Spatiotemporal analysis of urban road congestion during and post COVID-19 pandemic in Shanghai, China.

Coronavirus Disease 2019 (COVID-19) has become one of the most serious global health crises in decades and tremendously influence the human mobility. Many residents changed their travel behavior during and after the pandemic, especially for a certain percentage of public transport users who chose to drive their owned vehicles. Thus, urban roadway congestion has been getting worse, and the spatiotemporal congestion patterns has changed significantly. Understanding spatiotemporal heterogeneity of urban roadway congestion during and post the pandemic is essential for mobility management. In this study, an analytical framework was proposed to investigate the spatiotemporal heterogeneity of urban roadway congestion in Shanghai, China. First, the matrix of average speed in each traffic analysis zones (TAZs) was calculated to extract spatiotemporal heterogeneity variation features. Second, the heterogenous component of each TAZ was extracted from the overall traffic characteristics using robust principal component analysis (RPCA). Third, clustering analysis was employed to explain the spatiotemporal distribution of heterogeneous traffic characteristics. Finally, fluctuation features of these characteristics were analyzed by iterated cumulative sums of squares (ICSS). The case study results suggested that the urban road traffic state evolution was complicated and varied significantly in different zones and periods during the long-term pandemic. Compared with suburban areas, traffic conditions in city central areas are more susceptible to the pandemic and other events. In some areas, the heterogeneous component shows opposite characteristics on working days and holidays with others. The key time nodes of state change for different areas have commonness and individuality. The proposed analytical framework and empirical results contribute to the policy decision-making of urban road transportation system during and post the COVID-19 pandemic.

Myoglobin and troponin as prognostic factors in patients with COVID-19 pneumonia.

BACKGROUND: The outbreak of novel coronavirus pneumonia 2019 (COVID-19) has caused millions of deaths worldwide. It is well documented that troponin predicts the prognosis of patients. Myoglobin is not only an important marker of myocardial injury, but it indicates systemic muscle damage. However, its relationship with COVID-19 was rarely reported. The present study compared the predictive value of troponin and myoglobin on the final prognosis of COVID-19 patients by analyzing the clinical characteristics and serum levels of myoglobin and troponin in severe/critical COVID-19 patients. METHODS: We enrolled 499 consecutive eligible hospitalized patients with severe/critical COVID-19 from February 14 to March 24, 2020 at Leishenshan Hospital, Wuhan, China. Clinical characteristics and laboratory data were collected and compared between the patients who died and survived. We analyzed the receiver operating characteristic curves of myoglobin and troponin. Then, the patients were divided into myo+ group, myo- group, tro+ group, and tro- group, and survival curves were analyzed. The prognostic predictable values of myoglobin and troponin were further analyzed using Cox multifactorial analysis. RESULTS: Myoglobin and troponin were significantly elevated in the death group (134.4 [interquartile range (IQR) 24.80, 605] vs 38.02 [IQR 3.87, 11.73] ng/ml, p < 0.001), and troponin was also significantly elevated in the death group (0.01 [IQR 0.01, 0.01] vs 0.04 [IQR 0.02, 0.15] ng/ml, p < 0.001). The ROC curves demonstrated that the area under the curve when using myoglobin to predict patient death was 0.911, with a threshold of 1.17, which was equivalent to troponin. Kaplan-Meier survival analysis revealed a significantly lower survival curve in the myo+ group than the myo- group. Multifactor Cox survival analysis showed that troponin was no longer significant (HR = 0.98, 95% CI 0.92-1.03, p = 0.507), but elevated myoglobin was an independent predictor of death in COVID-19 patients (HR = 1.001, 95% CI 1.001-1.002, p < 0.001). The analysis of the Cox model for predicting patient death and plotting decision curves suggested that the single factor myoglobin model was superior to troponin, and the predictive value of the multifactor model was superior to the single-factor analyses. CONCLUSIONS: In severe/critical COVID-19 patients, myoglobin and troponin were predictors of mortality and the probability of conversion to critical illness, and myoglobin may be superior to troponin for predictive value.

ANTECEDENTES: El brote de la nueva neumonía por coronavirus 2019 (COVID-19) ha causado millones de muertes en todo el mundo. Está bien documentado que la troponina predice el pronóstico de los pacientes. La mioglobina no solo es un importante marcador de lesión miocárdica, sino que indica daño muscular sistémico. Sin embargo, su relación con la COVID-19 ha sido raramente comunicada. En el presente estudio se ha comparado el valor predictivo de la troponina y la mioglobina en el pronóstico final de los pacientes con COVID-19, analizando las características clínicas y los niveles séricos de mioglobina y troponina en pacientes con COVID-19 en estado grave o crítico. MÉTODOS: Se inscribió a 499 pacientes consecutivos elegibles hospitalizados con COVID-19 en estado grave o crítico del 14 de febrero al 24 de marzo de 2020 en el Hospital Leishenshan (Wuhan, China). Se recogieron las características clínicas y los datos de laboratorio y se compararon entre los pacientes que murieron y los que sobrevivieron. Se analizaron las curvas de características operativas del receptor de mioglobina y troponina. A continuación, se dividió a los pacientes en grupo myo+, grupo myo−, grupo tro+ y grupo tro−, y se analizaron las curvas de supervivencia. Los valores pronósticos de la mioglobina y la troponina se analizaron además mediante un análisis multifactorial de Cox. RESULTADOS: La mioglobina y la troponina estaban significativamente elevadas en el grupo de muerte (134,4; rango intercuartílico [RIQ: 24,80; 605] vs. 38,02; [RIQ: 3,87; 11,73] ng/ml; p < 0,001), y la troponina también estaba significativamente elevada en el grupo de muerte (0,01 [RIQ: 0,01; 0,01] vs. 0,04 [RIQ: 0,02; 0,15] ng/ml; p < 0,001). Las curvas ROC demostraron que el área bajo la curva al utilizar la mioglobina para predecir la muerte de los pacientes era de 0,911, con un umbral de 1,17, equivalente al de la troponina. El análisis de supervivencia de Kaplan-Meier reveló una curva de supervivencia significativamente menor en el grupo myo+ que en el grupo myo−. El análisis de supervivencia multifactorial de Cox mostró que la troponina ya no era significativa (HR = 0,98; IC 95%: 0,92-1,03; p = 0,507), pero la mioglobina elevada era un predictor independiente de muerte en los pacientes COVID-19 (HR = 1,001; IC 95%: 1,001-1,002; p < 0,001). El análisis del modelo de Cox para predecir la muerte de los pacientes y el trazado de las curvas de decisión indicaron que el modelo de mioglobina de un solo factor era superior al de la troponina y que el valor predictivo del modelo multifactorial era superior a los análisis de un solo factor. CONCLUSIONES: En los pacientes graves o críticos de COVID-19, la mioglobina y la troponina fueron predictores de la mortalidad y de la probabilidad de conversión a enfermedad crítica, y la mioglobina puede ser superior a la troponina en cuanto al valor predictivo.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

Influence of Classroom Colour Environment on College Students' Emotions during Campus Lockdown in the COVID-19 Post-Pandemic Era-A Case Study in Harbin, China

Campus lockdown during COVID-19 and the post-pandemic era has had a huge negative effect on college students. As a vital part of interior teaching spaces, colour deeply influences college students' mental health and can be used for healing. Nevertheless, research on this topic has been limited. Based on colour psychology and colour therapy, this paper discusses the relationship between interior teaching space colours (hue and brightness) and emotions among college students. The HAD scale and questionnaire survey method were used. It was concluded that: (1) Anxiety and depression were prominent among the college student population during the quarantine of the university due to the epidemic. (2) Warm colours have an advantage over both cold and neutral colours in creating pleasure, relaxation, and mental attention, with the second in line being the cold and the last being the neutral. Warm colours make it pleasant for individuals while cold colours boost attention. (3) When subjects have higher values of anxiety and depression, they are less satisfied with the colour of the teaching space. (4) In most cases, there is no significant difference in the colour preference of teaching spaces across the gender, grade, and major groups, with females having a higher preference for warm high-brightness classrooms than males. These findings provide crucial ideas for future interior teaching space design and enrich the theories in colour psychology.

Decoy Nanozymes Enable Multitarget Blockade of Proinflammatory Cascades for the Treatment of Multi-Drug-Resistant Bacterial Sepsis.

Sepsis is a life-threatening organ dysfunction characterized by severe systemic inflammatory response to infection. Effective treatment of bacterial sepsis remains a paramount clinical challenge, due to its astonishingly rapid progression and the prevalence of bacterial drug resistance. Here, we present a decoy nanozyme-enabled intervention strategy for multitarget blockade of proinflammatory cascades to treat multi-drug-resistant (MDR) bacterial sepsis. The decoy nanozymes (named MCeC@MΦ) consist mesoporous silica nanoparticle cores loaded with CeO2 nanocatalyst and Ce6 photosensitizer and biomimetic shells of macrophage membrane. By acting as macrophage decoys, MCeC@MΦ allow targeted photodynamic eradication of MDR bacteria and realize simultaneous endotoxin/proinflammatory cytokine neutralization. Meanwhile, MCeC@MΦ possess intriguing superoxide dismutase and catalase-like activities as well as hydroxyl radical antioxidant capacity and enable catalytic scavenging of multiple reactive oxygen species (ROS). These unique capabilities make MCeC@MΦ to collaboratively address the issues of bacterial infection, endotoxin/proinflammatory cytokine secretion, and ROS burst, fully cutting off the path of proinflammatory cascades to reverse the progression of bacterial sepsis. In vivo experiments demonstrate that MCeC@MΦ considerably attenuate systemic hyperinflammation and rapidly rescue organ damage within 1 day to confer higher survival rates (>75%) to mice with progressive MDR Escherichia coli bacteremia. The proposed decoy nanozyme-enabled multitarget collaborative intervention strategy offers a powerful modality for bacterial sepsis management and opens up possibilities for the treatment of cytokine storm in the COVID-19 pandemic and immune-mediated inflammation diseases.

Fatality assessment and variant risk monitoring for COVID-19 using three new hospital occupancy related metrics.

BACKGROUND: Though case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, its use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to assess the severity of COVID-19, but HMR data is not directly available globally. Alternative metrics are needed for COVID-19 severity and fatality assessment. METHODS: We introduce new metrics for COVID-19 fatality risk measurements/monitoring and a new mathematical model to estimate average hospital length of stay for deaths (Ldead) and discharges (Ldis). Multiple data sources were used for our analyses. FINDINGS: We propose three, new metrics: hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR), and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated Ldead and Ldis for 501,079 COVID-19 hospitalizations in 34 US states between 7 August 2020 and 1 March 2021 were 18·2(95%CI:17·9-18·5) and 14·0(95%CI:13·9-14·0) days, respectively. We found the dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant (58·6% of US new cases as of 25 December 2021) decreased 65·16% in the US as of 16 January 2022. INTERPRETATION: The new and reliable measurements described here could be useful for COVID-19 fatality risk and variant-associated risk monitoring. FUNDING: No specific funding was associated with the present study.

[Overview of Meta-analysis of Lianhua Qingwen preparations in treatment of viral diseases].

This study aims to obtain higher-level evidence by overviewing the Meta-analysis of Lianhua Qingwen preparations in the treatment of viral diseases including influenza, coronavirus disease 2019(COVID-19), and hand, foot and mouth disease(HFMD). CNKI, Wanfang, VIP, China Clinical Trial Registry(ChiCTR), PubMed, EMbase, Web of Science, and Cochrane Library were searched for the Meta-analysis about the treatment of viral diseases with Lianhua Qingwen preparations from the database establishment to April 1, 2022. After literature screening and data extraction, AMSTAR2 and the grading of recommendations assessment, development and evaluations(GRADE) system were used to assess the methodological quality and evidence quality, respectively, and then the efficacy and safety outcomes of Lianhua Qingwen preparations in the treatment of viral diseases were summarized. Thirteen Meta-analysis were finally included, three of which were rated as low grade by AMSTAR2 and ten as very low grade. A total of 75 outcome indicators were obtained, involving influenza, COVID-19, and HFMD. According to the GRADE scoring results, the 75 outcome indicators included 5(6.7%) high-level indicators, 18(24.0%) mediate-level indicators, 25(33.3%) low-level evidence indicators, and 27(36.0%) very low-level indicators.(1)In the treatment of influenza, Lianhua Qingwen preparations exhibited better clinical efficacy than other Chinese patent medicines and Ribavirin and had similar clinical efficacy compared with Oseltamivir. Lianhua Qingwen preparations were superior to other Chinese patent medicines, Oseltamivir, and Ribavirin in alleviating clinical symptoms. They showed no significant differences from Oseltamivir or conventional anti-influenza treatment in terms of the time to and rate of negative result of viral nucleic acid test.(2)In the treatment of COVID-19, Lianhua Qingwen preparation alone or combined with conventional treatment was superior to conventional treatment in terms of total effective rate, main symptom subsidence rate and time, fever clearance rate, duration of fever, time to fever clearance, cough subsidence rate, time to cough subsidence, fatigue subsidence rate, time to fatigue subsidence, myalgia subsidence rate, expectoration subsidence rate, chest tightness subsidence rate, etc. Lianhua Qingwen preparations no difference from conventional treatment in terms of subsiding sore throat, nausea, diarrhea, loss of appetite, headache, and dyspnea. In terms of chest CT improvement rate, rate of progression to severe case, cure time, and hospitalization time, Lianhua Qingwen alone or in combination with conventional treatment was superior to conventional treatment.(3)In the treatment of HFMD, Lianhua Qingwen Granules was superior to conventional treatment in terms of total effective rate, average fever clearance time, time to herpes subsidence, and time to negative result of viral nucleic acid test.(4)In terms of safety, Lianhua Qingwen preparations led to low incidence of adverse reactions, all of which were mild and disappeared after drug withdrawal. The available evidence suggests that in the treatment of influenza, COVID-19, and HFMD, Lianhua Qingwen preparations can relieve the clinical symptoms, shorten the hospitalization time, and improve the chest CT. They have therapeutic effect and good safety in the treatment of viral diseases. However, due to the low quality of available studies, more high-quality clinical trials are needed to support the above conclusions.

Changes in Physical Fitness during COVID-19 Pandemic Lockdown among Adolescents: A Longitudinal Study.

The negative impact of COVID-19 on physical activity has been improved, while the research on changes in physical fitness that may be caused by physical inactivity is still scarce. This study aims to explore the impact of the COVID-19 pandemic lockdown on physical fitness, and the impact of initial physical fitness indicators on their changes during the lockdown in adolescents. A longitudinal study including 265 adolescents aged 14.1 ± 0.4 years old was conducted in China. Physical fitness measurement at baseline and follow-up were respectively measured before (November 2019) and after the lockdown (July 2020). Several physical fitness indicators including aerobic fitness (i.e., 800-m or 1000-m run) and explosive force (i.e., 50-m sprint) deteriorated during the lockdown. Whereas the performances of vital capacity, flexibility (i.e., sit and reach), and muscular strength (i.e., pull-ups) were significantly improved during the lockdown. Furthermore, the reduction in physical fitness for adolescents with higher physical fitness before the lockdown was greater than that for others. These findings may contribute to the development of targeted intervention strategies for physical fitness promotion during the lockdown caused by the public health emergency.

Structural and energetic features of the dimerization of the main proteinase of SARS-CoV-2 using molecular dynamic simulations.

The COVID-19 pandemic caused by SARS-CoV-2 has been declared a global health crisis. The development of anti-SARS-CoV-2 drugs heavily depends on the systematic study of the critical biological processes of key proteins of coronavirus among which the main proteinase (Mpro) dimerization is a key step for virus maturation. Because inhibiting the Mpro dimerization can efficiently suppress virus maturation, the key residues that mediate dimerization can be treated as targets of drug and antibody developments. In this work, the structure and energy features of the Mpro dimer of SARS-CoV-2 and SARS-CoV were studied using molecular dynamics (MD) simulations. The free energy calculations using the Generalized Born (GB) model showed that the dimerization free energy of the SARS-CoV-2 Mpro dimer (-107.5 ± 10.89 kcal mol-1) is larger than that of the SARS-CoV Mpro dimer (-92.83 ± 9.81 kcal mol-1), indicating a more stable and possibly a quicker formation of the Mpro dimer of SARS-CoV-2. In addition, the energy decomposition of each residue revealed 11 key attractive residues. Furthermore, Thr285Ala weakens the steric hindrance between the two protomers of SARS-CoV-2 that can form more intimate interactions. It is interesting to find 11 repulsive residues which effectively inhibit the dimerization process. At the interface of the Mpro dimer, we detected three regions that are rich in interfacial water which stabilize the SARS-CoV-2 Mpro dimer by forming hydrogen bonds with two protomers. The key residues and rich water regions provide important targets for the future design of anti-SARS-CoV-2 drugs through inhibiting Mpro dimerization.

Key residues of the receptor binding domain in the spike protein of SARS-CoV-2 mediating the interactions with ACE2: a molecular dynamics study.

The widespread coronavirus disease 2019 (COVID-19) has been declared a global health emergency. As one of the most important targets for antibody and drug developments, the Spike RBD-ACE2 interface has received extensive attention. Here, using molecular dynamics simulations, we explicitly analyzed the energetic features of the RBD-ACE2 complex of both SARS-CoV and SARS-CoV-2. Despite the high structural similarity, the binding strength of SARS-CoV-2 to the ACE2 receptor is estimated to be -16.35 kcal mol-1 stronger than that of SARS-CoV. Energy decomposition analyses identified three binding patches in SARS-CoV-2 RBD and eleven key residues (F486, Y505, N501, Y489, Q493, L455, etc.), which are believed to be the main targets for drug development. The dominating forces arise from van der Waals attractions and dehydration of these residues. Compared with SARS-CoV, we found seven mutational sites (K417, L455, A475, G476, E484, Q498 and V503) on SARS-CoV-2 that unexpectedly weakened the RBD-ACE2 binding. Interestingly, the E484 site is recognized to be the most repulsive residue at the RBD-ACE2 interface, indicating that from the energy point of view, a mutation of E484 would be beneficial to RBD-ACE2 binding. This is in line with recent findings that it is mutated by lysine (E484K mutation) in the rapidly spreading variants of COVID-19 belonging to the B.1.351 and P.1 lineages. In addition, this mutation is reported to cause virus neutralization escapes from highly neutralizing COVID-19 convalescent plasma. Thus, further efforts are required to probe its functional relevance. Overall, our results present a systematic understanding of the energetic binding features of SARS-CoV-2 RBD with the ACE2 receptor, which can provide a valuable insight for the design of SARS-CoV-2 drugs and identification of cross-active antibodies.

Myoglobin and troponin as prognostic factors in patients with COVID-19 pneumonia.

BACKGROUND: The outbreak of novel coronavirus pneumonia 2019 (COVID-19) has caused millions of deaths worldwide. It is well documented that troponin predicts the prognosis of patients. Myoglobin is not only an important marker of myocardial injury, but it indicates systemic muscle damage. However, its relationship with COVID-19 was rarely reported. The present study compared the predictive value of troponin and myoglobin on the final prognosis of COVID-19 patients by analyzing the clinical characteristics and serum levels of myoglobin and troponin in severe/critical COVID-19 patients. METHODS: We enrolled 499 consecutive eligible hospitalized patients with severe/critical COVID-19 from February 14 to March 24, 2020 at Leishenshan Hospital, Wuhan, China. Clinical characteristics and laboratory data were collected and compared between the patients who died and survived. We analyzed the receiver operating characteristic curves of myoglobin and troponin. Then, the patients were divided into myo+ group, myo- group, tro+ group, and tro- group, and survival curves were analyzed. The prognostic predictable values of myoglobin and troponin were further analyzed using Cox multifactorial analysis. RESULTS: Myoglobin and troponin were significantly elevated in the death group (134.4 [interquartile range (IQR) 24.80, 605] vs 38.02 [IQR 3.87, 11.73]ng/ml, p<0.001), and troponin was also significantly elevated in the death group (0.01 [IQR 0.01, 0.01] vs 0.04 [IQR 0.02, 0.15]ng/ml, p<0.001). The ROC curves demonstrated that the area under the curve when using myoglobin to predict patient death was 0.911, with a threshold of 1.17, which was equivalent to troponin. Kaplan-Meier survival analysis revealed a significantly lower survival curve in the myo+ group than the myo- group. Multifactor Cox survival analysis showed that troponin was no longer significant (HR=0.98, 95% CI 0.92-1.03, p=0.507), but elevated myoglobin was an independent predictor of death in COVID-19 patients (HR=1.001, 95% CI 1.001-1.002, p<0.001). The analysis of the Cox model for predicting patient death and plotting decision curves suggested that the single factor myoglobin model was superior to troponin, and the predictive value of the multifactor model was superior to the single-factor analyses. CONCLUSIONS: In severe/critical COVID-19 patients, myoglobin and troponin were predictors of mortality and the probability of conversion to critical illness, and myoglobin may be superior to troponin for predictive value.

Urban Road Congestion Patterns under the COVID-19 Pandemic: A Case Study in Shanghai

The novel coronavirus (COVID-19) pandemic has had a significant impact on human mobility around the world. Many cities issued “stay-at-home” orders during the outbreak of COVID-19, and many commuters have also changed their travel modes in the post pandemic period;e.g., transit/bus passengers have switched to driving or car-sharing. Urban road traffic congestion patterns are significantly different than they were pre-pandemic, and understanding such changes can be an opportunity to improve future emergency traffic management and control. Previous studies on this topic have focused on natural disasters or major accidents/incidents. However, very few studies have analyzed the empirical traffic congestion patterns that have occurred during a pandemic. This study takes Shanghai as an example, and conducts a retrospective analysis of empirical spatio-temporal road traffic congestion during the COVID-19 pandemic. The three-month road traffic speed data in the 446 Traffic Analysis Zones (TAZs) collected from Baidu Maps was used in this study. The algorithm of Singular Value Decomposition (SVD) was employed to investigate the inherent composition of the spatio-temporal variation simultaneously influenced by several factors. Three principal components were identified from the spatio-temporal variation, including the stable, main part of variation;the part of the variation that is affected by commuting;and the part of the variation that is affected by migrant populations and the pandemic. The results may suggest ways to improve the emergency management and control of urban roadways in other metropolitan areas worldwide during and after the COVID-19 pandemic period.

Intracity Pandemic Risk Evaluation Using Mobile Phone Data: The Case of Shanghai during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the framework: identification of high-risk groups, calculation of dynamic population flow and construction of a human mobility network, exposure and transmission risk assessment, and pandemic prevention guidelines. First, high-risk groups were extracted from mobile phone data based on multi-day activity chains. Second, daily human mobility networks were created by aggregating population and origin-destination (OD) flows. Third, clustering analysis, time series analysis, and network analysis were employed to evaluate pandemic risk. Finally, several solutions are proposed to control the pandemic. The outbreak period of COVID-19 in Shanghai was used to verify the proposed framework and methodology. The results show that the evaluation method is able to reflect the different spatiotemporal patterns of pandemic risk. The proposed framework and methodology may help prevent future public health emergencies and localized epidemics from evolving into global pandemics.

A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients.

BACKGROUND: COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. METHODS: COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. RESULTS: A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). CONCLUSIONS: A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.