Estimating the basic reproduction number for COVID-19 in Western Europe

On optimal control at the onset of a new viral outbreak

We propose a versatile model with a flexible choice of control for an early-pandemic outbreak prevention when vaccine/drug is not yet available. At that stage, control is often limited to non-medical interventions like social distancing and other behavioral changes. For the SIR optimal control problem, we show that the running cost of control satisfying mild, practically justified conditions generates an optimal strategy, u(t), t ∈ [0, T], that is sustainable up until some moment τ ∈ [0, T). However, for any t ∈ [τ, T], the function u(t) will decline as t approaches T, which may cause the number of newly infected people to increase. So, the window from 0 to τ is the time for public health officials to prepare alternative mitigation measures, such as vaccines, testing, antiviral medications, and others. In addition to theoretical study, we develop a fast and stable computational method for solving the proposed optimal control problem. The efficiency of the new method is illustrated with numerical examples of optimal control trajectories for various cost functions and weights. Simulation results provide a comprehensive demonstration of the effects of control on the epidemic spread and mitigation expenses, which can serve as invaluable references for public health officials.

A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana

In this paper, we suggest a mathematical model of COVID-19 with multiple variants of the virus under optimal control. Mathematical modeling has been used to gain deeper insights into the transmission of COVID-19, and various prevention and control strategies have been implemented to mitigate its spread. Our model is a SEIR-based model for multi-strains of COVID-19 with 7 compartments. We also consider the circulatory structure to account for the termination of immunity for COVID-19. The model is established in terms of the positivity and boundedness of the solution and the existence of equilibrium points, and the local stability of the solution. As a result of fitting data of COVID-19 in Ghana to the model, the basic reproduction number of the original virus and Delta variant was estimated to be 1.9396, and the basic reproduction number of the Omicron variant was estimated to be 3.4905, which is 1.8 times larger than that. We observe that even small differences in the incubation and recovery periods of two strains with the same initial transmission rate resulted in large differences in the number of infected individuals. In the case of COVID-19, infections caused by the Omicron variant occur 1.5 to 10 times more than those caused by the original virus. In terms of the optimal control strategy, we formulate three control strategies focusing on social distancing, vaccination, and testing-treatment. We have developed an optimal control model for the three strategies outlined above for the multi-strain model using the Pontryagin's Maximum Principle. Through numerical simulations, we analyze three optimal control strategies for each strain and also consider combinations of the two control strategies. As a result of the simulation, all control strategies are effective in reducing disease spread, in particular, vaccination strategies are more effective than the other two control strategies. In addition the combination of the two strategies also reduces the number of infected individuals by 1/10 compared to implementing one strategy, even when mild levels are implemented. Finally, we show that if the testing-treatment strategy is not properly implemented, the number of asymptomatic and unidentified infections may surge. These results could help guide the level of government intervention and prevention strategy formulation.

Parameter estimation for contact tracing in graph-based models

We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is R0. The estimator is tested in a simulation study and is furthermore applied to COVID-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical COVID-19 data, we compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution fit the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency of the estimates on the reproduction number. Finally, we discuss the relevance of our findings.

A Brighton Collaboration standardized template with key considerations for a benefit/risk assessment for the Novavax COVID-19 Vaccine (NVX-CoV2373), a recombinant spike protein vaccine with Matrix-M adjuvant to prevent disease caused by SARS-CoV-2 viruses

Novavax, a global vaccine company, began evaluating NVX-CoV2373 in human studies in May 2020 and the pivotal placebo-controlled phase 3 studies started in November 2020; five clinical studies provided adult and adolescent clinical data for over 31,000 participants who were administered NVX-CoV2373. This extensive data has demonstrated a well-tolerated response to NVX-CoV2373 and high vaccine efficacy against mild, moderate, or severe COVID-19 using a two-dose series (Dunkle et al., 2022) [1], (Heath et al., 2021) [2], (Keech et al., 2020) [3], (Mallory et al., 2022) [4]. The most common adverse events seen after administration with NVX-CoV2373 were injection site tenderness, injection site pain, fatigue, myalgia, headache, malaise, arthralgia, nausea, or vomiting. In addition, immunogenicity against variants of interest (VOI) and variants of concern (VOC) was established with high titers of ACE2 receptor-inhibiting and neutralizing antibodies in these studies (EMA, 2022) [5], (FDA, 2023) [6]. Further studies on correlates of protection determined that titers of anti-Spike IgG and neutralizing antibodies correlated with efficacy against symptomatic COVID-19 established in clinical trials (p < 0.001 for recombinant protein vaccine and p = 0.005 for mRNA vaccines for IgG levels) (Fong et al., 2022) [7]. Administration of a booster dose of the recombinant protein vaccine approximately 6 months following the primary two-dose series resulted in substantial increases in humoral antibodies against both the prototype strain and all evaluated variants, similar to or higher than the antibody levels observed in phase 3 studies that were associated with high vaccine efficacy (Dunkle et al., 2022) [1], (Mallory et al., 2022) [4]. These findings, together with the well tolerated safety profile, support use of the recombinant protein vaccine as primary series and booster regimens.

Using mortuary and burial data to place COVID-19 in Lusaka, Zambia within a global context

Reported COVID-19 cases and associated mortality remain low in many sub-Saharan countries relative to global averages, but true impact is difficult to estimate given limitations around surveillance and mortality registration. In Lusaka, Zambia, burial registration and SARS-CoV-2 prevalence data during 2020 allow estimation of excess mortality and transmission. Relative to pre-pandemic patterns, we estimate age-dependent mortality increases, totalling 3212 excess deaths (95% CrI: 2104-4591), representing an 18.5% (95% CrI: 13.0-25.2%) increase relative to pre-pandemic levels. Using a dynamical model-based inferential framework, we find that these mortality patterns and SARS-CoV-2 prevalence data are in agreement with established COVID-19 severity estimates. Our results support hypotheses that COVID-19 impact in Lusaka during 2020 was consistent with COVID-19 epidemics elsewhere, without requiring exceptional explanations for low reported figures. For more equitable decision-making during future pandemics, barriers to ascertaining attributable mortality in low-income settings must be addressed and factored into discourse around reported impact differences.

The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada

We propose a new method to estimate the change of the effective reproduction number with time, due to either disease control measures or seasonally varying transmission rate. We validate our method using a simulated epidemic curve and show that our method can effectively estimate both sudden changes and gradual changes in the reproduction number. We apply our method to the COVID-19 case counts in British Columbia, Canada in 2020, and we show that strengthening control measures had a significant effect on the reproduction number, while relaxations in May (business reopening) and September (school reopening) had significantly increased the reproduction number from around 1 to around 1.7 at its peak value. Our method can be applied to other infectious diseases, such as pandemics and seasonal influenza.

Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19

This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.

An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

Understanding the challenges to COVID-19 vaccines and treatment options, herd immunity and probability of reinfection

Unlike pandemics in the past, the outbreak of coronavirus disease 2019 (COVID-19), which rapidly spread worldwide, was met with a different approach to control and measures implemented across affected countries. The lack of understanding of the fundamental nature of the outbreak continues to make COVID-19 challenging to manage for both healthcare practitioners and the scientific community. Challenges to vaccine development and evaluation, current therapeutic options, convalescent plasma therapy, herd immunity, and the emergence of reinfection and new variants remain the major obstacles to combating COVID-19. This review discusses these challenges in the management of COVID-19 at length and highlights the mechanisms needed to provide better understanding of this pandemic.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images-Focussing Attention on the Lungs

Due to the primary affection of the respiratory system, COVID-19 leaves traces that are visible in plain chest X-ray images. This is why this imaging technique is typically used in the clinic for an initial evaluation of the patient's degree of affection. However, individually studying every patient's radiograph is time-consuming and requires highly skilled personnel. This is why automatic decision support systems capable of identifying those lesions due to COVID-19 are of practical interest, not only for alleviating the workload in the clinic environment but also for potentially detecting non-evident lung lesions. This article proposes an alternative approach to identify lung lesions associated with COVID-19 from plain chest X-ray images using deep learning techniques. The novelty of the method is based on an alternative pre-processing of the images that focuses attention on a certain region of interest by cropping the original image to the area of the lungs. The process simplifies training by removing irrelevant information, improving model precision, and making the decision more understandable. Using the FISABIO-RSNA COVID-19 Detection open data set, results report that the opacities due to COVID-19 can be detected with a Mean Average Precision with an IoU > 0.5 (mAP@50) of 0.59 following a semi-supervised training procedure and an ensemble of two architectures: RetinaNet and Cascade R-CNN. The results also suggest that cropping to the rectangular area occupied by the lungs improves the detection of existing lesions. A main methodological conclusion is also presented, suggesting the need to resize the available bounding boxes used to delineate the opacities. This process removes inaccuracies during the labelling procedure, leading to more accurate results. This procedure can be easily performed automatically after the cropping stage.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible-exposed-infected-recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.

Systematic review of outbreaks of COVID-19 within households in the European region when the child is the index case

OBJECTIVES

This systematic review aims to identify the secondary attack rates (SAR) to adults and other children when children are the index cases within household settings.

METHODS

This literature review assessed European-based studies published in Medline and Embase between January 2020 and January 2022 that assessed the secondary transmission of SARS-CoV-2 within household settings. The inclusion criteria were based on the Population, Exposure, Outcome framework for systematic reviews. Thus, the study population was restricted to humans within the household setting in Europe (population), in contact with paediatric index cases 1-17 years old (exposure) that led to the transmission of SARS-CoV-2 reported as either an SAR or the probability of onward infection (outcome).

RESULTS

Of 1819 studies originally identified, 19 met the inclusion criteria. Overall, the SAR ranged from 13% to 75% in 15 studies, while there was no evidence of secondary transmission from children to other household members in one study. Evidence indicated that asymptomatic SARS-CoV-2 index cases also have a lower SAR than those with symptoms and that younger children may have a lower SAR than adolescents (>12 years old) within household settings.

CONCLUSIONS

SARS-CoV-2 secondary transmission from paediatric index cases ranged from 0% to 75%, within household settings between January 2020 and January 2022, with differences noted by age and by symptomatic/asymptomatic status of the index case. Given the anticipated endemic circulation of SARS-CoV-2, continued monitoring and assessment of household transmission is necessary.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical

Most models of COVID-19 are implemented at a single micro or macro scale, ignoring the interplay between immune response, viral dynamics, individual infectiousness and epidemiological contact networks. Here we develop a data-driven model linking the within-host viral dynamics to the between-host transmission dynamics on a multilayer contact network to investigate the potential factors driving transmission dynamics and to inform how school closures and antiviral treatment can influence the epidemic. Using multi-source data, we initially determine the viral dynamics and estimate the relationship between viral load and infectiousness. Then, we embed the viral dynamics model into a four-layer contact network and formulate an agent-based model to simulate between-host transmission. The results illustrate that the heterogeneity of immune response between children and adults and between vaccinated and unvaccinated infections can produce different transmission patterns. We find that school closures play a significant effect on mitigating the pandemic as more adults get vaccinated and the virus mutates. If enough infected individuals are diagnosed by testing before symptom onset and then treated quickly, the transmission can be effectively curbed. Our multiscale model reveals the critical role played by younger individuals and antiviral treatment with testing in controlling the epidemic.

The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions

Unlike previous viral diseases, COVID-19 has an "asymptomatic" group that has no symptoms but can still spread the disease to others at the same rate as symptomatic patients who are infected. In the literature, the mass action or standard incidence rates are considered for compartmental models with asymptomatic compartment for studying the transmission dynamics of COVID-19, but the quarantined adjusted incidence rate is not. To bridge this gap, we developed a Susceptible Asymptomatic Infectious Quarantined (SAIQ) model with a Quarantine-Adjusted (QA) incidence to investigate the emergence and containment of COVID-19. COVID-19 models are investigated using various methods, but only a few studies take into account closed-form solutions. The knowledge of closed-form solutions simplifies the construction of the various epidemic indicators that describe the epidemic phenomenon and makes the sensitivity analysis to variations in the data under consideration possible. The closed-form solutions of the systems of four nonlinear first-order ordinary differential equations (ODEs) are established. The Epidemic Peak (EP), Force of Infection (FOI) and Rate of Infection (ROI) are the important indicators for the control and prevention of disease. We examined these indicators using closed-form solutions and particular parameter values. Different disease control scenarios are thoroughly examined. The four scenarios to analyze COVID-19 propagation and containment are (i) lockdown, (ii) quarantine and other preventative measures, (iii) stabilizing the basic reproduction rate to a level where the pandemic can be contained and (iv) containing the epidemic through an appropriate combination of lockdown, quarantine and other preventative measures.

Modeling COVID-19 Transmission Dynamics: A Bibliometric Review

A good amount of research has evolved just in three years in COVID-19 transmission, mortality, vaccination, and some socioeconomic studies. A few bibliometric reviews have already been performed in the literature, especially on the broad theme of COVID-19, without any particular area such as transmission, mortality, or vaccination. This paper fills this gap by conducting a bibliometric review on COVID-19 transmission as the first of its kind. The main aim of this study is to conduct a bibliometric review of the literature in the area of COVID-19 transmission dynamics. We have conducted bibliometric analysis using descriptive and network analysis methods to review the literature in this area using RStudio, Openrefine, VOSviewer, and Tableau. We reviewed 1103 articles published in 2020-2022. The result identified the top authors, top disciplines, research patterns, and hotspots and gave us clear directions for classifying research topics in this area. New research areas are rapidly emerging in this area, which needs constant observation by researchers to combat this global epidemic.

Investigation of turning points in the effectiveness of Covid-19 social distancing

Covid-19 is the first digitally documented pandemic in history, presenting a unique opportunity to learn how to best deal with similar crises in the future. In this study we have carried out a model-based evaluation of the effectiveness of social distancing, using Austria and Slovenia as examples. Whereas the majority of comparable studies have postulated a negative relationship between the stringency of social distancing (reduction in social contacts) and the scale of the epidemic, our model has suggested a varying relationship, with turning points at which the system changes its predominant regime from 'less social distancing-more cumulative deaths and infections' to 'less social distancing-fewer cumulative deaths and infections'. This relationship was found to persist in scenarios with distinct seasonal variation in transmission and limited national intensive care capabilities. In such situations, relaxing social distancing during low transmission seasons (spring and summer) was found to relieve pressure from high transmission seasons (fall and winter) thus reducing the total number of infections and fatalities. Strategies that take into account this relationship could be particularly beneficial in situations where long-term containment is not feasible.

Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS)

OBJECTIVES

Geographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. We applied GIS to analyze patterns of spread and hotspots of COVID-19 cases in the Vellore district in South India.

METHODS

Laboratory-confirmed COVID-19 cases from the Vellore district and neighboring taluks from March 2020 to June 2021 were geocoded and spatial maps were generated. Time trends exploring urban-rural burden with an age-sex distribution of cases and other variables were correlated with outcomes.

RESULTS

A total of 45,401 cases of COVID-19 were detected, with 20,730 cases during the first wave and 24,671 cases during the second wave. The overall incidence rates of COVID-19 were 462.8 and 588.6 per 100,000 population during the first and second waves, respectively. The spread pattern revealed epicenters in densely populated urban areas with radial spread sparing rural areas in the first wave. The case fatality rate was 1.89% and 1.6% during the first and second waves, which increased with advancing age.

CONCLUSIONS

Modern surveillance systems like GIS can accurately predict the trends and spread patterns during future pandemics. In addition, real-time mapping can help design risk mitigation strategies, thereby preventing the spread to rural areas.

Estimation of the basic reproduction number of COVID-19 from the incubation period distribution

The estimates of the future course of spreading of the SARS-CoV-2 virus are frequently based on Markovian models in which the duration of residence in any compartment is exponentially distributed. Accordingly, the basic reproduction number R 0 is also determined from formulae where it is related to the parameters of such models. The observations show that the start of infectivity of an individual appears nearly at the same time as the onset of symptoms, while the distribution of the incubation period is not an exponential. Therefore, we propose a method for estimation of R 0 for COVID-19 based on the empirical incubation period distribution and assumed very short infectivity period that lasts only few days around the onset of symptoms. We illustrate this venerable approach to estimate R 0 for six major European countries in the first wave of the epidemic. The calculations show that even if the infectivity starts 2 days before the onset of symptoms and stops instantly when they appear (immediate isolation), the value of R 0 is larger than that from the classical, SIR model. For more realistic cases, when only individuals with mild symptoms spread the virus for few days after onset of symptoms, the respective values are even larger. This implies that calculations of R 0 and other characteristics of spreading of COVID-19 based on the classical, Markovian approaches should be taken very cautiously.

Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification of Infectious Diseases - The Experience with SARS-CoV-2/COVID-19

Over the last 2 years, the economic and infrastructural damage incurred by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exposed several limitations in the world’s preparedness for a pandemic-level virus. Conventional diagnostic techniques that were key in minimizing the potential transmission of SARS-CoV-2 were limited in their overall effectiveness as on-site diagnostic devices due to systematic inefficiencies. The most prevalent of said inefficiencies include their large turnaround times, operational costs, the need for laboratory equipment, and skilled personnel to conduct the test. This left many people in the early stages of the pandemic without the means to test themselves readily and reliably while minimizing further transmission. This unmet demand created a vacuum in the healthcare system, as well as in industry, that drove innovation in several types of diagnostic platforms, including microfluidic and non-microfluidic devices. In this chapter, we will explore how integrated microfluidic technologies have facilitated the improvements of previously existing diagnostic platforms for fast and accurate on-site detection of infectious diseases.

An open-access database of infectious disease transmission trees to explore superspreader epidemiology

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders.

SARS-CoV-2 Risk Quantification Model and Validation Based on Large-Scale Dutch Test Events

In response to the outbreak of SARS-CoV-2, many governments decided in 2020 to impose lockdowns on societies. Although the package of measures that constitute such lockdowns differs between countries, it is a general rule that contact between people, especially in large groups of people, is avoided or prohibited. The main reasoning behind these measures is to prevent healthcare systems from becoming overloaded. As of 2021 vaccines against SARS-CoV-2 are available, but these do not guarantee 100% risk reduction and it will take a while for the world to reach a sufficient immune status. This raises the question of whether and under which conditions events like theater shows, conferences, professional sports events, concerts, and festivals can be organized. The current paper presents a COVID-19 risk quantification method for (large-scale) events. This method can be applied to events to define an alternative package of measures replacing generic social distancing.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts

We consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the COVID-19 pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections. Results for Germany illustrate that our estimates based on death counts are often similar to classical estimates based on confirmed cases; however, considering death counts allows to disentangle effects of adapted testing policies from transmission dynamics. In particular, during the second wave of infections, classical estimates suggest a flattening infection curve following the “lockdown light” in November 2020, while our results indicate that infections continued to rise until the “second lockdown” in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the “lockdown light”, which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

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.

Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach

The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named "susceptible-infectious-recovered (SIR)" and an additive regression model named "Facebook PROPHET Procedure" were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.

Time-discrete SIR model for COVID-19 in Fiji

Using the data provided by Fiji's ministry of health and medical services, we apply an implicit time-discrete SIR (susceptible people–infectious people–removed people) model that tracks the transmission and recovering rate at time, t to predict the trend of the coronavirus disease 2019 (COVID-19) pandemic in Fiji. The model implied time-varying transmission and recovery rates were calculated from 4 May 2021 to 9 October 2021. The estimator functions for these rates were determined, and a short-term (30 days) forecast was done. The model was validated with observed values of the active and recovered cases from 11 October 2021 to 9 December 2021. Statistical results reveal a good fit of profiles between model simulated and the reported COVID-19 data. The gradual decrease of the time-varying basic reproduction number with values below one towards the end of the study period suggest the government's success in controlling the epidemic. The mean reproduction number for the second wave of COVID-19 in Fiji was estimated as 2.7818. The results from this study can be used by researchers, the Fijian government, and the relevant health policy makers in making informed decisions should a third COVID-19 wave occur.

Optimal COVID-19 lockdown strategies in an age-structured SEIR model of Northern Ireland

An age-structured SEIR model simulates the propagation of COVID-19 in the population of Northern Ireland. It is used to identify optimal timings of short-term lockdowns that enable long-term pandemic exit strategies by clearing the threshold for herd immunity or achieving time for vaccine development with minimal excess deaths.

The basic reproduction number of COVID-19 across Africa

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31-4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

Great Expectations of COVID-19 Herd Immunity

There is a common preconception that reaching an estimated herd immunity threshold through vaccination will end the COVID-19 pandemic. However, the mathematical models underpinning this estimate make numerous assumptions that may not be met in the real world. The protection afforded by vaccines is imperfect, particularly against asymptomatic infection, which can still result in transmission and propagate pandemic viral spread. Immune responses wane and SARS-COV-2 has the capacity to mutate over time to become more infectious and resistant to vaccine elicited immunity. Human behavior and public health restrictions also vary over time and among different populations, impacting the transmissibility of infection. These ever-changing factors modify the number of secondary cases produced by an infected individual, thereby necessitating constant revision of the herd immunity threshold. Even so, vaccination remains a powerful strategy to slow down the pandemic, save lives, and alleviate the burden on limited health care resources.

Prevalence of COVID-19 Infection among Patients with Diabetes and Their Vaccination Coverage Status in Saudi Arabia: A Cross-Sectional Analysis from a Hospital-Based Diabetes Registry

Patients with diabetes have a higher risk of severe infection and mortality due to COVID-19. Considering the current limited effective pharmacological treatments, vaccination remains one of the most effective means to control the pandemic. The current study aimed to determine the prevalence of COVID-19 infection and the rate of COVID-19 vaccination coverage among patients with type 2 diabetes mellitus. The patients were identified from a diabetes hospital registry at Prince Sultan Military Medical City, Riyadh, Saudi Arabia in July 2021. The history of COVID-19 infection and the vaccination status were retrieved from the National Health Electronic Surveillance Network (HESN) program and the Seha platform, respectively. A total of 11,573 patients were included in this study (representing 99.5% of all patients in the registry). A total of 1981 patients (17.1%) had a history of confirmed COVID-19 infection. The rate of vaccination with a 1st dose was 84.8% (n = 9811), while the rate of full vaccination with the 2nd dose was 55.5% (n = 6422). The analysis showed that a higher proportion of male patients were fully vaccinated than female patients (61.0% versus 51.2%, p < 0.001). There were statistically significant differences among the age groups, with the full vaccination rate ranging from 59.0% for the 61–70-year-old age group to 49.0% for the > 80-year-old age group (p < 0.001). The patients with no previous history of COVID-19 infection were more likely to get fully vaccinated than those with a previous history of the infection (63.9% versus 14.6%, respectively, p < 0.001). The factors associated with a higher likelihood of unvaccinated status included the female gender (adjusted odds ratio (aOR) = 1.705 (95% confidence interval (CI): 1.528–1.902)), elderly patients in the age group of 61–70 (aOR (95% CI) = 1.390 (1.102–1.753)), the age group of 71–80 (aOR (95% CI) = 1.924 (1.499–2.470)) and the age group of >80 (aOR (95% CI) = 3.081 (2.252–4.214), and prior history of COVID-19 infection (aOR (95% CI) = 2.501 (2.223–2.813)). In conclusion, a considerable proportion of patients with type 2 diabetes had confirmed COVID-19 infection. Continued targeted efforts are needed to accelerate vaccination coverage rates among patients with diabetes in general and the particular subgroups identified in this study.

Anxiety Linked to COVID-19: A Systematic Review Comparing Anxiety Rates in Different Populations

The COVID-19 pandemic has incited a rise in anxiety, with uncertainty regarding the specific impacts and risk factors across multiple populations. A qualitative systematic review was conducted to investigate the prevalence and associations of anxiety in different sample populations in relation to the COVID-19 pandemic. Four databases were utilised in the search (Medline, EMBASE, CINAHL, and PsycINFO). The review period commenced in April 2021 and was finalised on 5 July 2021. A total of 3537 studies were identified of which 87 were included in the review (sample size: 755,180). Healthcare workers had the highest prevalence of anxiety (36%), followed by university students (34.7%), the general population (34%), teachers (27.2%), parents (23.3%), pregnant women (19.5%), and police (8.79%). Risk factors such as being female, having pre-existing mental conditions, lower socioeconomic status, increased exposure to infection, and being younger all contributed to worsened anxiety. The review included studies published before July 2021; due to the ongoing nature of the COVID-19 pandemic, this may have excluded relevant papers. Restriction to only English papers and a sample size > 1000 may have also limited the range of papers included. These findings identify groups who are most vulnerable to developing anxiety in a pandemic and what specific risk factors are most common across multiple populations.

The effects of super spreading events and movement control measures on the COVID-19 pandemic in Malaysia

This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson’s correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p < 0.001 and r = 0.81, p < 0.001 during the validation period The Rt increased to reach the highest values at 3.40 (95% CI 1.47, 6.14) and 1.72 (95% CI 1.54, 1.90) due to the Sri Petaling and Sabah electoral process during the second and third waves of COVID-19 respectively. The MCOs was able to reduce the Rt values by 63.2 to 77.1% and 37.0 to 47.0% during the second and third waves of COVID-19, respectively. Mass gathering events were one of the important drivers of the COVID-19 outbreak in Malaysia. However, COVID-19 transmission can be fuelled by noncompliance to Standard Operating Procedure, population mobility, ventilation and environmental factors.

Caveats on COVID-19 herd immunity threshold: the Spain case

After a year of living with the COVID-19 pandemic and its associated consequences, hope looms on the horizon thanks to vaccines. The question is what percentage of the population needs to be immune to reach herd immunity, that is to avoid future outbreaks. The answer depends on the basic reproductive number, R₀, a key epidemiological parameter measuring the transmission capacity of a disease. In addition to the virus itself, R₀ also depends on the characteristics of the population and their environment. Additionally, the estimate of R₀ depends on the methodology used, the accuracy of data and the generation time distribution. This study aims to reflect on the difficulties surrounding R₀ estimation, and provides Spain with a threshold for herd immunity, for which we considered the different combinations of all the factors that affect the R₀ of the Spanish population. Estimates of R₀ range from 1.39 to 3.10 for the ancestral SARS-CoV-2 variant, with the largest differences produced by the method chosen to estimate R₀. With these values, the herd immunity threshold (HIT) ranges from 28.1 to 67.7%, which would have made 70% a realistic upper bound for Spain. However, the imposition of the delta variant (B.1.617.2 lineage) in late summer 2021 may have expanded the range of R₀ to 4.02–8.96 and pushed the upper bound of the HIT to 90%.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

Assay of Fatty Acids and Their Role in the Prevention and Treatment of COVID-19

Since the emergence of COVID-19, concerted worldwide efforts have taken place to minimize global spread of the contagion. Its widespread effects have also facilitated evolution of new strains, such as the delta and omicron variants, which emerged toward the end of 2020 and 2021, respectively. While these variants appear to be no more deadly than the previous alpha, beta, and gamma strains, and widespread population vaccinations notwithstanding, greater virulence makes the challenge of minimizing spread even greater. One of the peculiarities of this virus is the extreme heath impacts, with the great majority of individuals minimally affected, even sometimes unaware of infection, while for a small minority, it is deadly or produces diverse long-term effects. Apart from vaccination, another approach has been an attempt to identify treatments, for those individuals for whom the virus represents a threat of particularly severe health impact(s). These treatments include anti-SARS-CoV-2 monoclonal antibodies, anticoagulant therapies, interleukin inhibitors, and anti-viral agents such as remdesivir. Nutritional factors are also under consideration, and a variety of clinical trials are showing promise for the use of specific fatty acids, or related compounds such as fat-soluble steroid vitamin D, to mitigate the more lethal aspects of COVID-19 by modulating inflammation and by anti-viral effects. Here we explore the potential protective role of fatty acids as a potential prophylactic as well as remedial treatment during viral infections, particularly COVID-19. We present a multiplexed method for the analysis of free and phospholipid bound fatty acids, which may facilitate research into the role of fatty acids as plasma biomarkers and therapeutic agents in minimizing pre- and post-infection health impacts.

Lessons from a pandemic

Several interventions have been used around the world trying to contain the SARS-CoV-2 pandemic, such as quarantine, prohibition of mass demonstrations, isolation of sick people, tracing of virus carriers, semi-containment, promotion of barrier gestures, development of rapid self-tests and vaccines among others. We propose a simple model to evaluate the potential impact of such interventions. A model for the reproduction number of an infectious disease including three main contexts of infection (indoor mass events, public indoor activities and household) and seven parameters is considered. We illustrate how these parameters could be obtained from the literature or from expert assumptions, and we apply the model to describe 20 scenarios that can typically occur during the different phases of a pandemic. This model provides a useful framework for better understanding and communicating the effects of different (combinations of) possible interventions, while encouraging constant updating of expert assumptions to better match reality. This simple approach will bring more transparency and public support to help governments to think, decide, evaluate and adjust what to do during a pandemic.

Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come.

One day at a time

In this issue of Biomedical Journal we get to know measures to prevent a nosocomial COVID-19 outbreak, a compound that is able to stall SARS-CoV-2 replication, and the connection between air pollution and COVID-19 cases. Another article allows an insight into the potential of treating HIV combining a conventional drug and low level laser therapy. Furthermore, the advantages of awake craniotomy are presented, the efficacy of IRES is examined, and plant extracts are on the one hand explored as a nociceptive agent and on the other hand as therapeutic approach against breast cancer. We learn about drug resistance in liver cancer, a mutation involved in a rare inflammatory disorder, and lung surgery related unilateral vocal fold paralysis. Finally, the success of emergency endotracheal intubations across different hospital units is compared, the importance of monitoring cerebral blood flow in asphyxiated neonates is elucidated, and resistance variants in hepatitis C virus are examined. A study about the necessity to perform quantitative cardiac MRI in Asian population is presented, and an approach is shown on how to augment the effect of platelet-rich plasma injections in chronic knee osteoarthritis.

Reduction of influenza in Osaka, Japan during the COVID-19 outbreak: a population-based ORION registry study

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No reports using a population-based registry to evaluate COVID-19 impact on influenza

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The Osaka Prefecture government created the ORION registry

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ORION is the Osaka Emergency Information Research Intelligent Operation Network

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ORION records emergency patients treated by emergency medical service (EMS) personnel

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Number of influenza patients transported by EMS decreased during COVID-19 pandemic

Objectives

Methods

Results

Conclusions

Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

Corona-virus disease 2019 (COVID-19) is an infectious disease that has affected different groups of humankind such as farmers, soldiers, drivers, educators, students, healthcare workers and many others. The transmission rate of the disease varies from one group to another depending on the contact rate. Healthcare workers are at a high risk of contracting the disease due to the high contact rate with patients. So far, there exists no mathematical model which combines both public control measures (as a parameter) and healthcare workers (as an independent compartment). Combining these two in a given mathematical model is very important because healthcare workers are protected through effective use of personal protective equipment, and control measures help to minimize the spread of COVID-19 in the community. This paper presents a mathematical model named SWEI s I a HR; susceptible individuals (S), healthcare workers (W), exposed (E), symptomatic infectious (I s ), asymptomatic infectious (I a ), hospitalized (H), recovered (R). The value of basic reproduction numberR 0 for all parameters in this study is 2.8540. In the absence of personal protective equipment ξ and control measure in the public θ , the value ofR 0 ≈ 4 . 6047 which implies the presence of the disease. When θ and ξ were introduced in the model, basic reproduction number is reduced to 0.4606, indicating the absence of disease in the community. Numerical solutions are simulated by using Runge-Kutta fourth-order method. Sensitivity analysis is performed to presents the most significant parameter. Furthermore, identifiability of model parameters is done using the least square method. The results indicated that protection of healthcare workers can be achieved through effective use of personal protective equipment by healthcare workers and minimization of transmission of COVID-19 in the general public by the implementation of control measures. Generally, this paper emphasizes the importance of using protective measures.

Risk Factors of In-Hospital Mortality in Non-Specialized Tertiary Center Repurposed for Medical Care to COVID-19 Patients in Russia

The purpose of our study is to investigate the risk factors of in-hospital mortality among patients who were admitted in an emergency setting to a non-specialized tertiary center during the first peak of coronavirus disease in Moscow in 2020. The Federal Center of Brain and Neurotechnologies of the Federal Medical and Biological Agency of Russia was repurposed for medical care for COVID-19 patients from 6th of April to 16th of June 2020 and admitted the patients who were transported by an ambulance with severe disease. In our study, we analyzed the data of 635 hospitalized patients aged 59.1 ± 15.1 years. The data included epidemiologic and demographic characteristics, laboratory, echocardiographic and radiographic findings, comorbidities, and complications of the COVID-19, developed during the hospital stay. Results of our study support previous reports that risk factors of mortality among hospitalized patients are older age, male gender (OR 1.91, 95% CI 1.03–3.52), previous myocardial infarction (OR 3.15, 95% CI 1.47–6.73), previous acute cerebrovascular event (stroke, OR = 3.78, 95% CI 1.44–9.92), known oncological disease (OR = 3.39, 95% CI 1.39–8.26), and alcohol abuse (OR 6.98, 95% CI 1.62–30.13). According to the data collected, high body mass index and smoking did not influence the clinical outcome. Arterial hypertension was found to be protective against in-hospital mortality in patients with coronavirus pneumonia in the older age group. The neutrophil-to-lymphocyte ratio showed a significant increase in those patients who died during the hospitalization, and the borderline was found to be 2.5. CT pattern of “crazy paving” was more prevalent in those patients who died since their first CT scan, and it was a 4-fold increase in the risk of death in case of aortic and coronal calcinosis (4.22, 95% CI 2.13–8.40). Results largely support data from other studies and emphasize that some factors play a major role in patients’ stratification and medical care provided to them.

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

Natural variants in SARS-CoV-2 Spike protein pinpoint structural and functional hotspots with implications for prophylaxis and therapeutic strategies

In December 2019, a novel coronavirus, termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of pneumonia with severe respiratory distress and outbreaks in Wuhan, China. The rapid and global spread of SARS-CoV-2 resulted in the coronavirus 2019 (COVID-19) pandemic. Earlier during the pandemic, there were limited genetic viral variations. As millions of people became infected, multiple single amino acid substitutions emerged. Many of these substitutions have no consequences. However, some of the new variants show a greater infection rate, more severe disease, and reduced sensitivity to current prophylaxes and treatments. Of particular importance in SARS-CoV-2 transmission are mutations that occur in the Spike (S) protein, the protein on the viral outer envelope that binds to the human angiotensin-converting enzyme receptor (hACE2). Here, we conducted a comprehensive analysis of 441,168 individual virus sequences isolated from humans throughout the world. From the individual sequences, we identified 3540 unique amino acid substitutions in the S protein. Analysis of these different variants in the S protein pinpointed important functional and structural sites in the protein. This information may guide the development of effective vaccines and therapeutics to help arrest the spread of the COVID-19 pandemic.

Epidemiology of COVID-19: What have we learnt until now?

The first case in the outbreak of atypical pneumonia of unknown etiology, later confirmed as disease caused by SARS-CoV-2, was described in Wuhan (China) on December 8, 2019. The rapid expansion of COVID-19 cases prompted the World Health Organization (WHO) to declare a global health emergency, and on March 11, 2020, COVID-19 was officially classified as a pandemic disease by the WHO. It is generally accepted that both genders and all ages in the population are susceptible to SARS-CoV-2 infection. Data from the real life also show difficulties in reaching the threshold of herd immunity. Thanks to the vaccination, some populations are approaching the theoretical threshold of immunity, but the spread of the virus is still difficult to stop. If we add to that the fact that we still do not know how long immunity lasts after the infection, the conclusion is that vaccination is unlikely to completely stop the spread of the virus, and that we must think about it. Vaccines certainly significantly reduce the hospitalization rate and mortality rate, and the assumption is that the virus will not disappear soon, but the severity of the disease and its fatality will be of marginal importance. The development of the epidemiological situation related to the COVID-19 is constantly changing and it significantly differs in various parts of the world, which is affected by differences in financial resources, health infrastructure and awareness of prevention and control of the COVID-19. Attempts are being made to make dynamically adjusted strategies in response to the COVID-19 pandemic, that is, the new normality.