Epidemiological impact of travel enhancement on the inter-prefectural importation dynamics of COVID-19 in Japan, 2020

Mobility restrictions were widely practiced to reduce contact with others and prevent the spatial spread of COVID-19 infection. Using inter-prefectural mobility and epidemiological data, a statistical model was devised to predict the number of imported cases in each Japanese prefecture. The number of imported cases crossing prefectural borders in 2020 was predicted using inter-prefectural mobility rates based on mobile phone data and prevalence estimates in the origin prefectures. The simplistic model was quantified using surveillance data of cases with an inter-prefectural travel history. Subsequently, simulations were carried out to understand how imported cases vary with the mobility rate and prevalence at the origin. Overall, the predicted number of imported cases qualitatively captured the observed number of imported cases over time. Although Hokkaido and Okinawa are the northernmost and the southernmost prefectures, respectively, they were sensitive to differing prevalence rate in Tokyo and Osaka and the mobility rate. Additionally, other prefectures were sensitive to mobility change, assuming that an increment in the mobility rate was seen in all prefectures. Our findings indicate the need to account for the weight of an inter-prefectural mobility network when implementing countermeasures to restrict human movement. If the mobility rates were maintained lower than the observed rates, then the number of imported cases could have been maintained at substantially lower levels than the observed, thus potentially preventing the unnecessary spatial spread of COVID-19 in late 2020.

Within-host delay differential model for SARS-CoV-2 kinetics with saturated antiviral responses

The present study discussed a model to describe the SARS-CoV-2 viral kinetics in the presence of saturated antiviral responses. A discrete-time delay was introduced due to the time required for uninfected epithelial cells to activate a suitable antiviral response by generating immune cytokines and chemokines. We examined the system's stability at each equilibrium point. A threshold value was obtained for which the system switched from stability to instability via a Hopf bifurcation. The length of the time delay has been computed, for which the system has preserved its stability. Numerical results show that the system was stable for the faster antiviral responses of epithelial cells to the virus concentration, i.e., quick antiviral responses stabilized patients' bodies by neutralizing the virus. However, if the antiviral response of epithelial cells to the virus increased, the system became unstable, and the virus occupied the whole body, which caused patients' deaths.

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.

Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages

COVID-19 has been spreading widely since January 2020, prompting the implementation of non-pharmaceutical interventions and vaccinations to prevent overwhelming the healthcare system. Our study models four waves of the epidemic in Munich over two years using a deterministic, biology-based mathematical model of SEIR type that incorporates both non-pharmaceutical interventions and vaccinations. We analyzed incidence and hospitalization data from Munich hospitals and used a two-step approach to fit the model parameters: first, we modeled incidence without hospitalization, and then we extended the model to include hospitalization compartments using the previous estimates as a starting point. For the first two waves, changes in key parameters, such as contact reduction and increasing vaccinations, were enough to represent the data. For wave three, the introduction of vaccination compartments was essential. In wave four, reducing contacts and increasing vaccinations were critical parameters for controlling infections. The importance of hospitalization data was highlighted, as it should have been included as a crucial parameter from the outset, along with incidence, to avoid miscommunication with the public. The emergence of milder variants like Omicron and a significant proportion of vaccinated people has made this fact even more evident.

Impact of antibody-level on viral shedding in B.1.617.2 (Delta) variant-infected patients analyzed using a joint model of longitudinal and time-to-event data

Knowledge of viral shedding remains limited. Repeated measurement data have been rarely used to explore the influencing factors. In this study, a joint model was developed to explore and validate the factors influencing the duration of viral shedding based on longitudinal data and survival data. We divided 361 patients infected with Delta variant hospitalized in Nanjing Second Hospital into two groups (≤ 21 days group and > 21 days group) according to the duration of viral shedding, and compared their baseline characteristics. Correlation analysis was performed to identify the factors influencing the duration of viral shedding. Further, a joint model was established based on longitudinal data and survival data, and the Markov chain Monte Carlo algorithm was used to explain the influencing factors. In correlation analysis, patients having received vaccination had a higher antibody level at admission than unvaccinated patients, and with the increase of antibody level, the duration of viral shedding shortened. The linear mixed-effects model showed the longitudinal variation of logSARS-COV-2 IgM sample/cutoff (S/CO) values, with a parameter estimate of 0.193 and a standard error of 0.017. Considering gender as an influencing factor, the parameter estimate of the Cox model and their standard error were 0.205 and 0.1093 (P = 0.608), the corresponding OR value was 1.228. The joint model output showed that SARS-COV-2 IgM (S/CO) level was strongly associated with the risk of a composite event at the 95% confidence level, and a doubling of SARS-COV-2 IgM (S/CO) level was associated with a 1.38-fold (95% CI: [1.16, 1.72]) increase in the risk of viral non-shedding. A higher antibody level in vaccinated patients, as well as the presence of IgM antibodies in serum, can accelerate shedding of the mutant virus. This study provides some evidence support for vaccine prevention and control of COVID-19 variants.

A generalized distributed delay model of COVID-19: An endemic model with immunity waning

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

CLINICAL AND EPIDEMIOLOGICAL FEATURES OF COVID-19 IN CHILDREN FOR THE PERIOD 2020-2022

OBJECTIVE

The aim: To investigate the epidemiological and clinical characteristics of COVID-19 in children for the period 2020-2022.

PATIENTS AND METHODS

Materials and methods: A retrospective analysis of 1144 case histories of children who were hospitalized at the St. Zinaida Children's Clinical Hospital (Sumy, Ukraine) for coronavirus disease for 2020-2022 was carried out. The observed patients were divided into 3 groups corresponding to the 3 waves of the pandemic: group 1 - 120 children, group 2 - 311 children, and group 3 - 713. The diagnosis of COVID-19 was established based on clinical, medical histories, laboratory and instrumental data. The etiology of coronavirus disease was determined based on the detection of antigens of the SARS-CoV-2 virus using PCR reverse transcription of a nasopharyngeal swab.

RESULTS

Results: An analysis of the clinical and epidemiological indicators of children who were treated for COVID-19 during 2020-2022 was conducted, depending on the outbreak of the pandemic. The frequency of lesions in children of different age groups was determined, and the main clinical symptoms and the frequency of complications in the form of pneumonia during different waves of COVID-19 were determined.

CONCLUSION

Conclusions: The incidence of coronavirus infection was mainly observed in children of the younger group (0-5 years). A more severe course of the disease and a higher frequency of complications in the form of pneumonia in children were determined during the 3rd wave of the COVID-19 pandemic.

Global dynamics of IAV/SARS-CoV-2 coinfection model with eclipse phase and antibody immunity

Coronavirus disease 2019 (COVID-19) and influenza are two respiratory infectious diseases of high importance widely studied around the world. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while influenza is caused by one of the influenza viruses, A, B, C, and D. Influenza A virus (IAV) can infect a wide range of species. Studies have reported several cases of respiratory virus coinfection in hospitalized patients. IAV mimics the SARS-CoV-2 with respect to the seasonal occurrence, transmission routes, clinical manifestations and related immune responses. The present paper aimed to develop and investigate a mathematical model to study the within-host dynamics of IAV/SARS-CoV-2 coinfection with the eclipse (or latent) phase. The eclipse phase is the period of time that elapses between the viral entry into the target cell and the release of virions produced by that newly infected cell. The role of the immune system in controlling and clearing the coinfection is modeled. The model simulates the interaction between nine compartments, uninfected epithelial cells, latent/active SARS-CoV-2-infected cells, latent/active IAV-infected cells, free SARS-CoV-2 particles, free IAV particles, SARS-CoV-2-specific antibodies and IAV-specific antibodies. The regrowth and death of the uninfected epithelial cells are considered. We study the basic qualitative properties of the model, calculate all equilibria, and prove the global stability of all equilibria. The global stability of equilibria is established using the Lyapunov method. The theoretical findings are demonstrated via numerical simulations. The importance of considering the antibody immunity in the coinfection dynamics model is discussed. It is found that without modeling the antibody immunity, the case of IAV and SARS-CoV-2 coexistence will not occur. Further, we discuss the effect of IAV infection on the dynamics of SARS-CoV-2 single infection and vice versa.

Mathematical assessment of the role of waning and boosting immunity against the BA.1 Omicron variant in the United States

Three safe and effective vaccines against SARS-CoV-2 have played a major role in combating COVID-19 in the United States. However, the effectiveness of these vaccines and vaccination programs has been challenged by the emergence of new SARS-CoV-2 variants of concern. A new mathematical model is formulated to assess the impact of waning and boosting of immunity against the Omicron variant in the United States. To account for gradual waning of vaccine-derived immunity, we considered three vaccination classes that represent high, moderate and low levels of immunity. We showed that the disease-free equilibrium of the model is globally-asymptotically, for two special cases, if the associated reproduction number is less than unity. Simulations of the model showed that vaccine-derived herd immunity can be achieved in the United States via a vaccination-boosting strategy which entails fully vaccinating at least 59% of the susceptible populace followed by the boosting of about 72% of the fully-vaccinated individuals whose vaccine-derived immunity has waned to moderate or low level. In the absence of boosting, waning of immunity only causes a marginal increase in the average number of new cases at the peak of the pandemic, while boosting at baseline could result in a dramatic reduction in the average number of new daily cases at the peak. Specifically, for the fast immunity waning scenario (where both vaccine-derived and natural immunity are assumed to wane within three months), boosting vaccine-derived immunity at baseline reduces the average number of daily cases at the peak by about 90% (in comparison to the corresponding scenario without boosting of the vaccine-derived immunity), whereas boosting of natural immunity (at baseline) only reduced the corresponding peak daily cases (in comparison to the corresponding scenario without boosting of natural immunity) by approximately 62%. Furthermore, boosting of vaccine-derived immunity is more beneficial (in reducing the burden of the pandemic) than boosting of natural immunity. Finally, boosting vaccine-derived immunity increased the prospects of altering the trajectory of COVID-19 from persistence to possible elimination.

Binding Affinity Measurements Between DNA Aptamers and their Virus Targets Using ELONA and MST

Aptamers have been selected with strong affinity and high selectivity for a wide range of targets, as recently highlighted by the development of aptamer-based sensors that can differentiate infectious from non-infectious viruses, including human adenovirus and SARS-CoV-2. Accurate determination of the binding affinity between the DNA aptamers and their viral targets is the first step to understanding the molecular recognition of viral particles and the potential uses of aptamers in various diagnostics and therapeutic applications. Here, we describe protocols to obtain the binding curve of the DNA aptamers to SARS-CoV-2 using Enzyme-Linked Oligonucleotide Assay (ELONA) and MicroScale Thermophoresis (MST). These methods allow for the determination of the binding affinity of the aptamer to the infectious SARS-CoV-2 and the selectivity of this aptamer against the same SARS-CoV-2 that has been rendered non-infectious by UV inactivation, and other viruses. Compared to other techniques like Electrophoretic Mobility Shift Assay (EMSA), Surface Plasmon Resonance (SPR), and Isothermal Titration Calorimetry (ITC), these methods have advantages for working with larger particles like viruses and with samples that require biosafety level 2 facilities.

Basic reproduction number of the COVID-19 Delta variant: Estimation from multiple transmission datasets

The basic reproduction number, $ R_0 $, plays a central role in measuring the transmissibility of an infectious disease, and it thus acts as the fundamental index for planning control strategies. In the present study, we apply a branching process model to meticulously observed contact tracing data from Wakayama Prefecture, Japan, obtained in early 2020 and mid-2021. This allows us to efficiently estimate $ R_0 $ and the dispersion parameter $ k $ of the wild-type COVID-19, as well as the relative transmissibility of the Delta variant and relative transmissibility among fully vaccinated individuals, from a very limited data. $ R_0 $ for the wild type of COVID-19 is estimated to be 3.78 (95% confidence interval [CI]: 3.72-3.83), with $ k = 0.236 $ (95% CI: 0.233-0.240). For the Delta variant, the relative transmissibility to the wild type is estimated to be 1.42 (95% CI: 0.94-1.90), which gives $ R_0 = 5.37 $ (95% CI: 3.55-7.21). Vaccine effectiveness, determined by the reduction in the number of secondary transmissions among fully vaccinated individuals, is estimated to be 91% (95% CI: 85%-97%). The present study highlights that basic reproduction numbers can be accurately estimated from the distribution of minor outbreak data, and these data can provide further insightful epidemiological estimates including the dispersion parameter and vaccine effectiveness regarding the prevention of transmission.

To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.

Estimating relative generation times and reproduction numbers of Omicron BA.1 and BA.2 with respect to Delta variant in Denmark

The Omicron variant spreads fastest as ever among the severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) we had so far. The BA.1 and BA.2 sublineages of Omicron are circulating worldwide and it is urgent to evaluate the transmission advantages of these sublineages. Using a mathematical model describing trajectories of variant frequencies that assumes a constant ratio in mean generation times and a constant ratio in effective reproduction numbers among variants, trajectories of variant frequencies in Denmark from November 22, 2021 to February 26, 2022 were analyzed. We found that the mean generation time of Omicron BA.1 is 0.44-0.46 times that of Delta and the effective reproduction number of Omicron BA.1 is 1.88-2.19 times larger than Delta under the epidemiological conditions at the time. We also found that the mean generation time of Omicron BA.2 is 0.76-0.80 times that of BA.1 and the effective reproduction number of Omicron BA.2 is 1.25-1.27 times larger than Omicron BA.1. These estimates on the ratio of mean generation times and the ratio of effective reproduction numbers have epidemiologically important implications. The contact tracing for Omicron BA.2 infections must be done more quickly than that for BA.1 to stop further infections by quarantine. In the Danish population, the control measures against Omicron BA.2 need to reduce 20-21% of additional contacts compared to that against BA.1.

Global dynamics of SARS-CoV-2/malaria model with antibody immune response

Coronavirus disease 2019 (COVID-19) is a new viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Malaria is a parasitic disease caused by Plasmodium parasites. In this paper, we explore a within-host model of SARS-CoV-2/malaria coinfection. This model consists of seven ordinary differential equations that study the interactions between uninfected red blood cells, infected red blood cells, free merozoites, uninfected epithelial cells, infected epithelial cells, free SARS-CoV-2 particles, and antibodies. We show that the model has bounded and nonnegative solutions. We compute all steady state points and derive their existence conditions. We use appropriate Lyapunov functions to confirm the global stability of all steady states. We enhance the reliability of the theoretical results by performing numerical simulations. The steady states reflect the monoinfection and coinfection with malaria and SARS-CoV-2. The shared immune response reduces the concentrations of malaria merozoites and SARS-CoV-2 particles in coinfected patients. This response reduces the severity of SARS-CoV-2 infection in this group of patients.

Prioritizing COVID-19 vaccination. Part 1: Final size comparison between a single dose and double dose

In response to the coronavirus disease 2019 (COVID-19) pandemic, Japan conducted mass vaccination. Seventy-two million doses of vaccine (i.e., for 36 million people if a double dose is planned per person) were obtained, with initial vaccination of the older population (≡ 65 years). Because of the limited number of vaccines, the government discussed shifting the plan to administering only a single dose so that younger individuals (<65 years) could also be vaccinated with one shot. This study aimed to determine the optimal vaccine distribution strategy using a simple mathematical method. After accounting for age-dependent relative susceptibility after single- and double-dose vaccination (vs and vd, respectively, compared with unvaccinated), we used the age-dependent transmission model to compute the final size for various patterns of vaccine distributions. Depending on the values of vs, the cumulative risk of death would be lower if all 72 million doses were used as a double dose for older people than if a single-dose program was conducted in which half is administered to older people and the other half is administered to adults (i.e., 1,856,000 deaths in the former program and 1,833,000-2,355,000 deaths [depending on the values of vs] in the latter). Even if 90% of older people were vaccinated twice and 100% of adults were vaccinated once, the effective reproduction number would be reduced from 2.50 to1.14. Additionally, the cumulative risk of infection would range from 12.0% to 54.6% and there would be 421,000-1,588,000deaths (depending on the values of vs). If an epidemic appears only after completing vaccination, vaccination coverage using a single-dose program with widespread vaccination among adults will not outperform a double-dose strategy.

Epidemiology of coronavirus disease 2019 (COVID-19) in Japan during the first and second waves

Following the emergence and worldwide spread of coronavirus disease 2019 (COVID-19), each country has attempted to control the disease in different ways. The first patient with COVID-19 in Japan was diagnosed on 15 January 2020, and until 31 October 2020, the epidemic was characterized by two large waves. To prevent the first wave, the Japanese government imposed several control measures such as advising the public to avoid the 3Cs (closed spaces with poor ventilation, crowded places with many people nearby, and close-contact settings such as close-range conversations) and implementation of "cluster buster" strategies. After a major epidemic occurred in April 2020 (the first wave), Japan asked its citizens to limit their numbers of physical contacts and announced a non-legally binding state of emergency. Following a drop in the number of diagnosed cases, the state of emergency was gradually relaxed and then lifted in all prefectures of Japan by 25 May 2020. However, the development of another major epidemic (the second wave) could not be prevented because of continued chains of transmission, especially in urban locations. The present study aimed to descriptively examine propagation of the COVID-19 epidemic in Japan with respect to time, age, space, and interventions implemented during the first and second waves. Using publicly available data, we calculated the effective reproduction number and its associations with the timing of measures imposed to suppress transmission. Finally, we crudely calculated the proportions of severe and fatal COVID-19 cases during the first and second waves. Our analysis identified key characteristics of COVID-19, including density dependence and also the age dependence in the risk of severe outcomes. We also identified that the effective reproduction number during the state of emergency was maintained below the value of 1 during the first wave.

Transmission dynamics of varicella before, during and after the COVID-19 pandemic in Japan: a modelling study

Public health and social measures (PHSMs) targeting the coronavirus disease 2019 (COVID-19) pandemic have potentially affected the epidemiological dynamics of endemic infectious diseases. In this study, we investigated the impact of PHSMs for COVID-19, with a particular focus on varicella dynamics in Japan. We adopted the susceptible-infectious-recovered type of mathematical model to reconstruct the epidemiological dynamics of varicella from Jan. 2010 to Sep. 2021. We analyzed epidemiological and demographic data and estimated the within-year and multi-year component of the force of infection and the biases associated with reporting and ascertainment in three periods: pre-vaccination (Jan. 2010-Dec. 2014), pre-pandemic vaccination (Jan. 2015-Mar. 2020) and during the COVID-19 pandemic (Apr. 2020-Sep. 2021). By using the estimated parameter values, we reconstructed and predicted the varicella dynamics from 2010 to 2027. Although the varicella incidence dropped drastically during the COVID-19 pandemic, the change in susceptible dynamics was minimal; the number of susceptible individuals was almost stable. Our prediction showed that the risk of a major outbreak in the post-pandemic era may be relatively small. However, uncertainties, including age-related susceptibility and travel-related cases, exist and careful monitoring would be required to prepare for future varicella outbreaks.

Computational identification of Shenshao Ningxin Yin as an effective treatment for novel coronavirus infection (COVID-19) with myocarditis

BACKGROUND

The newly identified betacoronavirus SARS-CoV-2 is the causative pathogen of the 2019 coronavirus disease (COVID-19), which has killed more than 4.5 million people. SARS-CoV-2 causes severe respiratory distress syndrome by targeting the lungs and also induces myocardial damage. Shenshao Ningxin Yin (SNY) has been used for more than 700 years to treat influenza. Previous randomized controlled trials (RCTs) have demonstrated that SNY can improve the clinical symptoms of viral myocarditis, reverse arrhythmia, and reduce the level of myocardial damage markers.

METHODS

This work uses a rational computational strategy to identify existing drug molecules that target host pathways for the treatment of COVID-19 with myocarditis. Disease and drug targets were input into the STRING database to construct proteinɃprotein interaction networks. The Metascape database was used for GO and KEGG enrichment analysis.

RESULTS

SNY signaling modulated the pathways of coronavirus disease, including COVID-19, Ras signaling, viral myocarditis, and TNF signaling pathways. Tumor necrosis factor (TNF), cellular tumor antigen p53 (TP53), mitogen-activated protein kinase 1 (MAPK1), and the signal transducer and activator of transcription 3 (STAT3) were the pivotal targets of SNY. The components of SNY bound well with the pivotal targets, indicating there were potential biological activities.

CONCLUSION

Our findings reveal the pharmacological role and molecular mechanism of SNY for the treatment of COVID-19 with myocarditis. We also, for the first time, demonstrate that SNY displays multi-component, multi-target, and multi-pathway characteristics with a complex mechanism of action.

Geographical network model for COVID-19 spread among dynamic epidemic regions

Pandemic due to SARS-CoV-2 (COVID-19) has affected to world in several aspects: high number of confirmed cases, high number of deaths, low economic growth, among others. Understanding of spatio-temporal dynamics of the virus is helpful and necessary for decision making, for instance to decide where, whether and how, non-pharmaceutical intervention policies are to be applied. This point has not been properly addressed in literature since typical strategies do not consider marked differences on the epidemic spread across country or large territory. Those strategies assume similarities and apply similar interventions instead. This work is focused on posing a methodology where spatio-temporal epidemic dynamics is captured by means of dividing a territory in time-varying epidemic regions, according to geographical closeness and infection level. In addition, a novel Lagrangian-SEIR-based model is posed for describing the dynamic within and between those regions. The capabilities of this methodology for identifying local outbreaks and reproducing the epidemic curve are discussed for the case of COVID-19 epidemic in Jalisco state (Mexico). The contagions from July 31, 2020 to March 31, 2021 are analyzed, with monthly adjustments, and the estimates obtained at the level of the epidemic regions present satisfactory results since Relative Root Mean Squared Error RRMSE is below 15% in most of regions, and at the level of the whole state outstanding with RRMSE below 5%.

Using the SEIR model to constrain the role of contaminated fomites in spreading an epidemic: An application to COVID-19 in the UK

The use of the SEIR model of compartmentalized population dynamics with an added fomite term is analysed as a means of statistically quantifying the contribution of contaminated fomites to the spread of a viral epidemic. It is shown that for normally expected lifetimes of a virus on fomites, the dynamics of the populations are nearly indistinguishable from the case without fomites. With additional information, such as the change in social contacts following a lockdown, however, it is shown that, under the assumption that the reproduction number for direct infection is proportional to the number of social contacts, the population dynamics may be used to place meaningful statistical constraints on the role of fomites that are not affected by the lockdown. The case of the Spring 2020 UK lockdown in response to COVID-19 is presented as an illustration. An upper limit is found on the transmission rate by contaminated fomites of fewer than 1 in 30 per day per infectious person (95% CL) when social contact information is taken into account. Applied to postal deliveries and food packaging, the upper limit on the contaminated fomite transmission rate corresponds to a probability below 1 in 70 (95% CL) that a contaminated fomite transmits the infection. The method presented here may be helpful for guiding health policy over the contribution of some fomites to the spread of infection in other epidemics until more complete risk assessments based on mechanistic modelling or epidemiological investigations may be completed.

A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics

In the context of 2019 coronavirus disease (COVID-19), considerable attention has been paid to mathematical models for predicting country- or region-specific future pandemic developments. In this work, we developed an SVICDR model that includes a susceptible, an all-or-nothing vaccinated, an infected, an intensive care, a deceased, and a recovered compartment. It is based on the susceptible-infectious-recovered (SIR) model of Kermack and McKendrick, which is based on ordinary differential equations (ODEs). The main objective is to show the impact of parameter boundary modifications on the predicted incidence rate, taking into account recent data on Germany in the pandemic, an exponential increasing vaccination rate in the considered time window and trigonometric contact and quarantine rate functions. For the numerical solution of the ODE systems a model-specific non-standard finite difference (NSFD) scheme is designed, that preserves the positivity of solutions and yields the correct asymptotic behaviour.

SIRVVD model-based verification of the effect of first and second doses of COVID-19/SARS-CoV-2 vaccination in Japan

As of August 2021, COVID-19 is still spreading in Japan. Vaccination, one of the key measures to bring COVID-19 under control, began in February 2021. Previous studies have reported that COVID-19 vaccination reduces the number of infections and mortality rates. However, simulations of spreading infection have suggested that vaccination in Japan is insufficient. Therefore, we developed a susceptible-infected-recovered-vaccination1-vaccination2-death model to verify the effect of the first and second vaccination doses on reducing the number of infected individuals in Japan; this includes an infection simulation. The results confirm that appropriate vaccination measures will sufficiently reduce the number of infected individuals and reduce the mortality rate.

What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases?

The COVID-19 outbreak, which started in late December 2019 and rapidly spread around the world, has been accompanied by an unprecedented release of data on reported cases. Our objective is to offer a fresh look at these data by coupling a phenomenological description to the epidemiological dynamics. We use a phenomenological model to describe and regularize the reported cases data. This phenomenological model is combined with an epidemic model having a time-dependent transmission rate. The time-dependent rate of transmission involves changes in social interactions between people as well as changes in host-pathogen interactions. Our method is applied to cumulative data of reported cases for eight different geographic areas. In the eight geographic areas considered, successive epidemic waves are matched with a phenomenological model and are connected to each other. We find a single epidemic model that coincides with the best fit to the data of the phenomenological model. By reconstructing the transmission rate from the data, we can understand the contributions of the changes in social interactions (contacts between individuals) on the one hand and the contributions of the epidemiological dynamics on the other hand. Our study provides a new method to compute the instantaneous reproduction number that turns out to stay below 3.5 from the early beginning of the epidemic. We deduce from the comparison of several instantaneous reproduction numbers that the social effects are the most important factor in understanding the epidemic wave dynamics for COVID-19. The instantaneous reproduction number stays below 3.5, which implies that it is sufficient to vaccinate 71% of the population in each state or country considered in our study. Therefore, assuming the vaccines will remain efficient against the new variants and adjusting for higher confidence, it is sufficient to vaccinate 75-80% to eliminate COVID-19 in each state or country.

Not all fun and games: Potential incidence of SARS-CoV-2 infections during the Tokyo 2020 Olympic Games

The Tokyo 2020 Olympic and Paralympic Games represent the most diverse international mass gathering event held since the start of the coronavirus disease 2019 (COVID-19) pandemic. Postponed to summer 2021, the rescheduled Games were set to be held amidst what would become the highest-ever levels of COVID-19 transmission in the host city of Tokyo. At the same time, the Delta variant of concern was gaining traction as the dominant viral strain and Japan had yet to exceed fifteen percent of its population fully vaccinated against COVID-19. To quantify the potential number of secondary cases that might arise during the Olympic Games, we performed a scenario analysis using a multitype branching process model. We considered the different contributions to transmission of Games accredited individuals, the general Tokyo population, and domestic spectators. In doing so, we demonstrate how transmission might evolve in these different groups over time, cautioning against any loosening of infection prevention protocols and supporting the decision to ban all spectators. If prevention measures were well observed, we estimated that the number of new cases among Games accredited individuals would approach zero by the end of the Games. However, if transmission was not controlled our model indicated hundreds of Games accredited individuals would become infected and daily incidence in Tokyo would reach upwards of 4,000 cases. Had domestic spectators been allowed (at 50% venue capacity), we estimated that over 250 spectators might have arrived infected to Tokyo venues, potentially generating more than 300 additional secondary infections while in Tokyo/at the Games. We also found the number of cases with infection directly attributable to hypothetical exposure during the Games was highly sensitive to the local epidemic dynamics. Therefore, reducing and maintaining transmission levels below epidemic levels using public health measures would be necessary to prevent cross-group transmission.

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study

Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that there is a significant benefit in adhering to the COVID-19 NPIs.

SIR model-based verification of effect of COVID-19 Contact-Confirming Application (COCOA) on reducing infectors in Japan

As of April 2021, the coronavirus disease (COVID-19) continues to spread in Japan. To overcome COVID-19, the Ministry of Health, Labor, and Welfare of the Japanese government developed and released the COVID-19 Contact-Confirming Application (COCOA) on June 19, 2020. COCOA users can know whether they have come into contact with infectors. If persons who receive a contact notification through COCOA undertake self-quarantine, the number of infectors in Japan will decrease. However, the effectiveness of COCOA in reducing the number of infectors depends on the usage rate of COCOA, the rate of fulfillment of contact condition, the rate of undergoing the reverse transcription polymerase chain reaction (RT-PCR) test, the false negative rate of the RT-PCR test, the rate of infection registration, and the self-quarantine rate. Therefore, we developed a Susceptible-Infected-Removed (SIR) model to estimate the effectiveness of COCOA. In this paper, we introduce the SIR model and report the simulation results for different scenarios that were assumed for Japan.

Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.

EPIDEMIOLOGICAL DATA ON THE DETECTION OF IMMUNOGLOBULINS OF CLASS IGM, IGG TO SARS-COV-2 AMONG POPULATION OF POLTAVA REGION

OBJECTIVE

The aim: Of this study was to analyze epidemiological data on the detection of immunoglobulins of class M and G (IgM, IgG) to SARS-CoV-2 among urban and rural population of Poltava region.

PATIENTS AND METHODS

Materials and methods: We have analyzed the research results of 2841 patients to determine IgM and IgG levels to SARS-CoV-2. The study included the results of patients in Poltava and nearby villages of Poltava region, obtained during July - December 2020.

RESULTS

Results: Thus, 84% of patients applied for detection of IgM in the serum of patients with the pathogen COVID-2019. We have found only 135 positive results for the detection of IgM to SARS-CoV-2, which was 5.7% of the total number of people who underwent this study from July to December 2020. Moreover, women received a positive result more often than men. The IP samples for the detection of IgM to SARS-CoV-2 in the serum of patients averaged 2.5 ± 1.04. It was found that patients went to the laboratory to detect IgG to SARS-CoV-2 with the vast majority among them were residents of Poltava. However, in this case the share of positive results was 47.7%, among which the female population outnumbered the male.

CONCLUSION

Conclusions: The frequency of detection of positive results on IgM to SARS-CoV-2 is about 6%. The share of positive results on IgG to SARS-CoV-2 was 47.7%, among them 76.2% were women. The frequency of detection of IgM and IgG to SARS-CoV-2 during October-December 2020 significantly exceeds the indices in July-September of the same year.

[Lung lesions in severe COVID-19 : anatomoclinical confrontation]

We report the fatal outcome of two patients infected by SARS-CoV-2 and exhibiting severe lung lesions at the thoracic imaging and autopsic examination. We also describe the biosecurity measures to adopt when performing autopsies during the Covid-19 pandemia.

[Six cases of acute pulmonary embolism associated with COVID-19]

Rising from the province of Wuhan in China, the new coronavirus SARS-CoV-2 broke out in winter 2019, causing a global pandemic. In most cases reported, COVID-19 symptoms include cough, dyspnea, myalgia and asthenia. In some cases, the disease can also cause severe respiratory distress syndrome, requiring intensive care. Recent studies suggest that SARS-CoV-2 infection predisposes to thromboembolic event such as pulmonary embolism. Moreover, there is an overlap between signs and symptoms of pulmonary embolism and COVID-19, which brings a challenge for the diagnosis and could potentially be fatal. Nevertheless, the incidence rate of pulmonary embolism in cases of COVID-19 is currently not known. In this paper we describe six cases of pulmonary embolism associated with COVID-19.

[The Leon Fredericq Foundation against COVID-19 in Liege]

The Leon Fredericq Foundation gives support to the clinicians and the scientists of the Uliege and of the CHU of Liege in order to push back the frontiers of biomedical science and to contribute to improve the care and cure of patients. Since the outbreak due to COVID-19, the Foundation has given out a call for donations in order to support urgent procedures for taking care of COVID-19 suffering patients. Furthermore, by raising important financial means, the Foundation has selected thirteen research projects aiming at a better understanding of the SARS-CoV-2-induced disease.

[The COVID-19 breaker : PCR to the rescue !]

Chronicle of a crisis management at the Clinical Microbiology Laboratory of CHU Liège The SARS-CoV-2 outbreak in December 2019 in China and its expansion across the world and Europe have requested the participation of clinical laboratories as major players in the diagnosis of COVID-19, to perform PCR tests mainly on nasopharyngeal swabs. In Belgium, the first confirmed COVID-19 patient was diagnosed in early February, the first of many, especially travelers returning from winter sports. In order to meet the ever-increasing demands for testing, the Clinical Microbiology Laboratory of the CHU of Liege had to adapt to this situation: firstly, by developing manual PCR tests and then automated solutions, permitting to increase the number of analyzes by ensuring a short turnaround time of results. Then, a system for the communication of results on a large scale has been set up, and finally solutions to deal with the lack of sampling devices have been found. This first wave of the pandemic has also highlighted an unprecedented solidarity within the institution. In this article, we recount the chronology of the management of this unprecedented health crisis within the Clinical Microbiology Laboratory of the CHU of Liege.

Mathematical modeling and analysis of COVID-19 pandemic in Nigeria

A mathematical model is designed and used to study the transmission dynamics and control of COVID-19 in Nigeria. The model, which was rigorously analysed and parametrized using COVID-19 data published by the Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in some jurisdictions within Nigeria (notably the states of Kano and Lagos, and the Federal Capital Territory, Abuja). Numerical simulations of the model showed that COVID-19 can be effectively controlled in Nigeria using moderate levels of social-distancing strategy in the jurisdictions and in the entire nation. Although the use of face masks in public can significantly reduce COVID-19 in Nigeria, its use, as a sole intervention strategy, may fail to lead to a substantial reduction in disease burden. Such substantial reduction is feasible in the jurisdictions (and the entire Nigerian nation) if the public face mask use strategy is complemented with a social-distancing strategy. The community lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.

Mathematical modeling and analysis of COVID-19 pandemic in Nigeria

A mathematical model is designed and used to study the transmission dynamics and control of COVID-19 in Nigeria. The model, which was rigorously analysed and parametrized using COVID-19 data published by the Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in some jurisdictions within Nigeria (notably the states of Kano and Lagos, and the Federal Capital Territory, Abuja). Numerical simulations of the model showed that COVID-19 can be effectively controlled in Nigeria using moderate levels of social-distancing strategy in the jurisdictions and in the entire nation. Although the use of face masks in public can significantly reduce COVID-19 in Nigeria, its use, as a sole intervention strategy, may fail to lead to a substantial reduction in disease burden. Such substantial reduction is feasible in the jurisdictions (and the entire Nigerian nation) if the public face mask use strategy is complemented with a social-distancing strategy. The community lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.

Lifting mobility restrictions and the effect of superspreading events on the short-term dynamics of COVID-19

SARS-CoV-2 has now infected 15 million people and produced more than six hundred thousand deaths around the world. Due to high transmission levels, many governments implemented social distancing and confinement measures with different levels of required compliance to mitigate the COVID-19 epidemic. In several countries, these measures were effective, and it was possible to flatten the epidemic curve and control it. In others, this objective was not or has not been achieved. In far too many cities around the world, rebounds of the epidemic are occurring or, in others, plateaulike states have appeared, where high incidence rates remain constant for relatively long periods of time. Nonetheless, faced with the challenge of urgent social need to reactivate their economies, many countries have decided to lift mitigation measures at times of high incidence. In this paper, we use a mathematical model to characterize the impact of short duration transmission events within the confinement period previous but close to the epidemic peak. The model also describes the possible consequences on the disease dynamics after mitigation measures are lifted. We use Mexico City as a case study. The results show that events of high mobility may produce either a later higher peak, a long plateau with relatively constant but high incidence or the same peak as in the original baseline epidemic curve, but with a post-peak interval of slower decay. Finally, we also show the importance of carefully timing the lifting of mitigation measures. If this occurs during a period of high incidence, then the disease transmission will rapidly increase, unless the effective contact rate keeps decreasing, which will be very difficult to achieve once the population is released.

Dynamics of SARS-CoV-2 infection model with two modes of transmission and immune response

In this paper, we propose a new within-host model which describes the interactions between SARS-CoV-2, host pulmonary epithelial cells and cytotoxic T lymphocyte (CTL) cells. Furthermore, the proposed model takes into account the lytic and nonlytic immune responses and also incorporates both modes of transmission that are the virus-to-cell infection through extracellular environment and the cell-to-cell transmission via virological synapses. The well-posedness of the model as well as the existence of equilibria are established rigorously. Moreover, the dynamical behaviour of the model is further examined by two threshold parameters, and the biological aspects of the analytical results are further presented.

The within-host viral kinetics of SARS-CoV-2

In this work, we use a within-host viral dynamic model to describe the SARS-CoV-2 kinetics in the host. Chest radiograph score data are used to estimate the parameters of that model. Our result shows that the basic reproductive number of SARS-CoV-2 in host growth is around 3.79. Using the same method we also estimate the basic reproductive number of MERS virus is 8.16 which is higher than SARS-CoV-2. The PRCC method is used to analyze the sensitivities of model parameters. Moreover, the drug effects on virus growth and immunity effect of patients are also implemented to analyze the model.

The within-host viral kinetics of SARS-CoV-2

In this work, we use a within-host viral dynamic model to describe the SARS-CoV-2 kinetics in the host. Chest radiograph score data are used to estimate the parameters of that model. Our result shows that the basic reproductive number of SARS-CoV-2 in host growth is around 3.79. Using the same method we also estimate the basic reproductive number of MERS virus is 8.16 which is higher than SARS-CoV-2. The PRCC method is used to analyze the sensitivities of model parameters. Moreover, the drug effects on virus growth and immunity effect of patients are also implemented to analyze the model.

UHMS Position Statement: Hyperbaric Oxygen (HBO2) for COVID-19 Patients

There have been numerous recent inquiries regarding use of hyperbaric oxygen (HBO2) for patients with COVID-19. Questions have been raised pertinent to two possible mechanisms for HBO2 in this clinical context. The UHMS Hyperbaric Oxygen Therapy Committee, UHMS Executive Committee, with collaborative input from multiple senior UHMS members and researchers have drafted this position statement.