Multi-weight Susceptible-Infected Model for Predicting COVID-19 in China

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

Multi-weight susceptible-infected model for predicting COVID-19 in China.

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

Factors Influencing the Acceptance of Robo-Taxi Services in China: An Extended Technology Acceptance Model Analysis

With the development of autonomous driving technologies, robo-taxis (shared autonomous vehicles) are being tested on real roads. In China, in particular, people in some cities such as Beijing and Shanghai can book a robo-taxi online and experience the service. To examine the influential factors on user acceptance of robo-taxi services, this study proposes and employs an extended technology acceptance model (TAM) with four external factors: perceived trust, government support, social influence, and perceived enjoyment. Data were collected through an online questionnaire in China, and responses from 403 respondents were analyzed using structural equation modeling. Both typical TAM factors—including perceived ease of use, perceived usefulness, and attitude—and external factors were found to play significant roles in predicting users’ intention to use robo-taxis. The four external factors influenced the user acceptance indirectly via typical TAM factors. Improving users’ perceived trust is important for increasing public adoption. A greater emphasis by manufacturers on safety concerns, wider dissemination of information on data protection and safety systems, and government support through incentives for manufacturers and users can help improve public adoption of robo-taxi services.

Outcomes and prognostic factors in 70 non-survivors and 595 survivors with COVID-19 in Wuhan, China.

Since the first outbreak of coronavirus disease 2019 (COVID-19) occurred in December 2019, more than 51 million cases had been reported globally. We aimed to identify the risk factors for in-hospital fatal outcome and severe pneumonia of this disease. This is a retrospective, multicentre study, which included all confirmed cases of COVID-19 with definite outcomes (died or discharged) hospitalized between 1 January and 4 March 2020 in Wuhan. Of all 665 patients included, 70 died and 595 discharged (including 333 mild and 262 severe cases). Underlying comorbidity was more commonly observed among deaths (72.9%) than mild (26.4%) and severe (61.5%) survivors, with hypertension, diabetes and cardiovascular as dominant diseases. Fever and cough were the primary clinical magnifications. Older age (≥65 years) (OR = 3.174, 95% CI = 1.356-7.755), diabetes (OR = 2.540, 95% CI = 0.995-6.377), dyspnoea (OR = 7.478, 95% CI = 3.031-19.528), respiratory failure (OR = 10.528, 95% CI = 4.484-25.829), acute cardiac injury (OR = 25.103, 95% CI = 9.057-76.590) and acute respiratory distress syndrome (OR = 7.308, 95% CI = 1.501-46.348) were associated with in-hospital fatal outcome. In addition, older age (OR = 2.149, 95% CI = 1.424-3.248), diabetes (OR = 3.951, 95% CI = 2.077-7.788), cardiovascular disease (OR = 3.414, 95% CI = 1.432-8.799), nervous system disease (OR = 4.125, 95% CI = 1.252-18.681), dyspnoea (OR = 31.944, 95% CI = 18.877-92.741), achieving highest in-hospital temperature of >39.0°C (OR = 37.450, 95% CI = 7.402-683.403) and longer onset of illness to diagnosis (≥9 days) were statistically associated with higher risk of developing severe COVID-19. In conclusion, the potential risk factors forolder age, diabetes, dyspnoea, respiratory failure, acute cardiac injury and acute respiratory distress syndrome could help clinicians to identify patients with poor prognosis at an early stage.

Hypophosphatemia is an independent risk factor for AKI among hospitalized patients with COVID-19 infection.

BACKGROUND: This study sought to investigate incidence and risk factors for acute kidney injury (AKI) in hospitalized COVID-19. METHODS: In this retrospective study, we enrolled 823 COVID-19 patients with at least two evaluations of renal function during hospitalization from four hospitals in Wuhan, China between February 2020 and April 2020. Clinical and laboratory parameters at the time of admission and follow-up data were recorded. Systemic renal tubular dysfunction was evaluated via 24-h urine collections in a subgroup of 55 patients. RESULTS: In total, 823 patients were enrolled (50.5% male) with a mean age of 60.9 ± 14.9 years. AKI occurred in 38 (40.9%) ICU cases but only 6 (0.8%) non-ICU cases. Using forward stepwise Cox regression analysis, we found eight independent risk factors for AKI including decreased platelet level, lower albumin level, lower phosphorus level, higher level of lactate dehydrogenase (LDH), procalcitonin, C-reactive protein (CRP), urea, and prothrombin time (PT) on admission. For every 0.1 mmol/L decreases in serum phosphorus level, patients had a 1.34-fold (95% CI 1.14-1.58) increased risk of AKI. Patients with hypophosphatemia were likely to be older and with lower lymphocyte count, lower serum albumin level, lower uric acid, higher LDH, and higher CRP. Furthermore, serum phosphorus level was positively correlated with phosphate tubular maximum per volume of filtrate (TmP/GFR) (Pearson r = 0.66, p < .001) in subgroup analysis, indicating renal phosphate loss via proximal renal tubular dysfunction. CONCLUSION: The AKI incidence was very low in non-ICU patients as compared to ICU patients. Hypophosphatemia is an independent risk factor for AKI in patients hospitalized for COVID-19 infection.

A validation study of UCSD-Mayo risk score in predicting hospital-acquired acute kidney injury in COVID-19 patients.

INTRODUCTION: Acute kidney injury (AKI) in coronavirus disease 2019 (COVID-19) patients is associated with poor prognosis. Early prediction and intervention of AKI are vital for improving clinical outcome of COVID-19 patients. As lack of tools for early AKI detection in COVID-19 patients, this study aimed to validate the USCD-Mayo risk score in predicting hospital-acquired AKI in an extended multi-center COVID-19 cohort. METHODS: Five hundred seventy-two COVID-19 patients from Wuhan Tongji Hospital Guanggu Branch, Wuhan Leishenshan Hospital, and Wuhan No. Ninth Hospital was enrolled for this study. Patients who developed AKI or reached an outcome of recovery or death during the study period were included. Predictors were evaluated according to data extracted from medical records. RESULTS: Of all patients, a total of 44 (8%) developed AKI. The UCSD-Mayo risk score achieved excellent discrimination in predicting AKI with the C-statistic of 0.88 (95%CI: 0.84-0.91). Next, we determined the UCSD-Mayo risk score had good overall performance (Nagelkerke R2 = 0.32) and calibration in our cohort. Further analysis showed that the UCSD-Mayo risk score performed well in subgroups defined by gender, age, and several chronic comorbidities. However, the discrimination of the UCSD-Mayo risk score in ICU patients and patients with mechanical ventilation was not good which might be resulted from different risk factors of these patients. CONCLUSIONS: We validated the performance of UCSD-Mayo risk score in predicting hospital-acquired AKI in COVID-19 patients was excellent except for patients from ICU or patients with mechanical ventilation.

Anemia is associated with severe illness in COVID-19: A retrospective cohort study.

Anemia commonly aggravates the severity of respiratory diseases, whereas thus far, few studies have elucidated the impact of anemia on coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the clinical characteristics of patients with anemia, and to further explore the relationship between anemia and the severity of COVID-19. In this single-center, retrospective, observational study, a total of 222 confirmed patients admitted to Wuhan Ninth Hospital from 1 December 2019 to 20 March 2020 were recruited, including 79 patients with anemia and 143 patients without anemia. Clinical characteristics, laboratory findings, disease progression and prognosis were collected and analyzed. Risk factors associated with the severe illness in COVID-19 were established by univariable and multivariable logistic regression models. In our cohort, compared to patients without anemia, patients with anemia were more likely to have one or more comorbidities and severe COVID-19 illness. More patients demonstrated elevated levels of C-reactive protein (CRP), procalcitonin (PCT) and creatinine in anemia group. Levels of erythrocyte sedimentation rate, D-dimer, myoglobin, T-pro brain natriuretic peptide (T-pro-BNP) and urea nitrogen in patients with anemia were significantly higher than those without. In addition, the proportion of patients with dyspnea, elevated CRP, and PCT was positively associated with the severity of anemia. The odd ratio of anemia related to the severe condition of COVID-19 was 3.47 (95% confidence interval [CI]: 1.02-11.75; P = .046) and 3.77 (95% CI: 1.33-10.71; P = .013) after adjustment for baseline date and laboratory indices, respectively. Anemia is an independent risk factor associated with the severe illness of COVID-19, and healthcare professionals should be more sensitive to the hemoglobin levels of COVID-19 patients on admission. Awareness of anemia as a risk factor for COVID-19 was of great significance.

Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis.

The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries. Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes. Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied. The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment. In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers. The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted. Results show the feasibility and scalability of this model. On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22, 2020, would be spent for the number of commercial flights to recover back to 58.52%-60.39% on different interventions. This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport.

A risk score based on baseline risk factors for predicting mortality in COVID-19 patients.

BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.

In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19.

Statins are lipid-lowering therapeutics with favorable anti-inflammatory profiles and have been proposed as an adjunct therapy for COVID-19. However, statins may increase the risk of SARS-CoV-2 viral entry by inducing ACE2 expression. Here, we performed a retrospective study on 13,981 patients with COVID-19 in Hubei Province, China, among which 1,219 received statins. Based on a mixed-effect Cox model after propensity score-matching, we found that the risk for 28-day all-cause mortality was 5.2% and 9.4% in the matched statin and non-statin groups, respectively, with an adjusted hazard ratio of 0.58. The statin use-associated lower risk of mortality was also observed in the Cox time-varying model and marginal structural model analysis. These results give support for the completion of ongoing prospective studies and randomized controlled trials involving statin treatment for COVID-19, which are needed to further validate the utility of this class of drugs to combat the mortality of this pandemic.

Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes.

Type 2 diabetes (T2D) is a major comorbidity of COVID-19. However, the impact of blood glucose (BG) control on the degree of required medical interventions and on mortality in patients with COVID-19 and T2D remains uncertain. Thus, we performed a retrospective, multi-centered study of 7,337 cases of COVID-19 in Hubei Province, China, among which 952 had pre-existing T2D. We found that subjects with T2D required more medical interventions and had a significantly higher mortality (7.8% versus 2.7%; adjusted hazard ratio [HR], 1.49) and multiple organ injury than the non-diabetic individuals. Further, we found that well-controlled BG (glycemic variability within 3.9 to 10.0 mmol/L) was associated with markedly lower mortality compared to individuals with poorly controlled BG (upper limit of glycemic variability exceeding 10.0 mmol/L) (adjusted HR, 0.14) during hospitalization. These findings provide clinical evidence correlating improved glycemic control with better outcomes in patients with COVID-19 and pre-existing T2D.

Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19.

RATIONALE: Use of ACEIs (angiotensin-converting enzyme inhibitors) and ARBs (angiotensin II receptor blockers) is a major concern for clinicians treating coronavirus disease 2019 (COVID-19) in patients with hypertension. OBJECTIVE: To determine the association between in-hospital use of ACEI/ARB and all-cause mortality in patients with hypertension and hospitalized due to COVID-19. METHODS AND RESULTS: This retrospective, multi-center study included 1128 adult patients with hypertension diagnosed with COVID-19, including 188 taking ACEI/ARB (ACEI/ARB group; median age 64 [interquartile range, 55-68] years; 53.2% men) and 940 without using ACEI/ARB (non-ACEI/ARB group; median age 64 [interquartile range 57-69]; 53.5% men), who were admitted to 9 hospitals in Hubei Province, China from December 31, 2019 to February 20, 2020. In mixed-effect Cox model treating site as a random effect, after adjusting for age, gender, comorbidities, and in-hospital medications, the detected risk for all-cause mortality was lower in the ACEI/ARB group versus the non-ACEI/ARB group (adjusted hazard ratio, 0.42 [95% CI, 0.19-0.92]; P=0.03). In a propensity score-matched analysis followed by adjusting imbalanced variables in mixed-effect Cox model, the results consistently demonstrated lower risk of COVID-19 mortality in patients who received ACEI/ARB versus those who did not receive ACEI/ARB (adjusted hazard ratio, 0.37 [95% CI, 0.15-0.89]; P=0.03). Further subgroup propensity score-matched analysis indicated that, compared with use of other antihypertensive drugs, ACEI/ARB was also associated with decreased mortality (adjusted hazard ratio, 0.30 [95% CI, 0.12-0.70]; P=0.01) in patients with COVID-19 and coexisting hypertension. CONCLUSIONS: Among hospitalized patients with COVID-19 and coexisting hypertension, inpatient use of ACEI/ARB was associated with lower risk of all-cause mortality compared with ACEI/ARB nonusers. While study interpretation needs to consider the potential for residual confounders, it is unlikely that in-hospital use of ACEI/ARB was associated with an increased mortality risk.