Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants.

Our efforts as a society to combat the ongoing COVID-19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high-resolution agent-based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID-19, in which multiple variants will coexist and residents continue to suffer from infections.

Biofunctional roles of estrogen in coronavirus disease 2019: Beyond a steroid hormone.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), epidemic poses a major global public health threat with more than one million daily new infections and hundreds of deaths. To combat this global pandemic, efficient prevention and management strategies are urgently needed. Together with the main characteristics of COVID-19, impaired coagulation with dysfunctions of the immune response in COVID-19 pathophysiology causes high mortality and morbidity. From recent clinical observations, increased expression of specific types of estrogen appears to protect patients from SARS-CoV-2 infection, thereby, reducing mortality. COVID-19 severity is less common in women than in men, particularly in menopausal women. Furthermore, estrogen levels are negatively correlated with COVID-19 severity and mortality. These findings suggest that estrogen plays a protective role in the pathophysiology of COVID-19. In this review, we discuss the potential roles of estrogen in blocking the SARS-CoV-2 from invading alveolar cells and replicating, and summarize the potential mechanisms of anti-inflammation, immune modulation, reactive oxygen species resistance, anti-thrombosis, vascular dilation, and vascular endothelium protection. Finally, the potential therapeutic effects of estrogen against COVID-19 are reviewed. This review provides insights into the role of estrogen and its use as a potential strategy to reduce the mortality associated with COVID-19, and possibly other viral infections and discusses the possible challenges and pertinent questions.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains.

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Research progress on angiotensin converting enzyme 2 as the receptor of SARS-CoV -2

There have been nearly '80,000 cases in the world since the outbreak of novel coronavirus pneumonia. With the study on viral structure and infection mechanism, researchers have found that angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2. According to previous studies, ACE2 is one of the key enzymes in the RAS system. It plays a regulatory role with its homologue, namely ACE. The physiological function can be found in angiocarpy, kidney, liver, intestine and so on. The outbreak of novel coronavirus pneumonia makes people eager to know the viral source, intermediate host, epidemiological analysis and therapeutic targets. Then ACE2 becomes a research hotspot. In this review, we discuss the research advance of ACE2 in SARS - Coy - 2 and cardiovascular diseases to provide references for tracing, MSS species transmission, epidemiological analysis and antiviral treatment of SARS-CoV-2.

Advances in rapid detection of SARS-CoV-2 by mass spectrometry.

COVID-19 has already been lasting for more than two years and it has been severely affecting the whole world. Still, detection of SARS-CoV-2 remains the frontline approach to combat the pandemic, and the reverse transcription polymerase chain reaction (RT-PCR)-based method is the well recognized detection method for the enormous analytical demands. However, the RT-PCR method typically takes a relatively long time, and can produce false positive and false negative results. Mass spectrometry (MS) is a very commonly used technique with extraordinary sensitivity, specificity and speed, and can produce qualitative and quantitative information of various analytes, which cannot be achieved by RT-PCR. Since the pandemic outbreak, various mass spectrometric approaches have been developed for rapid detection of SARS-CoV-2, including the LC-MS/MS approaches that could allow analysis of several hundred clinical samples per day with one MS system, MALDI-MS approaches that could directly analyze clinical samples for the detection, and efforts for the on-site detection with portable devices. In this review, these mass spectrometric approaches were summarized, and their pros and cons as well as further development were also discussed.

Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling (Adv. Theory Simul. 6/2022)

Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling Mathematical models have proven to be indispensable in our fight against COVID-19. In article 2100521, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers expand on a high-resolution agent-based model published previously in this journal to study the effectiveness of the booster shot campaign in preventing new COVID-19 waves in the town of New Rochelle, NY. Image by Anna Sawulska and Maurizio Porfiri.

Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State.

The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective immunization efforts. Here, we perform a computational analysis of COVID-19 spread in three cities of similar size in New York State (Colonie, New Rochelle, and Utica) aiming to isolate urban determinants of infections and deaths. We develop detailed digital representations of the cities and simulate COVID-19 spread using a complex agent-based model, taking into account differences in spatial layout, mobility, demographics, and occupational structure of the population. By critically comparing pandemic outcomes across the three cities under equivalent initial conditions, we provide compelling evidence in favor of the central role of hospitals. Specifically, with highly efficacious testing and detection, the number and capacity of hospitals, as well as the extent of vaccination of hospital employees are key determinants of COVID-19 spread. The modulating role of these determinants is reduced at lower efficacy of testing and detection, so that the pandemic outcome becomes equivalent across the three cities.

Predicting the Effects of Waning Vaccine Immunity Against COVID‐19 through High‐Resolution Agent‐Based Modeling

The potential waning of the vaccination immunity to COVID‐19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near‐complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID‐19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent‐based model tuned on a medium‐sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid‐spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed. Mathematical models have been proven to be indispensable in our fight against COVID‐19. This paper expands on a high‐resolution agent‐based model published previously in this journal to study the effectiveness of the booster shot campaign in preventing a new wave in the town of New Rochelle, NY during this fall and the coming spring.

Smart Charging Management for Autonomous Vehicles: A Smart Solution for Smart Cities & Societies: COVID 19 Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling

Summary Smart Cities have a high quality of life determined by several variables with renewable transport networks, protection and health, open space connectivity, and other critical facilities. The key elements of smart city classification are transportation, traffic management, and parking. Among those elements, the intelligent transportation system plays a vital role. Big data analytics can create smart cities to achieve a smart transport system, autonomous vehicles, and crowdsourcing. The coronavirus disease (COVID-19) has spread quickly through China since December 2019 and later globally. As this article was written, the disease was reported to globally infect 100 countries. Evening transmission from person to person is a practical approach for controlling and preventing the epidemic. Specific everyday tasks, such as logistics that move goods in our daily lives, ultimately require interaction between men. Using an automated logistic vehicle has been the preferred option for achieving the contactless transportation of goods. This chapter describes Hercules, an autonomous logistic vehicle used to move goods without touch during the outbreak of COVID-19. The vehicle shall be equipped for autonomous navigation capability. The research introduces the mechanism for smart charging of shared autonomous electric vehicles. Researchers include information on the hardware and software and the algorithms for achieving autonomous navigation, including vision, planning, and control. Autonomous vehicles (AVs) are believed to provide many profits for individuals and society, with increased road safety, reduced traffic congestion, and an improved ecological footprint. However, many barriers still hinder the widespread acceptance of autonomous driving. During this pandemic situation, decision-making towards adopting autonomous driving imposes a more accessible and reliable way to deliver things, such as food and medical supplies, in an emergency than policymakers and automakers have devoted in recent years. Innovative AVs emerge as potential energy savers in the transportation system. There is a cumulative emphasis on AVs together in terms of rapid technological advances and evaluating these technologies and their significant effects on society and transportation systems during COVID-19 around the world. The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Role of a lower cutoff of high sensitivity troponin I in identification of early cardiac damage in non-severe patients with COVID-19.

Cardiac damage in non-severe patients with coronavirus disease 2019 (COVID-19) is poorly explored. This study aimed to explore the manifestations of cardiac damage at presentation in non-severe patients with COVID-19. In this study, 113 non-severe patients with COVID-19 were grouped according to the duration from symptoms onset to hospital admission: group 1 (≤ 1 week, n = 27), group 2 (> 1 to 2 weeks, n = 28), group 3 (> 2 to 3 weeks, n = 27), group 4 (> 3 weeks, n = 31). Clinical, cardiovascular, and radiological data on hospital admission were compared across the four groups. The level of high sensitivity troponin I (hs-cTnI) in group 2 [10.25 (IQR 6.75-15.63) ng/L] was significantly higher than those in group 1 [1.90 (IQR 1.90-8.80) ng/L] and group 4 [1.90 (IQR 1.90-5.80) ng/L] (all Pbonferroni < 0.05). The proportion of patients who had a level of hs-cTnI ≥ 5 ng/L in group 2 (85.71%) was significantly higher than those in the other three groups (37.04%, 51.85%, and 25.81%, respectively) (all Pbonferroni < 0.05). Compared with patients with hs-cTnI under 5 ng/L, those with hs-cTnI ≥ 5 ng/L had lower lymphocyte count (P = 0.000) and SpO2 (P = 0.002) and higher CRP (P = 0.000). Patients with hs-cTnI ≥ 5 ng/L had a higher incidence of bilateral pneumonia (P = 0.000) and longer hospital length of stay (P = 0.000). In conclusion, non-severe patients with COVID-19 in the second week after symptoms onset were most likely to suffer cardiac damage. A detectable level of hs-cTnI ≥ 5 ng/L might be a manifestation of early cardiac damage in the patients.

Clinical Progress on Management of Pneumonia Due to COVID-19 With Chinese Traditional Patent Medicines.

Background: The outbreak of new coronavirus has tremendously threatened the public health system worldwide, including China. Chinese patent medicines (CPMs) have greatly contributed to the prevention and treatment of this viral infection, as well as the recovery of patients with COVID-19 infection. Therefore, numerous experts and guidelines recommend to take CPMs to treat pneumonia due to COVID-19. Aim of the Study: The present study reviewed CPMs recommended by the < Guidelines for diagnosis and management of COVID-19 (8th edition)> regarding evidence of their efficacy from clinical studies and the underlying mechanisms, which will lay the foundation for clinical use of these CPMs for COVID-19. Methods: The composition, efficacy, indications, history of use, and relevant clinical research on 14 recommended CPMs, including Huoxiangzhengqi capsules (pills, liquid, oral solution), Jinhuaqinggan granules, Lianhuaqingwen capsules (granules), Shufengjiedu capsules, Xiyanping injections, Xuebijing injections, Reduning injections, Tanreqing injections, Xingnaojing injections, Shenfu injections, Shengmai injections, Angongniuhuang pills, Suhexiang pills, were searched in both Chinese and English databases based on differences in stages of the disease and manifestations of such patients. Advantages of these CPMs over conventional treatments and their underlying mechanisms were explored by analyzing results from published articles and undergoing clinical trials. Results: Findings from clinical studies and Chinese experience in using these CPMs showed that CPMs, when used in combination with conventional treatments, were effective in managing COVID-19 with few side effects. Conclusion: CPMs have excellent efficacy in managing COVID-19 with a great potential for clinical use.

Predicting the effects of waning vaccine immunity against COVID-19 through high-resolution agent-based modeling (preprint)

The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, we might witness a resurgent COVID-19 wave in winter 2021/2022. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. In a projected study with an outlook of six months, we explore the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized U.S. town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to continuously evolving nature of the pandemics. Our study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Designing the Safe Reopening of US Towns Through High-Resolution Agent-Based Modeling.

As COVID-19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high-resolution agent-based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll-out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY - representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll-out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.

A clinical staging proposal of the disease course over time in non-severe patients with coronavirus disease 2019.

Information on the clinical staging of coronavirus disease 2019 (COVID-19) is still limited. This study aimed to propose a clinical staging proposal of the disease course in non-severe patients with COVID-19. In this retrospective study, 108 non-severe patients with COVID-19 were grouped according to the duration from symptoms onset to hospital admission: ≤ 1 week, > 1 to 2 weeks, > 2 to 3 weeks, > 3 to 5 weeks, respectively. The dynamic changes of clinical signs were profiled across the four groups. A clinical staging proposal of the disease course over time was proposed from the perspective of the interaction between the virus and host. The prodromal phase, characterized by pneumonia, significant lymphopenia, and slightly elevated inflammatory markers, occurred in the first week after symptoms onset. In the second week, all the hematological and inflammatory markers were at the peak or bottom. Meanwhile, progressive pneumonia as well as the secondary damage of other organs (e.g. cardiac damage, coagulopathy, etc.) was significant during this period, making the disease progress into the apparent manifestation phase. In the third week, the improvement of the majority of clinical signs accompanied by a relatively high degree of inflammatory response defined the remission phase. After 3 weeks, patients were in the convalescent phase, in which all the indicators were maintained at a relatively normal level. We concluded that the disease course over time in non-severe patients with COVID-19 could be divided into four phases: the prodromal phase (in the first week), the apparent manifestation phase (in the second week), the remission phase (in the third week), and the convalescent phase (after 3 weeks), respectively. In clinical practice, tailored therapies should be considered seriously in different stages of the disease course.

Association Between Socioeconomic Factors and the COVID-19 Outbreak in the 39 Well-Developed Cities of China.

Background: Socioeconomic factors play an indispensable role in the spread of emerging infectious diseases. Few studies have investigated the role of socioeconomic factors in the spread of COVID-19. Methods: The number of COVID-19 cases in the 39 well-developed cities of China was aggregated by searching the publicly available sources. Socioeconomic indicators (e.g., population, population density, gross domestic product, rural-to-urban migrants, urbanization rate, per-person disposable income, and level of health care) in these cities were also aggregated from the Bureau of Statistics. The data referring to travelers from Wuhan were collected from the Baidu Migration database. A multiple stepwise linear regression model was performed to identify the independent risk factors of the number of cases. Results: As of Mar 19, 2020, a total of 5,939 cases were reported in the 39 well-developed cities with almost half of total cases in China outside of Hubei. The number of cases ranged 20-576, and the median number of cases was 93 (IQR 54-180) in these cities. Nine socioeconomic variables including the number of travelers from Wuhan, population, native population, gross domestic product, Per-person GDP, the number of hospitals, the number of rural-to-urban migrants, traffic capacity, and person-disposable income were recognized as potential contributors of the number of cases. Results of multiple linear regression showed a statistically significant association between the number of cases and the number of travelers from Wuhan (t = 6.746, P = 0.000) and the number of rural-to-urban migrants (t = 3.776, P = 0.001) in these cities. However, other seven potential contributors were not associated with the number of cases. Moreover, a well-fitted multiple regression model was built in this study, and a regression equation was as follows: Y = 0.007Xt + 0.200Xm (adjusted R2 = 0.833). Conclusions: Travelers from Wuhan and rural-to-urban migrants were independently associated with the COVID-19 outbreak in the 39 well-developed cities of China. These findings suggested that travelers from an epicenter and rural-to-urban migrants should be paid more attention in the early stage of the COVID-19 outbreak in the well-developed cities.

No Statistically Apparent Difference in Antiviral Effectiveness Observed Among Ribavirin Plus Interferon-Alpha, Lopinavir/Ritonavir Plus Interferon-Alpha, and Ribavirin Plus Lopinavir/Ritonavir Plus Interferon-Alpha in Patients With Mild to Moderate Coronavirus Disease 2019: Results of a Randomized, Open-Labeled Prospective Study.

BACKGROUND: Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, causing an unprecedented pandemic. However, there is no specific antiviral therapy for coronavirus disease 2019 (COVID-19). We conducted a clinical trial to compare the effectiveness of three antiviral treatment regimens in patients with mild to moderate COVID-19. METHODS: This was a single-center, randomized, open-labeled, prospective clinical trial. Eligible patients with mild to moderate COVID-19 were randomized into three groups: ribavirin (RBV) plus interferon-α (IFN-α), lopinavir/ritonavir (LPV/r) plus IFN-α, and RBV plus LPV/r plus IFN-α at a 1:1:1 ratio. Each patient was invited to participate in a 28-d follow-up after initiation of an antiviral regimen. The outcomes include the difference in median interval to SARS-CoV-2 nucleic acid negativity, the proportion of patients with SARS-CoV-2 nucleic acid negativity at day 14, the mortality at day 28, the proportion of patients re-classified as severe cases, and adverse events during the study period. RESULTS: In total, we enrolled 101 patients in this study. Baseline clinical and laboratory characteristics of patients were comparable among the three groups. In the analysis of intention-to-treat data, the median interval from baseline to SARS-CoV-2 nucleic acid negativity was 12 d in the LPV/r+IFN-α-treated group, as compared with 13 and 15 d in the RBV+IFN-α-treated group and in the RBV+LPV/r+ IFN-α-treated group, respectively (p=0.23). The proportion of patients with SARS-CoV-2 nucleic acid negativity in the LPV/r+IFN-α-treated group (61.1%) was higher than the RBV+ IFN-α-treated group (51.5%) and the RBV+LPV/r+IFN-α-treated group (46.9%) at day 14; however, the difference between these groups was calculated to be statistically insignificant. The RBV+LPV/r+IFN-α-treated group developed a significantly higher incidence of gastrointestinal adverse events than the LPV/r+ IFN-α-treated group and the RBV+ IFN-α-treated group. CONCLUSIONS: Our results indicate that there are no significant differences among the three regimens in terms of antiviral effectiveness in patients with mild to moderate COVID-19. Furthermore, the combination of RBV and LPV/r is associated with a significant increase in gastrointestinal adverse events, suggesting that RBV and LPV/r should not be co-administered to COVID-19 patients simultaneously. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, ID: ChiCTR2000029387. Registered on January 28, 2019.

COVID-19 Modeling: High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town (Adv. Theory Simul. 3/2021).

Since 2020, COVID-19 has wreaked havoc across the planet, taking the lives of more than one million people. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that can support effective policy-making. In article number 2000277, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers report a high-resolution, agent-based modeling platform to simulate the spreading of COVID-19 in the city of New Rochelle, NY-one of the first outbreaks registered in the United States. Image by Anna Sawulska, Agnieszka Truszkowska, Beata Truszkowska, and Maurizio Porfiri.

Risk factors associated with acute respiratory distress syndrome in COVID-19 patients outside Wuhan: A double-center retrospective cohort study of 197 cases in Hunan, China.

BACKGROUND: There have been few reports on the risk factors for acute respiratory distress syndrome (ARDS) in coronavirus disease 2019 (COVID-19), and there were obvious differences regarding the incidence of ADRS between Wuhan and outside Wuhan in China. AIM: To investigate the risk factors associated with ARDS in COVID-19, and compare the characteristics of ARDS between Wuhan and outside Wuhan in China. METHODS: Patients were enrolled from two medical centers in Hunan Province. A total of 197 patients with confirmed COVID-19, who had either been discharged or had died by March 15, 2020, were included in this study. We retrospectively collected the patients' clinical data, and the factors associated with ARDS were compared by the χ² test, Fisher's exact test, and Mann-Whitney U test. Significant variables were chosen for the univariate and multivariate logistic regression analyses. In addition, literature in the PubMed database was reviewed, and the characteristics of ARDS, mortality, and biomarkers of COVID-19 severity were compared between Wuhan and outside Wuhan in China. RESULTS: Compared with the non-ARDS group, patients in the ARDS group were significantly older, had more coexisting diseases, dyspnea, higher D-dimer, lactate dehydrogenase (LDH), and C-reactive protein. In univariate logistic analysis, risk factors associated with the development of ARDS included older age [odds ratio (OR) = 1.04), coexisting diseases (OR = 3.94), dyspnea (OR = 17.82), dry/moist rales (OR = 9.06), consolidative/mixed opacities (OR = 2.93), lymphocytes (OR = 0.68 for high lymphocytes compared to low lymphocytes), D-dimer (OR = 1.41), albumin (OR = 0.69 for high albumin compared to low albumin), alanine aminotransferase (OR = 1.03), aspartate aminotransferase (OR = 1.02), LDH (OR = 1.02), C-reactive protein (OR = 1.04) and procalcitonin (OR = 17.01). In logistic multivariate analysis, dyspnea (adjusted OR = 27.10), dry/moist rales (adjusted OR = 9.46), and higher LDH (adjusted OR = 1.02) were independent risk factors. The literature review showed that patients in Wuhan had a higher incidence of ARDS, higher mortality rate, and higher levels of biomarkers associated with COVID-19 severity than those outside Wuhan in China. CONCLUSION: Dyspnea, dry/moist rales and higher LDH are independent risk factors for ARDS in COVID-19. The incidence of ARDS in Wuhan seems to be overestimated compared with outside Wuhan in China.

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town.

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY-one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches-in hospitals or drive-through facilities-and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.