Prognostic value of SOFA combined with chest CT severity score in patients with critical COVID-19 pneumonia: a retrospective study (preprint)

Background: Either sequential organ failure assessment (SOFA) score or chest CT severity score (CT-SS) is often used alone to evaluate the prognosis of patients with critical coronavirus disease 2019 (COVID-19), but each of them has intrinsic deficiency. Herein, we attempted to investigate the predictive value of the combination of SOFA and CT-SS for the prognosis of COVID-19. Materials and Methods: A single-center retrospective study was performed in the Second Affiliated Hospital of Zhejiang University School of Medicine from December 2022 to January 2023. Patients with critical COVID-19 pneumonia were divided into two groups of survival or non-survival of hospitalization. The data including clinical characteristics, CT-SS, SOFA score, laboratory results on admission day were collected and analyzed. In addition, the predictive value of SOFAscore, chest CT-SS, or their combination for in-hospital mortality of COVID-19 pneumonia were compared by receiver operating characteristic (ROC) curve. Results: A total of 424 patients with a mean age of 75.46 years and a major proportion of male (69.10%) were finally enrolled, and the total in-hospital mortality was 43.40% (184/424). In comparison with survival group, significant higher proportions of older age (>75 years), comorbidities including obesity, diabetes, and cerebrovascular disease, more needs of mechanical ventilation and continuous renal replacement therapy (CRRT) were observed in the non-survival group (all P﹤0.05). In addition, non-survival patients had a higher value of creatinine, procalcitonin, C-reactive protein, interleukin-6 , SOFA score , CT-SS  (all P﹤0.05) on admission day. Multivariate logistic regression analysis further showed that older age, obesity, diabetes, SOFA score, CT-SS, mechanical ventilation, and lymphocytopenia (all P﹤0.05) were independently related with in-hospital mortality. Moreover, the area under the curve (AUC) of combination of SOFA score and chest CT-SS became significant higher than their respective alone (P＜0.01). Conclusion: A simple combination of SOFA scorewith chest CT-SS on admission elicits a better predictive value for in-hospital mortality of critical COVID-19 patients, which could also serve as a promising indicator for prognosis prediction of other severe lung diseases like severe pneumonia and acute lung injury.

Multimorbidity characteristics in elderly inpatients and their associated factors in complex networks: a cross-sectional study (preprint)

Background Multimorbidity of chronic diseases has become an increasingly serious public health problem. However, the research on the current situation of multimorbidity in the elderly in Jiangsu, China is relatively lacking. Methods We surveyed a total of 229,926 inpatients aged above 60 and with two or more chronic diseases in the First Affiliated Hospital with Nanjing Medical University from January 1, 2015 to December 31, 2021. The Apriori algorithm was used to analyze the association rules of the multimorbidity patternsin old adults. Results The mean age of these patients was 72.0±8.7 years, and the male-to-female ratio was 1:1.53. These patients during the COVID-19 period(from 2020 to 2021) displayed younger, higher male rate, shorter median length of hospital stay, higher ≥6 multimorbidities rate and lower median cost than those not during the COVID-19 period (from 2015 to 2019). In all of these patients, the top 5 chronic diseases were "Hypertensive diseases(I10-I15)", "Other forms of heart disease(I30-I52)", "Diabetes mellitus(E10-E14)", "lschaemic heart diseases(I20-I25)" and "Cerebrovascular diseases(I60-I69)". The complex networks of multimorbidity showed that Hypertensive diseases had a higher probability of co-occurrence with multiple diseases in all these patients, followed by Diabetes mellitus, Other forms of heart disease, and lschaemic heart diseases(I20-I25). Conclusion In conclusion, the patterns of multimorbidity among the aged varied by COVID-19. Our results highlighted the importance of control of hypertensive diseases, diabetes, and heart disease in gerontal patients. More efforts to improve the understanding of multimorbidity patterns would help us develop new clinical and family care models.

Phenome-wide association study to explore the long-term symptoms after infection with novel coronavirus in the UK Biobank (preprint)

Background Observational research studies have shown that even after the acute phase, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can affect patients, and increase the risk of cardiovascular, mental, metabolic, and other disorders. However, the spectrum of diseases for individuals with a genetic predisposition to COVID-19 remains unclear. Methods We leveraged individual-level data from UK Biobank to implement a phenome-wide association study to explore the relationships between COVID-19 and 1061 diseases. Then, the inverse-variance weighted (IVW) method was adopted with summary-level data from global consortiums as sensitivity analyses combined with other MR methods with different model assumptions to identify robust associations. Findings The PheWAS found severe respiratory, hospitalized, and susceptibility COVID-19 had detrimental effects on 36, 37, and 51 kinds of diseases, separately. The IVW test found severe respiratory COVID-19 had detrimental effects on breast cancer [OR 95% CI: 1.065 (1.000-1.133) ], pan-cancer [OR 95% CI: 1.002 (1.000-1.004) ], and Alzheimer's disease [OR 95% CI: 1.042 (1.005-1.081) ], etc. Hospitalized COVID-19 had detrimental effects on ischemic stroke (IS) [OR 95%CI: 1.049 (1.001-1.100) ], breast cancer [OR 95%CI: 1.139 (1.011-1.283) ], and pan-cancer [OR 95%CI: 1.003 (1.000-1.006) ], etc. Susceptibility COVID-19 had detrimental effects on deep vein thrombosis (DVT) of lower extremities [OR 95%CI: 2.392 (1.167-4.902) ], venous thromboembolism [OR 95%CI: 1.962 (1.115-3.453) ], pulmonary heart disease/diseases of pulmonary circulation [OR 95%CI: 1.767 (1.142-2.733) ], IS (large artery atherosclerosis) [OR 95%CI: 1.405 (1.025-1.927) ], myocardial infarction [OR 95%CI: 1.235 (1.012-1.509) ], heart failure [OR 95%CI: 1.140 (1.009-1.287) ], etc. Interpretation This study describes the extensive link between genetically determined COVID-19 and a broad range of diseases, especially those of the circulatory system, neuropsychiatric system, neoplasms, immune system, and digestive systems. Early detection and management of post-COVID-19 conditions could be tremendously beneficial to public health.

The Equilibrium and Pandemic Waves of COVID-19 in the US (preprint)

Importance: Removing the epidemic waves and reducing the instability level of an endemic critical point of COVID-19 dynamics are fundamental to the control of COVID-19 in the US. Objective: To develop new mathematic models and investigate when and how will the COVID-19 in the US be evolved to endemic. Design, Setting, and Participants: To solve the problem of whether mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic, we defined a set of nonlinear ordinary differential equations as a mathematical model of transmission dynamics of COVID-19 with vaccination. Multi-stability analysis was conducted on the data for the daily reported new cases of infection from January 12, 2021 to December 12, 2022 across 50 states in the US using the developed dynamic model of COVID-19 and limit cycle theory. Main Outcomes and Measures: Eigenvalues and the reproduction number under the disease-free equilibrium point and endemic equilibrium point were used to assess the stability of the disease-free equilibrium point and endemic equilibrium point. Both analytic analysis and numerical methods were used to determine the instability level of new cases of COVID-19 in the US under the different types of equilibrium points and to investigate how the system moves back and forth between stable and unstable states of the system and how the pandemic COVD-19 will evolve to endemic in the US. Results: Multi-stability analysis identified two types of critical equilibrium points, disease-free endemic equilibrium points in the COVID-19 transmission dynamic system. The transmissional, recovery, vaccination rates and vaccination effectiveness during the major transmission waves of COVID-19 across 50 states in the US were estimated. These parameters in the model varied over time and across the 50 states. The eigenvalues and the reproduction numbers R0 and R0end in the disease-free equilibrium point and endemic equilibrium point were estimated to assess stability and classify equilibrium points. They also varied from state to state. The impacts of the transmission and vaccination parameters on the stability of COVID-19 were simulated, and stability attractor regions of these parameters were found and ranked for all 50 states in the US. The US experienced five major epidemic waves, endemic equilibrium points of which across 50 states were all in unstable states. However, the combination of re-infection and vaccination (hybrid immunity) may provide strong protection against COVID-19 infection, and stability analysis showed that these unstable equilibrium points were toward stable points. Theoretical analysis and real data analysis showed that additional epidemic waves may be possible in the future, but COVID-19 across all 50 sates in the US is rapidly moving toward stable endemicity. Conclusions and Relevance: Both stability analysis and observed epidemic waves in the US indicated that the pandemic might not end with the disappearance of the virus. However, after enough people gained immune protection from vaccination and from natural infection, COVID-19 would become an endemic disease, as the stability analysis showed. Educating the population about multiple epidemic waves of the transmission dynamics of COVID-19 and designing optimal vaccine rollout are crucial for controlling the pandemic of COVID-19 and its evolving to endemic.

Dust events affect the occurrence of influenza: A case-crossover study in an arid city of China (preprint)

Background Air aerosol is believed to be an important pathway for infectious disease transmission like COVID-19 as well as influenza. Therefore, we hypothesized that there might be a strong association between dust events and influenza, especially in semi-arid areas. This study aims to explore the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza cases in a semi-arid city.Methods A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters and dust events were conducted from 2014 to 2019 in Lanzhou, China. The Case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza.Results We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased, the greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.06 1(1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found the low humidity and temperature had an interaction with PM, which could aggravate the risk of influenza.Conclusions Ambient PM and dust events exposure may increase the risk of laboratory-confirmed influenza, and the risk of laboratory-confirmed influenza increased with the dust events duration. These findings will provide additional epidemiological evidence for future influenza prevention and environmental protection.

A flexible, efficient, and scalable platform to produce circular RNAs as new therapeutics (preprint)

Messenger RNA (mRNA) has recently emerged as a new drug modality with great therapeutic potential. However, linear mRNAs are relatively unstable and also require base modification to reduce their immunogenicity, imposing a limitation to the broad application. With improved stability, the circular RNA (circRNA) presents a better alternative for prolonged expression of the proteins, however the in vitro circularization of RNA at a large scale is technically challenging. Here we developed a new self-catalyzed system to efficiently produce circRNAs in a co-transcriptional fashion. By rational sequence design, we can efficiently produce scarless circRNAs that do not contain foreign sequences. The resulting circRNAs are very stable and have low immunogenicity, enabling prolonged protein translation in different cells without cellular toxicity. The circRNAs generated from this platform can be encapsulated in lipid nanoparticles and efficiently delivered into mice to direct robust protein expression. Finally, the circRNAs encoding RBD of SARS-CoV-2 S protein induced strong antibody productions, with neutralization antibody titers higher than the preclinical data from the linear mRNAs. Collectively, this study provided a general platform for efficient production of circRNAs, demonstrating the potential of circRNAs as the new generation of mRNA therapy.

Facemasks and the COVID-19 pandemic: The harm caused by improper use of facemasks is a matter of concern (preprint)

Abstract: The use of facemasks has played an important role in the prevention of COVID-19. However, inappropriate use of facemasks also brings people certain problems. Therefore, the reasonable use of facemasks is a necessary measure to protect oneself and others in the current epidemic prevention and control.

Automated Target-Driven Molecular Machine-Based Engineering Process For SARS-CoV-2 Monitoring (preprint)

Nucleic acid structured electrochemiluminescence (ECL) biosensors provide valuable, versatile, programmable support for the development of medical diagnostics. Here, we constructed an automated DNA molecular machine with just one thiol-modified DNA probe and three nucleic acid probes on the surface of Ti 3 C 2 -based composites to monitor SARS-CoV-2, free from the limitations of motion tracks on the DNA molecular machine and significantly decreasing the probe modification fee. In the presence of the target, the designed DNA molecular machine conducted a modular reaction to transduce the target concentration information into an enhancement of the ECL signal based on DNA hybridization calculations. Modular, scalable reactions occur on DNA automata, reducing complex bioanalytical reactions to a single nucleic acid probe unit and eliminating the tedious steps of laying down motion tracks required by traditional molecular machines. By designing three capture probes, it is possible to extend the application ranges of the protocol from single to multi-target monitoring. Moreover, the strategy implements the bioanalysis of the SARS-CoV-2 in complex environments such as saliva dilutions and serum dilutions, which is valuable in promoting public health development and evaluating the environmental hazards of SARS-CoV-2.

COVID-19 Symptoms and Deaths Among Health Care Workers in the United States (preprint)

Background: While most COVID-19 research has focused on older individuals with multi-comorbidities, few studies have assessed the predictors of fatality among health care workers (HCWs). This study evaluated if demographics and COVID-19 symptomatology predicted COVID-19 fatality and the temporal trends and spatial distribution among HCWs.Methods: We used a case-control design to compare HCW deaths related to COVID-19 (laboratory-confirmed) with three control groups (i.e., Non-HCW deaths, HCW non-deaths, and non-HCW non-deaths). Patient-level data with 33 variables, including COVID-19 confirmed cases, deaths, demographics, and various specific COVID symptoms reported by all states in the US, have been obtained from the Restricted Access Dataset by the US CDC since January 2020. A logistic regression model was used by regressing the outcome variable against each predictor while controlling for gender, age group, race, and ethnicity.Results: The percentages of 50-69 years old, Hispanics (8.7%), Black (32%), and Asian (23.1%) in HCW death were significantly higher than in their respective controls. The fatality and all severe indicators were higher among the deaths than non-deaths, but not different for HCWs than non-HCWs. Significantly increased risks for deaths were observed with pre-existing medical conditions (RR: 7.24, 95% CI: 5.40-9.70), shortness of breath (RR: 5.73, 95% CI:4.50-7.31), fever (RR:3.52, 95% CI: 2.71-4.56), cough (RR:2.02, 95% CI: 1.54-2.65), and diarrhea (RR: 1.57, 95% CI:1.20-2.05).  Conclusion: Older and minority HCWs experienced relatively higher COVID-19 fatality. Severe symptoms are similarly prevalent among HCW deaths and non-HCW deaths. Pre-existing medical conditions, shortness of breath and fever symptoms may be critical COVID indicators for HCWs.

Incidence, Risk Factors, and Outcomes of Patients with COVID‑19‑Associated Pulmonary Aspergillosis (CAPA) in Intensive Care Units: A Systematic Review and Meta-Analysis Of 29 Cohort Studies (preprint)

Background Patients with Coronavirus disease 2019 (COVID-19) admitted to an intensive care unit (ICU) might develop COVID-19-related pulmonary Aspergillosis (CAPA). We aimed to identify studies systematically that describe the incidence and risks factors of CAPA, and to assess its outcome. Methods Two authors independently searched ScienceDirect, PubMed, CNKI, MEDLINE (OVID), and MedRXIV from December 31, 2019 to Feb 27, 2021. We included observational cohort studies that investigated patients with CAPA admitted to an ICU. We assessed the quality of all included studies using the Newcastle–Ottawa Scale). The meta-analysis was registered with PROSPERO (CRD42021242179).ResultsTwenty-nine cohort studies with 2095 patients with COVID-19 admitted to an ICU and 264 patients who developed to CAPA were included (Pooled incidence: 0.14, 95% confidence interval [CI] = 0.11–0.17). The overall mortality and case fatality rate of CAPA were 0.07 (0.05–0.09) and 0.51 (0.44–0.58), respectively. Patients with COVID‑19 would develop CAPA at 7.28 days after mechanical ventilation (range, 5.48–9.08). Compared with patients without CAPA, those with CAPA had a significantly lower median body mass index (27.32 vs. 28.97 kg/m2, P = 0.034), higher median creatinine level (127.94 vs. 88.23 µmol/L, P = 014), and were more likely to receive corticosteroids therapy (41.0% vs. 38.0%, risk ratio [RR] = 1.98, 95% CI=1.08–3.63) and renal replacement therapy (42.0% vs. 28.2%, RR = 1.61, 95% CI=1.04–2.50) during admission. Remarkably, patients with CAPA were associated significantly with a 1.66‑fold higher mortality (RR = 1.66, 95% CI=1.31–2.12) without significant heterogeneity and publication bias. ConclusionsPatients with COVID-19 admitted to an ICU might develop CAPA and have higher all‑cause mortality. We recommend conducting prospective screening for CAPA among patients with severe COVID-19, especially for those who receive mechanical ventilation over 7 days. 

Population Vaccine Effectiveness and its Implication for Control of the Spread of COVID-19 in the US (preprint)

Realized vaccine efficacy in population is highly different from the individual vaccine efficacy measured in clinical trial. The realized vaccine efficacy in population is substantially affected by the vaccine age-stratified prioritization strategy, population age-structure, non-pharmaceutical intervention (NPI). We proposed a population vaccine efficacy which integrated individual vaccine efficacy, vaccine prioritization strategy and NPI to measure and monitor the control of the spread of COVID-19. We found that 11 states in the US had low population vaccine efficacy and 20 states had high population efficacy. We demonstrated that although the proportion of the population who received at least one dose of COVID-19 vaccine across 11 low population vaccine efficacy states, in general, was greater than that in 20 high population vaccine efficacy states, the 11 low population vaccine efficacy states experienced the recent COVID-19 surge, while the number of new cases in the 20 high population vaccine efficacy states exponentially decreased. We demonstrated that the proportions of adults in the population across 50 states were significantly associated with the forecasted ending date of the COVID-19. We show that it was recent low proportion of adults vaccinated in Michigan that caused its COVID-19 surge. Using population vaccination efficacy, we forecasted that the earliest COVID-19 ending states were Hawaii, Arizona, Arkansas, and California (in the end of June, 2021) and the last COVID-19 ending states were Colorado, New York and Michigan (in the Spring, 2022).

The effects of different travel modes on COVID-19 transmission in global cities (preprint)

Previous studies have been focused primarily on modelling and predicting the transmission of COVID-19. While little research has been conducted to understand the impacts of different travel modes on the transmission of COVID-19, without an explicit understanding of the travel mode effects, many people intuitively perceive non-motorized travel modes to be safer than public transit as passengers in public transit are confined to small, enclosed spaces where the virus can transmit more easily. During the period when urban mobility gradually returns towards what was called ‘normal’ and transit systems and urban facilities reopen, new waves of the pandemic might be generated as travel mode choices significantly differ across cities and different travel behaviors are associated with diverse infectious sources. Thus, the current study focuses on understanding the impact of different travel modes on the transmission of COVID-19 in the long-term and at world-wide scales, aspects that have not received much attention in the research literature. Accordingly, a multivariate time series analysis has been developed to examine the impacts of daily confirmed cases and travel modes, based on driving, public transit, and walking as recorded in the Apple Mobility Trends Reports on COVID-19 transmission risks in 71 cities throughout the world from January to November 2020. The impact of population density in built-up areas and the degree to which the `wearing' of facemasks affects infections are also investigated. Among the three travel modes we examine, driving is the safest way to commute because drivers are physically separate from crowds. Unexpectedly, walking has a relatively low risk when the population density in built-up areas is high, which suggests that, globally, people have increased awareness of pandemic prevention. Although the general public is more worried about using public transit, this mode can still be safe in many large cities, a factor that is vital for informing policy making and developing trust among citizens so they will continue to commute using public transit when strict preventative measures are in place. From another perspective, infectious sources make the largest contribution to daily confirmed cases, thus demonstrating the importance of strict quarantine measures to block the source of infection. The results and conclusions presented herein are based on an analysis of spatio-temporal data that helps inform policy making and enable cities to be kept open when controlling the pandemic, which has become an urgent task for the international community when rebuilding the economy.

Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network (preprint)

We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients lies in the great variability and irregular sampling of patient's vitals and labs observed in the clinical setting. Existing methods have strong limitations in handling time-dependent features' complex dynamics, either oversimplifying temporal data with summary statistics that lose information or over-engineering features that lead to less robust outcomes. We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients. The model incorporates the Multi-task Gaussian Process using observed values to learn the posterior joint multi-variant conditional probability and infer the missing values on a unified time grid. The temporal imputed data is fed into a multi-objective self-attention network for the prediction task. A novel positional encoding layer is proposed and added to the network for producing in-time predictions. The positional layer outputs a risk score at each user-defined time point during the entire hospital stay of an inpatient. We frame the prediction task into a multi-objective learning framework, and the risk scores at all time points are optimized altogether, which adds robustness and consistency to the risk score trajectory prediction. Our experimental evaluation on a large database with nationwide in-hospital patients with COVID-19 also demonstrates that it improved the state-of-the-art performance in terms of AUC (Area Under the receiver operating characteristic Curve) and AUPRC (Area Under the Precision-Recall Curve) performance metrics, especially at early times after hospital admission.

Antibody response among COVID-19 patient with and without re-detectable positive RT-PCR results in convalescent phases (preprint)

Objective: To analyze the dynamic of total, IgA, IgM and IgG antibody of the confirmed COVID-19 patients during convalescent phases to understand the kinetics of antibody response among recovered patients.Methods: From March 4 to April 29, 2020, a total of 143 recovered COVID-19 patients with clear date of illness onset available were enrolled in this study. Nasopharyngeal and anal swabs were collected for SARS-CoV-2 RNA testing. Blood samples were collected for antibodies testing. Results: A total of 275 blood samples up to 96 days after illness onset were collected from 143 recovered patients. High titers of total and IgG antibodies continued to persist for over 3 months, with 100% and 99.3% patients remaining positive for total and IgG antibody. IgM antibody declined rapidly with a median time to seronegative at 67 (95%CI: 59, 75) days after illness onset. Around 25% patients were seronegative for IgA antibody at month 3 after illness onset. No statistical significance difference was founded in the antibody kinetics between patients with and without re-detectable positive RT-PCR results during in convalescent phases. Conclusion: Similar high antibody titers of total and IgG antibody continued to persist for over 3 months among recovered COVID-19 patients with and without re-detectable positive RT-PCR results.

Association of Menopausal Status with COVID-19 outcomes: a Propensity Score-Matching Analysis (preprint)

Background: Despite the growing number of studies on the Coronavirus Disease-19 (COVID-19), little is known about the association of menopausal status with COVID-19 outcomes. Materials: and methods: In this retrospective study, we included 336 COVID-19 in-patients between February 15, 2020 and April 30, 2020 at the Taikang Tongji Hospital (Wuhan), China. Electronic medical records, including patient demographics, laboratory results, and chest computed tomography (CT) images were reviewed. Results: : In total, 300 patients with complete clinical outcomes were included for analysis. The mean age was 65.3 years and most patients were women (n=167, 55.7%). Over 50% of patients presented with comorbidities, with hypertension (63.5%) being the most common comorbidity. After propensity-score matching, results showed that men had significantly higher odds than premenopausal women for developing severe disease type (23.7% vs. 0%, OR 17.12, 95% CI 1.00–293.60; p =0.003) and bilateral lung infiltration (86.1% vs. 64.7%, OR 3.39, 95% CI 1.08–10.64; p = 0.04), but not for mortality (2.0% vs. 0%, OR 0.88, 95% CI 0.04–19.12, p =1.00). However, non-significant difference was observed among men and post-menopause women in the percentage of severe disease type (32.7% vs. 41.7%, OR 0.68, 95% CI 0.37–1.24, p =0.21) and bilateral lung infiltration (86.1% vs. 91.7%, OR 0.56, 95% CI 0.22–1.47, p =0.24), mortality (2.0% vs. 6.0%, OR 0.32, 95% CI 0.06–1.69, p =0.25). Conclusions: : Men had higher disease severity than premenopausal women, while the differences disappeared between postmenopausal women and men. These findings support aggressive treatment for the poor-prognosis of postmenopausal women in clinical practice.

Clinical characteristics and manifestations in older patients with COVID-19 (preprint)

Background: To investigate the clinical characteristics and manifestations of older patients with coronavirus disease 2019 (COVID-19). Methods: : In this retrospective study, 566 patients with confirmed COVID-19 were enrolled and the clinical characteristics, laboratory findings, complications and outcome data were collected and analyzed. Results: : Among the 566 patients (median age, 61.5 years) with COVID-19, 267 (47.2%) patients were male and 307 (54.2%) were elderly. Compared with younger patients, older patients had more underlying comorbidities and laboratory abnormalities. A higher rate of acute respiratory distress syndrome (ARDS), acute cardiac injury and heart failure was observed in the older group as compared with younger and middle-aged groups, particularly those oldest-old patients (>75 years) had more multi-organ damage. Older patients with COVID-19 were more likely to suffer from acute cardiac injury in cases with preexistenting cardiovascular diseases, while there was no difference among the three groups when patients had no history of cardiovascular diseases. Older patients present more severe with the mortality of 18.6%, which was higher than that in younger and middle-aged patients ( P <0.05). Multivariable analysis showed that age, lymphopenia, ARDS, acute cardiac injury, heart failure and skeletal muscle injury were associated with death in older patients, while glucocorticoids may be harmful. Conclusions: : Older patients, especially the oldest-old patients were more likely to exhibit significant systemic inflammation, pulmonary and extrapulmonary organ damage and a higher mortality. Advanced age, lymphopenia, ARDS, acute cardiac injury, heart failure and skeletal muscle injury were independent predictors of death in older patients with COVID-19 and glucocorticoids should be carefully administered in older patients.

Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America (preprint)

As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit) , tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

Psychological Impact and Associated Factors During the Containment Stage of Covid-19 Epidemic Among College Students in China (preprint)

Background College students are a uniquely vulnerable group and may experience high stress levels due to COVID-19. This study aims to identify the the psychological state and related factors on Chinese college students during the initial phases of the COVID-19 pandemic. Methods From February 23 to March 5, 2020, a cross-sectional online survey was conducted among 3606 college students from seven provinces in China using standard questionnaires measuring adverse psychological outcomes and related factors including Impact of Event Scale-6 (IES-6), Depression, Anxiety and Stress Scale (DASS), Perceived Social Support Scale (PSSS) and Simplified Coping Style Questionnaire (SCSQ). Exploratory factor analysis (EFA) were used to determine underlying constructs of the perceived threat items. Multivariate regression was used to explore the determinants of adverse psychological impact. Results Posttraumatic stress (PTS) were prevalent in this sample of college students, and 34.22% met the cut-off for posttraumatic stress disorder (PTSD). The proportion of having mild to extremely severe symptoms of depression, anxiety and stress were 15.70%, 13.31% and 7.10%, respectively. The impact of closed-off management on life, perceived threat and passive coping strategies were positively correlated to PTS and DASS scores, while knowledge score, perceived social support and active coping strategies were negatively correlated to DASS scores. Conclusions In summary, adverse psychological symptoms were prevalent among college students in China during the COVID-19 epidemic. Identifying vulnerable populations and formulating correspondingly psychological interventions would be beneficial to improve the mental health during the COVID-19 epidemic.

Recurrent Neural Reinforcement Learning for Counterfactual Evaluation of Public Health Interventions on the Spread of Covid-19 in the world (preprint)

As the Covid-19 pandemic soars around the world, there is urgent need to forecast the expected number of cases worldwide and the length of the pandemic before receding and implement public health interventions for significantly stopping the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission of epidemics, their prediction accuracies are quite low. Alternative to the epidemiological models, the reinforcement learning (RL) and causal inference emerge as a powerful tool to select optimal interventions for worldwide containment of Covid-19. Therefore, we formulated real-time forecasting and evaluation of multiple public health intervention problems into off-policy evaluation (OPE) and counterfactual outcome forecasting problems and integrated RL and recurrent neural network (RNN) for exploring public health intervention strategies to slow down the spread of Covid-19 worldwide, given the historical data that may have been generated by different public health intervention policies. We applied the developed methods to real data collected from January 22, 2020 to June 28, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world. We forecasted that the number of laboratory confirmed cumulative cases of Covid-19 will pass 26 million as of August 14, 2020.