Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.

Impact of COVID-19 in Cervical and Breast Cancer Screening and Systemic Treatment in Sao Paulo, Brazil: An Interrupted Time Series Analysis

PURPOSE COVID-19 caused a disruption in cancer management around the world, resulting in an estimated excess burden secondary to screening disruption and excess lag time for treatment initiation. METHODS We gathered information from primary reimbursement data sets of the public health system of Sao Paulo, Brazil, from April 2020 to November 2021, and compared these data with those of the pre-COVID-19 period. We used an interrupted time series model to estimate the effect of the COVID-19 pandemic on the rate of key procedures of breast and cervical cancer health care chain. RESULTS We estimated that 1,149,727, 2,693, and 713,616 pap smears, conizations, and mammograms, respectively, were missed or delayed during the COVID-19 pandemic, compared with those in the years immediately before the COVID-19 stay-at-home restrictions. Specifically, we observed an acute decrease of procedures after the COVID-19 stay-at-home restrictions, with a trend to recovery in the long term. Regarding the systemic treatment analysis, we observed a 25% reduction in the rate of initiation of adjuvant systemic treatment for early breast cancer (stage I/II). However, we did not find a clear effect on the other settings of systemic treatment for breast cancer. We estimated an excess of 156 patients starting palliative care for cervical cancer after the COVID-19 stay-at-home restrictions. CONCLUSION The COVID-19 pandemic significantly reduced the performance rate of pap smears, conizations, and mammograms. The initiation of adjuvant treatment for early-stage breast cancer was most susceptible to COVID-19's health system disruption. Furthermore, the downward trend of treatment of advanced cervical cancer was interrupted. Therefore, public health policies are urgently needed to decrease the incidence of advanced cervical and breast cancers caused by delayed diagnosis and treatment initiation. The COVID-19 control policies resulted in reduction of cancer patients' delivery of care. This study evaluated the pandemic's influence in key procedures of breast and cervical cancer chain of care in Sao Paulo, Brazil. We observed a substantial reduction in the number of mammograms, pap smears, and conizations performed since the onset of the COVID-19 pandemic. In addition, stage I and II breast cancer adjuvant treatment presented a reduced realization rate, whereas palliative treatment delivered for advanced cervical cancer increased. Our results support the need for public health policies focused on mitigating the long-term effects of COVID-19 in cancer-related mortality. (C) 2022 by American Society of Clinical Oncology

Psychosocial well-being during the COVID-19 pandemic among women with and without breast cancer.

OBJECTIVE: Treatment delays in combination with general social distancing practices to reduce transmission may have negative impacts on the mental health of women with breast cancer who may need more social and emotional support. We sought to elucidate the psychosocial effects of the COVID-19 pandemic among women with and without breast cancer in New York City. METHODS: We conducted a prospective cohort study among women aged 18+ across the spectrum of breast health care at New York Presbyterian (NYP)-Weill Cornell, NYP-Brooklyn Methodist Hospital and NYP-Queens. Women were contacted between June and October 2021 to assess their self-reported depression, stress, and anxiety during the COVID-19 pandemic. We compared women who were recently diagnosed, those with a history of breast cancer, and women without cancer whose other health visits were delayed during the pandemic. RESULTS: There were 85 women who completed the survey. Breast cancer survivors (42%) were the least likely to report a delay in care due to COVID compared to breast cancer patients who were recently diagnosed (67%) and women without cancer (67%). Compared to women without cancer and breast cancer survivors, women recently diagnosed with breast cancer reported higher levels of anxiety and depression with a statistically significant difference in perceived stress. CONCLUSIONS: Our findings highlight the need to identify and risk-stratify patients facing a new breast cancer diagnosis in and around the COVID-19 pandemic who may benefit from additional resources to mitigate the adverse impacts of the pandemic and a breast cancer diagnosis on psychosocial health.

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands.

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

E-Ticketing in Highway Construction: Reasons for Delayed Implementation

The evolution of technology and the use of mobile devices for performing daily operations has benefited the construction industry. E-Ticketing can automate most administrative processes in highway construction, provide valuable insights into daily operations, and increase inspector safety. Many state departments of transportation (DOTs) have conducted pilot testing of the technology and have opted not to install or acquire it for a variety of reasons, and a few have discontinued their pilot studies. Thus, the goal of this research is to identify and pinpoint the misconceptions surrounding the implementation of e-Ticketing platform and to explore ways to make it attractive to more users. To achieve this, an extensive literature review was conducted of studies that investigated the implementation of technology. Then, semi-structured interviews were held with 13 individuals who are employed in the highway construction industry. Inductive thematic analysis of the interview transcripts revealed two primary causes for the delays in implementing the technology: (1) a misunderstanding resulting from the limited implementation of the platform during COVID-19;and (2) a high initial investment cost for state DOTs because of overlapping fleet management functionalities. This research will help DOT decision-makers and engineers in re-define the functionalities of the e-Ticketing platform, adopt regulations and standards, minimize project costs, provide initial funding, performing pilot testing, and enhance inspector safety.

Investigation of Project Delays: Towards a Sustainable Construction Industry

The construction industry is one of the key industries for any country. It has been observed that this industry is suffering from sustainable solutions during project execution. It is evident from the literature that most of the construction projects are seriously affected by delays. Pakistan's construction industry also suffers similar challenges. After the COVID-19 pandemic, the construction industry is experiencing several challenges which have resulted in project delays. Thus, this study investigates the key challenges affecting the timely completion of construction projects. The challenges were discovered from the literature and investigated to analyze their significance towards a sustainable construction project. This study also observes the relationships between the key challenges using Partial Least Squares Structural Equation Modeling (PLS-SEM). A structural model was developed based on the 55 common challenges identified from literature. Data collection was administered through a structured questionnaire survey using a 5-point Likert-scale. The challenges were grouped into six constructs. The outcome reported 20 critical challenges, with information and communication-related factors being the most important challenge in the construction industry. Contract management also significantly affects project time overrun. The created model served as a starting point for academics, researchers, and practitioners to create an effective system for regulating time overrun challenges.

Patient, Diagnosis, and Treatment Delays Among Tuberculosis Patients Before and During COVID-19 Epidemic - China, 2018-2022.

What is already known about this topic?: The coronavirus disease (COVID-19) pandemic could have a damaging impact on access to tuberculosis (TB) diagnosis and treatment. What is added by this report?: The overall delay experienced by TB patients during the COVID-19 pandemic has shown a modest decrease in comparison to the period before the pandemic. Notably, higher patient delays were observed among agricultural workers and those identified through passive case-finding methods. Furthermore, the patient delay in eastern regions was shorter compared to western and central regions. What are the implications for public health practice?: The observed increase in patient delay in 2022 should be of concern for ongoing TB control efforts. Health education and active screening initiatives must be enhanced and broadened among high-risk populations and regions characterized by extended patient delays.

Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE

Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways

Predictive analytics is an increasingly popular tool for enhancing decision-making processes but is in many business settings based on rule-based models. These rule-based models reach their limits in complex settings. This study compares the performance of a rule-based system with a customised LSTM encoder-decoder deep learning model for predicting train delays. For this, we use a purposefully built real-world dataset on railway transportation, where trains' interdependence over the network makes delay prediction more difficult. Results show that the deep learning model, which incorporates rich spatiotemporal interdependency information in real-time, outperforms the rule-based system by 18%, with the difference increasing to above 23% with higher complexity. The study also dissects the performance difference across different settings: dense versus rural areas, peak versus off-peak hours, low versus high delay, and before versus during the COVID-19 pandemic. The deep learning model is implemented as a proof of concept for decision support within Belgium's railway infrastructure company Infrabel. © 2023 Elsevier B.V.

On the Aspects of Vitamin D and COVID-19 Infections and Modeling Time-Delay Body's Immune System with Time-Dependent Effects of Vitamin D and Probiotic

This chapter provides a summary of recent views on the aspects of vitamin D levels and the relationship between the prevalence rates of vitamin D deficiency and COVID-19 death toll of several countries in Europe and Asia. The chapter also discusses a new modified time-delay immune system model with time-dependent of the body's immune healthy cells, vitamin D, and probiotic. The model can be used to assess the timely progression of healthy immune cells with the effects of the levels of vitamin D and probiotics supplement. It also can help to predict when the infected cells and virus particles free state can ever be reached as time progresses with and without considering the vitamin D and probiotic supplements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Airline flight delays using artificial intelligence in COVID-19 with perspective analytics

This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Navigating health systems for burn care: Patient journeys and delays in Uttar Pradesh, India.

BACKGROUND: India has one of the highest burden of burns. The health systems response to burn care is sometimes patchy and highly influenced by social determinants. Delay in access to acute care and rehabilitation adversely affects recovery outcomes. Evidence on underlying factors for delays in care are limited. In this study, we aim to explore patients' journeys to analyse their experiences in accessing burn care in Uttar Pradesh, India. METHODS: We conducted qualitative inquiry using the patient journey mapping approach and in-depth interviews (IDI). We purposively selected a referral burn centre in Uttar Pradesh, India and included a diverse group of patients. A chronological plot of the patient's journey was drawn and confirmed with respondents at the end of the interview. A detailed patient journey map was drawn for each patient based on interview transcripts and notes. Further analysis was done in NVivo 12 using a combination of inductive and deductive coding. Similar codes were categorised into sub-themes, which were distributed to one of the major themes of the 'three delays' framework. RESULTS: Six major burns patients (4 female and 2 males) aged between 2 and 43 years were included in the study. Two patients had flame burns, and one had chemical, electric, hot liquid, and blast injury, respectively. Delay in seeking care (delay 1) was less common for acute care but was a concern for rehabilitation. Accessibility and availability of services, costs of care and lack of financial support influenced delay (1) for rehabilitation. Delay in reaching an appropriate facility (delay 2) was common due to multiple referrals before reaching an appropriate burn facility. Lack of clarity on referral systems and proper triaging influenced this delay. Delay in getting adequate care (delay 3) was mainly due to inadequate infrastructure at various levels of health facilities, shortage of skilled health providers, and high costs of care. COVID-19-related protocols and restrictions influenced all three delays. CONCLUSIONS: Burn care pathways are adversely affected by barriers to timely access. We propose using the modified 3-delays framework to analyse delays in burns care. There is a need to strengthen referral linkage systems, ensure financial risk protection, and integrate burn care at all levels of health care delivery systems.

A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile.

Background: One of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease. Materials and methods: Epidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination. Results: We show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset. Conclusion: This work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions.

Framework for the Classification of COVID-19 Force Majeure Delay Events

Time is always of the essence in the construction industry, and all projects depend on timely execution for their success. All stakeholders aim to achieve a project's goals within the time constraints set during the planning stage. However, delays do occur, and often some could be due to foreseen or unforeseen risks or an act of God. Moreover, the current outbreak of COVID-19 has meant that the construction industry suffered even more delays. Most of the delays are labelled as force majeure which are events that cannot be foreseen by any party due to the outbreak;however, significant consideration of an event's material circumstances and the forecast-ability of the specific reasons for the delay are required before this labelling is considered in its legal sense. Thus, this study aimed to develop a framework for the identification of delay events during the outbreak of COVID-19 via a logistic regression model, and assessed if such events could be labelled as force majeure. The study examined the literature to identify the factors that lead to the delay event and the force majeure conditions. Moreover, data was collected from industry experts in semi-structured interviews to identify and confirm the factors that are usually considered when a claim of force majeure is evaluated. Finally, the study developed a framework and a Claims Scoring Metric (CSM). The CSM saves contractors the cost of preparing ineligible claims and saves the project owner the cost of reviewing such claims. It assists in the initial identification of delays caused by the COVID-19 pandemic before a thorough evaluation is needed. The framework was validated via a case study with results matching the outcome of the delay analysis. © 2022 International Conference on Construction in the 21st Century. All rights reserved.

An Intelligent Food Serving Robot Prototype with Android Application for Canteens

As the world faces a COVID epidemic, one of the most critical rules to observe is social separation. There are some situations where social separation is difficult to maintain, such as canteens. The proposed technology equips a college canteen with an autonomous food serving robot, allowing us to preserve social distance. People in canteens confront challenges such as long lines and food service delays. When it comes to college canteens, students only have a limited amount of time for refreshment, resulting in a rush at the canteen. Our self-serving food robot will serve the food to the clients without fail;all they have to do is order meals using the mobile app. The system relies on a mobile application to place orders and a robot to deliver the food. Users will be able to summon the robot using the help button in the mobile app, which will result in canteen trash management. For routing and finding the best way to the table, we employ a combination of sensors and Radio Frequency Identifier (RFID) technology. Our solution will benefit the admin in addition to keeping the customers happy. Making a robot will be less expensive than hiring a human waiter. The system not only has a rechargeable wallet payment interface, but also net banking, card payment, and UPI payment possibilities. © 2022 IEEE.

Stability and Bifurcation Analysis of the Caputo Fractional-Order Asymptomatic COVID-19 Model with Multiple Time-Delays

Throughout the last few decades, fractional-order models have been used in many fields of science and engineering, applied mathematics, and biotechnology. Fractional-order differential equations are beneficial for incorporating memory and hereditary properties into systems. Our paper proposes an asymptomatic COVID-19 model with three delay terms τ1,τ2,τ3 and fractional-order α. Multiple constant time delays are included in the model to account for the latency of infection in a vector. We study the necessary and sufficient criteria for stability of steady states and Hopf bifurcations based on the three constant time-delays, τ1, τ2, and τ3. Hopf bifurcation occurs in the addressed model at the estimated bifurcation points τ10, τ20, τ30, and τ10*. The numerical simulations fit to real observations proving the effectiveness of the theoretical results. Fractional-order and time-delays successfully enhance the dynamics and strengthen the stability condition of the asymptomatic COVID-19 model. © 2023 World Scientific Publishing Company.

Stability analysis of a fractional-order SEIR epidemic model with general incidence rate and time delay

In this paper, we investigate the qualitative behavior of a class of fractional SEIR epidemic models with a more general incidence rate function and time delay to incorporate latent infected individuals. We first prove positivity and boundedness of solutions of the system. The basic reproduction number (Formula presented.) of the model is computed using the method of next generation matrix, and we prove that if (Formula presented.), the healthy equilibrium is locally asymptotically stable, and when (Formula presented.), the system admits a unique endemic equilibrium which is locally asymptotically stable. Moreover, using a suitable Lyapunov function and some results about the theory of stability of differential equations of delayed fractional-order type, we give a complete study of global stability for both healthy and endemic steady states. The model is used to describe the COVID-19 outbreak in Algeria at its beginning in February 2020. A numerical scheme, based on Adams–Bashforth–Moulton method, is used to run the numerical simulations and shows that the number of new infected individuals will peak around late July 2020. Further, numerical simulations show that around 90% of the population in Algeria will be infected. Compared with the WHO data, our results are much more close to real data. Our model with fractional derivative and delay can then better fit the data of Algeria at the beginning of infection and before the lock and isolation measures. The model we propose is a generalization of several SEIR other models with fractional derivative and delay in literature. © 2023 John Wiley & Sons, Ltd.

The Impact of a Delay on the Evolution of Epidemics

: Based on a discrete model of the spread of infection in a closed population, the corresponding form of differential equations with delay is found. It is shown that the development of the epidemic is determined by four key parameters: the number of infectious persons, the average number of dangerous contacts of one infectious person per day, the probability of infection as a result of such contact, and the average time interval during which the sick person is able to infect others. The decision also depends on the size of the population and on the initial number of infected persons. The four named parameters have a clear meaning and are related to the well-known concept of reproductive number in the continuous Susceptible–Infectious–Recovered (SIR) and Susceptible–Infected–Infectious–Recovered (SEIR) models. The epidemic saturation conditions are established by solving the obtained differential equations. It is shown that, due to the long virus carrying characteristic of COVID-19, the solutions proposed here differ significantly from the SIR model. © 2022, Pleiades Publishing, Ltd.

Improving Safety by Evaluating the Impact of the Supply Chain and Drug Shortages on Health-Systems.

Background: The COVID-19 pandemic has shown how fragile our healthcare supply chain is with product delays, drug shortages, and labor shortages being exacerbated in recent years. Objective: This article reviews current threats to the healthcare supply chain that impact patient safety and highlights potential solutions for the future. Method: A review of the literature was conducted, and important up-to-date resources associated with drug shortages and supply chain were analyzed to build foundational knowledge. Potential supply chain threats and solutions were then explored through further literature analyses. Conclusions: The information provided in this article helps to brief pharmacy leaders on current supply chain issues and solutions that can be integrated throughout the healthcare supply chain in the future.

Dynamic behavior analysis of an SVIR epidemic model with two time delays associated with the COVID-19 booster vaccination time.

Since the outbreak of COVID-19, there has been widespread concern in the community, especially on the recent heated debate about when to get the booster vaccination. In order to explore the optimal time for receiving booster shots, here we construct an SVIR model with two time delays based on temporary immunity. Second, we theoretically analyze the existence and stability of equilibrium and further study the dynamic properties of Hopf bifurcation. Then, the statistical analysis is conducted to obtain two groups of parameters based on the official data, and numerical simulations are carried out to verify the theoretical analysis. As a result, we find that the equilibrium is locally asymptotically stable when the booster vaccination time is within the critical value. Moreover, the results of the simulations also exhibit globally stable properties, which might be more beneficial for controlling the outbreak. Finally, we propose the optimal time of booster vaccination and predict when the outbreak can be effectively controlled.