An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019(COVID-19)has exerted a profound adverse impact on human health.Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people.Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks.Monitoring of pathogenic microorganisms in the air,especially in densely populated areas,may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage.The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation,allocate health resources,and formulate epidemic response policies.This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission,which lays a theoretical foundation for the monitoring and prediction of epidemic development.Secondly,the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized.Subsequently,this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology,atmospheric sciences,environmental sciences,sociology,demography,etc.By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere,this review proposes suggestions for epidemic response,namely,the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

MODELS:a six-step framework for developing an infectious disease model

Since the COVID-19 pandemic began,a plethora of modeling studies relatedto COVID-19 have been released.While some models stand out due to their innovative approaches,others are flawed in their methodology.To assist novices,frontline healthcare workers,and public health policymakers in navigating the complex landscape of these models,we introduced a structured framework named MODELS.This framework is designed to detail the essential steps and considerations for creating a dependable epidemic model,offering direction to researchers engaged in epidemic modeling endeavors.

Theoretical Epidemiology Needs Urgent Attention in China

The mathematical method to which theoretical epidemiology belongs is one of the three major methodologies in epidemiology.It is of great value in diagnosing infectious disease epidemic trends and evaluating the effectiveness of prevention and control measures.This paper aims to summarize the brief history of the development of theoretical epidemiology,common types of mathematical models,and key steps to develop a mathematical model.It also provides some thoughts and perspectives on the development and application of theoretical epidemiology in China.

Trends in Respiratory Infectious Pathogens in Children Under the Age of 14—Xiamen City,Fujian Province,China,2017-2023

Summary What is already known about this topic?Respiratory infections pose a significant burden on public health.Despite recent outbreaks occurring in various locations,there is limited information available on the prevalence trends of multiple common respiratory pathogens in China beyond 2022.What is added by this report?

Tuberculosis Prevalence Trends from a Predictive Modelling Study-10 High-Burden Countries,1980-2035

What is already known about this topic?Given the challenges presented by drug-resistant strains of tuberculosis(TB)and the rising mobility of the population,achieving the objective of eradicating TB appears uncertain.What is added by this report?The examination of TB incidence trends in 10 highburden countries(HBCs)indicated a steady rise in cases,with India and China jointly accounting for nearly 70%of the burden.Projections for the future show diverse trajectories in these countries,with potential difficulties in reaching the TB elimination target,especially in Nigeria,Congo,and South Africa.What are the implications for public health practice?The number of TB cases is on the rise.It is crucial to learn from successful strategies to improve TB prevention and control worldwide through collaborative efforts.

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.

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.

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.

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.

Analysis of the effect of PCR testing and antigen testing on controlling the transmission for Omicron based on different scenarios

After the policy adjustment,China no longer carries out COVID-19 PCR testing for all people,and antigen testing has become the main way to detect and manage infectious sources.We developed a dynamic model to evaluate and compare the effects between PCR and antigen testing for controlling the pandemic.Due to the increase of contact degree,the peak reduction effect of PCR testing in population is lower than that of antigen testing.Even if it was only 20%of people isolated at home after antigen testing,the peak of the epidemic could be reduced by 9.46%.If the proportion of antigen testing is further increased to 80%,the peak of the pandemic can be reduced by 31.41%.Antigen testing performed better effects in school(reduction proportion 29.27%)and community(29.34%)than in workplace(27.75%).Therefore,we recommend that antigen testing in the popu-lation should be encouraged during the pandemic,and home isolation of infected persons should be advocated,especially in crowded places.To improve the availability of antigen,the testing proportion should be further enhanced.

Advancements in Defining and Estimating the Reproduction Number in Infectious Disease Epidemiology

The reproduction number(R)serves as a fundamental metric in the examination of infectious disease outbreaks,epidemics,and pandemics.Despite an array of available methods for estimating,both newcomers and established public health professionals often encounter difficulties in comprehending the circumstances for their use and their constrictions.Consequently,this review intends to offer elementary guidance on’s selection and estimation approaches.To facilitate our review,we executed an extensive search on PubMed and Web of Science applying the following search approach:[“Basic Reproduction Number/classification”(Mesh)]AND[“Basic Reproduction Number/prevention and control”(Mesh)]OR[“Basic Reproduction Number/statistics and numerical data”(Mesh)].

Nowcasting and Forecasting Seasonal Influenza Epidemics—China,2022-2023

Background:Seasonal influenza resurged in China in February 2023,causing a large number of hospitalizations.While influenza epidemics occurred across China during the coronavirus disease 2019(COVID-19)pandemic,the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023.Methods:Using a mathematical model incorporating influenza activity as measured by influenza-like illness(ILI)data for northern and southern regions of China,we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic.Using this trained model,we predicted influenza activities in northern and southern China from March to September 2023.Results:We estimated the effective reproduction number Re as 1.08[95%confidence interval(CI):0.51,1.65]in northern China and 1.10(95%CI:0.55,1.67)in southern China at the start of the 2022-2023 influenza season.We estimated the infection attack rate of this influenza wave as 18.51%(95%CI:0.00%,37.78%)in northern China and 28.30%(95%CI:14.77%,41.82%)in southern China.Conclusions:The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

Transmissibility of tuberculosis among students and non-students:an occupational-specific mathematical modelling

Background: Recently, despite the steady decline in the tuberculosis (TB) epidemic globally, school TB outbreaks have been frequently reported in China. This study aimed to quantify the transmissibility ofMycobacterium tuberculosis (MTB) among students and non-students using a mathematical model to determine characteristics of TB transmission.Methods: We constructed a dataset of reported TB cases from four regions (Jilin Province, Xiamen City, Chuxiong Prefecture, and Wuhan City) in China from 2005 to 2019. We classified the population and the reported cases under student and non-student groups, and developed two mathematical models [nonseasonal model (Model A) and seasonal model (Model B)] based on the natural history and transmission features of TB. The effective reproduction number (R(eff)) of TB between groups were calculated using the collected data.Results: During the study period, data on 456,423 TB cases were collected from four regions: students accounted for 6.1% of cases. The goodness-of-fit analysis showed that Model A had a better fitting effect (P < 0.001). The average R_(eff) of TB estimated from Model A was 1.68 [interquartile range (IQR): 1.20–1.96] in Chuxiong Prefecture, 1.67 (IQR: 1.40–1.93) in Xiamen City, 1.75 (IQR: 1.37–2.02) in Jilin Province, and 1.79 (IQR: 1.56–2.02) in Wuhan City. The average R_(eff) of TB in the non-student population was 23.30 times (1.65/0.07) higher than that in the student population.Conclusions: The transmissibility of MTB remains high in the non-student population of the areas studied, which is still dominant in the spread of TB. TB transmissibility from the non-student-to-student-population had a strong influence on students. Specific interventions, such as TB screening, should be applied rigorously to control and to prevent TB transmission among students.

Control measures during the COVID-19 outbreak reduced the transmission of hand,foot,and mouth disease

Control measures during the coronavirus disease 2019(COVID-19)outbreak may have limited the spread of infectious diseases.This study aimed to analyze the impact of COVID-19 on the spread of hand,foot,and mouth disease(HFMD)in China.A mathematical model was established to fit the reported data of HFMD in six selected cities in China's Mainland from 2015 to 2020.The absolute difference(AD)and relative difference(RD)between the reported incidence in 2020,and simulated maximum,minimum,or median incidence of HFMD in 2015-2019 were calculated.The incidence and R effof HFMD have decreased in six selected cities since the outbreak of COVID-19,and in the second half of 2020,the incidence and R effof HFMD have rebounded.The results show that the total attack rate(TAR)in 2020 was lower than the maximum,minimum,and median TAR fitted in previous years in six selected cities(except Changsha City).For the maximum,median,minimum fitted TAR,the range of RD(%)is 42·20-99·20%,36·35-98·41%48·35-96·23%(except Changsha City)respectively.The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.

Modelling the Emerging COVID-19 Epidemic and Estimating Intervention Effectiveness—Taiwan,China,2021

Introduction:The coronavirus disease 2019(COVID-19)pandemic recently affected Taiwan,China.This study aimed to calculate the transmissibility of COVID-19 to predict trends and evaluate the effects of interventions.Methods:The data of reported COVID-19 cases was collected from April 20 to May 26,2021,which included daily reported data(Scenario I)and reported data after adjustment(Scenario II).A susceptibleexposed-symptomatic-asymptomatic-recovered model was developed to fit the data.The effective reproductive number(Reff)was used to estimate the transmissibility of COVID-19.Results:A total of 4,854 cases were collected for the modelling.In Scenario I,the intervention has already taken some effects from May 17 to May 26(the Reff reduced to 2.1).When the Reff was set as 0.1,the epidemic was projected to end on July 4,and a total of 1,997 cases and 855 asymptomatic individuals would have been reported.In Scenario II,the interventions were projected as having been effective from May 24 to May 26(the Reff reduced to 0.4).When the Reff was set as 0.1,the epidemic was projected to end on July 1,and a total of 1,482 cases and 635 asymptomatic individuals would have been reported.Conclusion:The epidemic of COVID-19 was projected to end after at least one month,even if the most effective interventions were applied in Taiwan,China.Although there were some positive effects of intervention in Taiwan,China.

Shigellosis seasonality and transmission characteristics in different areas of China: A modelling study

Objective:In China,the burden of shigellosis is unevenly distributed,notably across various ages and geographical areas.Shigellosis temporal trends appear to be seasonal.We should clarify seasonal warnings and regional transmission patterns.Method:This study adopted a Logistic model to assess the seasonality and a dynamics model to compare the transmission in different areas.The next-generation matrix was used to calculate the effective reproduction number(Reff)to quantify the transmissibility.Results:In China,the rate of shigellosis fell from 35.12 cases per 100,000 people in 2005 to 7.85 cases per 100,000 people in 2017,peaking in June and August.After simulation by the Logistic model,the‘peak time’is mainly concentrated from mid-June to mid-July.China's‘early warning time’is primarily focused on from April to May.We predict the‘peak time’of shigellosis is the 6.30th month and the‘early warning time’is 3.87th month in 2021.According to the dynamics model results,the water/food transfer pathway has been mostly blocked off.The transmissibility of different regions varies greatly,such as the mean Reff of Longde County(3.76)is higher than Xiamen City(3.15),higher than Chuxiong City(2.52),and higher than Yichang City(1.70).Conclusion:The‘early warning time’for shigellosis in China is from April to May every year,and it may continue to advance in the future,such as the early warning time in 2021 is in mid-March.Furthermore,we should focus on preventing and controlling the personto-person route of shigellosis and stratified deploy prevention and control measures according to the regional transmission.

Effectiveness of Interventions to Control Transmission of Reemergent Cases of COVID-19--Jilin Province,China,2020

What is already known about this topic?COVID-19 has a high transmissibility calculated by mathematical model.The dynamics of the disease and the effectiveness of intervention to control the transmission remain unclear in Jilin Province,China.What is added by this report?This is the first study to report the dynamic characteristics and to quantify the effectiveness of interventions implemented in the second outbreak of COVID-19 in Jilin Province,China.The effective reproduction number of the disease before and after May 10 was 4.00 and p<0.01,respectively.The combined interventions reduced the transmissibility of COVID-19 by 99% and the number of cases by 98.36%.What are the implications for public health practice?The findings of this study would add data on the transmission of COVID-19 and provide evidence to prepare the second outbreak transmission of the disease in other areas of China even in many other countries.

Feasibility of Booster Vaccination in High-Risk Populations for Controlling Coronavirus Variants—China,2021

Introduction:Vaccination booster shots are completely necessary for controlling breakthrough infections of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)in China.The study aims to estimate effectiveness of booster vaccines for high-risk populations(HRPs).Methods:A vaccinated Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed(SEIAR)model was developed to simulate scenarios of effective reproduction number(Reff)from 4 to 6.Total number of infectious and asymptomatic cases were used to evaluated vaccination effectiveness.Results:Our model showed that we could not prevent outbreaks when covering 80%of HRPs with booster unless Reff=4.0 or the booster vaccine had efficacy against infectivity and susceptibility of more than 90%.The results were consistent when the outcome index was confirmed cases or asymptomatic cases.Conclusions:An ideal coronavirus disease 2019(COVID-19)booster vaccination strategy for HRPs would be expected to reach the initial goal to control the transmission of the Delta variant in China.Accordingly,the recommendation for the COVID-19 booster vaccine should be implemented in HRPs who are already vaccinated and could prevent transmission to other groups.

Mathematical Models Supporting Control of COVID-19

Mathematical models have played an important role in the management of the coronavirus disease 2019(COVID-19)pandemic.The aim of this review is to describe uses of COVID-19 mathematical models,their classification,and the advantages and disadvantages of different types of models.We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms“COVID-19,”“Mathematical Statistical Model,”“Model,”“Modeling,”“Agent-based Model,”and“Ordinary Differential Equation Model”and classified and analyzed the scientific literature retrieved in the search.We categorized the models as data-driven or mechanism-driven.Data-driven models are mainly used for predicting epidemics,and have the advantage of rapid assessment of disease instances.However,their ability to determine transmission mechanisms is limited.Mechanism-driven models include ordinary differential equation(ODE)and agent-based models.ODE models are used to estimate transmissibility and evaluate impact of interventions.Although ODE models are good at determining pathogen transmission characteristics,they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data.Agent-based models consider influences of individual differences,but they require large amounts of data and can take a long time to develop fully.Many COVID-19 mathematical modeling studies have been conducted,and these have been used for predicting trends,evaluating interventions,and calculating pathogen transmissibility.Successful infectious disease modeling requires comprehensive considerations of data,applications,and purposes.

The collaboration between infectious disease modeling and public health decision-making based on the COVID-19

Public health decision-making may have great uncertainty especially in dealing with emerging infectious diseases,so it is necessary to establish a collaborative mechanism among modelers,epidemiologists,and public health decision-makers to reduce the uncertainty as much as possible.We searched the relevant studies on transmission dynamics modeling of infectious diseases,SARS,MERS,and COVID-19 as of March 1,2021 based on PubMed.We compared the key health decision-making time points of SARS,MERS,and COVID-19 prevention and control,and the publication time points of modeling research,to reveal the collaboration between infectious disease modeling and public health decision-making in the context of the COVID-19 pandemic.Searching with infectious disease and mathematical model as keywords,there were 166,81 and 1289 studies on the modeling of infectious disease transmission dynamics of SARS,MERS,and COVID-19 were retrieved respectively.Based on the modeling application framework of public health practice proposed in the current study,the collaboration among modelers,epidemiologists and public health decision-makers should be strengthened in the future.