A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling.

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Application of the Aho-Korasik Algorithm for the Selection of Primers for Loop Isothermal Amplification

This paper presents a program which allows user to do primer design for identifying DNA target site or a whole genome with a goal of performing loopmediated isothermal amplification. The review of the most popular existing primer design programs for LAMP is carried out. Recommended conditions are presented in the paper. They are required to be taken in consideration during the process of primer design for loop-mediated isothermal amplification. These are the conditions: primer's length, GC-content, amplicon average size, annealing temperature and distance between primers. A search for primer positions in genome is needed since loop-mediated isothermal amplification requires primer kits that consist of 6 primers in order for primer design to be done. The Aho–Corasick algorithm was proposed for a search implementation. This algorithm is capable of simultaneous search for a number of sample (primer) entries in a longer sequence (a fragment or a whole genome). This software allows the search for primers in genomes of various length and it groups primers by kits, which in turn could be applied in laboratory experiments. These kits are formed according both to the recommended conditions of primer selection for performing loop-mediated isothermal amplification and to the initial conditions, which are determined by the user before the process. After that, the user may choose the best option for their case from a list of primer kits that are being created as a result of performed computer analysis. The test run of the program was done during the search for a specific primer kit that is meant to be used for performing loop-mediated isothermal amplification of genome with a goal of detection of novel coronavirus infection SARS-CoV-2, a virus that triggers a dangerous disease, COVID-19. The software was developed using Python with BioPython and Pyahocorasick libraries and available at the link: https://cloud.mail.ru/public/C7av/QCkSiUomz © 2022,Mathematical Biology and Bioinformatics.All Rights Reserved.

Patterns of statin adherence in primary cardiovascular disease prevention during the pandemic.

Background: Study of medication adherence patterns can help identify patients who would benefit from effective interventions to improve adherence. Objectives: To identify and compare groups of statin users based on their adherence patterns before and during the COVID-19 pandemic, to characterize the profile of users in each group, and to analyze predictors of distinct adherence patterns. Methods: Participants of the CARhES (CArdiovascular Risk factors for HEalth Services research) cohort, comprising individuals aged >16 years, residing in Aragón (Spain), with hypertension, diabetes mellitus and/or dyslipidemia, took part in this observational longitudinal study. Individuals who began statin therapy during January-June 2019 were selected and followed up until June 2021. Those with a cardiovascular event before or during follow-up were excluded. Data were obtained from healthcare system data sources. Statin treatment adherence during the implementation phase was estimated bimonthly using the Continuous Medication Availability (CMA9) function in the AdhereR package. Group-based trajectory models were developed to group statin users according to their adherence pattern during July 2019-June 2021. Group characteristics were compared and predictors of each adherence pattern were analyzed using multinomial logistic regression. Results: Of 15,332 new statin users, 30.8% had a mean CMA9 ≥80% for the entire study period. Four distinct adherence patterns were identified: high adherence (37.2% of the study population); poor adherence (35.6%); occasional use (14.9%); and gradual decline (12.3%). The latter two groups included users who showed a change in adherence (increase or decrease) during the pandemic emergence. Users with suboptimal adherence were likely to be younger, not pensioners, not institutionalized, with low morbidity burden and a low number of comorbidities. Female sex and switching between statins of different intensity increased the likelihood of belonging to the occasional use group, in which improved adherence coincided with the pandemic. Conclusion: We identified four distinct adherence patterns in a population of new statin users; two of them modified their adherence during the pandemic. Characterization of these groups could enable more effective distribution of resources in future similar crisis and the routine implementation of patient-centered interventions to improve medication adherence.

Possibilities of computer simulation in optimizing the work of the inpatient emergency department in emergency situations

Relevance. The development of computer technology in recent years is increasingly being introduced into the medical field. Modern programs make it possible to perform imitation modeling of a medical unit in the mode of daily activities and in emergency situations, allow predicting the required number of personnel and bed capacity. Intention. To study the possibilities of computer simulation to optimize the work of an inpatient emergency department in emergency conditions. Methodology. With the help of software, a simulation model of a real inpatient emergency department was developed, experiments were carried out with the formation of emergency situations, the results were compared with data obtained in practice. Results and Discussion. Upon admission of 50 patients per hour, the optimal solution was the conversion of 5 beds of the dynamic observation ward into intensive care beds and the placement of 10 additional beds in the waiting room, as well as the allocation of additional personnel in case of an emergency: 8 doctors, 6 nurses and 2 paramedics, 4 medical registrars. In the experiment of work in the conditions of the first wave of the COVID-19 pandemic, the capacity of the department was sufficient to admit 164 patients in 24 hours, the duration of their stay in the department was (110.0 ± 4.6) minutes. The second wave demonstrated the need to apply simulation modeling as a whole for the entire medical institution, and not just for individual structural units. Conclusion. Planning the work of an inpatient emergency department in an emergency requires preparedness for massive admission of patients. In this case, it is advisable to solve tactical issues in advance, using modern technologies, such as computer simulation. © 2022 Proceedings of the National Academy of Sciences of Belarus. Medical series. All rights reserved.

Simulation of prevention and control of COVID-19 epidemic based on computer modeling

Taking Henan Province as the research object, this paper discusses the temporal and spatial distribution of COVID-19 and its spreading laws and characteristics. Through computer modeling and intelligent fitting, the Moran'I and Moran's I exponential distributions are obtained to describe the global space and local space density. Establish SEIRD model and use simulated annealing algorithm to predict its development trend. At the same time, taking into account the development of the epidemic and the infection rate under different conditions, as well as the local testing capabilities and testing costs, combined with mathematical expectations, design a reasonable virus testing program. © 2021 IEEE.

Research on Improved SEIR Model Predicting UK Coronavirus Variant Omicron Outbreak

As it is reported, the detailed COVID-19 cases have exceeded 400 million worldwide. And there is another outbreak of the COVID-19 infection in England due to the emergence of a new variant: Omicron. Through implementing distinctive control measures, most vaccination has been accomplished to an expansive levels in this country and is as of now in advance. Due to the popularity of vaccines and the success of anti-epidemic measures, the English government decide to announce the last legal restrictions being lifted by February 24, which means that English will be the first country to declare victory over COVID-19. To judge whether the decision is correct or not, we estimate confirmed cases, death, daily new confirmed cases and trend through modeling and simulating method. Considering the effectiveness of vaccines, we raise a new epidemic model named SEIR-V. Our results appear that if transmission rate increases by 15% compared to the current rate due to unwinding of social distancing conditions, the daily new cases can crest to 200k per day around April 1, 2022. The combination of vaccination and controlled legal restrictions is the key to tackling the emergency of the new variant Omicron epidemic. Considering that the new variant strains increase transmissibility and have high resistance to vaccines, the English government should continue the current epidemic prevention measures to avoid the emergence of a second wave of the epidemic. © 2022 SPIE

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19.

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects.

The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients' rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes.

Estimation of undetected symptomatic and asymptomatic cases of COVID-19 infection and prediction of its spread in the USA.

The reported COVID-19 cases in the United States of America have crossed over 10 million and a large number of infected cases are undetected whose estimation can be done if country-wide antibody testing is performed. In this study, we estimate this undetected fraction of the population by a modeling and simulation approach. We employ an epidemic model SIPHERD in which three categories of infection carriers, symptomatic, purely asymptomatic, and exposed are considered with different transmission rates that are taken dependent on the social distancing conditions, and the detection rate of the infected carriers is taken dependent on the tests done per day. The model is first validated for Germany and South Korea and then applied for prediction of the total number of confirmed, active and dead, and daily new positive cases in the United States. Our study predicts the possible outcomes of the infection if social distancing conditions are relaxed or kept stringent. We estimate that around 30.1 million people are already infected, and in the absence of any vaccine, 66.2 million (range: 64.3-68.0) people, or 20% (range: 19.4-20.5) of the population will be infected by mid-February 21 if social distancing conditions are not made stringent. We find the infection-to-fatality ratio to be 0.65% (range: 0.63-0.67).

SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial?

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world, causing serious illness and death and creating a heavy burden on the healthcare systems of many countries. Since the virus first emerged in late November 2019, its spread has coincided with peak circulation of several seasonal respiratory viruses, yet some studies have noted limited coinfections between SARS-CoV-2 and other viruses. We use a mathematical model of viral coinfection to study SARS-CoV-2 coinfections, finding that SARS-CoV-2 replication is easily suppressed by many common respiratory viruses. According to our model, this suppression is because SARS-CoV-2 has a lower growth rate (1.8/d) than the other viruses examined in this study. The suppression of SARS-CoV-2 by other pathogens could have implications for the timing and severity of a second wave.

Oscillatory Dynamics in Infectivity and Death Rates of COVID-19.

The analysis of systematically collected data for coronavirus disease 2019 (COVID-19) infectivity and death rates has revealed, in many countries around the world, a typical oscillatory pattern with a 7-day (circaseptan) period. Additionally, in some countries, 3.5-day (hemicircaseptan) and 14-day periodicities have also been observed. Interestingly, the 7-day infectivity and death rate oscillations are almost in phase, showing local maxima on Thursdays/Fridays and local minima on Sundays/Mondays. These observations are in stark contrast to a known pattern correlating the death rate with the reduced medical staff in hospitals on the weekends. While we cannot exclude the possibility that a significant portion of the observed oscillations is associated with the reporting of the individual cases, other reasons might contribute at least partly to these data. One possible hypothesis addressing these observations is that they reflect gradually increasing stress with the progressing week, which can trigger the higher death rates on Thursdays/Fridays. Moreover, assuming the weekends provide the likely time for new infections, the maximum number of new cases might fall, again, on Thursdays/Fridays. These observations deserve further study to provide a better understanding of COVID-19 dynamics.IMPORTANCE The infectivity and death rates for COVID-19 have been observed in many countries around the world as well as in the collective data of the whole world. These oscillations show distinct circaseptan periodicity, which could be associated with numerous biological reasons as well as with improper reporting of the data collected. Since very different results are observed in different countries and even continents, such as Sweden (very significant oscillations) or India (almost no oscillations), these data provide a very important message about different conditions under which the disease is spread or is reported, which, in turn, could serve as guidance tools in future epidemics. It is necessary that follow-up studies track the observed differences and fully reliably address their origins.

An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India.

Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread.

Estimating the effects of asymptomatic and imported patients on COVID-19 epidemic using mathematical modeling.

The epidemic of Coronavirus Disease 2019 has been a serious threat to public health worldwide. Data from 23 January to 31 March at Jiangsu and Anhui provinces in China were collected. We developed an adjusted model with two novel features: the asymptomatic population and threshold behavior in recovery. Unbiased parameter estimation identified faithful model fitting. Our model predicted that the epidemic for asymptomatic patients (ASP) was similar in both provinces. The latent periods and outbreak sizes are extremely sensitive to strongly controlled interventions such as isolation and quarantine for both asymptomatic and imported cases. We predicted that ASP serve as a more severe factor with faster outbreaks and larger outbreak sizes compared with imported patients. Therefore, we argued that the currently strict interventions should be continuously implemented, and unraveling the asymptomatic pool is critically important before preventive strategy such as vaccines.