A more accurate Parameterization based on cosmic Age(MAPAge)

Recently,several statistically significant tensions between different cosmological datasets have raised doubts about the standard Lambda cold dark matter(ACDM)model.A recent letter(Huang 2020)suggests to use"Parameterization based on cosmic Age"(PAge)to approximate a broad class of beyondACDM models,with a typical accuracy~1%in angular diameter distances at z■10.In this work,we extend PAge to a More Accurate Parameterization based on cosmic Age(MAPAge)by adding a new degree of freedomη2.The parameterη2 describes the difference between physically motivated models and their phenomenological PAge approximations.The accuracy of MAPAge,typically of order 10-3 in angular diameter distances at z■10,is significantly better than PAge.We compare PAge and MAPAge with current observational data and forecast data.The conjecture in Huang(2020),that PAge approximation is sufficiently good for current observations,is quantitatively confirmed in this work.We also show that the extension from PAge to MAPAge is important for future observations,which typically require sub-percent accuracy in theoretical predictions.

Effectiveness of potential antiviral treatments in COVID-19 transmission control:a modelling study

Background:Novel coronavirus disease 2019(COVID-19)causes an immense disease burden.Although public health countermeasures effectively controlled the epidemic in China,non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally.Vaccination is mainly used to prevent COVID-19,and most current antiviral treatment evaluations focus on clinical efficacy.Therefore,we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.

Feasibility of controlling hepatitis E in Jiangsu Province, China: a modelling study

Background:Hepatitis E,an acute zoonotic disease caused by the hepatitis E virus(HEV),has a relatively high burden in developing countries.The current research model on hepatitis E mainly uses experimental animal models(such as pigs,chickens,and rabbits)to explain the transmission of HEV.Few studies have developed a multi-host and multiroute transmission dynamic model(MHMRTDM)to explore the transmission feature of HEV.Hence,this study aimed to explore its transmission and evaluate the effectiveness of intervention using the dataset of Jiangsu Province.

Correlation between mumps and meteorological factors in Xiamen City,China:A modelling study

Objective:Mumps is a seasonal infectious disease,always occurring in winter and spring.In this study,we aim to analyze its epidemiological characteristics,transmissibility,and its correlation with meteorological variables.Method:A seasonal Susceptiblee Exposede Infectious/Asymptomatice Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number(Rt).Results:The seasonal double peak of annual incidence was mainly in May to July and November to December.There was high transmission at the median of Rt¼1.091(ranged:0 to 4.393).Rt was seasonally distributed mainly from February to April and from September to November.Correlations were found between temperature(Pearson correlation coefficient[r]ranged:from 0.101 to 0.115),average relative humidity(r¼0.070),average local pressure(r¼-0.066),and the number of new cases.In addition,average local pressure(r¼0.188),average wind speed(r¼0.111),air temperature(r ranged:-0.128 to-0.150),average relative humidity(r¼-0.203)and sunshine duration(r¼-0.075)were all correlated with Rt.Conclusion:A relatively high level of transmissibility has been found in Xiamen City,leading to a continuous epidemic of mumps.Meteorological factors,especially air temperature and relative humidity,may be more closely associated with mumps than other factors.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019(COVID-19)continues to threaten human health security,exerting considerable pressure on healthcare systems worldwide.While prognostic models for COVID-19 hospitalized or intensive care patients are currently available,prognostic models developed for large cohorts of thousands of individuals are still lacking.Methods Between February 4 and April 16,2020,we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital,Hubei Province,China.(1)Screening of key prognostic factors:A univariate Cox regression analysis was performed on 2,649 patients in the training set,and factors affecting prognosis were initially screened.Subsequently,a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis.Finally,multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis.(2)Establishment of a scoring system:The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors,calculated the C index,drew calibration curves and drew training set patient survival curves.(3)Verification of the scoring system:The scoring system assessed 1,325 patients in the test set,splitting them into high-and low-risk groups,calculated the C-index,and drew calibration and survival curves.Results The cross-sectional study found that age,clinical classification,sex,pulmonary insufficiency,hypoproteinemia,and four other factors(underlying diseases:blood diseases,malignant tumor;complications:digestive tract bleeding,heart dysfunction)have important significance for the prognosis of the enrolled patients with COVID-19.Herein,we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19.Meanwhile,the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high-and low-risk groups(using a scoring threshold of 117.77,a score below which is considered low risk).The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines(C indexes,0.95 vs.0.89).Conclusions Age,clinical typing,sex,pulmonary insufficiency,hypoproteinemia,and four other factors were important for COVID-19 survival.Compared with general statistical methods,this method can quickly and accurately screen out the relevant factors affecting prognosis,provide an order of importance,and establish a scoring system based on the nomogram model,which is of great clinical significance.