Uncovering the interplay between hypertension and the inflammatory response for the patient affected by COVID-19 via mathematical modeling and computer-based analysis

We explore here the systems-based regulatory mechanisms that determine human blood pressure patterns. This in the context of the reported negative association between hypertension and COVID-19 disease. We are particularly interested in the key role that plays angiotensin converting enzyme 2 (ACE2), one of the first identified receptors that enable the entry of the SARS-CoV-2 virus into a cell. Taking into account the two main systems involved in the regulation of blood pressure, that is, the Renin-Angiotensin system and the Kallikrein-Kinin system, we follow a Bottom-Up systems biology modeling approach in order to built the discrete Boolean model of the gene regulatory network that underlies both the typical hypertensive phenotype and the hypotensive/normotensive phenotype. These phenotypes correspond to the dynamic attractors of the regulatory network modeled on the basis of publicly available experimental information. Our model recovers the observed phenotypes and shows the key role played by the inflammatory response in the emergence of hypertension. Source code go to the next url: https://github.com/cxro-cc/red_ras_kks © 2023, EasyChair. All rights reserved.

COVID-19 SEIAR Model with Sensitivity Analysis

Beside the unexpected toll of mortality and morbidity caused by COVID-19 worldwide, low- and middle-income countries are more suffering from the devastating issues on economic and social life. This disease has fostered mathematical modelling. In this paper, a SEIAR mathematical model is presented to illustrate how policymakers may apply efficient strategies to end or at least to control the devastating wide spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

THEORETICAL MODELLING OF THE EPIDEMIC COVID-2019 IN AZERBAIJAN.

A mathematical model of the coronavirus COVID-2019 epidemic in Azerbaijan is proposed. Analysis of the proposed mathematical model shows that the dynamic behavior of the epidemic is quite sensitive to parameters (rate constant of stages), which reflect different measures against the epidemic. This fact suggests that the lifting of all restrictive measures can aggravate the situation with COVID-19 in the republic and one should not expect the complete disappearance of the Covid-19 coronavirus in Azerbaijan.Copyright © 2023 Ministry of Health. All rights reserved.

Threshold conditions for curbing COVID-19 with a dynamic zero-case policy derived from 101 outbreaks in China.

By 31 May 2022, original/Alpha, Delta and Omicron strains induced 101 outbreaks of COVID-19 in mainland China. Most outbreaks were cleared by combining non-pharmaceutical interventions (NPIs) with vaccines, but continuous virus variations challenged the dynamic zero-case policy (DZCP), posing questions of what are the prerequisites and threshold levels for success? And what are the independent effects of vaccination in each outbreak? Using a modified classic infectious disease dynamic model and an iterative relationship for new infections per day, the effectiveness of vaccines and NPIs was deduced, from which the independent effectiveness of vaccines was derived. There was a negative correlation between vaccination coverage rates and virus transmission. For the Delta strain, a 61.8% increase in the vaccination rate (VR) reduced the control reproduction number (CRN) by about 27%. For the Omicron strain, a 20.43% increase in VR, including booster shots, reduced the CRN by 42.16%. The implementation speed of NPIs against the original/Alpha strain was faster than the virus's transmission speed, and vaccines significantly accelerated the DZCP against the Delta strain. The CRN ([Formula: see text]) during the exponential growth phase and the peak time and intensity of NPIs were key factors affecting a comprehensive theoretical threshold condition for DZCP success, illustrated by contour diagrams for the CRN under different conditions. The DZCP maintained the [Formula: see text] of 101 outbreaks below the safe threshold level, but the strength of NPIs was close to saturation especially for Omicron, and there was little room for improvement. Only by curbing the rise in the early stage and shortening the exponential growth period could clearing be achieved quickly. Strengthening China's vaccine immune barrier can improve China's ability to prevent and control epidemics and provide greater scope for the selection and adjustment of NPIs. Otherwise, there will be rapid rises in infection rates and an extremely high peak and huge pressure on the healthcare system, and a potential increase in excess mortality.

COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021.

BACKGROUND: Policy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period. METHODS: An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions. RESULTS: Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures. CONCLUSIONS: Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.

Risk estimation of lifted mask mandates and emerging variants using mathematical model.

Background: More than half of the population in Korea had a prior COVID-19 infection. In 2022, most nonpharmaceutical interventions, except mask-wearing indoors, had been lifted. And in 2023, the indoor mask mandates were eased. Methods: We developed an age-structured compartmental model that distinguishes vaccination history, prior infection, and medical staff from the rest of the population. Contact patterns among hosts were separated based on age and location. We simulated scenarios with the lifting of the mask mandate all at once or sequentially according to the locations. Furthermore, we investigated the impact of a new variant assuming that it has higher transmissibility and risk of breakthrough infection. Results: We found that the peak size of administered severe patients may not exceed 1100 when the mask mandate is lifted everywhere, and 800 if the mask mandate only remains in the hospital. If the mask mandate is lifted in a sequence (except hospital), then the peak size of administered severe patients may not exceed 650. Moreover, if the new variant has both higher transmissibility and immune reduction, the effective reproductive number of the new variant is approximately 3 times higher than that of the current variant, and additional interventions may be needed to keep the administered severe patients from exceeding 2,000, which is the critical level we set. Conclusion: Our findings showed that the lifting of the mask mandate, except in hospitals, would be more manageable if implemented sequentially. Considering a new variant, we found that depending on the population immunity and transmissibility of the variant, wearing masks and other interventions may be necessary for controlling the disease.

Comparative Analysis of Mathematical Model of Covid-Sars Using Atangana-Baleanu and Yang-Abdel-Cattani Fractional Derivative Operators

Today's world is suffering from a disease known as the Corona Virus (COVID-19). Since this virus has turned into a pandemic at a global level, it is required to investigate the virus and its related attributes to anticipate future outbreaks and also to make strategies for its control through mathematical models. In this article, we perform a comparative analysis of the model using the Atangana-Baleanu and Yang-Abdel-Cattani fractional derivative operators with the help of Sumudu transform. We also compute the numerical results with graphical representation to show the behavior of the operators.

Method for Quantitative Assessment of Protective Immunity against Sars-Cov-2, Its Duration and Antibody Dynamics

The level and duration of protective immunity are often analyzed qualitatively or semi-quantitatively. The same strategy is applied to the analysis of antibody dynamics. At some point in time t after exposure or immunization, the presence of immunity against the infection is inferred from the level of specific antibodies by comparing it to a reference value. This approach does not account for the stochastic nature of human disease after exposure to a pathogen. At the same time, it is not fully clear what antibody level should be considered protective. The aim of this study was to develop a mathematical model for quantitative determination of protective immunity against SARS-CoV-2 and its duration. We demonstrate that the problem of describing protective immunity in quantitative terms can be broken down into 2 interrelated problems: describing the quantitative characteristics of a pathogen's virulence (in our case, the pathogen is SARS-CoV-2) and describing the dynamics of antibody titers in a biological organism. Below, we provide solutions for these problems and identify parameters of the model which describes such dynamics. Using the proposed model, we offer a theoretical solution to the problem of protective immunity and its duration. We also note that in order to quantitatively determine the studied parameters in a homogenous population group, it is necessary to know 5 parameters of the bivariate probability density function for correlated continuous random variables: the infective dose of the pathogen and the antibody titer at which the disease develops and which are still unknown.Copyright © Extreme Medicine.All right reserved.

Mathematical Evaluation of Covid-19 Detection Technique Using Cxr Radiographs of Patients, Using Densenet-201

A current COVID-19 detection tool is CXR imaging, which has been developing since 2019 to provide early diagnosis;it can be performed in any health unit and is more affordable than Real Time Polymerase Chain Reaction (RT-PCR) tests. However, diagnosis with Chest X Ray (CXR) images had not achieved the predictive capacity required to replace the RT-PCR test;previous studies with a limited number of images have evaluated their models. This research seeks to contribute to the detection of COVID-19 from CXR images, with the evaluation of a convolutional neural network model from CXR images, through the use of open source code on a free dataset of approximately 30 thousand images. The algorithm and mathematical model used was DenseNet-201. The results of the experiment show a precision and accuracy of more than 95% and specificity, sensitivity, predictive ability and F1 measurement of more than 90%.Copyright © 2022, Anka Publishers. All rights reserved.

Mathematical evaluation of COVID-19 detection technique using CXR radiographs of patients, using CNN vggnet-16

The detection of COVID-19 by CXR imaging is a support tool for physicians and specialists since the pandemic and has been evolving rapidly because it provides early diagnosis, can be performed in any health center, and is more affordable than Real-Time Polymerase Chain Reaction (RT-PCR) tests. However, Chest X-Ray (CXR) imaging had not achieved the predictive capacity needed to replace the RT-PCR test;previous studies have evaluated their models with a limited amount of images. This study aims to contribute to the evaluation of a convolutional neural network (CNN) model to detect COVID-19 from CXR images, using open source and a free dataset containing approximately 30,000 images. The mathematical model or algorithm used was VGGNet-16. The results of the experiments show accuracy and precision of more than 95% and sensitivity, specificity, F1-measure,andthedictive ability of more than 90%.Copyright © 2022, Anka Publishers. All rights reserved.

Outbreak trends of fatality rate into coronavirus disease-2019 using deep learning

The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.Copyright © 2023 Inderscience Enterprises Ltd.

Covid19 Impact OnTwelve Sectors Of The Indian Economy

This study aims to investigate the reaction of COVID-19 cases (confirmed, deaths, recovered, & active) on twelve sectors of Indian economy by using sectoral indices of national stock exchange. Daily frequency of COVID-19 case categories was obtained from Worldometer from January 30, 2020 to June 30, 2020 and dataset of daily closing prices of twelve sectoral indices (auto, banks, financial services, fast moving consumer goods, information technology, media, metal, oil & gas, pharmaceutical, public sector banks, private banks, realty sector) was obtained from national stock exchange web portal for the same period as of COVID-19. In this study, the ordinary least square regression was used to study the significance of COVID-19 cases (confirmed, deaths, recovered, & active) on twelve sectoral indices. Empirical evidence suggested no significant impact of COVID-19 cases on daily returns of twelve major sectors represented by sectoral indices except in the case of pharmaceutical sector, where daily growth in number of deaths is impacting daily returns on pharmaceutical sectoral index in a positive way. The twelve sectoral indices went into a downward spiral at the beginning of COVID-19 pandemic, but as government and central bank introduced various policy measures, the impact of COVID-19 pandemic on sectoral indices faded away.Copyright © 2020 Ubiquity Press. All rights reserved.

Forecasting COVID-19 cases in Egypt using ARIMA-based time-series analysis

Objectives: The World Health Organization declared the novel coronavirus (COVID-19) outbreak a public health emer-gency of international concern on January 30, 2020. Since it was first identified, COVID-19 has infected more than one hundred million people worldwide, with more than two million fatalities. This study focuses on the interpretation of the distribution of COVID-19 in Egypt to develop an effective forecasting model that can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of COVID-19. Method(s): A model was developed using the data collected by the Egyptian Ministry of Health and used it to predict possible COVID-19 cases in Egypt. Result(s): Statistics obtained based on time-series and kinetic model analyses suggest that the total number of CO-VID-19 cases in mainland Egypt could reach 11076 per week (March 1, 2020 through January 24, 2021) and the number of simple regenerations could reach 12. Analysis of the ARIMA (2, 1, 2) and (2, 1, 3) sequences shows a rise in the number of COVID-19 events. Conclusion(s): The developed forecasting model can help the government and medical personnel plan for the imminent conditions and ensure that healthcare systems are prepared to deal with them.Copyright © 2021 by Eurasian Journal of Medicine and Oncology.

Analysis of Benefits and Potential Damage from Covid-19 Vaccination

Intro: COVID-19 Vaccination has proven to be very effective in preventing infection and progression to severity and death. However, there were concerns about very rare but potentially fatal adverse reactions after vaccination;myocarditis/pericarditis, TTS/VITT et al. It suggested that the evaluation of the two values of personal safety and public benefit is necessary. Method(s): The benefit of vaccination was measured by the number of critically ill patients prevented from vaccination. The number of critically ill patients predicted in the future was measured through two Methods: based on a fixed scenario, and using a mathematical model. Damage through vaccination was calculated as the occurrence of TTS/VITT, Myocarditis/Pericarditis, and of severe cases. Finding(s): The evaluation results on vaccine safety and effectiveness were made in the form of age restrictions for vaccination by each vaccine platform. As a result of the evaluation, the AstraZeneca vaccine was limited to those under the age of 30 but there was no restriction on the age of mRNA vaccination. In addition, the risks and benefits of vaccination for children aged 5-11 years and 12-17 years of age were evaluated respectively, and it was confirmed that the benefits of vaccination outweigh the potential harm in children and adolescents. Conclusion(s): Our nation has the own policy for COVID 19 vaccination from the results. The pandemic situation has presented a new approach to the benefits and risks of large-scale vaccination. In particular, the method of comparing the risks and benefits of vaccination was considered as a useful method for health communication.Copyright © 2023

Projecting the development of antimicrobial resistance in Neisseria gonorrhoeae from antimicrobial surveillance data: a mathematical modelling study.

BACKGROUND: The World Health Organization recommends changing the first-line antimicrobial treatment for gonorrhoea when ≥ 5% of Neisseria gonorrhoeae cases fail treatment or are resistant. Susceptibility to ceftriaxone, the last remaining treatment option has been decreasing in many countries. We used antimicrobial resistance surveillance data and developed mathematical models to project the time to reach the 5% threshold for resistance to first-line antimicrobials used for N. gonorrhoeae. METHODS: We used data from the Gonococcal Resistance to Antimicrobials Surveillance Programme (GRASP) in England and Wales from 2000-2018 about minimum inhibitory concentrations (MIC) for ciprofloxacin, azithromycin, cefixime and ceftriaxone and antimicrobial treatment in two groups, heterosexual men and women (HMW) and men who have sex with men (MSM). We developed two susceptible-infected-susceptible models to fit these data and produce projections of the proportion of resistance until 2030. The single-step model represents the situation in which a single mutation results in antimicrobial resistance. In the multi-step model, the sequential accumulation of resistance mutations is reflected by changes in the MIC distribution. RESULTS: The single-step model described resistance to ciprofloxacin well. Both single-step and multi-step models could describe azithromycin and cefixime resistance, with projected resistance levels higher with the multi-step than the single step model. For ceftriaxone, with very few observed cases of full resistance, the multi-step model was needed to describe long-term dynamics of resistance. Extrapolating from the observed upward drift in MIC values, the multi-step model projected ≥ 5% resistance to ceftriaxone could be reached by 2030, based on treatment pressure alone. Ceftriaxone resistance was projected to rise to 13.2% (95% credible interval [CrI]: 0.7-44.8%) among HMW and 19.6% (95%CrI: 2.6-54.4%) among MSM by 2030. CONCLUSIONS: New first-line antimicrobials for gonorrhoea treatment are needed. In the meantime, public health authorities should strengthen surveillance for AMR in N. gonorrhoeae and implement strategies for continued antimicrobial stewardship. Our models show the utility of long-term representative surveillance of gonococcal antimicrobial susceptibility data and can be adapted for use in, and for comparison with, other countries.

Modeling a traffic light warning system for acute respiratory infections.

The high morbidity of acute respiratory infections constitutes a crucial global health burden. In particular, for SARS-CoV-2, non-pharmaceutical intervention geared to enforce social distancing policies, vaccination, and treatments will remain an essential part of public health policies to mitigate and control disease outbreaks. However, the implementation of mitigation measures directed to increase social distancing when the risk of contagion is a complex enterprise because of the impact of NPI on beliefs, political views, economic issues, and, in general, public perception. The way of implementing these mitigation policies studied in this work is the so-called traffic-light monitoring system that attempts to regulate the application of measures that include restrictions on mobility and the size of meetings, among other non-pharmaceutical strategies. Balanced enforcement and relaxation of measures guided through a traffic-light system that considers public risk perception and economic costs may improve the public health benefit of the policies while reducing their cost. We derive a model for the epidemiological traffic-light policies based on the best response for trigger measures driven by the risk perception of people, instantaneous reproduction number, and the prevalence of a hypothetical acute respiratory infection. With numerical experiments, we evaluate and identify the role of appreciation from a hypothetical controller that could opt for protocols aligned with the cost due to the burden of the underlying disease and the economic cost of implementing measures. As the world faces new acute respiratory outbreaks, our results provide a methodology to evaluate and develop traffic light policies resulting from a delicate balance between health benefits and economic implications.

Age-dependent final size equation to anticipate mortality impact of COVID-19 in China.

Before reopening society in December 2022, China had not achieved sufficiently high vaccination coverage among people aged 80 years and older, who are vulnerable to severe infection and death owing to COVID-19. Suddenly ending the zero-COVID policy was anticipated to lead to substantial mortality. To investigate the mortality impact of COVID-19, we devised an age-dependent transmission model to derive a final size equation, permitting calculation of the expected cumulative incidence. Using an age-specific contact matrix and published estimates of vaccine effectiveness, final size was computed as a function of the basic reproduction number, R0. We also examined hypothetical scenarios in which third-dose vaccination coverage was increased in advance of the epidemic, and also in which mRNA vaccine was used instead of inactivated vaccines. Without additional vaccination, the final size model indicated that a total of 1.4 million deaths (half of which were among people aged 80 years and older) were anticipated with an assumed R0 of 3.4. A 10% increase in third-dose coverage would prevent 30,948, 24,106, and 16,367 deaths, with an assumed second-dose effectiveness of 0%, 10%, and 20%, respectively. With mRNA vaccine, the mortality impact would have been reduced to 1.1 million deaths. The experience of reopening in China indicates the critical importance of balancing pharmaceutical and non-pharmaceutical interventions. Ensuring sufficiently high vaccination coverage is vital in advance of policy changes.

Equity-Based Optimization for Covid-19 Vaccine Allocation

Background: Despite the development of safe and effective vaccines and antiviral treatments against COVID- 19, marginalized racial/ethnic groups in the United States continue to be disproportionally burdened by COVID-19. In response to this inequity, public health officials in several states designed, usually in an ad-hoc manner, policies aimed to be more equitable in both access and distribution of COVID-19 interventions. Method(s): We constructed an age- and race-stratified mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. We fit our model to data from Oregon at the beginning of 2021. Next, we explored counterfactual scenarios where we determined the optimal use of limited amounts of vaccine over the first 4 months of 2021 with the goal of minimizing 1) number of deaths or Years of Life Lost (YLL), 2) the inequity in mortality or YYL between race groups, 3) a combination of both. We compared them to a base-case scenario without vaccination. Result(s): When vaccine supply is very limited (enough to cover 10% of the population), there is a trade-off between minimizing mortality or minimizing inequity (Fig.1). For minimizing mortality, it is optimal to allocate vaccine to the oldest age group, irrespective of race. To minimize inequity, vaccine needs to be allocated first to the marginalized populations in the young- and middle-aged groups, incurring significantly more deaths in all groups, including the marginalized ones, compared to minimizing mortality (Fig.1). When minimizing both deaths and inequity, the optimal vaccination strategy achieved a significant reduction in inequity while preserving most of the reduction in mortality (Fig.1). When minimizing YYL and inequity, the optimal allocation resulted in a more equitable distribution of resources and outcomes across race groups. Once vaccine supply was enough to cover 20% of the population, our results suggest that it is possible to minimize both mortality (or YYL) and inequity, by protecting marginalized communities and the oldest populations at the same time. Conclusion(s): With low vaccine supply, there is a trade-off between being more equitable and reducing mortality. This is true because COVID-19 related mortality is concentrated in the oldest population while marginalized populations are predominately young. This trade-off quickly disappears when more vaccine is available. An interdisciplinary approach is needed to address the inequitable distribution of resources and outcomes in public health. Mortality rate (left), Years of Life Lost (center) and Indices of Disparity (right) with no vaccination (top row), minimizing deaths (2nd row), inequity (3rd row) or both (4th row) with enough vaccine to cover 10% of the population.

Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.

Unpacking Modeling Studies

There is increasing recognition of the usefulness of mathematical models in informing public health intervention strategies and policymakers on how to address different epidemics. Over the years, we have seen many models testing different interventions to prevent the spread of HIV. Lately, mathematical models have been used to assess interventions focused on controlling the spread of COVID-19 and, more recently, of Mpox. However, as we will show in this presentation, we saw considerable pressure to fast produce mathematical models for COVID-19 to inform on different policies to control the spread of this disease. Consequently, we saw that several of these models missed the mark, and others, although not perfect, were useful in making predictions based on different interventions. In this presentation, I will describe how mathematical models have informed public health Interventions. We will provide an overview of the use of mathematical models to prevent HIV, COVID-19 and Mpox. I will propose a list of criteria to evaluate and write publications involving mathematical models. Finally, I will discuss some of the lessons learned for properly using mathematical models and interpreting results and propose a pathway moving forward.