Selection of forecasting models for infectious diseases in large warships.

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

The Value-Based Price for a Bivalent COVID-19 Booster Vaccine in the United Kingdom

Objectives: The United Kingdom (UK) implemented an autumn 2022 booster programme that allowed those at higher risk from COVID-19, including those >= 50 years, to receive a booster to increase protection against infection and subsequent severe outcomes. As the UK transitions out of the pandemic, future booster campaigns may be required to maintain protection against such outcomes. The objective of this analysis was to estimate the value-based price (VBP) for a bivalent COVID-19 vaccine used in a future autumn 2023 campaign in the UK to protect people aged >= 50 years. Method(s): A Susceptible-Exposed-Infected-Recovered (SEIR) model was used to predict infections across a 1-year time horizon starting September 2023 with and without an autumn booster campaign. Initial effectiveness was predicted to be 89% and 97% against infection and hospitalization respectively based on BA.4/BA.5 antibody titers and correlates of protection. A monthly decline in protection of 1.4% and 4.8%, respectively, was assumed based on monovalent vaccine data. A decision tree was used to predict the quality-adjusted life-years (QALY) lost and costs associated with infections. Result(s): Considering a willingness-to-pay (WTP) threshold of 20,000/QALY, the VBP associated with an autumn 2023 booster campaign is 343/dose. Considering a WTP threshold of 30,000, the VBP increases to 476. In sensitivity analyses, excluding the post-infection costs (e.g., long COVID), reduces the VBP by 11%. Varying the hospitalization rates by +/-25% changes the VBP by +/- 6%. Varying hospitalization unit costs only impacts the VBP by 1%. Doubling the rate of waning for booster effectiveness increases the VBP by 54% because the effectiveness provided from past campaigns falls faster and an autumn 2023 booster becomes more valuable. Conclusion(s): While the trajectory of COVID-19 incidence is highly uncertain, pricing the bivalent booster lower than the VBP is expected to result in a cost-effective strategy for the UK.Copyright © 2023

A systematic approach for COVID-19 predictions and parameter estimation.

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.

Health-Related Quality of Life for Jordanian-Recovered Individuals During Post-COVID-19 Era: A Cross-Sectional Study.

Aim: This study aims to determine health-related quality of life (HRQoL) that includes the physical and mental health of recovered patients of COVID-19 and examines the significant impact of variables such as period of infection, sample demographics characteristics, hospitalization past, and chronic disease past and the other variables on HRQoL of COVID-19-recovered patients. Methods: An exploratory, community-based, cross-sectional research layout was adopted, using an electronic self-reporting survey disseminated online to recovered COVID-19 patients in Jordan. Targeted COVID-19 patients were individuals who were 18 years old or above. They had a documented background of COVID-19 illness, as stated in the following inclusion requirements: Those who had not been formally proven to have been afflicted by COVID-19 were excluded. Results: The mean of the physical well-being of study participants during COVID-19 was M=68.00 (SD=6.95), representing medium physical well-being status. The mean of psychological well-being of study participants during COVID-19 was M=60.20 (SD=8.85), representing medium physical health. Multiple regression showed that female recovered patients who are not working, with a low level of income, married women, and who getting COVID-19 more than once are having lower health-related quality of life compared to other recovered patients. Conclusion: The HRQoL of COVID-19 patients was significantly impacted, independent of the period since hospitalization or rehabilitation. Policymakers and health workers should research strong ways to enhance the HRQoL of COVID-19 patients as soon as possible. Elderly patients and those who have been infected more than one time and being hospitalized have a greater probability of decreased HRQoL after infection.

Probability of Infectious Disease in Humans during Epidemic

Popular SIR models and their modifications used to generate predictions about epidemics and, specifically, the COVID-19 pandemic, are inadequate. The aim of this study was to find the laws describing the probability of infection in a biological object. Using theoretical methods of research based on the probability theory, we constructed the laws describing the probability of infection in a human depending on the infective dose and considering the temporal characteristics of a given infection. The so-called generalized time-factor law, which factors in the time of onset and the duration of an infectious disease, was found to be the most general. Among its special cases are the law describing the probability of infection developing by some point in time t, depending on the infective dose, and the law that does not factor in the time of onset. The study produced a full list of quantitative characteristics of pathogen virulence. The laws described in the study help to solve practical tasks and should lie at the core of mathematical epidemiological modeling.Copyright © 2022 Obstetrics, Gynecology and Reproduction. All rights reserved.

Data Visualization for Corona Patients Globally using Real-Time APIs

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

Evaluation of cognitive functions in a group of Egyptian recovered COVID-19 patients

Background: Numerous investigations have found that cognitive deficits in COVID-19 survivors may be reversible;hence, early detection is essential. These cognitive deficiencies should be targeted with scaled cognitive therapies that can be widely used even in patients' homes, supporting the best possible cognitive and functional outcomes. In the meanwhile, it has been observed that COVID-19 patients may experience worry, fear, depression, and other mental health problems. Therefore, subjective cognitive difficulties may be due to emotional discomfort. As a result, these data highlight the significance of early diagnosis of anxiety symptoms and depression symptoms in COVID-19 patients in order to prevent subsequent cognitive problems. All patients were selected in accordance with the case definition and used the following tools after 1, 3, and 6 months after being cleared of COVID-19 infection: developed questionnaire for both clinical and demographic data, the Wisconsin Card Sorting Test, the Wechsler Memory Scale-Revised, the Wechsler Adult Intelligence Scale, the Hamilton Anxiety Rating Scale, the Beck Depression Inventory, and Structured Clinical Interview for DSM-IV disorders. Results: Fifty patients were participated in this study from both gender, different levels of education, and the major group was nonsmokers (82%). A total of 88% of participants had confirmed COVID-19, and 12% had contact with them. Wisconsin Card Sorting Test for preservative parameters revealed that the 2nd follow-up showed nonsignificant comparison to the 1st follow-up, while the 3rd showed highly significant comparison to the 1st follow-up. While for non-preservative errors, the 2nd follow-up showed significant comparison to the 1st, while the 3rd showed highly significant comparison to the 1st follow-up. Conceptual level response parameters showed that both the 2nd and the 3rd follow-ups showed nonsignificant comparison to the 1st follow-up. There was no significant correlation between Hamilton Anxiety Scale (HAS) and any parameter of Wisconsin Card Sorting Test or any parameter of Wechsler Memory Scale-Revised. Conclusions: While there was negative impact of COVID-19 infection on cognitive functions in Egyptian recovered COVID-19 patients which improves gradually by time, there was nonsignificant correlations between anxiety symptoms, depressive symptoms, and Wisconsin Card Sorting Test as well as Wechsler Memory Scale-Revised parameters in tested individuals through three consecutive follow-ups of COVID-19 in Egypt. Further testing using other scales or larger sample is mandatory to elucidate further potential impact of COVID-19 on cognitive functions of recovered patients. [ FROM AUTHOR] Copyright of Middle East Current Psychiatry is the property of Springer Nature 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.)

Selection of forecasting models for infectious diseases in large warships

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Analysis and simulation of modified susceptible-infected-recovered model with vaccination for COVID-19 outbreak

In this paper, we develop and analyse a modified susceptible-infected-recovered (SIR) compartment model by integrating the vaccination factor as a model parameter to investigate the effect of vaccination parameter on the long-term outcomes of the COVID-19 pandemic. Mathematical analysis is used to determine the disease-free equilibrium, the endemic equilibrium, and the basic reproduction number of the developed model. The stability of the model is studied using the Routh-Hurwitz criterion, and numerical simulations are conducted to assess the impact of vaccination on the disease at different rates. The findings suggest that vaccination rate influences the transmission dynamics, and the vaccine can speed up the COVID-19 recovery and contain the outbreak. © 2023 Inderscience Enterprises Ltd.

Retrospective Assessment of the Accuracy of Mpox Case Projections in New York City

Background: In mid-2022, New York City (NYC) became the epicenter of the US mpox epidemic. Health authorities were in need of forecasts to anticipate the timing and magnitude of the outbreak. We provided mathematical modelbased projections with methodologies that evolved alongside the epidemic. Here, we retrospectively evaluate our mpox case projections and reflect on potential reasons for accuracies and inaccuracies. Method(s): Early in the outbreak (July 1 - 15, 2022), when the size of the at-risk population was unknown, we performed short-term (2-week) forecasting using exponential regression. Once epidemic growth was no longer exponential (July 15 - Aug. 9), we consulted with the NYC Department of Health and Mental Hygiene regarding populations most-at-risk of mpox based on available epidemiological data. Modelers and epidemiologists collaboratively developed an estimate of 70,180 people at risk, informed by estimates of LGBTQ adults with male sex at birth who had 2+ partners in the last 3 months. We combined this with NYC case count data, NYC vaccination data, and global mpox natural history data to develop a Susceptible-Exposed-Infected-Recovered (SEIR) model, taking into account immunity accrued through vaccination and prior exposure, for longer-term forecasting. Result(s): Initial exponential forecasts of the NYC mpox outbreak were only accurate for very short-term predictions (< 2 weeks) (Figure, top panel). Forecasts were more accurate after 1 week (mean absolute error: 83.0 cases/ wk) than after 2 weeks (mean absolute error: 351.4 cases/wk). In contrast, the SEIR model accurately predicted the decline in cases through the end of Sept. 2022, when cases fell to < 70/wk. Over the period from Aug. 10 to Sept. 24 2022, the mean absolute error of the SEIR-based projection was 8.2 cases per week (Figure, bottom panel). Conclusion(s): Model-based NYC mpox projections provided only short-term accuracy in the early epidemic, but long-term accuracy once the epidemic exited exponential growth and an SEIR model was developed. Cumulative cases and vaccinations when exiting exponential growth, and the epidemiology of those most-at-risk, provided evidence for the likely size of the most-at-risk population - a crucial input for an accurate SEIR model. The ability of the SEIR model to accurately forecast mpox cases was in part attributable to lack of vaccine or immune escape by mpox, in contrast to more rapidly-evolving viruses such as SARS-CoV-2.

Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID‐19

The impact that each individual non‐pharmaceutical intervention (NPI) had on the spread rate of COVID‐19 is difficult to estimate, since several NPIs were implemented in rapid succession in most countries. In this article, we analyze the detectability of sudden changes in a parameter of nonlinear dynamical systems, which could be used to represent NPIs or mutations of the virus, in the presence of measurement noise. Specifically, by taking an agnostic approach, we provide necessary conditions for when the best possible unbiased estimator is able to isolate the effect of a sudden change in a model parameter, by using the Hammersley–Chapman–Robbins (HCR) lower bound. Several simplifications to the calculation of the HCR lower bound are given, which depend on the amplitude of the sudden change and the dynamics of the system. We further define the concept of the most informative sample based on the largest ℓ2 distance between two output trajectories, which is a good indicator of when the HCR lower bound converges. These results are thereafter used to analyze the susceptible‐infected‐removed model. For instance, we show that performing analysis using the number of recovered/deceased, as opposed to the cumulative number of infected, may be an inferior signal to use since sudden changes are fundamentally more difficult to estimate and seem to require more samples. Finally, these results are verified by simulations and applied to real data from the spread of COVID‐19 in France. [ FROM AUTHOR] Copyright of International Journal of Robust & Nonlinear Control is the property of John Wiley & Sons, Inc. 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.)

Characterization of oral bacterial and fungal microbiome in recovered COVID-19 patients.

COVID-19 has emerged as a global pandemic, challenging the world's economic and health systems. Human oral microbiota comprises the second largest microbial community after the gut microbiota and is closely related to respiratory tract infections; however, oral microbiomes of patients who have recovered from COVID-19 have not yet been thoroughly studied. Herein, we compared the oral bacterial and fungal microbiota after clearance of SARS-CoV-2 in 23 COVID-19 recovered patients to those of 29 healthy individuals. Our results showed that both bacterial and fungal diversity were nearly normalized in recovered patients. The relative abundance of some specific bacteria and fungi, primarily opportunistic pathogens, decreased in recovered patients (RPs), while the abundance of butyrate-producing organisms increased in these patients. Moreover, these differences were still present for some organisms at 12 months after recovery, indicating the need for long-term monitoring of COVID-19 patients after virus clearance.

mRNA-1273 boost after BNT162b2 vaccination generates comparable SARS-CoV-2-specific functional responses in naïve and COVID-19-recovered individuals.

Introduction: COVID-19 vaccines based on mRNA have represented a revolution in the biomedical research field. The initial two-dose vaccination schedule generates potent humoral and cellular responses, with a massive protective effect against severe COVID-19 and death. Months after this vaccination, levels of antibodies against SARS-CoV-2 waned, and this promoted the recommendation of a third vaccination dose. Methods: We have performed an integral and longitudinal study of the immunological responses triggered by the booster mRNA-1273 vaccination, in a cohort of health workers previously vaccinated with two doses of the BNT162b2 vaccine at University Hospital La Paz located in Madrid, Spain. Circulating humoral responses and SARS-CoV-2-specific cellular reactions, after ex vivo restimulation of both T and B cells (cytokines production, proliferation, class switching), have been analyzed. Importantly, all along these studies, the analyses have been performed comparing naïve and subjects recovered from COVID-19, addressing the influence of a previous infection by SARS-CoV-2. Furthermore, as the injection of the third vaccination dose was contemporary to the rise of the Omicron BA.1 variant of concern, T- and B-cell-mediated cellular responses have been comparatively analyzed in response to this variant. Results: All these analyses indicated that differential responses to vaccination due to a previous SARS-CoV-2 infection were balanced following the boost. The increase in circulating humoral responses due to this booster dropped after 6 months, whereas T-cell-mediated responses were more stable along the time. Finally, all the analyzed immunological features were dampened in response to the Omicron variant of concern, particularly late after the booster vaccination. Conclusion: This work represents a follow-up longitudinal study for almost 1.5 years, analyzing in an integral manner the immunological responses triggered by the prime-boost mRNA-based vaccination schedule against COVID-19.

A Study to Assess the Level of Anxiety among COVID-19 Recovered Patient in Community Area

A new strain of the coronavirus, SARS-CoV-2, was identified as the cause by the Chinese authorities and the World Health Organization (WHO). At the time, it was referred to as a coronavirus disease 2019 and is now commonly referred to as COVID-19. Gamil Ghaleb Alrubaiee and associates 2020) coronaviruses are a family of enveloped RNA viruses that get their name from the outer edge of their envelope proteins that look like crowns ('corona' in Latin). The purpose of this study was to determine how anxious COVID-19 recovered patients in a specific community area were. A quantitative strategy with a descriptive study design for the research. Purposive sampling is used to collect 50 samples from COVID-19 recovered patients for this study. The average anxiety score among COVID-19 recovered patients was 26.76, with a standard deviation of 5.76 and a minimum anxiety score of 13.0 and a maximum anxiety score of 40.0. In patients who had recovered from COVID-19, the analysis showed that none of the demographic variables had a statistically significant relationship with the level of anxiety. ASEAN Journal of Psychiatry, Vol. 24 (1) January, 2023;1-8.

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates - the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people∗week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people∗week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%2.75% people∗week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people∗week infections and avoid vaccine waste when more vaccines are available. © 2013 IEEE.

The Impact of Restricting Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia

Sumatra Island is the third largest island with the second largest population in Indonesia which has the following eight provinces: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu and Lampung. The connectivity of these eight provinces in the economic field is very strong. This encourages high mobility between these provinces. During this Covid-19 pandemic, the high mobility between provinces affects the level of spread of Covid-19 on the island of Sumatra. The central government ordered local governments to implement a community activity restriction program called PPKM. In this article, a study is conducted on the impact of the PKKM program on the spread of Covid 19 on the island of Sumatra, Indonesia. The spread of Covid-19 is modeled using the Susceptible-Infected-Recovered-Death (SIRD) model which considers the mobility factor of the population. The model parameters were estimated using Approximate Bayesian Computation (ABC). The results of the study using this model show that the application of PKKM in several provinces in Sumatra can reduce the level of spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode

In 2019, COVID-19 (CoronaVirus Disease 2019) broke out all over the world. COVID-19 is an infectious disease, which has a huge impact on the global economy. It is very difficult to prevent and control the epidemic situation of this infectious disease. At present, many SEIR(Susceptible Exposed Infected Recovered)models are used to predict the number of infectious diseases, which has the shortcomings of low prediction accuracy and inaccurate inflection point prediction. Therefore, this paper proposes that the prediction and analysis of COVID-19 based on improved GEP algorithm and optimized SEIR model can improve the prediction accuracy and inflection point prediction accuracy, and provide a theoretical basis for epidemic prevention of large-scale infectious diseases in the future. The algorithm. First, establish SEIR (Susceptible Exposed Infected Recovered) model to analyze the epidemic trend, and then use improved GEP (Gene Expression Programming) algorithm to analyze the infection coefficient of SEIR model beta And coefficient of restitution y, perform parameter estimation to optimize the initial value I and recovery coefficient of the infected population y and so on to improve the accuracy of model prediction. The experimental data take the number of COVID-19 infected people in the United States, China, the United Kingdom and Italy as examples. The results show that the SEIR model optimized based on the improved GEP algorithm conforms to the inflection point of the actual data, and the average error value is 1.32%. The algorithm provides a theoretical basis for the future epidemic prevention. © 2022 IEEE.