The impact of EV71 vaccination program on hand,foot and mouth disease in Zhejiang Province,China:A negative control study

Objective:To estimate the potential causal impact of Enterovirus A71(EV71)vaccination program on the reduction of EV71-infected hand,foot,and mouth disease(HFMD)in Zhejiang Province.Methods:We utilized the longitudinal surveillance dataset of HFMD and EV71 vaccination in Zhejiang Province during 2010-2019.We estimated vaccine efficacy using a Bayesian structured time series(BSTS)model,and employed a negative control outcome(NCO)model to detect unmeasured confounding and reveal potential causal association.Results:We estimated that 20,132 EV71 cases(95%CI:16,733,23,532)were prevented by vaccination program during 2017-2019,corresponding to a reduction of 29%(95%CI:24%,34%).The effectiveness of vaccination increased annually,with reductions of 11%(95%CI:6%,16%)in 2017 and 66%(95%CI:61%,71%)in 2019.Children under 5 years old obtained greater benefits compared to those over 5 years.Cities with higher vaccination coverage experienced a sharper EV71 reduction compared to those with lower coverage.The NCO model detected no confounding factors in the association between vaccination and EV71 cases reduction.

Effects and interaction of temperature and relative humidity on the trend of influenza prevalence:A multi-central study based on 30 provinces in China's Mainland from 2013 to 2018

Background:Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions of China.Methods:We estimated the time-varying reproduction number(Rt)of influenza and explored the impact of temperature and relative humidity on Rt using generalized additive quasi-Poisson regression models combined with the distribution lag non-linear model(DLNM).The effect of temperature and humidity interaction on Rt of influenza was explored.The multiple random-meta analysis was used to evaluate region-specific association.The excess risk(ER)index was defined to investigate the correlation between Rt and each meteorological factor with the modification of seasonal and regional characteristics.Results:Low temperature and low relative humidity contributed to influenza epidemics on the national level,while shapes of merged cumulative effect plots were different across regions.Compared to that of median temperature,the merged RR(95%CI)of low tem-perature in northern and southern regions were 1.40(1.24,1.45)and 1.20(1.14,1.27),respectively,while those of high temperature were 1.10(1.03,1.17)and 1.00(0.95,1.04),respectively.There were negative interactions between temperature and relative humidity on national(SI=0.59,95%CI:0.57e0.61),southern(SI=0.49,95%CI:0.17e0.80),and northern regions(SI=0.59,95%CI:0.56,0.62).In general,with the increase of the change of the two meteorological factors,the ER of Rt also gradually increased.Conclusions:Temperature and relative humidity have an effect on the influenza epidemics in China,and there is an interaction between the two meteorological factors,but the effect of each factor is heterogeneous among regions.Meteorological factors may be considered to predict the trend of influenza epidemic.

Simulating potential outbreaks of Delta and Omicron variants based on contact-tracing data:A modelling study in Fujian Province,China

Although studies have compared the relative severity of Omicron and Delta variants by assessing the relative risks,there are still gaps in the knowledge of the potential COVID-19 burden these variations may cause.And the contact patterns in Fujian Province,China,have not been described.We identified 8969 transmission pairs in Fujian,China,by analyzing a contact-tracing database that recorded a SARS-CoV-2 outbreak in September 2021.We estimated the waning vaccine effectiveness against Delta variant infection,contact patterns,and epidemiology distributions,then simulated potential outbreaks of Delta and Omicron variants using a multi-group mathematical model.For instance,in the contact setting without stringent lockdowns,we estimated that in a potential Omicron wave,only 4.7%of infections would occur in Fujian Province among individuals aged>60 years.In comparison,58.75%of the death toll would occur in unvaccinated individuals aged>60 years.Compared with no strict lockdowns,combining school or factory closure alone reduced cumulative deaths of Delta and Omicron by 28.5%and 6.1%,respectively.In conclusion,this study validates the need for continuous mass immunization,especially among elderly aged over 60 years old.And it confirms that the effect of lockdowns alone in reducing infections or deaths is minimal.However,these measurements will still contribute to lowering peak daily incidence and delaying the epidemic,easing the healthcare system's burden.

Trends in disease burden of hepatitis B infection in Jiangsu Province,China,1990-2021

Background:The incidence of hepatitis B virus(HBV)has decreased year by year in China after the expansion of vaccination,but there is still a high disease burden in Jiangsu Province of China.Methods:The year-by-year incidence data of HBV in Jiangsu Province from 1990 to 2021 were collected.The incidence rates of males and females age groups were clustered by systematic clustering,and the incidence rates of each age group were analyzed and studied by using Joinpoint regression model and age-period-cohort effect model(APC).Results:Joinpoint regression model and APC model showed a general decrease in HBV prevalence in both males and females.In addition,the results of the APC model showed that the age,period,and cohort effects of patients all affected the incidence of HBV,and the incidence was higher in males than in females.The incidence is highest in the population between the ages of 15 and 30 years(mean:21.76/100,000),especially in males(mean:31.53/100,000)than in females(mean:11.67/100,000).Another high-risk group is those over 60 years of age(mean:21.40/100,000),especially males(mean:31.17/100,000)than females(mean:11.63/100,000).The period effect of the APC model suggests that HBV vaccination is effective in reducing the incidence of HBV in the population.Conclusions:The incidence of HBV in Jiangsu Province showed a gradual downward trend,but the disease burden in males was higher than that in females.The incidence is higher and increasing rapidly in the population between the ages of 15 and 30 years and people over 60 years of age.More targeted prevention and control measures should be imple-mented for males and the elderly.

A follow up study of cycle threshold values of SARS-CoV-2 in Hunan Province,China

Since the epidemic of the severe acute respiratory syndrome coronavirus 2(SARS-COV-2),many governments have used reverse transcription polymerase chain reaction(RT-PCR)to detect the virus.However,there are fewer measures of CT values information based on RT-PCR results,and the relationship between CT values and factors from consecutive tests is not clear enough.So in this study,we analyzed the connection between CT values and the factors based on cohort data from Delta variant of SARS-CoV-2 in Hunan Province.Previous studies have showed that the mean age of the cases was 33.34 years(±18.72 years),with a female predominance(55.03%,n=71),and the greatest proportion of clinical symptoms were of the common type(60.47%,n=78).There were statistical differences between the N and ORF1ab genes in the CT values for the cases.Based on the analysis of the association between CT values and the factors,the lowest CT values were obtained for the unvaccinated,older and clinically symptomatic group at 3e10 days,the maximum peak of viral load occurred.Therefore,it is recommended to use patient information to focus on older,clinically symptomatic,unvaccinated patients and to intervene promptly upon admission.

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.

Revisiting classical SIR modelling in light of the COVID-19 pandemic

Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.

A nonlinear relapse model with disaggregated contact rates:Analysis of a forward-backward bifurcation

Throughout the progress of epidemic scenarios,individuals in different health classes are expected to have different average daily contact behavior.This contact heterogeneity has been studied in recent adaptive models and allows us to capture the inherent differences across health statuses better.Diseases with reinfection bring out more complex scenarios and offer an important application to consider contact disaggregation.Therefore,we developed a nonlinear differential equation model to explore the dynamics of relapse phenomena and contact differences across health statuses.Our incidence rate function is formulated,taking inspiration from recent adaptive algorithms.It incorporates contact behavior for individuals in each health class.We use constant contact rates at each health status for our analytical results and prove conditions for different forward-backward bifurcation scenarios.The relationship between the different contact rates heavily in-fluences these conditions.Numerical examples highlight the effect of temporarily recov-ered individuals and initial conditions on infected population persistence.

Simulation of optimal dose regimens of photoactivated curcumin for antimicrobial resistance pneumonia in COVID-19 patients:A modeling approach

Background:Secondary antimicrobial resistance bacterial(AMR)pneumonia could lead to an increase in mortality in COVID-19 patients,particularly of geriatric patients with underlying diseases.The comedication of current medicines for AMR pneumonia with corticosteroids may lead to suboptimal treatment or toxicities due to drug-drug interactions(DDIs).Objective:This study aimed to propose new promising dosage regimens of photoactivated curcumin when co-administered with corticosteroids for the treatment of antimicrobial resistance(AMR)pneumonia in COVID-19 patients.Methods:A whole-body physiologically-based pharmacokinetic(PBPK)with the simplified lung compartments model was built and verified following standard model verification(absolute average-folding error or AAFEs).The pharmacokinetic properties of photo-activated were assumed to be similar to curcumin due to minor changes in physiochemical properties of compound by photoactivation.The acceptable AAFEs values were within 2-fold.The verified model was used to simulate new regimens for different formulations of photoactivated curcumin.Results:The AAFEs was 1.12-fold.Original formulation(120 mg once-daily dose)or new intramuscular nano-formulation(100 mg with a release rate of 10/h given every 7 days)is suitable for outpatients with MRSA pneumonia to improve patient adherence.New intravenous formulation(2000 mg twice-daily doses)is for hospitalized patients with both MRSA and VRSA pneumonia.Conclusion:The PBPK models,in conjunction with MIC and applied physiological changes in COVID-19 patients,is a potential tool to predict optimal dosage regimens of photo-activated curcumin for the treatment of co-infected AMR pneumonia in COVID-19 patients.Each formulation is appropriate for different patient conditions and pathogens.

A discrete-time susceptible-infectious-recovered-susceptible model for the analysis of influenza data

We develop a discrete time compartmental model to describe the spread of seasonal influenza virus.As time and disease state variables are assumed to be discrete,this model is considered to be a discrete time,stochastic,Susceptible-Infectious-RecoveredSusceptible(DT-SIRS)model,where weekly counts of disease are assumed to follow a Poisson distribution.We allow the disease transmission rate to also vary over time,and the disease can only be reintroduced after extinction if there is a contact with infected individuals from other host populations.To capture the variability of influenza activities from one season to the next,we define the seasonality with a 4-week period effect that may change over years.We examine three different transmission rates and compare their performance to that of existing approaches.Even though there is limited information for susceptible and recovered individuals,we demonstrate that the simple models for transmission rates effectively capture the behaviour of the disease dynamics.We use a Bayesian approach for inference.The framework is applied in an analysis of the temporal spread of influenza in the province of Manitoba,Canada,2012e2015.

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out:An application to the Italian scenario

In the context of SARS-CoV-2 pandemic,mathematical modelling has played a funda-mental role for making forecasts,simulating scenarios and evaluating the impact of pre-ventive political,social and pharmaceutical measures.Optimal control theory represents a useful mathematical tool to plan the vaccination campaign aimed at eradicating the pandemic as fast as possible.The aim of this work is to explore the optimal prioritisation order for planning vaccination campaigns able to achieve specific goals,as the reduction of the amount of infected,deceased and hospitalized in a given time frame,among age classes.For this purpose,we introduce an age stratified SIR-like epidemic compartmental model settled in an abstract framework for modelling two-doses vaccination campaigns and conceived with the description of COVID19 disease.Compared to other recent works,our model incorporates all stages of the COVID-19 disease,including death or recovery,without accounting for additional specific compartments that would increase computa-tional complexity and that are not relevant for our purposes.Moreover,we introduce an optimal control framework where the model is the state problem while the vaccine doses administered are the control variables.An extensive campaign of numerical tests,featured in the Italian scenario and calibrated on available data from Dipartimento di Protezione Civile Italiana,proves that the presented framework can be a valuable tool to support the planning of vaccination campaigns.Indeed,in each considered scenario,our optimization framework guarantees noticeable improvements in terms of reducing deceased,infected or hospitalized individuals with respect to the baseline vaccination policy.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy.One can use a variety of well-known and new mathematical models,taking into account a huge number of factors.However,complex models contain a large number of unknown parameters,the values of which must be determined using a limited number of observations,e.g.,the daily datasets for the accumulated number of cases.Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model,which contains 4 unknown parameters.Application of the original algo-rithm of the model parameter identification for the first waves of the COVID-19 pandemic in China,South Korea,Austria,Italy,Germany,France,Spain has shown its high accuracy in pre-dicting their duration and number of diseases.To simulate different epidemic waves and take into account the incompleteness of statistical data,the generalized SIR model and algorithms for determining the values of its parameters were proposed.The interference of the previous waves,changes in testing levels,quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface.Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period(e.g.,14 days).It will be possible to predict the daily number of new cases for the country as a whole or for its separate region,to estimate the number of carriers of the infection and the probability of facing such a carrier,as well as to estimate the number of deaths.Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed.The predicted accumulated and daily numbers of cases agree with the results of observations,especially for the simulation based on the datasets corresponding to the period from July 3 to July 16,2022.A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels,quarantine restrictions,social behavior,etc.on the numbers of new infections,death,and mortality rates.As example,the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020,2021 and 2022 is presented.The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave.The daily numbers of cases were about ten times higher than in the corresponding period of 2021.Nevertheless,the death per case ratio in 2022 was much lower than in 2020.

Contact pattern,current immune barrier,and pathogen virulence determines the optimal strategy of further vaccination

Background:The current outbreak of novel coronavirus disease 2019 has caused a seriousdisease burden worldwide.Vaccines are an important factor to sustain the epidemic.Although with a relatively high-vaccination worldwide,the decay of vaccine efficacy andthe arising of new variants lead us to the challenge of maintaining a sufficient immunebarrier to protect the population.Method:A case-contact tracking data in Hunan,China,is used to estimate the contactpattern of cases for scenarios including school,workspace,etc,rather than ordinary susceptible population.Based on the estimated vaccine coverage and efficacy,a multi-groupvaccinated-exposed-presymptomatic-symptomatic-asymptomatic-removed model(VEFIAR)with 8 age groups,with each partitioned into 4 vaccination status groups isdeveloped.The optimal dose-wise vaccinating strategy is optimized based on the currentlyestimated immunity barrier of coverage and efficacy,using the greedy algorithm thatminimizes the cumulative cases,population size of hospitalization and fatality respectivelyin a certain future interval.Parameters of Delta and Omicron variants are used respectivelyin the optimization.Results:The estimated contact matrices of cases showed a concentration on middle ages,and has compatible magnitudes compared to estimations from contact surveys in otherstudies.The VEFIAR model is numerically stable.The optimal controled vaccination strategy requires immediate vaccination on the un-vaccinated high-contact population of age30e39 to reduce the cumulative cases,and is stable with different basic reproductionnumbers(R_(0)).As for minimizing hospitalization and fatality,the optimized strategy requires vaccination on the un-vaccinated of both aged 30e39 of high contact frequencyand the vulnerable older.Conclusion:The objective of reducing transmission requires vaccination in age groups ofthe highest contact frequency,with more priority for un-vaccinated than un-fully or fullyvaccinated.The objective of reducing total hospitalization and fatality requires not only toreduce transmission but also to protect the vulnerable older.The priority changes byvaccination progress.For any region,if the local contact pattern is available,then with thevaccination coverage,efficacy,and disease characteristics of relative risks in heterogeneouspopulations,the optimal dose-wise vaccinating process will be obtained and gives hintsfor decision-making.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics.In this study,we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data.The impact of the choice of tree-priors,informative epidemiological priors,and evolutionary parameters on the transmission potential estimation is evaluated.North American Influenza A(H1N1)pdm09 hemagglutinin(HA)gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number(R_(0)).Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models.Path-sampling marginal likelihood estimation is conducted to assess model fit.A bibliographic search to gather surveillancebased R_(0)values were consistently lower(mean≤1.2)when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness(mean≥1.3 to≤2.88 days).The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates.While there was no certain impact of clock rate and tree height on the R_(0)estimation,an opposite relationship was observed between coalescent and birth-death tree priors.There was no significant difference(p=0.46)between the birth-death model and surveillance R0 estimates.This study concludes that treeprior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters.The study also reports a consensus between the sequence-based R_(0)estimation and surveillanceased R_(0)stimates.Altogether,these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Co-dynamics of COVID-19 and TB with COVID-19 vaccination and exogenous reinfection for TB:An optimal control application

COVID-19 and Tuberculosis(TB)are among the major global public health problems and diseases with major socioeconomic impacts.The dynamics of these diseases are spread throughout the world with clinical similarities which makes them difficult to be mitigated.In this study,we formulate and analyze a mathematical model containing several epidemiological characteristics of the co-dynamics of COVID-19 and TB.Sufficient conditions are derived for the stability of both COVID-19 and TB sub-models equilibria.Under certain conditions,the TB sub-model could undergo the phenomenon of backward bifurcation whenever its associated reproduction number is less than one.The equilibria of the full TBCOVID-19 model are locally asymptotically stable,but not globally,due to the possible occurrence of backward bifurcation.The incorporation of exogenous reinfection into our model causes effects by allowing the occurrence of backward bifurcation for the basic reproduction number R_(0)<1 and the exogenous reinfection rate greater than a threshold(η>η*).The analytical results show that reducing R_(0)<1 may not be sufficient to eliminate the disease from the community.The optimal control strategies were proposed to minimize the disease burden and related costs.The existence of optimal controls and their characterization are established using Pontryagin's Minimum Principle.Moreover,different numerical simulations of the control induced model are carried out to observe the effects of the control strategies.It reveals the usefulness of the optimization strategies in reducing COVID-19 infection and the co-infection of both diseases in the community.

Mathematical modeling for Delta and Omicron variant of SARS-CoV-2 transmission dynamics in Greece

A compartmental,epidemiological,mathematical model was developed in order to analyze the transmission dynamics of Delta and Omicron variant,of SARS-CoV-2,in Greece.The model was parameterized twice during the 4th and 5th wave of the pandemic.The 4th wave refers to the period during which the Delta variant was dominant(approximately July to December of 2021)and the 5th wave to the period during which the Omicron variant was dominant(approximately January to May of 2022),in accordance with the official data from the National Public Health Organization(NPHO).Fitting methods were applied to evaluate important parameters in connection with the transmission of the variants,as well as the social behavior of population during these periods of interest.Mathematical models revealed higher numbers of contagiousness and cases of asymptomatic disease during the Omicron variant period,but a decreased rate of hospitalization compared to the Delta period.Also,parameters related to the behavior of the population in Greece were also assessed.More specifically,the use of protective masks and the abidance of social distancing measures.Simulations revealed that over 5,000 deaths could have been avoided,if mask usage and social distancing were 20%more efficient,during the short period of the Delta and Omicron outbreak.Furthermore,the spread of the variants was assessed using viral load data.The data were recorded from PCR tests at 417 Army Equity Fund Hospital(NIMTS),in Athens and the Ct values from 746 patients with COVID-19 were processed,to explain transmission phenomena and disease severity in patients.The period when the Delta variant prevailed in the country,the average Ct value was calculated as 25.19(range:12.32e39.29),whereas during the period when the Omicron variant prevailed,the average Ct value was calculated as 28(range:14.41e39.36).In conclusion,our experimental study showed that the higher viral load,which is related to the Delta variant,may interpret the severity of the disease.However,no correlation was confirmed regarding contagiousness phenomena.The results of the model,Ct analysis and official data from NPHO are consistent.

EpiMix:A novel method to estimate effective reproduction number

Transmission potential of a pathogen,often quantified by the time-varying reproduction number R t,provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control.In this study,we proposed a novel method,EpiMix,for R t estimation,wherein we incorporated the impacts of exogenous factors and random effects under a Bayesian regression framework.Using Integrated Nested Laplace Approx-imation,EpiMix is able to efficiently generate reliable,deterministic R t estimates.In the simulations and case studies performed,we further demonstrated the method's robust-ness in low-incidence scenarios,together with other merits,including its flexibility in selecting variables and tolerance of varying reporting rates.All these make EpiMix a potentially useful tool for real-time R t estimation provided that the serial interval distri-bution,time series of case counts and external influencing factors are available.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Forecast for peak infections in the second wave of the Omicron after the adjustment of zero-COVID policy in the mainland of China

On December 7,2022,the Chinese government optimized the current epidemic prevention and control policy,and no longer adopted the zero-COVID policy and mandatory quarantine measures.Based on the above policy changes,this paper establishes a compartment dynamics model considering age distribution,home isolation and vaccinations.Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data.Then,using the estimated parameter values to predict a second wave of the outbreak,the peak of severe cases will reach on 8 May 2023,the number of severe cases will reach 206,000.Next,it is proposed that with the extension of the effective time of antibodies obtained after infection,the peak of severe cases in the second wave of the epidemic will be delayed,and the final scale of the disease will be reduced.When the effectiveness of antibodies is 6 months,the severe cases of the second wave will peak on July 5,2023,the number of severe cases is 194,000.Finally,the importance of vaccination rates is demonstrated,when the vaccination rate of susceptible people under 60 years old reaches 98%,and the vaccination rate of susceptible people over 60 years old reaches 96%,the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023,when the number of severe cases is 166,000.

SARS-CoV-2:Air pollution highly correlated to the increase in mortality.The case of Guadalajara,Jalisco,Mexico

Objectives:To determine whether air pollution or changes in SARS-CoV-2 lineages lead to an increase in mortality.Methods:Descriptive statistics were used to calculate rates of infection(2020-2021).RT ePCR was used to compare viral loads from October 2020 to February 2021.Nextgeneration sequencing(NGS)(n=92)was used to examine and phylogenetically map SARS-CoV-2 lineages.A correlative“air pollution/temperature”index(I)was developed using regression analysis.PM_(2.5),PM_(10),O_(3),NO_(2),SO_(2),and CO concentrations were analyzed and compared to the mortality.Results:The mortality rate during the last year was~32%.Relative SARS-CoV-2 viral loads increased in December 2020 and January 2021.NGS revealed that approximately 80%of SARS-CoV-2 linages were B.1.243(33.7%),B1.1.222(11.2%),B.1.1(9%),B.1(7%),B.1.1.159(7%),and B.1.2(7%).Two periods were analyzed,the prehigh-and high-mortality periods and no significant lineage differences or new lineages were found.Positive correlations of air pollution/temperature index values with mortality were found for IPM_(2.5) and IPM_(10).INO_(2).ISO_(2),and ICO but not for O_(3).Using ICO,we developed a model to predict mortality with an estimated variation of~±5 deaths per day.