The impact of COVID-19 vaccination campaign in Hong Kong SAR China and Singapore

Background:Vaccination has been the most important measure to mitigate the COVID-19 pandemic.The vaccination coverage was relatively low in Hong Kong Special Administrative Region China,compared to Singapore,in early 2022.Hypothetically,if the two regions,Hong Kong(HK)and Singapore(SG),swap their vaccination coverage rate,what outcome would occur?Method:We adopt the Susceptible e Vaccinated e Exposed e Infectious e Hospitalized e Death-Recovered model with a time-varying transmission rate and fit the model to weekly reported COVID-19 deaths(the data up to 2022 Nov 4)in HK and SG using R package POMP.After we obtain a reasonable fitting,we rerun our model with the estimated parameter values and swap the vaccination rates between HK and SG to explore what would happen.Results:Our model fits the data well.The reconstructed transmission rate was higher in HK than in SG in 2022.With a higher vaccination rate as in SG,the death total reported in HK would decrease by 37.5%and the timing of the peak would delay by 3 weeks.With a lower vaccination rate as in HK,the death total reported in SG would increase to 5.5-fold high with a peak 6 weeks earlier than the actual during the Delta variant period.Conclusions:Vaccination rate changes in HK and SG may lead to very different outcomes.This is likely due that the estimated transmission rates were very different in HK and SG which reflect the different control policies and dominant variants.Because of strong control measures,HK avoided large-scale community transmission of the Delta variant.Given the high breakthrough infection rate and transmission rate of the Omicron variant,increasing the vaccination rate in HK will likely yield a mild(but significant)contribution in terms of lives saved.While in SG,lower vaccination coverage to the level of HK will be disastrous.

Solving Interface Problems of the Helmholtz Equation by Immersed Finite Element Methods

This article reports our explorations for solving interface problems of the Helmholtz equation by immersed finite elements (IFE) on interface independent meshes. Two IFE methods are investigated: the partially penalized IFE (PPIFE) and discontinuous Galerkin IFE (DGIFE) methods. Optimal convergence rates are observed for these IFE methods once the mesh size is smaller than the optimal mesh size which is mainly dictated by the wave number. Numerical experiments also suggest that higher degree IFE methods are advantageous because of their larger optimal mesh size and higher convergence rates.

RESEARCH ANNOUNCEMENTS ON "MODERATE DEVIATIONS FOR GRENANDER ESTIMATOR NEAR BOUNDARIES OF THE SUPPORT"

1 Introduction and Main Results Let f be a decreasing density with support[0,∞).Denote by Fn the empirical distribution function of a sample X_(1),...,X_(n) from f.Let F_(n) be the concave majorant of F_(n) on[0,∞),i.e.

Distributed computations for large-scale networked systems using belief propagation

This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical learning.These algorithms are suitable for a class of computational problems in largescale networked systems,ranging from average consensus,sensor fusion,distributed estimation,distributed optimisation,distributed control,and distributed learning.By expressing the underlying computational problem as a sparse linear system,each algorithm operates at each node of the network graph and computes iteratively the desired solution.The behaviours of these algorithms are discussed in terms of the network graph topology and parameters of the corresponding computational problem.A number of examples are presented to illustrate their applications.Also introduced is a message-passing algorithm for distributed convex optimisation.

Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects:a modeling analysis

Despite most COVID-19 infections being asymptomatic,China's Mainland had a high increase in symptomatic cases at the end of 2022.In this study,we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model.Our model considers the epidemiological characteristics of SARS-CoV-2,particularly variolation,from non-pharmaceutical intervention(facial masking and social distance),demography,and disease mortality in China's Mainland.The increase in symptomatic proportions in China may be attributable to(1)higher sensitivity and vulnerability during winter and(2)enhanced viral inhalation due to spikes in SARS-CoV-2 infections(high transmissibility).These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022.Our study,therefore,can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts.Thus,we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals.However,further investigation is required to understand the variolation effect on disease severity.

Modelling the unexpected dynamics of COVID-19 in Manaus,Brazil

In late March 2020,SARS-CoV-2 arrived in Manaus,Brazil,and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates.Several key studies reported that∼76%of residents of Manaus were infected(attack rate AR≃76%)by October 2020,suggesting protective herd immunity had been reached.Despite this,an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first,creating a catastrophe for the unprepared population.It has been suggested that this could be possible if the second wave was driven by reinfections.However,it is widely reported that reinfections were at a low rate(before the emergence of Omicron),and reinfections tend to be mild.Here,we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared.The method fits a“flexible”reproductive numberR_(0)(t)that changes over the epidemic,and it is demonstrated that the method can successfully reconstruct R_(0)(t)from simulated data.For Manaus,the method finds AR≃34%by October 2020 for the first wave,which is far less than required for herd immunity yet in-line with seroprevalence estimates.The work is complemented by a two-strain model.Using genomic data,the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1.Moreover,an age class model variant that considers the high mortality rates of older adults show very similar results.These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates,which until now have only been found in negligible to moderate numbers in recent surveillance efforts.

Stationary distribution and probability density function of a stochastic waterborne pathogen model with logistic growth

Waterborne disease threatens public health globally.Previous studies mainly consider that the birth of pathogens in water sources arises solely by the shedding of infected individuals,However,for free-living pathogens,intrinsic growth without the presence of hosts in environment could be possible.In this paper,a stochastic waterborne disease model with a logistic growth of pathogens is investigated.We obtain the sufficient conditions for the extinction of disease and also the existence and uniqueness of an ergodic stationary distribution if the threshold R_(0)^(s)>1.By solving the Fokker-Planck equation,an exact expression of probability density function near the quasi-endemic equilibrium is obtained.Results suggest that the intrinsic growth in bacteria population induces a large reproduction number to determine the disease dynamics.Finally,theoretical results are validated by numerical examples.

The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale

Virus evolution is a common process of pathogen adaption to host population and environment.Frequently,a small but important fraction of virus mutations are reported to contribute to higher risks of host infection,which is one of the major determinants of infectious diseases outbreaks at population scale.The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly.Based on classic epidemiology theories of disease transmission,we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population.The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness.The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England.The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Large Eddy Simulation of a dilute particleladen turbulent flow over a backwardfacing step

Dilute gas-particle turbulent flows over a backward-facing step are numerically simulated by Large Eddy Simulation (LES) for the continuous phase and Lagran- gian particle trajectory method for the particle phase. Predicted results of mean velocities and fluctuating velocities of both phases agree well with the experimental data, and demonstrate that the main characteristics of the flow are accurately captured by the simulations. Characteristics of separation and reattachments as well as essential features of the coherent structure are obtained, in which the processes of vortex roll up, growth, pairing and breaking up are shown in details. Particle dispersions are then investigated through particles’ instantaneous distri- butions in coherent structure as well as the mean and fluctuating properties of particle number density (PND). The predicted mean PND agree well with experiment results. For small particles, the instantaneous distributions show much preferential concentration, while their mean PND shows more uniform distribution in down- stream region. On the contrary, for large particles, their instantaneous distributions are much uniform (without clear preferential concentration) due to less effect of large eddy coherent, while their mean PND across the section is not uniform for more particles are distributed in the main flow region. The preferential concentra- tion of particles by the large-scale eddies can lead to a high fluctuating PND.

Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19)in Africa

Background:Since the first case of coronavirus disease 2019(COVID-19)in Africa was detected on February 14,2020,the cumulative confirmations reached 15207 including 831 deaths by April 13,2020.Africa has been described as one of the most vulnerable region with the COVID-19 infection during the initial phase of the outbreak,due to the fact that Africa is a great commercial partner of China and some other EU and American countries.Which result in large volume of travels by traders to the region more frequently and causing African countries face even bigger health threat during the COVID-19 pandemic.Furthermore,the fact that the control and management of COVID-19 pandemic rely heavily on a country's health care system,and on average Africa has poor health care system which make it more vulnerable indicating a need for timely intervention to curtail the spread.In this paper,we estimate the exponential growth rate and basic reproduction number(R0)of COVID-19 in Africa to show the potential of the virus to spread,and reveal the importance of sustaining stringent health measures to control the disease in Africa.Methods:We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020,by using the simple exponential growth model.We examined the publicly available materials published by the WHO situation report to show the potential of COVID-19 to spread without sustaining strict health measures.The Poisson likelihood framework is adopted for data fitting and parameter estimation.We modelled the distribution of COVID-19 generation interval(GI)as Gamma distributions with a mean of 4.7 days and standard deviation of 2.9 days estimated from previous work,and compute the basic reproduction number.Results:We estimated the exponential growth rate as 0.22 per day(95%CI:0.20-0.24),and the basic reproduction number,R0,as 2.37(95%CI:2.22-2.51)based on the assumption that the exponential growth starting from 1 March 2020.With an R0 at 2.37,we quantified the instantaneous transmissibility of the outbreak by the time-varying effective reproductive number to show the potential of COVID-19 to spread across African region.Conclusions:The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries.Our estimates should be useful in preparedness planning against further spread of the COVID-19 epidemic in Africa.

Mathematical modeling of COVID-19 epidemic with effect of awareness programs

Severe acute respiratory syndrome coronavirus 2(SARS-COV-2)is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019(COVID-19).The epidemic has largely been controlled in China since March 2020,but continues to inflict severe public health and socioeconomic burden in other parts of the world.One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment(awareness)programs which play a vital role in the control of the COVID-19 pandemic.Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs.In this paper,we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria.Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases,to assess the effect of public awareness on the dynamics of COVID-19 infection.We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020.We find that the epidemic could increase if awareness programs are not properly adopted.We presumed that the effect of awareness programs could be estimated.Further,our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond.Finally,we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic.

Infection fatality rate and infection attack rate of COVID-19 in South American countries

Background:The ongoing COVID-19 pandemic hit South America badly with multiple waves.Different COVID-19 variants have been storming across the region,leading to more severe infections and deaths even in places with high vaccination coverage.This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate(IFR),infection attack rate(IAR)and reproduction number(R0)for twelve most affected South American countries.Methods:We fit a susceptible-exposed-infectious-recovered(SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities.Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization,Johns Hopkins Coronavirus Resource Center and Our World in Data.We investigate the COVID-19 mortalities in these countries,which could represent the situation for the overall South American region.We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR,IAR and R0 of COVID-19 for the South American countries.Results:We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR(varies between 0.303% and 0.723%),IAR(varies between 0.03 and 0.784)and R0(varies between 0.7 and 2.5)for the 12 South American countries.We observe that the severity,dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous.Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.Conclusions:This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America.We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths.Thus,strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

Regional heterogeneity of in-hospital mortality of COVID-19 in Brazil

Background The ongoing Coronavirus disease of 2019(COVID-19)pandemic has hit Brazil hard in period of different dominant variants.Different COIVD-19 variants have swept through the region,resulting that the total number of cases in Brazil is the third highest in the world.This study is aimed at investigating the regional heterogeneity of in-hospital mortality of COVID-19 in Brazil and the effects of vaccination and social inequality.Methods We fitted a multivariate mixed-effects Cox model to a national database of inpatient data in Brazil who were admitted for COVID-19 from February 27,2020 to March 15,2022.The in-hospital mortality risks of vaccinated and unvaccinated patients were compared,with adjustment for age,state,ethnicity,education and comorbidities.And the effects of variables to in-hospital mortality were also compared.Stratified analysis was conducted across different age groups and vaccine types.Results By fitting the multivariate mixed-effects Cox model,we concluded that age was the most important risk factor for death.With regards to educational level,illiterate patients(hazard ratio:1.63,95%CI:1.56–1.70)had a higher risk than those with a university or college degree.Some common comorbidities were more dangerous for hospitalized patients,such as liver disease(HR:1.46,95%CI:1.34–1.59)and immunosuppression(HR:1.32,95%CI:1.26–1.40).In addition,the states involving Sergipe(HR:1.75,95%CI:1.46–2.11),Roraima(HR:1.65,95%CI:1.43–1.92),Maranhão(HR:1.57,95%CI:1.38–1.79),Acre(HR:1.44,95%CI:1.12–1.86),and Rondônia(HR:1.26,95%CI:1.10–1.44)in the north and the northeast region tended to have higher hazard ratios than other area.In terms of vaccine protection,vaccination did not significantly reduce mortality among hospitalized patients.Sinovac and AstraZeneca offered different protection in different regions,and no vaccine provided high protection in all regions.Conclusion The study revealed the regional heterogeneity of in-hospital mortality of Covid-19 in Brazil and the effects of vaccination and social inequality.We found that ethnic concentrations were consistent with higher proportion of death cases relative to population size.White Brazilians had more frequent international travel opportunities.As race revealed the intersection of social connections,we speculated that uneven interactions with residential communities partially contribute to the spread of the epidemic.Additionally,the vaccine showed different protection in different regions.In the northern and northeastern regions,AstraZeneca was much more protective than Sinovac,while Sinovac was more protective for hospitalized patients with varying numbers of comorbidities in the Central-west,Southeast and South regions.

A Method of Lines Based on Immersed Finite Elements for Parabolic Moving Interface Problems

This article extends the finite element method of lines to a parabolic initial boundary value problem whose diffusion coefficient is discontinuous across an interface that changes with respect to time.The method presented here uses immersed finite element(IFE)functions for the discretization in spatial variables that can be carried out over a fixedmesh(such as a Cartesianmesh if desired),and this featuremakes it possible to reduce the parabolic equation to a system of ordinary differential equations(ODE)through the usual semi-discretization procedure.Therefore,with a suitable choice of the ODE solver,this method can reliably and efficiently solve a parabolic moving interface problem over a fixed structured(Cartesian)mesh.Numerical examples are presented to demonstrate features of this new method.

Visual inference of flow flux via free surface PDE model and image sequence assimilation

The free-surface flows,such as flows in rivers,lakes,and tides,play an important role in hydraulic engineering and environmental monitoring.However,due to their complex and changeable characters,the precise evolution procedure is quite difficult to reconstruct.In this study,the authors propose a novel framework to reconstruct the free-surface flow modelled by the shallow water equations by assimilating the images sequences.In particular,the ensemble Kalman filter framework is employed to implement the assimilation process.The efficiency of the proposed strategy has been verified through numerical simulations in which the accurate flow field in different situations could be obtained within limited assimilation steps.

Visualization of Coherent Structures in Acoustically Forced, Gas-Particle Turbulent Round Jet

To show the effects of the particles and forced disturbances on the instantaneous large-scale vortex structures in a gas-particle round jet, coherent structures in gas-particle turbulent round jets were investigated experimentally by flow visualization. The 45-μm and 350-μm diameter glass beads were used as the particles in the experiments. An acoustic speaker was used to introduce velocity perturbations at the jet exit. The Strouhal number based on the nozzle diameter, exit velocity, and forcing frequency was varied from 0,1 to 0.9. The Reynolds number was 9400. The coherent structures were visualized in unforced and forced single-phase jet flows and unforced and forced particle-laden jet flows with different diameter glass beads. The experimental results show that the particles have significant effects on the gas phase coherent structures. The coherent structures are controlled by the large 350-μm diameter particles, while the structures are mainly dominated by the forced disturbances in the flows w

Mathematical modeling and analysis of meningococcal meningitis transmission dynamics

Meningococcal meningitis(MCM)is one of the serious public health threats in the tropical and sub-tropical regions.In this paper,we propose an epidemic model to study the transmission dynamics of MCM with high-and low-risk susceptible populations.The model considers two different groups of susceptible individuals depending on the availability of medical resources(MR,including hospitals,health workers,etc.),which varies the infection risk.We find that the model exhibits the phenomenon of backward bifurcation(BB),which increases the difficulty of MCM control since the dynamics are not merely relying on the basic reproduction number,TZo.This study explores the effects of MR on the MCM epidemics by mathematical analysis and shows the existence of BB on MCM disease.Our findings suggest that providing adequate MR in a community is crucial in mitigating MCM incidences and deaths,especially,in the MCM endemic regions.

Mathematical modeling and analysis of schistosomiasis transmission dynamics

Schistosomiasis is a parasitic disease from the family of Schistosomatidae and genus Schistosoma,which is caused by blood flukes.The disease is endemic in many countries and still a serious threat to global public health and development.In this paper,a new deterministic model is designed and analyzed qualitatively to explore the dynamics of schistosomiasis transmission in human,cattle and snail populations.Results from our mathematical analysis show that the model has a disease-free equilibrium(DFE)which is locally asymptotically stable(LAS)whenever a particular epidemiological threshold quantity,also known as basic reproduction number(R0)is less than unity.Further analysis shows that the model has a unique endemic equilibrium(EE)which is globally asymptotically stable whenever R0>1 and unstable when R0<1.Furthermore,we adopt partial rank correlation coefficient for sensitivity analysis to reveal the most important parameters for effective control and mitigation of schistosomiasis disease in a community.Finally,we obtain some numerical results by simulating the entire dynamics of the model,which show that the infections in the compartments of each population decrease with respect to time.This further indicates that avoiding contact with infected human,cattle or infested water is vital to prevent the spread of schistosomiasis disease infection.

Testing heteroscedasticity by wavelets in a nonparametric regression model

In the nonparametric regression models, a homoscedastic structure is usually assumed. However, the homoscedasticity cannot be guaranteed a priori. Hence, testing the heteroscedasticity is needed. In this paper we propose a consistent nonparametric test for heteroscedasticity, based on wavelets. The empirical wavelet coefficients of the conditional variance in a regression model are defined first. Then they are shown to be asymptotically normal, based on which a test statistic for the heteroscedasticity is constructed by using Fan's wavelet thresholding idea. Simulations show that our test is superior to the traditional nonparametric test.

Optimizing COVID-19 vaccination programs during vaccine shortages

During the ongoing COVID-19 pandemic, vaccine shortages occur due to various types of constraints, including interruptions in production/supply, higher-than-expected demands, and a lack of resources such as healthcare capacity to administer vaccines. Scientifically informed epidemic models have been utilized as pivotal tools to optimize the immunization programs subject to vaccine shortages. The current paper reviews modelling methods to optimize the allocation strategies of vaccines with differential efficacies by using various model-based outcome measures. The models reviewed in this study are expected to be adopted and extended to make contributions on policy development for disease control under the vaccine shortage scenario.