Evaluation of the Effects of Cypermethrin on Female Reproductive Function by Using Rabbit Model and of the Protective Role of Chinese Propolis

The prophylactic effects of Chinese propolis against cypermethrin toxicity were evaluated by performing ovary and uterus histopathology, as well as by characterizing ovarian function, embryos, and litters. Cypermethrin induced atypia in the ovary and uterus, and decreased the ovulation sites and the number of embryos. Cypermethrin-induced oxidative stress during pregnancy, decreased the parturition rate as well as the number and weight of offspring and increased the incidence of morphological malformations in the offspring. Administration of propolis to cypermethrin-treated animals mitigated cypermethrin-induced reproductive toxicity.

The spatial distribution and breeding behavior of neighbors affect the reproductive success of tits

The spatial distribution and breeding behavior of neighboring birds(conspecific and heterospecific)may influence reproduction and the effects differ across species.In this study,we investigated intra-and inter-specific spatial distribution of breeding via artificial nestboxes and examined whether overlap with neighbors affected reproductive success of the focal breeding pair.Regarding spatial distribution,Varied Tits(Sittiparus varius)and Cinereous Tits(Parus cinereus)showed similar pattern:distances of nestboxes containing conspecific tits were greater than those with heterospecific ones.In terms of breeding behavior,reproductive success was not significantly related to the distance from their neighbor’s nest.The reproductive success of Varied Tits was significantly related to that of their neighbors,while in Cinereous Tits,no strong link was found to any recorded reproductive behavior of the neighbors.These findings explain spatial variation in nest site selection of Varied Tits and Cinereous Tits.Reproductive success rate of Varied Tits is affected by that of the neighbors while that of Cinereous Tits is not.The results highlight that there are differences in reproductive outcome among related species and offer suggestions on the use of artificial nestboxes in experiment and conservation programs.

Oxidative Stress and Role of Natural Plant Derived Antioxidants in Animal Reproduction

The experimental knowledge on the role of oxidative stress,and beneficial and detrimental effects of plant derived antioxidants in male and female animal reproduction are reviewed in this article.Free radical-induced oxidative stress in animal reproduction causes great loss to livestock industry.Antioxidant therapy has been implicated to be effective in preventing diseases resulted from oxidative stress.Considering the advantages of lower side effects of natural antioxidants than those of synthetic antioxidants,plants or their extracts have been extensively utilized in animals.Although many advances have been gained on application of plant derived antioxidants in alleviating oxidative stress,debatable issues still exist.Because many opposite effects were observed even using plant extracts containing similar bioactive substances in the same animal species.Therefore,plant derived antioxidants,like free radicals,are ＂double-edged swords＂ in animal reproduction,representing that they may exhibit beneficial or detrimental effects in animal reproduction,including spermatogenesis,semen functions,estrous cycles,ovulation,ovary functions,endometrium,embryo development,and pregnancy.Besides dose-dependent manner as an explanation of plant extracts＇ dual function,future studies are needed to investigate the mechanism of double-edged actions of plant derived antioxidants in different animal reproduction systems.

Mathematical Modelling of the COVID-19 Epidemic in Northern Ireland in 2020

In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1st March 2020 up to 25th December 2020, using several copies of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model, and compare it to a detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number. We also discuss the limitations and possible extensions of the employed model.

Estimating the Level of Asymptomatic COVID-19 Infections in Northern Ireland in 2020

The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R0, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.

The Method for Optimum Estimation of COVID-19 Variant Type Virus Infection Status Analysis by the Multivariate Analysis Considering the Environmental Variability Impact in Japan

Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.

Estimating effective reproduction number revisited

Accurately estimating the effective reproduction number is crucial for characterizing the transmissibility of infectious diseases to optimize interventions and responses during epidemic outbreaks.In this study,we improve the estimation of the effective reproduction number through two main approaches.First,we derive a discrete model to represent a time series of case counts and propose an estimation method based on this framework.We also conduct numerical experiments to demonstrate the effectiveness of the proposed discretization scheme.By doing so,we enhance the accuracy of approximating the underlying epidemic process compared to previous methods,even when the counting period is similar to the mean generation time of an infectious disease.Second,we employ a negative binomial distribution to model the variability of count data to accommodate overdispersion.Specifically,given that observed incidence counts follow a negative binomial distribution,the posterior distribution of secondary infections is obtained as a Dirichlet multinomial distribution.With this formulation,we establish posterior uncertainty bounds for the effective reproduction number.Finally,we demonstrate the effectiveness of the proposed method using incidence data from the COVID-19 pandemic.

Wastewater Surveillance Provides Spatiotemporal SARS-CoV-2 Infection Dynamics

Wastewater surveillance(WWS)can leverage its wide coverage,population-based sampling,and high monitoring frequency to capture citywide pandemic trends independent of clinical surveillance.Here we conducted a nine months daily WWS for severe acute respiratory syndrome coronavirus 2(SARSCoV-2)from 12 wastewater treatment plants(WWTPs),covering approximately 80%of the population,to monitor infection dynamics in Hong Kong,China.We found that the SARS-CoV-2 virus concentration in wastewater was correlated with the daily number of reported cases and reached two pandemic peaks three days earlier during the study period.In addition,two different methods were established to estimate the prevalence/incidence rates from wastewater measurements.The estimated results from wastewater were consistent with findings from two independent citywide clinical surveillance programmes(rapid antigen test(RAT)surveillance and serology surveillance),but higher than the cases number reported by the Centre for Health Protection(CHP)of Hong Kong,China.Moreover,the effective reproductive number(R_(t))was estimated from wastewater measurements to reflect both citywide and regional transmission dynamics.Our findings demonstrate that large-scale intensive WWS from WWTPs provides cost-effective and timely public health information,especially when the clinical surveillance is inadequate and costly.This approach also provides insights into pandemic dynamics at higher spatiotemporal resolutions,facilitating the formulation of effective control policies and targeted resource allocation.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities.Kuwait reported its first imported cases of COVID-19 on February 24,2020.Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management.The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave.Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed(SEAIR)transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait.We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices.The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness.The model can help inform future pandemic wave management,not only in Kuwait but for other countries as well.

Time-dependent force of infection and effective reproduction ratio in an age-structure dengue transmission model in Bandung City,Indonesia

Dengue virus infection is a leading health problem in many endemic countries,including Indonesia,characterized by high morbidity and wide spread.It is known that the risk factors that influence the transmission intensity vary among different age groups,which can have implications for dengue control strategies.A time-dependent four−age structure model of dengue transmission was constructed in this study.A vaccination scenario as control strategy was also applied to one of the age groups.Daily incidence data of dengue cases from Santo Borromeus Hospital,Bandung,Indonesia,from 2014 to 2016 was used to estimate the infection rate.We used two indicators to identify the changes in dengue transmission intensity for this period in each age group:the annual force of infection(FoI)and the effective reproduction ratio based on a time-dependent transmission rate.The results showed that the yearly FoI of children(age 0–4 years)increased significantly from 2014 to 2015,at 10.08%.Overall,the highest FoI before and after vaccination occurred in youngsters(age 5–14 years),with a FoI of about 6%per year.In addition,based on the daily effective reproduction ratio,it was found that vaccination of youngsters could reduce the number of dengue cases in Bandung city faster than vaccination of children.

Estimating the instantaneous reproduction number(R_(t))by using particle filter

Background:Monitoring the transmission of coronavirus disease 2019(COVID-19)requires accurate estimation of the effective reproduction number(Rt).However,existing methods for calculating Rt may yield biased estimates if important real-world factors,such as delays in confirmation,pre-symptomatic transmissions,or imperfect data observation,are not considered.Method:To include real-world factors,we expanded the susceptible-exposed-infectiousrecovered(SEIR)model by incorporating pre-symptomatic(P)and asymptomatic(A)states,creating the SEPIAR model.By utilizing both stochastic and deterministic versions of the model,and incorporating predetermined time series of Rt,we generated simulated datasets that simulate real-world challenges in estimating Rt.We then compared the performance of our proposed particle filtering method for estimating Rt with the existing EpiEstim approach based on renewal equations.Results:The particle filtering method accurately estimated Rt even in the presence of data with delays,pre-symptomatic transmission,and imperfect observation.When evaluating via the root mean square error(RMSE)metric,the performance of the particle filtering method was better in general and was comparable to the EpiEstim approach if perfectly deconvolved infection time series were provided,and substantially better when Rt exhibited short-term fluctuations and the data was right truncated.Conclusions:The SEPIAR model,in conjunction with the particle filtering method,offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies.This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.

An updated estimation of the risk of transmission of the novel coronavirus(2019-nCov)

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period,in a naïve,uninfected population.It is well-known that the estimation of this number may vary due to several methodological issues,including different assumptions and choice of parameters,utilized models,used datasets and estimation period.With the spreading of the novel coronavirus(2019-nCoV)infection,the reproduction number has been found to vary,reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate.Due to significant variations in the control strategies,which have been changing over time,and thanks to the introduction of detection technologies that have been rapidly improved,enabling to shorten the time from infection/symptoms onset to diagnosis,leading to faster confirmation of the new coronavirus cases,our previous estimations on the transmission risk of the 2019-nCoV need to be revised.By using time-dependent contact and diagnose rates,we refit our previously proposed dynamics transmission model to the data available until January 29th,2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions.We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak.Our updated findings suggest that the best measure is persistent and strict self-isolation.The epidemics will continue to grow,and can peak soon with the peak time depending highly on the public health interventions practically implemented.

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.

Coronavirus disease 2019 epidemic prediction in Shanghai under the “dynamic zero-COVID policy”using time-dependent SEAIQR model

It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-remov ed(SEAIQR)model with stage-specific interventions based on recent Shanghai epidemic data,considering a large number of asymptomatic infectious,the changing parameters,and control procedures.The data collected from March 1st,2022 to April 15th,2022 were used to fit the model,and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting.We then calculated the effective regeneration number(Rt)and analyzed the sensitivity of different measures scenarios.Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai,and Pudong is the district with the most positive cases.The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th,2022,with 1963 and 28,502 cases,respectively,and zero community transmission may be achieved in early to mid-May.The prediction errors for newly confirmed cases were considered to be reasonable,and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th.The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477~47,749 and 402,254~730,176,respectively.At the beginning of the outbreak,Rt was 6.69.Since the implementation of comprehensive control,Rt showed a gradual downward trend,dropping to below 1.0 on April 15th,2022.With the early implementation of control measures and the improvement of quarantine rate,recovery rate,and immunity threshold,the peak number of infections will continue to decrease,whereas the earlier the control is implemented,the earlier the turning point of the epidemic will arrive.The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well,which can provide a reference for decision making of the“dynamic zero-COVID policy”.

Risk estimation and prediction of the transmission of coronavirus disease-2019(COVID-19)in the mainland of China excluding Hubei province

Background In December 2019,an outbreak of coronavirus disease(later named as COVID-19)was identified in Wuhan,China and,later on,detected in other parts of China.Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures,estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed.COVID-19 daily data of the mainland of China excluding Hubei province,including the cumulative confirmed cases,the cumulative deaths,newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020,were archived from the National Health Commission of China(NHCC).We parameterize the model by using the Markov Chain Monte Carlo(MCMC)method and estimate the control reproduction number(Rc),as well as the effective daily reproduction ratio-Re(t),of the disease transmission in the mainland of China excluding Hubei province.Results The estimation outcomes indicate that Rc is 3.36(95%CI:3.20–3.64)and Re(t)has dropped below 1 since 31 January 2020,which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission.Moreover,our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected,and may even cause a second wave of epidemic or outbreaks.By calculating the effective reproduction ratio,we prove that the contact rate should be kept at least less than 30%of the normal level by April,2020.Conclusions To ensure the pandemic ending rapidly,it is necessary to maintain the current integrated restrict interventions and self-protection measures,including travel restriction,quarantine of entry,contact tracing followed by quarantine and isolation and reduction of contact,like wearing masks,keeping social distance,etc.People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April.If all the above conditions are met,the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.

Mathematical modeling of COVID-19 infection dynamics in Ghana: Impact evaluation of integrated government and individual level interventions

The raging COVID-19 pandemic is arguably the most important threat to global health presently.Although there is currently a vaccine,preventive measures have been proposed to reduce the spread of infection but the efficacy of these interventions,and their likely impact on the number of COVID-19 infections is unknown.In this study,we proposed the SEIQHRS model(susceptible-exposed-infectious-quarantine-hospitalized-recovered-susceptible)model that predicts the trajectory of the epidemic to help plan an effective control strategy for COVID-19 in Ghana.We provided a short-term forecast of the early phase of the epidemic trajectory in Ghana using the generalized growth model.We estimated the effective basic Reproductive number Re in real-time using three different estimation procedures and simulated worse case epidemic scenarios and the impact of integrated individual and government interventions on the epidemic in the long term using compartmental models.The maximum likelihood estimates of Re and the corresponding 95%confidence interval was 2.04[95%CI:1.82e2.27;12th March-7th April 2020].The Re estimate using the exponential growth method was 2.11[95%CI:2.00e2.24]within the same period.The Re estimate using time-dependent(TD)method showed a gradual decline of the Effective Reproductive Number since March 12,2020 when the first 2 index cases were recorded but the rate of transmission remains high(TD:Re=2.52;95%CI:[1.87e3.49]).The current estimate of Re based on the TD method is 1.74[95%CI:1.41 e2.10;(13th May 2020)]but with comprehensive integrated government and individual level interventions,the Re could reduce to 0.5 which is an indication of the epidemic dying out in the general population.Our results showed that enhanced government and individual-level interventions and the intensity of media coverage could have a substantial effect on suppressing transmission of new COVID-19 cases and reduced death rates in Ghana until such a time that a potent vaccine or drug is discovered.

Infer HIV transmission dynamics from gene sequences among young men who have sex with men in China

To investigate the transmission dynamics and temporal and spatial migration characteristics of HIV spread among men who have sex with men(MSM)in China,a total of 1012 HIV-1 partial pol sequences,including five subtypes,were studied.Bayesian analysis were applied for each subtype to infer its dynamic characters including the effective reproductive number(R_(e))and migration process.The mean curve of each R_(e) was almost always greater than 1(even the 95%highest posterior density(HPD)lower value)along with time,which supports the necessity for a comprehensive study about risk behaviors among young MSM group in China.We also should reappraise the free treatment strategy,especially the therapeutic effect during the free treatment policy.

Analysis of the outbreak of COVID-19 in Japan by SIQR model

The SIQR model is exploited to analyze the outbreak of COVID-19 in Japan where the number of the daily confirmed new cases is explicitly treated as an observable.It is assumed that the society consists of four compartments;susceptible individuals(S),infected individuals at large(I),quarantined patients(Q)and recovered individuals(R),and the time evolution of the pandemic is described by a set of ordinary differential equations.It is shown that the quarantine rate can be determined from the time dependence of the daily confirmed new cases,from which the number of infected individuals can be estimated.The infection rate and quarantine rate are determined for the period from mid-February to mid-April in Japan and transmission characteristics of the initial stages of the outbreak in Japan are analyzed in connection with the policies employed by the government.The effectiveness of different measures is discussed for controlling the outbreak and it is shown that identifying patients through PCR(Polymerase Chain Reaction)testing and isolating them in a quarantine is more effective than lockdown measures aimed at inhibiting social interactions of the general population.An effective reproduction number for infected individuals at large is introduced which is appropriate to epidemics controlled by quarantine measures.

QUALITATIVE STUDY FOR A MULTI-DRUG RESISTANT TB MODEL WITH EXOGENOUS REINFECTION AND RELAPSE

One tuberculosis transmission model is formulated by incorporating exogenous reinfec- tion, relapse, and two treatment stages of infectious TB cases. The global stability of the unique disease-free equilibrium is obtained by applying the comparison principle if the effective reproduction number for the full model is less than unity. The existence and stability of the boundary equilibria are given by introducing the invasion reproduction numbers. Furthermore, the existence and local stability of the endemic equilibrium are addressed under some conditions.

A COVID-19 mathematical model of at-risk populations with non-pharmaceutical preventive measures:The case of Brazil and South Africa

This work examines a mathematical model of COVID-19 among two subgroups:low-risk and high-risk populations with two preventive measures;non-pharmaceutical interventions including wearing masks,maintaining social distance,and washing hands regularly by the low-risk group.In addition to the interventions mentioned above,highrisk individuals must take extra precaution measures,including telework,avoiding social gathering or public places,etc.to reduce the transmission.Those with underlying chronic diseases and the elderly(ages 60 and above)were classified as high-risk individuals and the rest as low-risk individuals.The parameter values used in this study were estimated using the available data from the Johns Hopkins University on COVID-19 for Brazil and South Africa.We evaluated the effective reproduction number for the two countries and observed how the various parameters affected the effective reproduction number.We also performed numerical simulations and analysis of the model.Susceptible and infectious populations for both low-risk and high-risk individuals were studied in detail.Results were displayed in both graphical and table forms to show the dynamics of each country being studied.We observed that non-pharmaceutical interventions by highrisk individuals significantly reduce infections among only high-risk individuals.In contrast,non-pharmaceutical interventions by low-risk individuals have a significant reduction in infections in both subgroups.Therefore,low-risk individuals’preventive actions have a considerable effect on reducing infections,even among high-risk individuals.