The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

Assessing potential surge of COVID-19 cases and the need for booster vaccine amid emerging SARS-CoV-2 variants in Indonesia: A modelling study from West Java

Objectives

Primary and booster vaccinations are crucial in COVID-19 control. This study aimed to assess the minimum booster coverage to hamper potential surge of COVID-19 cases in 2023 in Indonesia, a low-resource setting country.

Methods

We used a modified SEIR compartment model to assess different scenarios in booster coverage across West Java population: 35%, 50%, and 70%. We fitted the model, then we calculated the potential active cases in 2023 if each scenario was met before 2022 ends. A heat map of predicted cases from various booster coverages and time frames was produced and matched with vaccination rate's function to determine feasible time frames.

Results

A minimum of 70% booster coverage in West Java is needed to reduce 90% of potential COVID-19 cases and avert possible surge in 2023. The booster doses should be distributed before February 2023 to achieve its optimum preventive effect. Delays in achieving minimum booster coverage is acceptable, but higher booster coverage will be required.

Conclusions

For better COVID-19 control in Indonesia, booster vaccination is warranted, as presented by a case study in West Java. Sufficient vaccine supplies, infrastructure, and healthcare workers should be ensured to support a successful booster vaccination program.

Gynecological prevention and control model based on ward rearrangement and zoning management in pandemic period of COVID-19

By April 25, 2020, the outbreak of COVID-19 caused 2,719,897 confirmed cases and 18,7705 deaths globally, remarkably more than severe acute respiratory syndrome (SARS) (8273 cases, 775 deaths) and middle east respiratory syndrome (MERS) (1139 cases, 431 deaths) in 2003 and 2013, respectively. Gynecology is a specialty department with many critical and severe patients. Consequently, it is of preeminent importance to formulate the in-patient management process. Rearranging the gynecological wards and managing ward partition, as well as the medical protection measures in specialized areas, are suitable for the current prevention and control for COVID-19 pandemic and the therapeutic requirements of patients. To effectively minimize nosocomial infections during the COVID-19 pandemic period, our department implemented a novel prevention strategy based on the ward redesign and partition management. With this model, our department effectively protected the safety and health of patients and medical care staff from cross and nosocomial infection in the hospital. Now we would like to share the experience and strategies we implemented as following.

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Inflammatory markers and COVID-19 disease progression

BACKGROUND

The COVID-19 pandemic has resulted in a global humanitarian crisis. Despite ongoing research, transmission risks and many disease characteristics remained unclear. Most patients have displayed elevated levels of certain inflammatory markers, which we sought to investigate further in relation to disease severity. The aim of this study was to examine the correlation between inflammatory markers and the severity of COVID-19 among patients.

METHODS

We conducted a cross-sectional study from April to September 2020, involving 143 COVID-19 PCR-positive patients from Ziauddin Hospital. Electronic patient records provided data on demographics, clinical status, and laboratory results.

RESULTS

The majority of PCR-positive patients were elderly males with comorbidities such as diabetes and hypertension. Almost all patients exhibited increased levels of various inflammatory markers, with procalcitonin (97.2%) being the most common. Statistically significant differences were observed in the levels of TLC (p = 0.005), CRP (p = 0.001), LDH (p = 0.001), Ferritin (p = 0.001), D-dimer (p = 0.001), and procalcitonin (p = 0.028), in relation to COVID-19 severity.

CONCLUSIONS

The data suggest a significant association between levels of inflammatory markers and COVID-19 severity. All markers, except procalcitonin, demonstrated a significant correlation with disease severity. These results could enhance our understanding of COVID-19 pathogenesis and help predict and manage severe cases.

Endothelial Glycocalyx Injury in SARS-CoV-2 Infection: Molecular Mechanisms and Potential Targeted Therapy

This review aims at summarizing state-of-the-art knowledge on glycocalyx and SARS-CoV-2. The endothelial glycocalyx is a dynamic grid overlying the surface of the endothelial cell (EC) lumen and consists of membrane-bound proteoglycans and glycoproteins. The role of glycocalyx has been determined in the regulation of EC permeability, adhesion, and coagulation. SARS-CoV-2 is an enveloped, single-stranded RNA virus belonging to β-coronavirus that causes the outbreak and the pandemic of COVID-19. Through the respiratory tract, SARS-CoV-2 enters blood circulation and interacts with ECs possessing angiotensin-converting enzyme 2 (ACE2). Intact glycolyx prevents SARS-CoV-2 invasion of ECs. When the glycocalyx is incomplete, virus spike protein of SARS-CoV-2 binds with ACE2 and enters ECs for replication. In addition, cytokine storm targets glycocalyx, leading to subsequent coagulation disorder. Therefore, it is intriguing to develop a novel treatment for SARS-CoV-2 infection through the maintenance of the integrity of glycocalyx. This review aims to summarize state-of-the-art knowledge of glycocalyx and its potential function in SARS-CoV-2 infection.

COVID-19: An Overview of SARS-CoV-2 Variants-The Current Vaccines and Drug Development

The world is presently in crisis facing an outbreak of a health-threatening microorganism known as COVID-19, responsible for causing uncommon viral pneumonia in humans. The virus was first reported in Wuhan, China, in early December 2019, and it quickly became a global concern due to the pandemic. Challenges in this regard have been compounded by the emergence of several variants such as B.1.1.7, B.1.351, P1, and B.1.617, which show an increase in transmission power and resistance to therapies and vaccines. Ongoing researches are focused on developing and manufacturing standard treatment strategies and effective vaccines to control the pandemic. Despite developing several vaccines such as Pfizer/BioNTech and Moderna approved by the U.S. Food and Drug Administration (FDA) and other vaccines in phase 4 clinical trials, preventive measures are mandatory to control the COVID-19 pandemic. In this review, based on the latest findings, we will discuss different types of drugs as therapeutic options and confirmed or developing vaccine candidates against SARS-CoV-2. We also discuss in detail the challenges posed by the variants and their effect on therapeutic and preventive interventions.

Longitudinal clinical phenotyping of post COVID condition in Mexican adults recovering from severe COVID-19: a prospective cohort study

Introduction

Few studies have evaluated the presence of Post COVID-19 conditions (PCC) in people from Latin America, a region that has been heavily afflicted by the COVID-19 pandemic. In this study, we describe the frequency, co-occurrence, predictors, and duration of 23 symptoms in a cohort of Mexican patients with PCC.

Methods

We prospectively enrolled and followed adult patients hospitalized for severe COVID-19 at a tertiary care centre in Mexico City. The incidence of PCC symptoms was determined using questionnaires. Unsupervised clustering of PCC symptom co-occurrence and Kaplan-Meier analyses of symptom persistence were performed. The effect of baseline clinical characteristics was evaluated using Cox regression models and reported with hazard ratios (HR).

Results

We found that amongst 192 patients with PCC, respiratory problems were the most prevalent and commonly co-occurred with functional activity impairment. 56% had ≥5 persistent symptoms. Symptom persistence probability at 360 days 0.78. Prior SARS-CoV-2 vaccination and infection during the Delta variant wave were associated with a shorter duration of PCC. Male sex was associated with a shorter duration of functional activity impairment and respiratory symptoms. Hypertension and diabetes were associated with a longer duration of functional impairment. Previous vaccination accelerated PCC recovery.

Discussion

In our cohort, PCC symptoms were frequent (particularly respiratory and neurocognitive ones) and persistent. Importantly, prior SARS-CoV-2 vaccination resulted in a shorter duration of PCC.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States

BACKGROUND

As we continue the fourth year of the COVID-19 epidemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade.

METHODS

We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus and account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider three exponentially distributed waning rates, with means of 365 days (1 year), 548 days (1.5 years), and 730 days (2 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome.

RESULTS

As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 81,000-164,600 annual reported deaths, depending on duration of immunity, are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with 52-80% annual vaccination coverage and below 10,000 annual deaths with 59-83% annual vaccination coverage, depending on duration of immunity. Universal treatment reduces hospitalizations by 88.6% and deaths by 93.1% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged.

CONCLUSIONS

Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Changing Severity and Epidemiology of Adults Hospitalized With Coronavirus Disease 2019 (COVID-19) in the United States After Introduction of COVID-19 Vaccines, March 2021-August 2022

INTRODUCTION

Understanding the changing epidemiology of adults hospitalized with coronavirus disease 2019 (COVID-19) informs research priorities and public health policies.

METHODS

Among adults (≥18 years) hospitalized with laboratory-confirmed, acute COVID-19 between 11 March 2021, and 31 August 2022 at 21 hospitals in 18 states, those hospitalized during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron-predominant period (BA.1, BA.2, BA.4/BA.5) were compared to those from earlier Alpha- and Delta-predominant periods. Demographic characteristics, biomarkers within 24 hours of admission, and outcomes, including oxygen support and death, were assessed.

RESULTS

Among 9825 patients, median (interquartile range [IQR]) age was 60 years (47-72), 47% were women, and 21% non-Hispanic Black. From the Alpha-predominant period (Mar-Jul 2021; N = 1312) to the Omicron BA.4/BA.5 sublineage-predominant period (Jun-Aug 2022; N = 1307): the percentage of patients who had ≥4 categories of underlying medical conditions increased from 11% to 21%; those vaccinated with at least a primary COVID-19 vaccine series increased from 7% to 67%; those ≥75 years old increased from 11% to 33%; those who did not receive any supplemental oxygen increased from 18% to 42%. Median (IQR) highest C-reactive protein and D-dimer concentration decreased from 42.0 mg/L (9.9-122.0) to 11.5 mg/L (2.7-42.8) and 3.1 mcg/mL (0.8-640.0) to 1.0 mcg/mL (0.5-2.2), respectively. In-hospital death peaked at 12% in the Delta-predominant period and declined to 4% during the BA.4/BA.5-predominant period.

CONCLUSIONS

Compared to adults hospitalized during early COVID-19 variant periods, those hospitalized during Omicron-variant COVID-19 were older, had multiple co-morbidities, were more likely to be vaccinated, and less likely to experience severe respiratory disease, systemic inflammation, coagulopathy, and death.

Delay-differential SEIR modeling for improved modelling of infection dynamics

SEIR (Susceptible-Exposed-Infected-Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.

Epidemiological Characteristics of a COVID-19 Outbreak in a Psychiatric Hospital in Chung-buk

This study investigated the causes and risks for infection spread in three psychiatric hospitals in Chung-buk, South Korea, to strategize measures to block transmission and prevent a large-scale epidemic. From December 2020 to January 2021, 358 inpatients of Psychiatric Hospitals A, B, and C were enrolled to identify the epidemiological characteristics of confirmed patients. Epidemic curves and propagation relationships were constructed and a genotype analysis was conducted. The index case inpatient from Hospital A transmitted the infection to patients in Hospitals B and C; the infection was confirmed in 47, 193, and 118 patients in Hospitals A, B, and C, respectively. The patient characteristics hampered communication and the close identification of symptom onset. The incidence rate was 10 (2.9%) among employees and 348 (35.8%) among inpatients. The relative risk was 12.1 (95% CI: 6.6-22.5) times higher among inpatients than employees. Next-generation sequencing confirmed the probable infection source as a genotype identical to that of two different outbreaks, although the infection spread was undetermined. Direct risk factors emerged from patient characteristics, wherein cohort isolation was meaningless due to uncontrolled communication. Indirect risk factors included hospital-specific problems due to external factors (non-patient system deficiencies or employee negligence). Prior inspections, a confirmation of non-infection, and institutional emergent measures are needed.

Unveiling a New Perspective on Distinguishing Omicron Breakthrough Cases and Postimmune COVID-19-Naive Individuals: Insights from Antibody Profiles

In the situation of mass vaccination against COVID-19, few studies have reported on the early kinetics of specific antibodies (IgG/IgM/IgA) of vaccine breakthrough cases. There is still a lack of epidemiological evidence about the value of serological indicators in the auxiliary diagnosis of COVID-19 infection, especially when the nucleic acid results were undetectable. Omicron breakthrough cases post-inactivated vaccination (n = 456) and COVID-19-naive individuals with two doses of inactivated vaccination (n = 693) were enrolled. Blood samples were collected and tested for SARS-CoV-2 antibody levels based on the magnetic chemiluminescence enzyme immunoassay. Among Omicron breakthrough cases, the serum IgG antibody level was 36.34 Sample/CutOff (S/CO) (95% confidence interval [CI], 31.89 to 40.79) in the acute phase and 88.45 S/CO (95% CI, 82.79 to 94.12) in the recovery phase. Serum IgA can be detected in the first week post-symptom onset (PSO) and showed an almost linear increase within 5 weeks PSO. Compared with those of breakthrough cases, IgG and IgA titers of the postimmune group were much lower (4.70 S/CO and 0.46 S/CO, respectively). Multivariate regression showed that serum IgG and IgA levels in Omicron breakthrough cases were mainly affected by the weeks PSO (P < 0.001). Receiver operating characteristic ROC0 curve analysis showed that the area under the curve (AUC) was 0.744 and 0.806 when the cutoff values of IgA and IgG were 1 S/CO and 15 S/CO, respectively. Omicron breakthrough infection can lead to a further increase in IgG and IgA levels relative to those of the immunized population. When nucleic acid real-time PCR was negative, we would use the kinetics of IgG and IgA levels to distinguish the breakthrough cases from the immunized population. IMPORTANCE This study fills a gap in the epidemiological evidence by investigating the value of serological indicators, particularly IgG and IgA levels, in the auxiliary diagnosis of COVID-19 infections when nucleic acid results are undetectable. The findings reveal that among Omicron breakthrough cases, both IgG and IgA antibody levels exhibit significant changes. Serum IgG levels increase during the acute phase and rise further in the recovery phase. Serum IgA can be detected as early as the first week post-symptom onset (PSO), showing a consistent linear increase within 5 weeks PSO. Furthermore, receiver operating characteristic (ROC) curve analysis demonstrates the potential of IgG and IgA cutoff values as diagnostic markers. The study's conclusion underscores the importance of monitoring IgG and IgA kinetics in distinguishing Omicron breakthrough cases from vaccinated individuals. These findings contribute to the development of more accurate diagnostic approaches and help inform public health strategies during the ongoing COVID-19 pandemic.

Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided with immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.

SIRC epidemic model with cross-immunity and multiple time delays

Multi-strain diseases lead to the development of some degree of cross-immunity among people. In the present paper, we propose a multi-delayed SIRC epidemic model with incubation and immunity time delays. Here we aim to examine and investigate the effects of incubation delay [Formula: see text] and the impact of vaccine which provides partial/cross-immunity with immunity delay parameter ([Formula: see text]) on the disease dynamics. Also, we study the impact of the strength of cross-immunity [Formula: see text] on the disease prevalence. The positivity and boundedness of the solutions of the epidemic model have been established. Two different types of equilibrium points (disease-free and endemic) have been deduced. Expression for basic reproduction number has been derived. The stability conditions and Hopf-bifurcation about both the equilibrium points in the absence and presence of both delays have been discussed. The Lyapunov stability conditions about the endemic equilibrium point have been established. Numerical simulations have been performed to support our analytical results. We quantitatively demonstrate how oscillations and Hopf-bifurcation allow time delays to alter the dynamics of the system. The combined impacts of both the delays on disease prevalence has been studied. Through parameter sensitivity analysis, we observe that the infected population decreases with an increase in vaccination rate and the system starts to stabilize early with the increase in cross-immunity rate. Global sensitivity analysis for the basic reproduction number has been performed using Latin hypercube sampling and partial rank correlation coefficients techniques. The combined effect of vaccination rate with transmission rate and vaccination rate with re-infection probability (i.e. strength of cross-immunity) on [Formula: see text] have been discussed. Our research underlines the need to take cross-immunity and time delays into account in the epidemic model in order to better understand disease dynamics.

A Mixture of Essential Oils from Three Cretan Aromatic Plants Inhibits SARS-CoV-2 Proliferation: A Proof-of-Concept Intervention Study in Ambulatory Patients

INTRODUCTION

The need for effective therapeutic regimens for non-critically ill patients during the COVID-19 pandemic remained largely unmet. Previous work has shown that a combination of three aromatic plants' essential oils (CAPeo) (Thymbra capitata (L.) Cav., Origanum dictamnus L., Salvia fruticose Mill.) has remarkable in vitro antiviral activity. Given its properties, it was urgent to explore its potential in treating mild COVID-19 patients in primary care settings.

METHODS

A total of 69 adult patients were included in a clinical proof-of-concept (PoC) intervention study. Family physicians implemented the observational study in two arms (intervention group and control group) during three study periods (IG2020, n=13, IG2021/22, n=25, and CG2021/22, n=31). The SARS-CoV-2 infection was confirmed by real-time PCR. The CAPeo mixture was administered daily for 14 days per os in the intervention group, while the control group received usual care.

RESULTS

The PoC study found that the number and frequency of general symptoms, including general fatigue, weakness, fever, and myalgia, decreased following CAPeo administration. By Day 7, the average presence (number) of symptoms decreased in comparison with Day 1 in IG (4.7 to 1.4) as well as in CG (4.0 to 3.1), representing a significant decrease in the cumulative presence in IC (-3.3 vs. -0.9, p < 0.001; η2 = 0.20) on Day 7 and on Day 14 (-4.2 vs. -2.9, p = 0.027; η2 = 0.08).

DISCUSSION/CONCLUSIONS

Our findings suggest that CAPeo possesses potent antiviral activity against SARS-CoV-2 in addition tο its effect against influenza A and B and human rhinovirus HRV14 strains. The early and effective impact on alleviating key symptoms of COVID-19 may suggest this mixture can act as a complementary natural agent for patients with mild COVID-19.

Impact of school operating scenarios on COVID-19 transmission under vaccination in the U.S.: an agent-based simulation model

At the height of the COVID-19 pandemic, K-12 schools struggled to safely operate under the fast-changing pandemic situation. However, little is known about the impact of different school operating scenarios considering the ongoing efforts of vaccination. In this study, we deployed an agent-based simulation model to mimic disease transmission in a mid-sized community consisting of 10,000 households. A total of eight school operating scenarios were simulated, in decreasing order of restrictiveness regarding COVID-19 mitigation measures. When masks were worn at school, work, and community environments, increasing in-person education from 50% to 100% would result in only 1% increase in cumulative infections. When there were no masks nor contact tracing while schools were 100% in person, the cumulative infection increased by 86% compared to the scenario when both masking and contact tracing were in place. In the sensitivity analysis for vaccination efficacy, we found that higher vaccination efficacy was essential in reducing overall infections. Our findings showed that full in-person education was safe, especially when contact tracing, masking, and widespread vaccination were in place. If no masking nor contact tracing was practiced, the transmission would rose dramatically but eventually slow down due to herd immunity.

Diagnostic of Patients with COVID-19 Pneumonia Using Passive Medical Microwave Radiometry (MWR)

BACKGROUND

Chest CT is widely regarded as a dependable imaging technique for detecting pneumonia in COVID-19 patients, but there is growing interest in microwave radiometry (MWR) of the lungs as a possible substitute for diagnosing lung involvement.

AIM

The aim of this study is to examine the utility of the MWR approach as a screening tool for diagnosing pneumonia with complications in patients with COVID-19.

METHODS

Our study involved two groups of participants. The control group consisted of 50 individuals (24 male and 26 female) between the ages of 20 and 70 years who underwent clinical evaluations and had no known medical conditions. The main group included 142 participants (67 men and 75 women) between the ages of 20 and 87 years who were diagnosed with COVID-19 complicated by pneumonia and were admitted to the emergency department between June 2020 to June 2021. Skin and lung temperatures were measured at 14 points, including 2 additional reference points, using a previously established method. Lung temperature data were obtained with the MWR2020 (MMWR LTD, Edinburgh, UK). All participants underwent clinical evaluations, laboratory tests, chest CT scans, MWR of the lungs, and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2.

RESULTS

The MWR exhibits a high predictive capacity as demonstrated by its sensitivity of 97.6% and specificity of 92.7%.

CONCLUSIONS

MWR of the lungs can be a valuable substitute for chest CT in diagnosing pneumonia in patients with COVID-19, especially in situations where chest CT is unavailable or impractical.

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.

Fear itself. Is fear a determinant of the efficacy oflockdowns?

Among non-pharmaceutical measures for fighting the COVID-19 pandemic, one of the most important is the implementation of lockdowns. The cost and effectiveness of this policy remains a much-debated topic in economics. In this study we investigate whether a 'fear effect' is at work in influencing the effectiveness of lockdowns. According to previous contributions on the topic, fear can increase protective habits, and for this reason we may imagine that a high number of COVID-19-caused deaths creates fear among the population, which may make people more likely to follow government prescriptions and observe lockdowns strictly. By means of a qualitative-quantitative analysis, we find that among the 46 countries that reported coronavirus-caused deaths before the implementation of a lockdown, the top quartile for per capita deaths has better results in terms of reducing new COVID-19 cases after a lockdown, compared to the worst quartile. This suggests that the number of reported deaths, as well as its communication to the population, are important determinants of the effectiveness of a lockdown.

Antiplatelet agents for the treatment of adults with COVID-19

BACKGROUND

Severe coronavirus disease 2019 (COVID-19) can cause thrombotic events that lead to severe complications or death. Antiplatelet agents, such as acetylsalicylic acid, have been shown to effectively reduce thrombotic events in other diseases: they could influence the course of COVID-19 in general.

OBJECTIVES

To assess the efficacy and safety of antiplatelets given with standard care compared to no treatment or standard care (with/without placebo) for adults with COVID-19.

SEARCH METHODS

We searched the Cochrane COVID-19 Study Register (which comprises MEDLINE (PubMed), Embase, ClinicalTrials.gov, WHO ICTRP, medRxiv, CENTRAL), Web of Science, WHO COVID-19 Global literature on coronavirus disease and the Epistemonikos COVID-19 L*OVE Platform to identify completed and ongoing studies without language restrictions to December 2022.

SELECTION CRITERIA

We followed standard Cochrane methodology. We included randomised controlled trials (RCTs) evaluating antiplatelet agents for the treatment of COVID-19 in adults with COVID-19, irrespective of disease severity, gender or ethnicity.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methodology. To assess bias in included studies, we used the Cochrane risk of bias tool (RoB 2) for RCTs. We rated the certainty of evidence using the GRADE approach for the outcomes.

MAIN RESULTS

Antiplatelets plus standard care versus standard care (with/without placebo) Adults with a confirmed diagnosis of moderate to severe COVID-19 We included four studies (17,541 participants) that recruited hospitalised people with a confirmed diagnosis of moderate to severe COVID-19. A total of 8964 participants were analysed in the antiplatelet arm (either with cyclooxygenase inhibitors or P2Y12 inhibitors) and 8577 participants in the control arm. Most people were older than 50 years and had comorbidities such as hypertension, lung disease or diabetes. The studies were conducted in high- to lower middle-income countries prior to wide-scale vaccination programmes. Antiplatelets compared to standard care: - probably result in little to no difference in 28-day mortality (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.85 to 1.05; 3 studies, 17,249 participants; moderate-certainty evidence). In absolute terms, this means that for every 177 deaths per 1000 people not receiving antiplatelets, there were 168 deaths per 1000 people who did receive the intervention (95% CI 151 to 186 per 1000 people); - probably result in little to no difference in worsening (new need for invasive mechanical ventilation or death up to day 28) (RR 0.95, 95% CI 0.90 to 1.01; 2 studies, 15,266 participants; moderate-certainty evidence); - probably result in little to no difference in improvement (participants discharged alive up to day 28) (RR 1.00, 95% CI 0.96 to 1.04; 2 studies, 15,454 participants; moderate-certainty evidence); - probably result in a slight reduction of thrombotic events at longest follow-up (RR 0.90, 95% CI 0.80 to 1.02; 4 studies, 17,518 participants; moderate-certainty evidence); - may result in a slight increase in serious adverse events at longest follow-up (Peto odds ratio (OR) 1.57, 95% CI 0.48 to 5.14; 1 study, 1815 participants; low-certainty evidence), but non-serious adverse events during study treatment were not reported; - probably increase the occurrence of major bleeding events at longest follow-up (Peto OR 1.68, 95% CI 1.29 to 2.19; 4 studies, 17,527 participants; moderate-certainty evidence). Adults with a confirmed diagnosis of asymptomatic SARS-CoV-2 infection or mild COVID-19 We included two RCTs allocating participants, of whom 4209 had confirmed mild COVID-19 and were not hospitalised. A total of 2109 participants were analysed in the antiplatelet arm (treated with acetylsalicylic acid) and 2100 participants in the control arm. No study included people with asymptomatic SARS-CoV-2 infection. Antiplatelets compared to standard care: - may result in little to no difference in all-cause mortality at day 45 (Peto OR 1.00, 95% CI 0.45 to 2.22; 2 studies, 4209 participants; low-certainty evidence); - may slightly decrease the incidence of new thrombotic events up to day 45 (Peto OR 0.37, 95% CI 0.09 to 1.46; 2 studies, 4209 participants; low-certainty evidence); - may make little or no difference to the incidence of serious adverse events up to day 45 (Peto OR 1.00, 95% CI 0.60 to 1.64; 1 study, 3881 participants; low-certainty evidence), but non-serious adverse events were not reported. The evidence is very uncertain about the effect of antiplatelets on the following outcomes (compared to standard care plus placebo): - admission to hospital or death up to day 45 (Peto OR 0.79, 95% CI 0.57 to 1.10; 2 studies, 4209 participants; very low-certainty evidence); - major bleeding events up to longest follow-up (no event occurred in 328 participants; very low-certainty evidence). Quality of life and adverse events during study treatment were not reported.

AUTHORS' CONCLUSIONS

In people with confirmed or suspected COVID-19 and moderate to severe disease, we found moderate-certainty evidence that antiplatelets probably result in little to no difference in 28-day mortality, clinical worsening or improvement, but probably result in a slight reduction in thrombotic events. They probably increase the occurrence of major bleeding events. Low-certainty evidence suggests that antiplatelets may result in a slight increase in serious adverse events. In people with confirmed COVID-19 and mild symptoms, we found low-certainty evidence that antiplatelets may result in little to no difference in 45-day mortality and serious adverse events, and may slightly reduce thrombotic events. The effects on the combined outcome admission to hospital or death up to day 45 and major bleeding events are very uncertain. Quality of life was not reported. Included studies were conducted in high- to lower middle-income settings using antiplatelets prior to vaccination roll-outs. We identified a lack of evidence concerning quality of life assessments, adverse events and people with asymptomatic infection. The 14 ongoing and three completed, unpublished RCTs that we identified in trial registries address similar settings and research questions as in the current body of evidence. We expect to incorporate the findings of these studies in future versions of this review.

A simulation framework for modeling the within-patient evolutionary dynamics of SARS-CoV-2

The global impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for positive selection are best performed via comparison of empirical data to simulated data wherein evolutionary factors, including mutation and recombination rates, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. While there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intra-host evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them to existing empirical data. Of these, 592 models (~5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intra-host SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed towards strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.

Epidemiological analysis of coronavirus disease (COVID-19) patients on ships arriving at Busan port in Korea, 2020

Quarantine played an important role in preventing the spread of infectious diseases between countries in the early stages of the COVID-19 outbreak. In particular, in ports, infection during transit can cause a large number of patients on board ships and can flow into the community. In this study investigated cause of the cause of transmission in ships and suggested the way of preventing secondary transmission by analyzing clinical and epidemiological characteristics of COVID-19 patients identified at Busan Port (South Korea) in 2020. During the study period, out of 19,396 ships that arrived at Busan Port, 50 ships had COVID-19 confirmed cases. Among the 50 ships, type of deep-sea fishing vessels (24 ships, 48.0%), ships weighing less than 5,000 tons (31 ships, 62.0%), and ships from Russia (41 ships, 82.0%) had the highest positivity rates. Total 283 of the 25,450 arrivals tested positive for COVID-19 (a positivity rate of 1.1%), and 270 (95.4%) were asymptomatic. Moreover, the number of COVID-19 patients increased with the duration of the waiting period between arrival and sample collection (12.7% to 37.4%), and the positivity rate was significantly higher for those working as stewards (64.3%). These results indicate secondary transmission was active on board ships and that infection among stewards importantly contributed to group outbreaks. In addition, onboard residence time after arrival significantly elevated to COVID-19 positivity rates, indicating that rapid isolation, as determined using various screening techniques, might be effective at preventing onboard transmission and subsequent community outbreaks.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.

Socioeconomic determinants of stay-at-home policies during the first COVID-19 wave

Introduction

The COVID-19 pandemic has had a significant impact on public health and social systems worldwide. This study aims to evaluate the efficacy of various policies and restrictions implemented by different countries to control the spread of the virus.

Methods

To achieve this objective, a compartmental model is used to quantify the "social permeability" of a population, which reflects the inability of individuals to remain in confinement and continue social mixing allowing the spread of the virus. The model is calibrated to fit and recreate the dynamics of the epidemic spreading of 42 countries, mainly taking into account reported deaths and mobility across the populations.

Results

The results indicate that low-income countries have a harder time slowing the advance of the pandemic, even if the virus did not initially propagate as fast as in wealthier countries, showing the disparities between countries in their ability to mitigate the spread of the disease and its impact on vulnerable populations.

Discussion

This research contributes to a better understanding of the socioeconomic and environmental factors that affect the spread of the virus and the need for equitable policy measures to address the disparities in the global response to the pandemic.

Does COVID-19 Infection within 1 Week after Total Knee Arthroplasty Affect Patients' Early Clinical Outcomes? A Matched Case-Control Study

Recent studies have reported the impact of previous COVID-19 infection on the early clinical outcome after total knee arthroplasty (TKA). However, the timing of infection before the surgery was not constant and a study on patients with COVID-19 infection within 1 week after TKA is lacking. This study aimed to determine the effect of COVID-19 infection within one week after TKA on the postoperative outcomes and to compare the early clinical outcomes to those who were not infected with COVID-19 before and after surgery. No significant differences were observed between the two groups in terms of clinical outcomes or complications. The length of the hospital stay (LOS) was significantly longer for the COVID-19 group than for the non-COVID-19 group (p < 0.05). The erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) levels were higher for the study group on postoperative days 9 and 12 than for those in the control group (p < 0.05). However, D-dimer levels were not significantly different between the two groups. We should cautiously consider that COVID-19 infection within 1 week after TKA may be associated with increased ESR, CRP levels, and length of hospital stay, but they are not associated with the worsening of early clinical outcomes or the occurrence of complications.

The role of modelling and analytics in South African COVID-19 planning and budgeting

BACKGROUND

The South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead.

METHODS

Our tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary.

RESULTS

Given the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely.

CONCLUSION

The SACMC's models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout.

An Increased Risk for Ischemic Stroke in the Short-Term Period following COVID-19 Infection: A Nationwide Population-Based Study

INTRODUCTION

The association disclosed between coronavirus disease 2019 (COVID-19) infection and ischemic stroke (IS) raises concern. The exact risk periods, which were not consistent between studies, require further investigation.

METHODS

We linked two national databases: the COVID-19 database and the Israeli National Stroke Registry. The self-controlled case series method was used to estimate the association between COVID-19 infection and a first IS. The study population included all Israeli residents who had both a first IS event and a first COVID-19 diagnosis during 2020. The date of the PCR test served to define the day of exposure, and the 28 days following it were categorized into three risk periods: days 1-7, 8-14, and 15-28. A relative incidence (RI) with a 95% confidence interval (95% CI) was calculated based on the incidence rate of events in a post-exposure period, compared to the incidence rate in a control period.

RESULTS

From January 1, 2020, to December 31, 2020, 308,015 Israelis aged 18+ were diagnosed with COVID-19 and 9,535 were diagnosed with a first IS. Linking the two databases, 555 persons had both diagnoses during 2020. The mean age of the study population was 71.5 ± 13.7, 55.1% were males, 77.8% had hypertension, 73.7% had hyperlipidemia, 51.9% had diabetes, and 28.5% had ischemic heart disease. Comparing the risk period and the control period, we found a very similar distribution of the cardiovascular risk factors. The risk for an acute IS was 3.3-fold higher in the first week following COVID-19 diagnosis, compared with a control period (RI = 3.3; 95% CI: 2.3-4.6). The RI among males (RI = 4.5; 95% CI: 2.9-6.8) was 2.2-fold higher compared to females. The increased risk did not last beyond the first week following exposure.

CONCLUSION

Physicians should be aware of the elevated risk for IS among patients experiencing COVID-19, particularly among men with high burden of cardiovascular risk factors.

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

Development of transmission-reducing behaviour adherence measure (TRAM) for monitoring and predicting transmission-reducing behaviours during the pandemic

There is a need for a measure to monitor adherence to transmission-reducing behaviours (TRBs) during pandemics. An adherence measure can monitor current TRBs, assess change over time and, potentially, predict later behaviours. The TRB adherence measure (scale consisting of seven items) includes questions based on government behavioural directives in Scotland that were common internationally, i.e., physical distancing, face covering and hand hygiene. Data were collected weekly for 6 weeks at the beginning of the pandemic, including a later follow-up repeated measure of some participants, in 20-minute structured telephone surveys with a nationally representative random sample of adults in Scotland. A total of 2969 people completed the adherence items and were highly adherent. Confirmatory factor analysis supported a unidimensional scale (CFI = .95; TLI = .93; RMSEA = .08; SRMR = .08), although internal consistency was low (Cronbach's alpha = .49). The adherence score significantly predicted adherence to a validity test item (ΔR2 = .114, F(1,2964) = 379.76, p < .001). It also predicted adherence to TRBs later over and above personal habitual styles (Creature of Habit Scale: COHS). The adherence score has been developed for routine monitoring of adherence to TRBs during the COVID-19 pandemic. It can be used to predict future similar behaviours and adherence to other behaviours, although it may be necessary to explore adherence to the specific behaviours occasionally. Adherent behaviour for one TRB is likely to be associated with adherence to government directives to other TRBs. Importantly, these TRBs are likely to be crucial in reducing COVID-19 case numbers, as well as protecting against other infectious diseases including influenza and the common cold.

Evaluating the frequency of neurological symptoms in COVID-19 patients: A cross-sectional study

Background and Aims

Due to the recent emergence of COVID-19, the exact pathology of this disease has not been determined. Therefore, this study evaluated the frequency of neurological symptoms in patients with COVID-19.

Methods

This cross-sectional study was conducted on 2200 in patients with COVID-19 who were selected from an educational hospital in Sanandaj, Iran, from April 2020 to March 2021. The research samples were selected by census, all patients with COVID-19 were admitted to the hospital. The data collection tool was a checklist of the studied variables (dizziness, headache, and impaired consciousness) prepared by the researchers based on the specialists' opinions. The researcher completed these checklists based on the patients' hospitalization records. The data were analyzed by descriptive and analytical statistical tests using SPSS Software Version 20. The quantitative variables were compared using the independent t-test. The χ ² test was also used to compare qualitative variables. A p Value of less than 0.05 was considered statistically significant.

Results

The mean age of the patients was 57.41 years old, of whom 53.1% were male. The average blood oxygen level of the patients was 88.10%, and most disease symptoms were related to shortness of breath and cough, with a frequency of 24.3%. In addition, 20.8% of patients needed hospitalization in intensive care unit. The highest frequency of central and peripheral nervous system manifestations was related to headache, ageusia (loss of sense of taste), hyposmia (A decreased sense of smell and anosmia (The complete loss of smell). Finally, 15.3% of patients died, and 84.7% recovered. The analytical findings showed a significant relationship between the disease outcome and patients' dizziness, consciousness disorder, seizure and ageusia. There was a significant relationship between gender and headache in patients. There was a significant difference between the mean age and oxygen level with central and peripheral nervous system manifestations (dizziness, headache, impaired consciousness, smell disorder) and the disease outcome in patients.

Conclusion

The pathophysiology of COVID-19 virus infection involving the central nervous system is not fully understood. Neurological symptoms of this virus include delirium, headache, decreased level of consciousness, and seizures. Identifying the symptoms and mechanisms of neurological complications of COVID-19 is necessary for proper screening and complete treatment because a patient infected by COVID-19 may not show respiratory failure signs but may be a carrier. A complete and accurate knowledge of the symptoms and complications of this infection for proper screening of patients to prevent transmission and spread of this disease is critically needed.

Research on urban country parks based on emergency medical facilities for public health emergencies-a case study of Guangzhou, China

As COVID-19 has swept across the world, the escalating number of confirmed and suspected cases overwhelmed the admission capacity of the designated hospitals. Faced with such a grim situation, governments made a quick decision to build emergency medical facilities to address the outbreak. However, the emergency medical facilities faced a huge risk of epidemic spread and improper site could lead to serious secondary transmission. Using the disaster prevention and risk avoidance function of urban green space can solve the problem of selecting the location of emergency medical facilities to a certain extent, with country parks having a high degree of compatibility with the latter. Based on the location requirements of emergency medical facilities, using Analytic Hierarchy Process and Delphi method, through analyzing the type of country parks, effective risk avoidance area, spatial fragmentation, distance from water sources, wind direction, and distance from the city, quantification of 8 impact factors such as hydrogeology and traffic duration was conducted to comprehensively compare 30 country parks in Guangzhou. The results showed that the overall quality of country parks approximated a normal distribution, with Lianma Forest Country Park having the highest comprehensive score and the most balanced distribution of scores for various impact factors. Considering safety, expandability, rehabilitation, convenience, pollution prevention, and fecal isolation, it is a preferred destination for emergency medical facility construction.

Trade-off Between Quarantine Length and Compliance to Optimize COVID-19 Control

BACKGROUND

Guidance on COVID-19 quarantine duration is often based on the maximum observed incubation periods assuming perfect compliance. However, the impact of longer quarantines may be subject to diminishing returns; the largest benefits of quarantine occur over the first few days. Additionally, the financial and psychological burdens of quarantine may motivate increases in noncompliance behavior.

METHODS

We use a deterministic transmission model to identify the optimal length of quarantine to minimize transmission. We modeled the relation between noncompliance behavior and disease risk using a time-varying function of leaving quarantine based on studies from the literature.

RESULTS

The first few days in quarantine were more crucial to control the spread of COVID-19; even when compliance is high, a 10-day quarantine was as effective in lowering transmission as a 14-day quarantine; under certain noncompliance scenarios a 5-day quarantine may become nearly protective as 14-day quarantine.

CONCLUSION

Data to characterize compliance dynamics will help select optimal quarantine strategies that balance the trade-offs between social forces governing behavior and transmission dynamics.

An Epidemic Model for Multi-Intervention Outbreaks

Modeling is an important tool to utilize at the beginning of an infectious disease outbreak, as it allows estimation of parameters - such as the basic reproduction number, R 0 -that can be used to postulate how the outbreak may continue to spread. However, there exist many challenges that need to be accounted for, such as an unknown first case date, retrospective reporting of 'probable' cases, changing dynamics between case count and death count trends, and the implementation of multiple control efforts and their delayed or diminished effects. Using the near-daily data provided from the recent outbreak of Sudan ebolavirus in Uganda as a case study, we create a model and present a framework aimed at overcoming these aforementioned challenges. The impact of each challenge is examined by comparing model estimates and fits throughout our framework. Indeed, we found that allowing for multiple fatality rates over the course of an outbreak generally resulted in better fitting models. On the other hand, not knowing the start date of an outbreak appeared to have large and non-uniform effects on parameter estimates, particularly at the beginning stages of an outbreak. While models that did not account for the decaying effect of interventions on transmission underestimated R 0 , all decay models run on the full dataset yielded precise R 0 estimates, demonstrating the robustness of R 0 as a measure of disease spread when examining data from the entire outbreak.

Role of immigration and emigration on the spread of COVID-19 in a multipatch environment: a case study of India

Human mobility has played a critical role in the spread of COVID-19. The understanding of mobility helps in getting information on the acceleration or control of the spread of disease. The COVID-19 virus has been spreading among several locations despite all the best efforts related to its isolation. To comprehend this, a multi-patch mathematical model of COVID-19 is proposed and analysed in this work, where-in limited medical resources, quarantining, and inhibitory behaviour of healthy individuals are incorporated into the model. Furthermore, as an example, the impact of mobility in a three-patch model is studied considering the three worst-hit states of India, i.e. Kerala, Maharashtra and Tamil Nadu, as three patches. Key parameters and the basic reproduction number are estimated from the available data. Through results and analyses, it is seen that Kerala has a higher effective contact rate and has the highest prevalence. Moreover, if Kerala is isolated from Maharashtra or Tamil Nadu, the number of active cases will increase in Kerala but reduce in the other two states. Our findings indicate that the number of active cases will decrease in the high prevalence state and increase in the lower prevalence states if the emigration rate is higher than the immigration rate in the high prevalence state. Overall, proper travel restrictions are to be implemented to reduce or control the spread of disease from the high-prevalence state to other states with lower prevalence rates.

Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

COVID-19 dynamics and immune response: Linking within-host and between-host dynamics

The global impact of COVID-19 has led to the development of numerous mathematical models to understand and control the pandemic. However, these models have not fully captured how the disease's dynamics are influenced by both within-host and between-host factors. To address this, a new mathematical model is proposed that links these dynamics and incorporates immune response. The model is compartmentalized with a fractional derivative in the sense of Caputo-Fabrizio, and its properties are studied to show a unique solution. Parameter estimation is carried out by fitting real-life data, and sensitivity analysis is conducted using various methods. The model is then numerically implemented to demonstrate how the dynamics within infected hosts drive human-to-human transmission, and various intervention strategies are compared based on the percentage of averted deaths. The simulations suggest that a combination of medication to boost the immune system, prevent infected cells from producing the virus, and adherence to COVID-19 protocols is necessary to control the spread of the virus since no single intervention strategy is sufficient.

Secondary fungal infections in SARS-CoV-2 patients: pathological whereabouts, cautionary measures, and steadfast treatments

The earliest documented COVID-19 case caused by the SARS-CoV-2 coronavirus occurred in Wuhan, China, in December 2019. Since then, several SARS-CoV-2 mutants have rapidly disseminated as exemplified by the community spread of the recent omicron variant. The disease already attained a pandemic status with ever-dwindling mortality even after two and half years of identification and considerable vaccination. Aspergillosis, candidiasis, cryptococcosis and mucormycosis are the prominent fungal infections experienced by the majority of SARS-CoV-2 high-risk patients. In its entirety, COVID-19's nexus with these fungal infections may worsen the intricacies in the already beleaguered high-risk patients, making this a topic of substantial clinical concern. Thus, thorough knowledge of the subject is necessary. This article focuses on the concomitant fungal infection(s) in COVID-19 patients, taking into account their underlying causes, the screening methods, manifested drug resistance, and long-term effects. The information and knowledge shared herein could be crucial for the management of critically ill, aged, and immunocompromised SARS-CoV-2 patients who have had secondary fungal infections (SFIs).

Trajectories of COVID-19: A longitudinal analysis of many nations and subnational regions

The COVID-19 pandemic is the first to be rapidly and sequentially measured by nation-wide PCR community testing for the presence of the viral RNA at a global scale. We take advantage of the novel "natural experiment" where diverse nations and major subnational regions implemented various policies including social distancing and vaccination at different times with different levels of stringency and adherence. Initially, case numbers expand exponentially with doubling times of ~1-2 weeks. In the nations where interventions were not implemented or perhaps lees effectual, case numbers increased exponentially but then stabilized around 102-to-103 new infections (per km2 built-up area per day). Dynamics under effective interventions were perturbed and infections decayed to low levels. They rebounded concomitantly with the lifting of social distancing policies or pharmaceutical efficacy decline, converging on a stable equilibrium setpoint. Here we deploy a mathematical model which captures this V-shape behavior, incorporating a direct measure of intervention efficacy. Importantly, it allows the derivation of a maximal estimate for the basic reproductive number Ro (mean 1.6-1.8). We were able to test this approach by comparing the approximated "herd immunity" to the vaccination coverage observed that corresponded to rapid declines in community infections during 2021. The estimates reported here agree with the observed phenomena. Moreover, the decay (0.4-0.5) and rebound rates (0.2-0.3) were similar throughout the pandemic and among all the nations and regions studied. Finally, a longitudinal analysis comparing multiple national and regional results provides insights on the underlying epidemiology of SARS-CoV-2 and intervention efficacy, as well as evidence for the existence of an endemic steady state of COVID-19.

Effects of fine particulate matter (PM2.5) and meteorological factors on the daily confirmed cases of COVID-19 in Bangkok during 2020-2021, Thailand

The ongoing global pandemic caused by the SARS-CoV-2 virus, known as COVID-19, has disrupted public health, businesses, and economies worldwide due to its widespread transmission. While previous research has suggested a possible link between environmental factors and increased COVID-19 cases, the evidence regarding this connection remains inconclusive. The purpose of this research is to determine whether or not there is a connection between the presence of fine particulate matter (PM2.5) and meteorological conditions and COVID-19 infection rates in Bangkok, Thailand. The study employs a statistical method called Generalized Additive Model (GAM) to find a positive and non-linear association between RH, AH, and R and the number of verified COVID-19 cases. The impacts of the seasons (especially summer) and rainfall on the trajectory of COVID-19 cases were also highlighted, with an adjusted R-square of 0.852 and a deviance explained of 85.60%, both of which were statistically significant (p < 0.05). The study results assist in preventing the future seasonal spread of COVID-19, and public health authorities may use these findings to make informed decisions and assess their policies.

The effect of non-pharmaceutical interventions on COVID-19 outcomes: A heterogeneous age-related generalisation of the SEIR model

Successive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic and the assessment of the public health measures adopted and named under the common umbrella of Non-Pharmaceutical Interventions (NPIs). So far, these generalisations have not been able to assess the ability of these measures to avoid infection by the SARS-CoV-2 and thus their contribution to contain the spread of the disease. This work proposes a new generalisation of SEIR model and includes a heterogeneous and age-related generation of infections that depends both on a probability that a contact generates the transmission of the disease and a contact rate. The results show (1) thanks to the universal wearing of facial coverings, the probability that a contact provokes the transmission of the disease was reduced by at least 50% and (2) the impact of the other NPI is so significant that otherwise Portugal would have gone into a non-sustainable situation of having 80% of its population infected in the first 300 days of the pandemic. This situation would have led to a number of deaths almost twenty times higher than the number that was actually recorded by December 26th, 2020. Moreover, the results suggest that even if the requirement of universal wearing of facial coverings was adopted sooner jointly with closing workplaces and resorting to teleworking would have postponed the peak of the incidence, altought the epidemic path would have result in a number of infections hardly managed by the National Health System. Complementary, results confirm that (3) the health authorities adopted a conservative approach on the criteria to consider an infected individual not infective any longer; and (4) the most effective NPIs and stringency levels either impacting on self-protection against infection or reducing the contacts that would eventually result in infection are, in decreasing order of importance, the use of Facial coverings, Workplace closing and Stay at home requirements.

Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico

Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.

Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.

Post-COVID-19 cholangiopathy: A systematic review

BACKGROUND

The recent and still ongoing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entailed various long-term complications, including post-infectious cholangiopathy.

AIM

To identify the available studies concerning post-coronavirus disease 2019 (COVID-19) cholangiopathy.

METHODS

An extensive bibliographical search was carried out in PubMed and in Cochrane Library to identify the articles (retrospective and prospective studies, cohort studies, case series and case reports) published between January 1, 2020 and August 22, 2022, using both MeSH terms and free-language keywords: cholangiopathy; COVID-19; post-COVID-19 cholangiopathy; SARS-CoV-2.

RESULTS

Thirteen studies fulfilled the inclusion criteria, which included 64 patients suffering from this condition. The patients were male in 82.8% of cases. Liver transplant was executed in 6 patients and scheduled in 7 patients, while 2 patients refused the surgical approach. Therefore in 23.4% of the cases, performing this procedure appeared to be necessary.

CONCLUSION

This review has revealed that generally the involvement of the liver in the course of SARS-CoV-2 infection is mild and transient, inducing cholestasis of cholangiocytes but can also be severe enough to cause organ failure in some cases.

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection

T cell responses precede antibody and may provide early control of infection. We analyzed the clonal basis of this rapid response following SARS-COV-2 infection. We applied T cell receptor (TCR) sequencing to define the trajectories of individual T cell clones immediately. In SARS-COV-2 PCR+ individuals, a wave of TCRs strongly but transiently expand, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains but not with circulating human coronaviruses. Many expanding CDR3s were present at high frequency in pre-pandemic repertoires. Early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the preinfection naive repertoire. High-frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns

BACKGROUND

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions.

METHODS

We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling of a short time-interval around Denmark's December 2020 lockdown. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data.

RESULTS

We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task.

CONCLUSIONS

Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Adjuvant Pomegranate Juice Intake Improves the Inflammatory Status of Hospitalized COVID-19 Patients: A Randomized and Placebo-Controlled Trial

BACKGROUND

This study aimed to evaluate the effect of pomegranate juice intake on the inflammatory status and complete blood count in hospitalized Covid-19 patients.

METHODS

This randomized, double-blinded placebo-controlled trial included 48 patients with two parallel arms. In addition to the standard care provided at the hospital, the patients consumed 500mL of whole pomegranate juice (PJ) daily or a placebo for 14 days. Inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR)) and complete blood count were determined at baseline and after the 14 days of intervention.

RESULTS

At the end of the intervention, a significant decreased was observed in primary outcomes [mean difference (95%CI)] including IL-6 [5.24(0.87 to 9.61)], CRP [23.19(11.93 to 34.44)] and ESR [10.52(1.54 to 19.50)] in the PJ group vs. before the intervention. In addition, significant changes were also observed in the some of the secondary outcomes, including neutrophils, lymphocytes, platelets, platelets-to-lymphocyte(PLR) and neutrophils-to-lymphocyte (NLR) ratios (p<0.05) in the PJ group compared to before the intervention. At the end of the intervention period, the mean change of IL-6 [-7.09(-12.21 to -1.96)], white blood cells [-3.09(-6.14 to -0.05)], neutrophils [-9.12(-18.08 to -0.15)], lymphocyte [7.05(0.17 to 13.92)], platelets [-94.54(-139.33 to -49.75)], PLR [-15.99(-29.31 to -2.67)], blood oxygen saturation [1.75(0.13 to 3.37)] and MCV [0.31(-0.25 to 0.88)] levels were significantly different between groups while no difference was observed between the two groups in other blood indices.

CONCLUSION

Our results suggest that pomegranate juice intake might slightly improve the inflammatory status and CBC outcomes of COVID-19 patients and it may be beneficial.

AVAILABILITY OF DATA AND MATERIALS

The final dataset of trial will be available upon request from the primary investigator via e-mail at panahande.b@gmail.com, after obtaining permission from Regional Ethics Committee.