Our data suggest that the scavenger can efficiently reduce NO2 in the circuit for NO delivery. We administered NO to 4 adult volunteers. The total number of NO administrations was 30. The average concentration of inspired NO was 163.9±10.1 ppm with NO2 levels of 0.75±0.08 ppm. During the administration methemoglobin levels increased from a baseline value of 0.97±0.6% to 2.17±0.43%. The subjects did not experience any adverse events. Conclusions We built a NO delivery system that provides a safe alternative to a ventilator-based system to give high dose NO to spontaneously breathing patients.

8.
SenSys - Proc. ACM Conf. Embedded Networked Sens. Syst. ; : 782-783, 2020.
Article in English | Scopus | ID: covidwho-991901

ABSTRACT

As the COVID-19 outbreak evolves around the world, the World Health Organization (WHO) and its Member States have been heavily relying on staying at home and lock down measures to control the spread of the virus. In last months, various signs showed that the COVID-19 curve was flattening, but the premature lifting of some containment measures (e.g., school closures and telecommuting) are favouring a second wave of the disease. The accurate evaluation of possible countermeasures and their well-timed revocation are therefore crucial to avoid future waves or reduce their duration. In this paper, we analyze patient and route data collected by the Korea Centers for Disease Control & Prevention (KCDC). We extract information from real-world data sets and use them to parameterize simulations and evaluate different what-if scenarios. © 2020 Owner/Author.

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