Background and Purpose The coronavirus disease 2019 (COVID-19) pandemic presents an unprecedented health crisis to the entire world. As reported, the body mass index (BMI) may play an important role in COVID-19; however, this still remains unclear. The aim of this study was to explore the association between BMI and COVID-19 severity and mortality. Methods The Medline, PubMed, Embase and Web of science were systematically searched until August 2020. Random-effects models and dose-response meta-analysis were used to synthesize the results. Combined odds ratios (ORs) with their 95% confidence intervals (CIs) were calculated, and the effect of covariates were analyzed using subgroup analysis and meta-regression analyses. Results A total of 16 observational studies involving 109,881 patients with COVID-19 were included in the meta-analysis. The pooled results showed that patients with a BMI≥30kg/m 2 had a 2.35-fold risk(OR = 2.35, 95%CI = 1.64 − 3.38, P < 0.001) for critical COVID-19 and a 2.68-fold risk for COVID-19 mortality (OR = 2.68, 95%CI = 1.65 −4.37, P < 0.001 ) compared with patients with a BMI <30kg/m 2 . Subgroup analysis results showed that patients with obesity and age > 60 years was associated with a significantly increased risk of critical COVID-19(OR = 3.11, 95%CI = 1.73 − 5.61, P < 0.001) and COVID-19 mortality(OR = 3.93, 95%CI = 2.18 − 7.09, P < 0.001). Meta-regression analysis results also showed that age had a significant influence on the association between BMI and COVID-19 mortality(Coef.=0.036, p=0.048). Random-effects dose-response meta-analysis showed a linear association between BMI and both critical COVID-19(P non-linearity = 0.242) and mortality (P non-linearity = 0.116). The risk of critical COVID-19 and mortality increased by 9%(OR = 1.09, 95%CI = 1.04 −1.14, P < 0.001) and 6%(OR = 1.06, 95%CI = 1.02 −1.10, P = 0.002) for each 1 kg/m 2 increase in BMI, respectively. Conclusions Evidence from this meta-analysis suggested that a linear dose-response association between BMI and both COVID-19 severity and mortality. Further, obesity(BMI≥30kg/m 2 ) was associated with a significantly increased risk of critical COVID-19 and in-hospital mortality of COVID-19.

Aims: As reported, hypertension may play an important role in adverse outcomes of coronavirus disease-2019 , but it still had many confounding factors. The aim of this study was to explore whether hypertension is an independent risk factor for critical COVID-19 and mortality. Data synthesis: The Medline, PubMed, Embase, and Web of Science databases were systematically searched until November 2020. Combined odds ratios (ORs) with their 95% confidence interval (CIs) were calculated by using random-effect models, and the effect of covariates was analyzed using the subgroup analysis and meta-regression analysis. A total of 24 observational studies with 99,918 COVID-19 patients were included in the meta-analysis. The proportions of hypertension in critical COVID-19 were 37% (95% CI: 0.27 À0.47) when compared with 18% (95% CI: 0.14 À0.23) of noncritical COVID-19 patients, in those who died were 46% (95%CI: 0.37 À0.55) when compared with 22% (95% CI: 0.16 À0.28) of survivors. Pooled results based on the adjusted OR showed that patients with hypertension had a 1.82-fold higher risk for critical COVID-19 (aOR: 1.82; 95% CI: 1.19 À 2.77; P Z 0.005) and a 2.17-fold higher risk for COVID-19 mortality (aOR: 2.17; 95% CI: 1.67 À 2.82; P < 0.001). Subgroup analysis results showed that male patients had a higher risk of developing to the critical condition than female patients (OR: 3.04; 95%CI: 2.06 À 4.49; P < 0.001) and age >60 years was associated with a significantly increased risk of COVID-19 mortality (OR: 3.12; 95% CI: 1.93 À 5.05; P < 0.001). Meta-regression analysis results also showed that age (Coef. Z 2.3Â10 À2 , P Z 0.048) had a significant influence on the association between hypertension and COVID-19 mortality. Conclusions: Evidence from this meta-analysis suggested that hypertension was independently associated with a significantly increased risk of critical COVID-19 and inhospital mortality of COVID-19.

SUMMARYThis paper discusses the problem of output feedback stabilization for a more general class of stochastic high‐order nonlinear systems with time‐varying delays. On the basis of a subtle homogeneous observer and controller construction, and the homogeneous domination approach, the closed‐loop system is globally asymptotically stable in probability, by choosing an appropriate Lyapunov–Krasovskii functional. An example is given to illustrate the effectiveness of the proposed design procedure. Copyright © 2013 John Wiley & Sons, Ltd.

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Varicella is an acute respiratory infectious diseases, with high transmissibility and quick dissemination. In this study, an SEIR (susceptible-exposed-infected-recovered) dynamic model was established to explore the optimal prevention and control measures according to the epidemiological characteristics about varicella outbreak in a school in a central city of China. Berkeley Madonna 8.3.18 and Microsoft Office Excel 2010 software were employed for the model simulation and data management, respectively. The result showed that the simulated result of SEIR model agreed well with the reported data when β (infected rate) equal to 0.067. Models showed that the cumulative number of cases was only 13 when isolation adopted when the infected individuals were identified (assuming isolation rate was up to 100%); the cumulative number of cases was only two and the TAR (total attack rate) was 0.56% when the vaccination coefficient reached 50%. The cumulative number of cases did not change significantly with the change of efficiency of ventilation and disinfection, but the peak time was delayed; when δ (vaccination coefficient) = 0.1, m (ventilation efficiency) = 0.7 or δ = 0.2, m = 0.5 or δ = 0.3, m = 0.1 or δ = 0.4 and above, the cumulative number of cases would reduce to one case and TAR would reduce to 0.28% with combined interventions. Varicella outbreak in school could be controlled through strict isolation or vaccination singly; combined interventions have been adopted when the vaccination coefficient was low.

Background: Mumps is an acute respiratory infectious disease with obvious regional and seasonal differences. Exploring the impact of climate factors on the incidence of mumps and predicting its incidence trend on this basis could effectively control the outbreak and epidemic of mumps. Methods: Considering the great differences of climate in the vast territory of China, this study divided the Chinese mainland into seven regions according to the administrative planning criteria, data of Mumps were collected from the China Disease Prevention and Control Information System, ARIMA model and ARIMAX model with meteorological factors were established to predict the incidence of mumps. Results: In this study, we found that precipitation, air pressure, temperature, and wind speed had an impact on the incidence of mumps in most regions of China and the incidence of mumps in the north and southwest China was more susceptible to climate factors. Considering meteorological factors, the average relative error of ARIMAX model was 10.87%, which was lower than ARIMA model (15.57%). Conclusions: Meteorology factors were the important factors which can affect the incidence of mumps, ARIMAX model with meteorological factors could better simulate and predict the incidence of mumps in China, which has certain reference value for the prevention and control of mumps.

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