Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9-80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0-34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

Cumulative incidence, prevalence, seroconversion, and associated factors for SARS-CoV-2 infection among healthcare workers of a University Hospital in Bogotá, Colombia

This study aimed to determine the cumulative incidence, prevalence, and seroconversion of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its associated factors among healthcare workers (HCWs) of a University Hospital in Bogotá, Colombia. An ambispective cohort was established from March 2020 to February 2021. From November 2020 to February 2021, SARS-CoV-2 antibodies were measured on two occasions 14-90 days apart to determine seroprevalence and seroconversion. We used multivariate log-binomial regression to evaluate factors associated with SARS-CoV-2 infection. Among 2,597 HCWs, the cumulative incidence of infection was 35.7%, and seroprevalence was 21.5%. A reduced risk of infection was observed among those aged 35-44 and ≥45 years (adjusted relative risks [aRRs], 0.84 and 0.83, respectively), physicians (aRR, 0.77), those wearing N95 respirators (aRR, 0.82) and working remotely (aRR, 0.74). Being overweight (aRR, 1.18) or obese (aRR, 1.24); being a nurse or nurse assistant (aRR, 1.20); working in the emergency room (aRR, 1.45), general wards (aRR, 1.45), intensive care unit (aRR, 1.34), or COVID-19 areas (aRR, 1.17); and close contact with COVID-19 cases (aRR, 1.47) increased the risk of infection. The incidence of SARS-CoV-2 infection found in this study reflects the dynamics of the first year of the pandemic in Bogotá. A high burden of infection calls for strengthening prevention and screening measures for HCWs, focusing especially on those at high risk.

Understanding the Role of Environmental Transmission on COVID-19 Herd Immunity and Invasion Potential

COVID-19 is caused by the SARS-CoV-2 virus, which is mainly transmitted directly between humans. However, it is observed that this disease can also be transmitted through an indirect route via environmental fomites. The development of appropriate and effective vaccines has allowed us to target and anticipate herd immunity. Understanding of the transmission dynamics and the persistence of the virus on environmental fomites and their resistive role on indirect transmission of the virus is an important scientific and public health challenge because it is essential to consider all possible transmission routes and route specific transmission strength to accurately quantify the herd immunity threshold. In this paper, we present a mathematical model that considers both direct and indirect transmission modes. Our analysis focuses on establishing the disease invasion threshold, investigating its sensitivity to both transmission routes and isolate route-specific transmission rate. Using the tau-leap algorithm, we perform a stochastic model simulation to address the invasion potential of both transmission routes. Our analysis shows that direct transmission has a higher invasion potential than that of the indirect transmission. As a proof of this concept, we fitted our model with early epidemic data from several countries to uniquely estimate the reproduction numbers associated with direct and indirect transmission upon confirming the identifiability of the parameters. As the indirect transmission possess lower invasion potential than direct transmission, proper estimation and necessary steps toward mitigating it would help reduce vaccination requirement.

Using Pandemic Behavior to Test the External Validity of Laboratory Measurements of Risk Aversion and Guilt

We test whether laboratory measures of individual preferences for risk and guilt relate to risk-connected behaviors in a pandemic, such as socializing, dining in at restaurants, and hand washing. We utilize a survey administrated to a nationally representative subject pool in the United States in April, 2020 - the month following the declaration of a national state of emergency in response to the global outbreak of COVID-19. We find that higher levels of risk aversion are associated with risk-reducing behaviors during the COVID-19 pandemic. Meanwhile, we do not find strong evidence that guilt relates to the same behavior.

Clinical presentation of COVID-19 at the time of testing and factors associated with pre-symptomatic cases in Cameroon

Objectives

To describe the clinical features at time of testing and explore factors associated with SARS-CoV-2 infection and pre-symptomatic cases in Cameroon.

Methods

Data was collected on people in Cameroon who participated in COVID-19 testing by real-time reverse transcriptase-polymerase chain reaction between 1 March and 5 October 2020. After descriptive analysis, multivariate logistic regression was used to identify factors associated with SARS-CoV-2 infection and pre-symptomatic cases.

Results

Of 85 206 test participants, 14 863 (17.4%) were infected with SARS-CoV-2. The median age for cases was 38.4 years (interquartile range 29.6-49.4); 6.1% were aged <19 years, and 6.3% were ≥65 years. Of these cases, 46.5% had at least one symptom/sign with a median time from illness onset to testing of 6 days (interquartile range 3-9). Cough (64.2%), headache (46.5%), fatigue/malaise (46.0%), shortness of breath (30.6%) and myalgia/arthralgia (25.6%) were the most commonly observed symptoms/signs. Pre-symptomatic SARS-CoV-2 infection was associated with age <50 years, being male and absence of comorbidities.

Conclusion

This study provides a comprehensive summary of the early clinical profile of SARS-CoV-2 infection during the first wave of COVID-19 in Cameroon, which was dominated by pre-symptomatic illness. These findings would be helpful for SARS-CoV-2 surveillance and control at a regional level.

SARS-CoV-2 in children and their accompanying caregivers: Implications for testing strategies in resource limited hospitals

Background

Identification of SARS-CoV-2 infected individuals is imperative to prevent hospital transmission, but symptom-based screening may fail to identify asymptomatic/mildly symptomatic infectious children and their caregivers.

Methods

A COVID-19 period prevalence study was conducted between 13 and 26 August 2020 at Tygerberg Hospital, testing all children and their accompanying asymptomatic caregivers after initial symptom screening. One nasopharyngeal swab was submitted for SARS-CoV-2 using real-time reverse-transcription polymerase chain reaction (rRT-PCR). An additional Respiratory Viral 16-multiplex rRT-PCR test was simultaneously done in children presenting with symptoms compatible with possible SARS-CoV-2 infection.

Results

SARS-Co-V 2 RT-PCR tests from 196 children and 116 caregivers were included in the analysis. The SARS-CoV-2 period prevalence in children was 5.6% (11/196) versus 15.5% (18/116) in asymptomatic caregivers (p<0.01). Presenting symptoms did not correlate with SARS-CoV-2 test positivity; children without typical symptoms of SARS-CoV-2 were more likely to be positive than those with typical symptoms (10.2% [10/99] vs 1% [1/97]; p<0.01). Children with typical symptoms (97/196; 49.5%) mainly presented with acute respiratory (68/97; 70.1%), fever (17/97; 17.5%), or gastro-intestinal complaints (12/97; 12.4%); Human Rhinovirus (23/81; 28.4%) and Respiratory Syncytial Virus (18/81; 22.2%) were frequently identified in this group. Children-caregiver pairs' SARS-CoV-2 tests were discordant in 83.3%; 15/18 infected caregivers' children tested negative. Symptom-based COVID-19 screening alone would have missed 90% of the positive children and 100% of asymptomatic but positive caregivers.

Conclusion

Given the poor correlation between SARS-CoV-2 symptoms and RT-PCR test positivity, universal testing of children and their accompanying caregivers should be considered for emergency and inpatient paediatric admissions during high COVID-19 community transmission periods. Universal PPE and optimising ventilation is likely the most effective way to control transmission of respiratory viral infections, including SARS-CoV-2, where universal testing is not feasible. In these settings, repeated point prevalence studies may be useful to inform local testing and cohorting strategies.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.

Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling

BACKGROUND

The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks.

OBJECTIVE

This paper describes a novel model for COVID-19 infection risks and policy evaluations.

METHODS

The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies.

RESULTS

The application of the model is demonstrated for a typical office environment and for a real-world case.

CONCLUSION

The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.

Non-pharmaceutical interventions and their relevance in the COVID-19 vaccine rollout in Saudi Arabia and Arab Gulf countries

In the early stages of the pandemic, Saudi Arabia and other countries in the Arab Gulf region relied on non-pharmaceutical therapies to limit the effect of the pandemic, much like other nations across the world. In comparison to other nations in the area or globally, these interventions were successful at lowering the healthcare burden. This was accomplished via the deterioration of the economy, education, and a variety of other societal activities. By the end of 2020, the promise of effective vaccinations against SARS-CoV-2 have been realized, and vaccination programs have begun in developed countries, followed by the rest of the world. Despite this, there is still a long way to go in the fight against the disease. In order to explore disease transmission, vaccine rollout and prioritisation, as well as behavioural dynamics, we relied on an age-structured compartmental model. We examine how individual and social behaviour changes in response to the initiation of vaccination campaigns and the relaxation of non-pharmacological treatments. Overall, vaccination remains the most effective method of containing the disease and resuming normal life. Additionally, we evaluate several vaccination prioritisation schemes based on age group, behavioural responses, vaccine effectiveness, and vaccination rollout speed. We applied our model to four Arab Gulf nations (Saudi Arabia, Bahrain, the United Arab Emirates, and Oman), which were chosen for their low mortality rate compared to other countries in the region or worldwide, as well as their demographic and economic settings. We fitted the model using actual pandemic data in these countries. Our results suggest that vaccinations focused on the elderly and rapid vaccine distribution are critical for reducing disease resurgence. Our result also reinforces the cautious note that early relaxation of safety measures may compromise the vaccine's short-term advantages.

BNT162b2 mRNA Vaccine Effectiveness Given Confirmed Exposure: Analysis of Household Members of Coronavirus Disease 2019 Patients

BACKGROUND

Although BNT162b2 vaccine-efficacy analyses have been published, the effectiveness of the vaccine in preventing coronavirus disease 2019 given confirmed exposure has not been previously demonstrated, even though it has policy implications, such as the need for self-quarantine when exposure has occurred.

METHODS

In a retrospective cohort study, we used data collected between 20 December 2020 and 17 March 2021 from the second largest healthcare provider in Israel to analyze the probability of an additional household infection occurring within 10 days after an index infection. In model 1, vaccine effectiveness was described for Fully Vaccinated individuals (7 or more days from second dose) vs either Unvaccinated individuals or those Recently Vaccinated Once (0-7 days from the first dose, presumably still unprotected). Secondary analyses included correction for differing testing rates. In model 2, we conducted a separate analysis of households comprised of only adults with the same vaccination status.

RESULTS

A total of 173 569 households were included, of which 6351 had an index infection (mean [standard deviation] age, 58.9 [13.5] years); 50% were women. Adjusted vaccine effectiveness of Fully Vaccinated compared with Unvaccinated participants was 80.3% (95% confidence interval [CI], 73.5-85.4) and 82.0% (95% CI, 75.6-86.8) compared with those Recently Vaccinated Once.

CONCLUSIONS

The BNT162b2 vaccine is effective in high-risk real-life exposure scenarios, but the protection afforded in these settings is lower than that previously described. Individuals with a confirmed significant exposure to severe acute respiratory syndrome are still at risk of being infected even if fully vaccinated.

Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world

BACKGROUND

The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic.

METHODS

In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region's SARS-COV-2 transmission dynamic.

RESULTS

We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI's, over and above the ones identified in i) and ii).

CONCLUSION

In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI's) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV.

High Seroprevalence of SARS-CoV-2 IgG and RNA among Asymptomatic Blood Donors in Makkah Region, Saudi Arabia

The gold-standard approach for diagnosing and confirming Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) infection is reverse transcription-polymerase chain reaction (RT-PCR). This method, however, is inefficient in detecting previous or dormant viral infections. The presence of antigen-specific antibodies is the fingerprint and cardinal sign for diagnosis and determination of exposure to infectious agents including Corona virus disease-2019 (COVID-19). This cross-sectional study examined the presence of SARS-CoV-2 spike-specific immunoglobulin G (IgG) among asymptomatic blood donors in Makkah region. A total of 4368 asymptomatic blood donors were enrolled. They were screened for spike-specific IgG using ELISA and COVID-19 RNA by real-time PCR. COVID-19 IgG was detected among 2248 subjects (51.5%) while COVID-19-RNA was detected among 473 (10.8%) subjects. The IgG frequency was significantly higher among males and non-Saudi residents (p < 0.001 each) with no significant variation in IgG positivity among blood donors with different blood groups. In addition, COVID-19 RNA frequency was significantly higher among donors below 40-years old (p = 0.047, χ2 = 3.95), and non-Saudi residents (p = 0.001, χ2 = 304.5). The COVID-19 IgG levels were significantly higher among the RNA-positive donors (p = 001), and non-Saudi residents (p = 0.041), with no variations with age or blood group (p > 0.05). This study reveals a very high prevalence of COVID-19 IgG and RNA among asymptomatic blood donors in Makkah, Saudi Arabia indicating a high exposure rate of the general population to COVID-19; particularly foreign residents. It sheds light on the spread on COVID-19 among apparently healthy individuals at the beginning of the pandemic and could help in designing various control measures to minimize viral spread.

Isolation, Behavioral Changes, and Low Seroprevalence of SARS-CoV-2 Antibodies in Patients With Systemic Lupus Erythematosus or Rheumatoid Arthritis

OBJECTIVE

Patients with chronic rheumatic diseases (CRDs), such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), require special attention during the COVID-19 pandemic as they are considered at risk of severe infections. Our objective was to assess the seroprevalence of SARS-CoV-2 in patients with SLE and RA and to assess patient behavior, disease-related symptoms, and mental health.

METHODS

More than 900 participants were included: 405 patients with RA or SLE (CRD patients) and 513 blood donors. All participants had blood SARS-CoV-2 total antibodies measured (sensitivity 96.7%, specificity 99.5%) and answered a questionnaire concerning behavior, anxiety, and symptoms of depression (Patient Health Questionnaire 9). The CRD patients were further asked about physical activity, adherence to medication, and disease-related symptoms.

RESULTS

CRD patients had a significantly lower seroprevalence of SARS-CoV-2 antibodies (n = 1 of 365, 0.3%) compared to blood donors (n = 10 of 513, 1.9%; P = 0.03). Almost 60% of patients were unable to exercise as usual, and increased pain and disease activity was experienced by 34% and 24% of patients, respectively. Almost 10% of patients reduced or discontinued their immunosuppressive treatments at their own initiative. Symptoms of moderate depression were present in 19% of patients compared to 6.8% of blood donors (P < 0.001).

CONCLUSION

Low seroprevalence in patients with CRDs indicates successful mitigation of exposure to SARS-CoV-2. However, this mitigation appears to occur at the expense of physical activity, experience of increased pain, disease activity, and symptoms of depression. There is a need for care providers to be aware of these negative side effects and for further studies to investigate the possible long-term consequences.

Fluorescence spectrophotometry for COVID-19 determination in clinical swab samples

Considering the limitations of the assays currently available for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its emerging variants, a simple and rapid method using fluorescence spectrophotometry was developed to detect coronavirus disease 2019 (COVID-19). Forty clinical swab samples were collected from the nasopharyngeal and oropharyngeal cavities of COVID-19-positive and -negative. Each sample was divided into two parts. The first part of the samples was analyzed using reverse transcription-polymerase chain reaction (RT-qPCR) as the control method to identify COVID-19-positive and -negative samples. The second part of the samples was analyzed using fluorescence spectrophotometry. Fluorescence measurements were performed at excitation and emission wavelengths ranging from 200 to 800 nm. Twenty COVID-19-positive samples and twenty COVID-19-negative samples were detected based on RT-qPCR results. The fluorescence spectrum data indicated that the COVID-19-positive and -negative samples had significantly different characteristics. All positive samples could be distinguished from negative samples by fluorescence spectrophotometry. Principal component analysis showed that COVID-19-positive samples were clustered separately from COVID-19-negative samples. The specificity and accuracy of this experiment reached 100%. Limit of detection (LOD) obtained 42.20 copies/ml (Ct value of 33.65 cycles) for E gene and 63.60 copies/ml (Ct value of 31.36 cycles) for ORF1ab gene. This identification process only required 4 min. Thus, this technique offers an efficient and accurate method to identify an individual with active SARS-CoV-2 infection and can be easily adapted for the early investigation of COVID-19, in general.

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

Review of concerned SARS-CoV-2 variants like Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529), as well as novel methods for reducing and inactivating SARS-CoV-2 mutants in wastewater treatment facilities

The coronavirus known as COVID-19, which causes pandemics, is causing a global epidemic at a critical stage today. Furthermore, novel mutations in the SARS-CoV-2 spike protein have been discovered in an entirely new strain, impacting the clinical and epidemiological features of COVID-19. Variants of these viruses can increase the transmission in wastewater, lead to reinfection, and reduce immunity provided by monoclonal antibodies and vaccinations. According to the research, a large quantity of viral RNA was discovered in wastewater, suggesting that wastewater can be a crucial source of epidemiological data and health hazards. The purpose of this paper is to introduce a few basic concepts regarding wastewater surveillance as a starting point for comprehending COVID-19's epidemiological aspects. Next, the observation of Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in wastewater is discussed in detail. Secondly, the essential information for the initial, primary, and final treating sewage in SARS-CoV-2 is introduced. Following that, a thorough examination is provided to highlight the newly developed methods for eradicating SARS-CoV-2 using a combination of solar water disinfection (SODIS) and ultraviolet radiation A (UVA (315-400 nm)), ultraviolet radiation B (UVB (280-315 nm)), and ultraviolet radiation C (UVC (100-280 nm)) processes. SARS-CoV-2 eradication requires high temperatures (above 56°C) and UVC. However, SODIS technologies are based on UVA and operate at cooler temperatures (less than 45°C). Hence, it is not appropriate for sewage treatment (or water consumption) to be conducted using SODIS methods in the current pandemic. Finally, SARS-CoV-2 may be discovered in sewage utilizing the wastewater-based epidemiology (WBE) monitoring method.

Effect of Travel Restrictions of Wuhan City Against COVID-19: A Modified SEIR Model Analysis

OBJECTIVE

Since December 2019, a new coronavirus viral was initially detected in Wuhan, China. Population migration increases the risk of epidemic transmission. Here, the objective of study is to estimate the output risk quantitatively and evaluate the effectiveness of travel restrictions of Wuhan city.

METHODS

We proposed a modified susceptible-exposed-infectious-recovered (SEIR) dynamics model to predict the number of coronavirus disease 2019 (COVID-19) symptomatic and asymptomatic infections in Wuhan. And, subsequently, we estimated the export risk of COVID-19 epidemic from Wuhan to other provinces in China. Finally, we estimated the effectiveness of travel restrictions of Wuhan city quantitatively by the export risk on the assumption that the measure was postponed.

RESULTS

The export risks of COVID-19 varied from Wuhan to other provinces of China. The peak of export risk was January 21-23, 2020. With the travel restrictions of Wuhan delayed by 3, 5, and 7 d, the export risk indexes will increase by 38.50%, 55.89%, and 65.63%, respectively.

CONCLUSIONS

The results indicate that the travel restrictions of Wuhan reduced the export risk and delayed the overall epidemic progression of the COVID-19 epidemic in China. The travel restrictions of Wuhan city may provide a reference for the control of the COVID-19 epidemic all over the world.

Hemophagocytic lymphohistiocytosis diagnosed by bone marrow trephine biopsy in living post-COVID-19 patients: case report and mini-review

Hemophagocytic lymphohistiocytosis (HLH) constitutes a life-threatening inflammatory syndrome. Postmortem histological findings of bone marrow (BM) from COVID-19 patients showed histiocytosis and hemophagocytosis and supported the hypothesis that secondary HLH (sHLH) may be triggered by SARS-CoV-2 infection. However, there are a limited number of sHLH cases in which trephine has been performed in living post-COVID-19 patients. Here we present a recent case and a mini-review of sHLH diagnosed by trephine biopsy in living patients after COVID-19. An 81-year-old man with a past medical history of hypertension, diabetes, ischemic stroke, was referred to the hospital to evaluate leukocytosis, pyuria, and elevation of inflammatory markers four weeks after recovering from COVID-19. Computed tomography of the abdomen did not reveal focal signs of infection or hepatosplenomegaly. The patient received intravenous meropenem and two packed red blood cell units. Leukocytes and C-reactive protein were gradually decreased. A BM biopsy was performed and the patient was discharged on cefixime. BM smear revealed severe anemia, lymphopenia, and dysplastic morphologic findings of erythroblasts, neutrophils, and megakaryocytes. Trephine biopsy revealed hypercellular marrow dyserythropoiesis, plasmacytosis, lymphocytosis, histiocytosis, hemophagocytosis, and the absence of granulomas or carcinoma. Immunohistochemistry documented a mixed population of T lymphocytes (CD3+) and B lymphocytes (CD20+). Strong positivity for CD68 confirmed histiocytosis. CD138 κ, λ staining proved polyclonal plasmacytosis. Perl's staining showed excess hemosiderin deposits. Based on our findings, we document sHLH in trephine BM biopsy of a living post-COVID-19 patient and persistent leukocytosis, underscoring the diagnostic value of trephine biopsy in preventing life-threatening conditions such as COVID-19.

Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Toxicology and Poisoning Research Centre, Tehran University of Medical science, Tehran, Iran, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania & Department of Hematology, Center of Hematology and Bone Marrow Transplantation, Fundeni Clinical Institute, 022328 Bucharest, Romania, Applied Virology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. Effect of Hydroxychloroquine, Favipiravir, Lopinavir/Ritonavir, Remdesivir, Umifenovir, and Interferon beta-1a in Covid-19 patients: A systematic review of Randomized Clinical Trials

"Background: Coronavirus disease 2019 (COVID-19) was recognized by the World Health Organization (WHO) as a global pandemic on March 11, 2020. Since then, researchers worldwide have focused their attention on identifying effective treatments and developing vaccines to combat this disease. Aim: To report the effectiveness of the drugs employed in the COVID-19 treatment protocols based on data from clinical trial studies conducted from the beginning of the pandemic until December 10, 2020. Methods: Following the PRISMA guidelines, we conducted an advanced search in several electronic databases. A total of 13553 studies was screened by two people simultaneously and separately based on the article title, abstract and full-text. The quality of the studies was evaluated using the Cochrane criteria. Results: Of the 13553 studies identified, 50 clinical trials were included in this systematic review. Of these, three studies explored the use of remdesivir, nine studies the use of hydroxychloroquine, five studies the use of lopinavir/ritonavir, six studies the use of favipiravir, one study the use of tocilizumab, two studies the use of interferon beta-1a and two studies the use of umifenovir. "

The Role of Lung Ultrasound in SARS-CoV-19 Pneumonia Management

Purpose: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). Patients and methods: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48−72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. Results: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20−36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F−TC score correlation coefficient of −0.762, p < 0.001, and a P/F−LUS score correlation coefficient of −0.689, p < 0.001. Conclusions: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.

Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan

The COVID-19 pandemic has shocked nations due to its exponential death rates in various countries. According to the United Nations (UN), in Russia, there were 895, in Mexico 303, in Indonesia 77, in Ukraine 317, and in Romania 252, and in Pakistan, 54 new deaths were recorded on the 5th of October 2021 in the period of months. Hence, it is essential to study the future waves of this virus so that some preventive measures can be adopted. In statistics, under uncertainty, there is a possibility to use probability models that leads to defining future pattern of deaths caused by COVID-19. Based on probability models, many research studies have been conducted to model the future trend of a particular disease and explore the effect of possible treatments (as in the case of coronavirus, the effect of Pfizer, Sinopharm, CanSino, Sinovac, and Sputnik) towards a specific disease. In this paper, varieties of probability models have been applied to model the COVID-19 death rate more effectively than the other models. Among others, exponentiated flexible exponential Weibull (EFEW) distribution is pointed out as the best fitted model. Various statistical properties have been presented in addition to real-life applications by using the total deaths of the COVID-19 outbreak (in millions) in Pakistan and Afghanistan. It has been verified that EFEW leads to a better decision rather than other existing lifetime models, including FEW, W, EW, E, AIFW, and GAPW distributions.

Diagnostic Tools for Rapid Screening and Detection of SARS-CoV-2 Infection

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely impacted human health and the health management system globally. The ongoing pandemic has required the development of more effective diagnostic strategies for restricting deadly disease. For appropriate disease management, accurate and rapid screening and isolation of the affected population is an efficient means of containment and the decimation of the disease. Therefore, considerable efforts are being directed toward the development of rapid and robust diagnostic techniques for respiratory infections, including SARS-CoV-2. In this article, we have summarized the origin, transmission, and various diagnostic techniques utilized for the detection of the SARS-CoV-2 virus. These higher-end techniques can also detect the virus copy number in asymptomatic samples. Furthermore, emerging rapid, cost-effective, and point-of-care diagnostic devices capable of large-scale population screening for COVID-19 are discussed. Finally, some breakthrough developments based on spectroscopic diagnosis that could revolutionize the field of rapid diagnosis are discussed.

Estimation of timing of infection from longitudinal SARS-CoV-2 viral load data: mathematical modelling study

BACKGROUND

Multiple waves of the COVID-19 epidemic have hit most countries by the end of 2021. Most of those waves are caused by emergence and importation of new variants. To prevent importation of new variants, combination of border control and contact tracing is essential. However, the timing of infection inferred by interview is influenced by recall bias and hinders the contact tracing process.

METHODS

We propose a novel approach to infer the timing of infection, by employing a within-host model to capture viral load dynamics after the onset of symptoms. We applied this approach to ascertain secondary transmission which can trigger outbreaks. As a demonstration, the 12 initial reported cases in Singapore, which were considered as imported because of their recent travel history to Wuhan, were analyzed to assess whether they are truly imported.

RESULTS

Our approach suggested that 6 cases were infected prior to the arrival in Singapore, whereas other 6 cases might have been secondary local infection. Three among the 6 potential secondary transmission cases revealed that they had contact history to previously confirmed cases.

CONCLUSIONS

Contact trace combined with our approach using viral load data could be the key to mitigate the risk of importation of new variants by identifying cases as early as possible and inferring the timing of infection with high accuracy.

Dysregulation of Innate and Adaptive Immune Responses in Asymptomatic SARS-CoV-2 Infection with Delayed Viral Clearance

Asymptomatic infection with SARS-CoV-2 is a major concern in the control of the COVID-19 pandemic. Many questions concerning asymptomatic infection remain to be answered, for example, what are the differences in infectivity and the immune response between asymptomatic and symptomatic infections? In this study, based on a cohort established by the Wuchang District Health Bureau of Wuhan in the early stage of the COVID-19 pandemic in Wuhan in 2019, we conducted a comprehensive analysis of the clinical, virological, immunological, and epidemiological data of asymptomatic infections. The major findings of this study included: 1) the asymptomatic cohort enrolled this study exhibited low-grade but recurrent activity of viral replication; 2) despite a lack of overt clinical symptoms, asymptomatic infections exhibited ongoing innate and adaptive immune responses; 3) however, the immune response from asymptomatic infections was not activated adequately, which may lead to delayed viral clearance. Given the fragile equilibrium between viral infection and host immunity, and the delayed viral clearance in asymptomatic individuals, close viral monitoring should be scheduled, and therapeutic intervention may be needed.

An Epidemic Model with Time Delay Determined by the Disease Duration

Immuno-epidemiological models with distributed recovery and death rates can describe the epidemic progression more precisely than conventional compartmental models. However, the required immunological data to estimate the distributed recovery and death rates are not easily available. An epidemic model with time delay is derived from the previously developed model with distributed recovery and death rates, which does not require precise immunological data. The resulting generic model describes epidemic progression using two parameters, disease transmission rate and disease duration. The disease duration is incorporated as a delay parameter. Various epidemic characteristics of the delay model, namely the basic reproduction number, the maximal number of infected, and the final size of the epidemic are derived. The estimation of disease duration is studied with the help of real data for COVID-19. The delay model gives a good approximation of the COVID-19 data and of the more detailed model with distributed parameters.

Bayesian inference for asymptomatic COVID-19 infection rates

To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.

A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics

The dynamic nature of the COVID-19 pandemic has demanded a public health response that is constantly evolving due to the novelty of the virus. Many jurisdictions in the USA, Canada, and across the world have adopted social distancing and recommended the use of face masks. Considering these measures, it is prudent to understand the contributions of subpopulations—such as “silent spreaders”—to disease transmission dynamics in order to inform public health strategies in a jurisdiction-dependent manner. Additionally, we and others have shown that demographic and environmental stochasticity in transmission rates can play an important role in shaping disease dynamics. Here, we create a model for the COVID-19 pandemic by including two classes of individuals: silent spreaders, who either never experience a symptomatic phase or remain undetected throughout their disease course; and symptomatic spreaders, who experience symptoms and are detected. We fit the model to real-time COVID-19 confirmed cases and deaths to derive the transmission rates, death rates, and other relevant parameters for multiple phases of outbreaks in British Columbia (BC), Canada. We determine the extent to which SilS contributed to BC’s early wave of disease transmission as well as the impact of public health interventions on reducing transmission from both SilS and SymS. To do this, we validate our model against an existing COVID-19 parameterized framework and then fit our model to clinical data to estimate key parameter values for different stages of BC’s disease dynamics. We then use these parameters to construct a hybrid stochastic model that leverages the strengths of both a time-nonhomogeneous discrete process and a stochastic differential equation model. By combining these previously established approaches, we explore the impact of demographic and environmental variability on disease dynamics by simulating various scenarios in which a COVID-19 outbreak is initiated. Our results demonstrate that variability in disease transmission rate impacts the probability and severity of COVID-19 outbreaks differently in high- versus low-transmission scenarios.

Results of the Cologne Corona surveillance (CoCoS) study - a prospective population-based cohort study: incidence data and potential underestimation of new SARS-CoV-2 adult infections by health authorities

BACKGROUND

Current incidence estimates of SARS-CoV-2 in Germany rely to a large extent on case notifications. However, the large number of mild or asymptomatic infections is likely to result in underestimation. Population-based studies can provide valid estimates of the SARS-CoV-2 incidence and thus support health authorities to monitor the epidemiological situation and to initiate, maintain, strengthen or relax effective countermeasures.

METHODS

This study was conducted in Cologne, Germany. Six-thousand randomly drawn Cologne residents, 18 years of age or older, were contacted by mail in March 2021. Study envelopes contained a kit for self-administered saliva sample and access details to a questionnaire on sociodemographic characteristics, previous positive SARS-CoV-2 RT-qPCR and completed COVID-19 vaccinations. Participants were again invited for a second round in June 2021, while those who declined participation were replaced by additional randomly drawn Cologne residents in order to reach a total of 6000 potential participants again. The saliva samples were sent to the laboratory by mail and tested for SARS-CoV-2 using RT-qPCR. The incidence estimates were adjusted for sensitivity and specificity of the test procedure and compared with the official numbers of new SARS-CoV-2 cases in the adult Cologne population.

RESULTS

The first surveillance round in March 2021 (response rate: 34.08%, N = 2045) showed a SARS-CoV-2 seven-day incidence of 85 cases per 100,000 adult Cologne residents (95% CI: 9 to 319). In the same period, the officially registered cases were 125 per 100,000. The second surveillance round in June 2021 (response rate: 36.53%, N = 2192) showed a seven-day incidence of 27 per 100,000 adult Cologne residents (95% CI: 1 to 142), while the official figures for newly registered SARS-CoV-2 cases in the same period were 15 per 100,000.

CONCLUSIONS

The incidence estimates do not indicate relevant underestimation of new SARS-CoV-2 infections based on case notification. Regular use of the surveillance method developed here may nevertheless complement the efforts of the health authorities to assess the epidemiological situation.

TRIAL REGISTRATION

DRKS.de, German Clinical Trials Register (DRKS), Identifier: DRKS00024046 , Registered on 25 February 2021.

Temporal trends and differences of SARS-CoV-2-specific antibody responses in symptomatic and asymptomatic subjects: a longitudinal study from Umbria in Italy

OBJECTIVES

Dynamics of antibody responses following SARS-CoV-2 infection are controversial in terms of immunity and persistence. We aimed to assess longitudinally the trend of antibody serological titres, their correlation with clinical severity as well as clinical reinfection during a follow-up.

DESIGN

Longitudinal cohort, 12 months follow-up study.

SETTING

USL Umbria 2.

PARTICIPANTS

Consecutive subjects aged 15-75 who were discharged with the diagnosis of Sars-Cov-2 from the hospitals of the AUSL Umbria 2, or resulted positive to a PCR test for SARS-CoV-2 infection with or without symptoms were recruited. SARS-CoV-2 serological testing for antibodies targeting the Nucleocapside and Spike proteins were determined.

RESULTS

Of 184 eligible subjects, 149 were available for evaluation: 17 were classified as oligo/asymptomatic, 107 as symptomatic, 25 as hospital admitted. Participants differed in terms of signs and symptoms as well as treatment. Overall there was a significant difference in terms of antibody titres between groups (anti-S: p<0.00; anti-N: p=0.019). Median anti-S titres in the symptomatic and hospital admitted participants were significantly higher compared with the oligo/asymptomatic participants. During follow-up, the median titre of anti-S antibodies did not show significant variations (p=0.500) and the difference within groups remained constant overtime. Subjects that showed an anti-S titre above the threshold of 12 U/mL were 88.7% at first visit and 88.2% at last follow-up. Anti-N values were higher in the hospital admitted participants compared with the other two groups. Anti-N titre reduced constantly overtime (p<0.001) and across the three groups of participants. The percentage of the subjects with serological titre above threshold (<1.4 U/mL) decreased from 74.5%% to 29.2% (p<0.001). None of the participants developed clinically evident reinfection.

CONCLUSION

Anti-N and anti-S correlate well with clinical severity. While anti-N declines overtime, anti-S antibodies persist for at least 1 year.

Epidemiological and Clinical Characteristics of COVID-19 Patients in Northern Ethiopia: A Retrospective Cohort Study

Purpose

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is an emerging global public health problem. The disease is believed to affect older people and is accompanied by clinical features such as fever, shortness of breath, and coughing. Currently, there is a lack of information regarding the characteristics of COVID-19 patients in Ethiopia. Thus, this paper aims to evaluate the epidemiological and clinical features of COVID-19 patients in Tigray, Northern Ethiopia.

Patients and Methods

A total of 6,637 symptomatic and asymptomatic COVID-19 patients collected from six isolation and treatment centers in Tigray between May 7 and October 28, 2020 were retrospectively analyzed. Chi-square test or Fisher’s exact test was used to compare the epidemiological and clinical characteristics of COVID-19 patients as appropriate. A p-value <0.05 was considered statistically significant.

Results

The mean age of the patients was 31.3±12.8. SARS-CoV-2 infects men more than women with a ratio of 1.85:1. About 16% of the patients were symptomatic, of which 13.3% (95% CI=11.3–15.4%) were admitted to intensive care units and 6.1% (95% CI=4.5–7.6%) were non-survivors. The mortality rate was increased up to 40.3% (95% CI=32.1–48.4%) among patients with severe illness. A higher proportion of deaths were observed in men (73.2%) and 55.4% were in the age group of ≥50 years. About 4.3% (282 of 6,637) had one or more coexisting comorbidities; the most common being cardiovascular diseases (30.1%) and diabetes mellitus (23.8%). The comorbidity rate in the non-survivor group was significantly higher than in the survivor group (p-value <0.001).

Conclusion

The proportion of symptomatic patients was low. Non-survival was linked with old age and the existence of comorbidities. The findings of this study can help in the design of appropriate management strategies for COVID-19 patients, such as giving due emphasis to COVID-19 patients who are old and with comorbidities.

Tumor biomarkers CEA, CA19.9, CA15.3 and AFP levels in the serum of patients with COVID-19

Early diagnosis is very important to reduce morbidity and mortality in COVID-19 infected patients. The aim of this study was to detect of tumor antigens CEA, CA19.9, CA15.3, and AFP and to compare their levels in the serum of 69 COVID-19 patients and 69 healthy individuals who did not have COVID-19. The levels of CEA, CA19.9, CA15.3, and AFP in the serum were measured using ELISA. The levels of the tumor biomarkers in the serum of COVID-19 patients were found to be 7.74 ± 4.65 ng/ml for CEA, 29.33 ± 16.35 U/ml for CA19.9, 23.24 ± 13.48 U/ml for CA15.3 and 7.46 ± 5.57 ng/ml for AFP, while in the serum of healthy control patients 9.73 ± 43.58 ng/ml for CEA, 20.66 ± 11.1 for CA19.9, 19.64 ± 10.99 U/ml for CA15.3, and 3.83 ± 9.20 ng/ml for AFP, indicating no differences in the levels of the studied tumor biomarkers in the two experimental groups. It is concluded that tumor biomarkers CEA, CA19.9, CA15.3, and AFP cannot be used as effective screening tools for patients with COVID-19.

Modeling scenarios for mitigating outbreaks in congregate settings

The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.

The value of contact tracing and isolation in mitigation of COVID-19 epidemic: findings from outbreak investigation of COVID-19 onboard Nile Cruise Ship, Egypt, March 2020

WHO informed Egypt health authorities of individuals of different nationalities who proved positive for COVID-19 after returning from Egypt. Patients were among touristic group who visited Cairo and spent 1-week onboard Nile cruise ship. Investigation performed to confirm outbreak, detect source, and implement containment measures. Active case finding and contact tracing performed among contacts of the index cases and their contacts. Contacts defined as anyone within 6 feet from confirmed or suspected COVID-19 case for ≥15 min. Overall, 331 contacts, including 201 ship boarders and 130 hotel guests, were listed and interviewed using semistructured questionnaire and tested for COVID-19 by PCR. Among them, 136 (41.1%) were close contacts of index cases and 195 (58.9%) contacted secondary cases. Their mean age was 34.6±11.5 years, 251 (75.8%) were males and 126 (38.1%) non-Egyptians. Of them, 67 (20.2%) tested positive for COVID-19, including 57 (28.4%) ship boarders and 10 (7.7%) hotel guests. Per cent positive was significantly higher in: contacts of index cases, Egyptians, ship boarders and in males than corresponding categories (35.3% vs 9.7%, 22.9% vs 15.9%, 27.4% vs 7.7%, 24.7% vs 6.3%), respectively. Of all positive cases, 40 (59.7%) were asymptomatic where ship boarders, non-Egyptians, >50 years old and females were more likely to be asymptomatic than corresponding categories (85.0 vs 48.9%, 72.7 vs 54.5%, 100.0 vs 56.5%), respectively. COVID-19 patients among group of tourists triggered an outbreak onboard Nile ship and hotel in Egypt. Outbreak quickly contained through lab testing, case isolation, strict infection control measures and contact tracing which proved effective in reducing COVID-19 transmission early in pandemic.

Influence of indoor airflow on particle spread of a single breath and cough in enclosures: Does opening a window really 'help'?

The spread of respiratory diseases via aerosol particles in indoor settings is of significant concern. The SARS-CoV-2 virus has been found to spread widely in confined enclosures like hotels, hospitals, cruise ships, prisons, and churches. Particles exhaled from a person indoors can remain suspended long enough for increasing the opportunity for particles to spread spatially. Careful consideration of the ventilation system is essential to minimise the spread of particles containing infectious pathogens. Previous studies have shown that indoor airflow induced by opened windows would minimise the spread of particles. However, how outdoor airflow through an open window influences the indoor airflow has not been considered. The aim of this study is to provide a clear understanding of the indoor particle spread across multiple rooms, in a situation similar to what is found in quarantine hotels and cruise ships, using a combination of HVAC (Heating, Ventilation and Air-Conditioning) ventilation and an opening window. Using a previously validated mathematical model, we used 3D CFD (computational fluid dynamics) simulations to investigate to what extent different indoor airflow scenarios contribute to the transport of a single injection of particles ( 1 . 3 μ m ) in a basic 3D multi-room indoor environment. Although this study is limited to short times, we demonstrate that in certain conditions approximately 80% of the particles move from one room to the corridor and over 60% move to the nearby room within 5 to 15 s. Our results provide additional information to help identifying relevant recommendations to limit particles from spreading in enclosures.

Optimize data-driven multi-agent simulation for COVID-19 transmission

Background

Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading.

Results

This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 90 \%$$\end{document}∼90% cache references and achieves \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 4.3$$\end{document}×4.3 speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 80$$\end{document}×80 speedup consequently.

Conclusions

In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future.

Exploring the scope and utility of digital proximity tracing in the effective containment of COVID-19 infection: A narrative review

The ongoing coronavirus disease-2019 (COVID-19) pandemic can be acknowledged as one of the most significant public health emergencies the world has encountered in the last few decades. The purpose of the current review is to understand the significance of contact tracing and explore the pros and cons of digital contact tracing in ensuring better containment of the COVID-19 outbreaks. A widespread search of published articles pertaining to the topic was done in the PubMed search engine and a total of 46 articles matching the objectives of the present review were identified. However, four articles were discarded because of the non-availability of the free full text, and thus 42 research papers were finally included. Digital contact tracing bridges the gap wherein we aim to expedite the process of contact tracing to identify the potential contacts of the confirmed cases. These applications are designed in such a way that they send a notification on the smartphone of a person, once the user is exposed to one or more confirmed cases of COVID-19. To conclude, in the battle against the COVID-19 infection, the international welfare agencies and national policy makers have been looking forward to the employment of digital technologies to support the ongoing public health measures for contact tracing. The approach of digital contact/proximity tracing should be considered as a supplement to conventional manual tracing. The need of the hour is to take specific measures to improve the inherent design of these apps, their implementation and demonstration of their effectiveness, which in turn will play a part in enhancing their acceptance and usability among the general population.

Knowledge, Attitude, and Perception of Cancer Patients towards COVID-19 in Pakistan: A Cross-Sectional Study

Background: Cancer patients, being immunocompromised, are at higher risk of coronavirus disease (COVID-19). The current study determines cancer patients’ knowledge, attitude, perception, and impact of the COVID-19 pandemic. Method: A cross-sectional online survey was conducted in Pakistan from 1 April 2020 to 1 May 2020. The study respondents were cancer patients with ages equal to or greater than 18 years. Following a request for participation, the URL for the survey was distributed on numerous channels. Other social media platforms, including WeChat, WhatsApp, Facebook, Twitter, Instagram, Messenger, and LinkedIn, were used to increase cancer patient interaction. The questionnaire comprised five different sections such as: (1) sociodemographic information, (2) knowledge, (3) attitude, (4) perception, and (5) impact of COVID-19 on cancer patients. Descriptive medical statistics such as frequency, percentage, mean, and standard deviation were used to illustrate the demographic characteristics of the study participants. To compare mean knowledge scores with selected demographic variables, independent sample t-tests and one-way analysis of variance (ANOVA) were used, which are also practical methods in epidemiological, public health and medical research. The cut-off point for statistical significance was set at a p-value of 0.05. Results: More than 300 cancer patients were invited, of which 208 agreed to take part. The response rate was 69.33% (208/300). Gender, marital status, and employment status had a significant association with knowledge scores. Of the total recruited participants, 96% (n = 200) (p < 0.01) knew about COVID-19, and 90% were aware of general symptoms of COVID-19 disease, such as route of transmission and preventive measurements. In total, 94.5% (n = 197) (p < 0.01) were willing to accept isolation if they were infected with COVID-19, and 98% (n = 204) (p < 0.01) had reduced their use of public transportation. More than 90% (n = 188) (p < 0.01) of cancer patients were found to be practicing preventative measures such as using a face mask, keeping social distance, and avoiding handshaking and hugging. Around 94.4% (n = 196) (p < 0.01) of cancer patients had been impacted by, stopped or had changed cancer treatment during this pandemic, resulting in COVID-related anxiety and depression. Conclusion: The included cancer patients exhibited a good level of COVID-19 knowledge, awareness, positive attitude, and perception. Large-scale studies and efforts are needed to raise COVID-19 awareness among less educated and high-risk populations. The present survey indicates that mass-level effective health education initiatives are required for developing countries to improve and reduce the gap between KAP and COVID-19.

The Value of Spiral Chest Computed Tomography Scan in the Diagnosis of Asymptomatic Coronavirus Carriers among Paranasal Sinus and Pharynx Surgery Candidates

Introduction  The nasopharynx and oropharynx are the main colonization sites of coronavirus. Therefore, patients with paranasal sinuses and pharyngeal problems (ear, nose, and throat [ENT] patients) predispose coronavirus infection.

Ear, nose, and throat patients with concomitant asymptomatic coronavirus infection may develop severe pneumonia following surgical procedures. As a result, presurgical screening for coronavirus infection is a substantial concern.

Objective  We evaluated the usefulness of a spiral chest computed tomography (CT) scan in the diagnosis of asymptomatic coronavirus infection in the presurgical assessment of ENT patients

Methods  In this study, candidates of paranasal sinus or pharyngeal surgery were evaluated for coronavirus infection. Patients with neither history of coronavirus disease 2019 (COVID-19) nor compatible symptoms and signs were screened for asymptomatic coronavirus infection. These patients composed two groups: the first group underwent a reverse transcription polymerase chain reaction (RT-PCR) test of nasopharyngeal sample and spiral chest CT scan, but for the second one, only the latter was performed.

Results  In the first group, which consisted of 106 patients, 11 (10.4%) cases had positive RT-PCR test results, and 17 (16%) patients showed positive findings in favor of coronavirus infection in the spiral chest CT scan. In the second group, which consisted of 173 patients, 34 (19.7%) cases had positive chest CT scan results.

Conclusion  The chest CT scan has a valuable role in the early diagnosis of asymptomatic coronavirus carriers in patients highly predisposed to infection, especially in low resource areas, where the RT-PCR test is unavailable.

Evaluation of RT-qPCR Primer-Probe Sets to Inform Public Health Interventions Based on COVID-19 Sewage Tests

Sewage surveillance is increasingly employed as a supplementary tool for COVID-19 control. Experiences learnt from large-scale trials could guide better interpretation of the sewage data for public health interventions. Here, we compared the performance of seven commonly used primer-probe sets in RT-qPCR and evaluated the usefulness in the sewage surveillance program in Hong Kong. All selected primer-probe sets reliably detected SARS-CoV-2 in pure water at 7 copies per μL. Sewage matrix did not influence RT-qPCR determination of SARS-CoV-2 concentrated from a small-volume sewage (30 mL) but introduced inhibitory impacts on a large-volume sewage (920 mL) with a ΔCt of 0.2-10.8. Diagnostic performance evaluation in finding COVID-19 cases showed that N1 was the best single primer-probe set, while the ORF1ab set is not recommended. Sewage surveillance using the N1 set for over 3200 samples effectively caught the outbreak trend and, importantly, had a 56% sensitivity and a 96% specificity in uncovering the signal sources from new cases and/or convalescent patients in the community. Our study paves the way for selecting detection primer-probe sets in wider applications in responding to the COVID-19 pandemic.

Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines

Around the world, disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis, modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper, we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform, the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national, regional, and provincial levels guided government actions; and conversely, how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic, simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories (r=94%-99%, p<.001). Model simulations were subsequently utilized to predict the outcomes of proposed interventions, including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized, flexible, and responsive mathematical modeling, as applied to pandemic intelligence and for data-driven policy-making in general.

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Psychological wellbeing and associated factors among nurses exposed to COVID 19: Findings from a cross sectional study

Background

The coronavirus pandemic known as COVID-2019 poses a global concern. The psychological well-being of front-line nurses and other healthcare providers is a major concern.

Aims

This study evaluated the psychological well-being and the associated factors among nurses in Dr. Soliman Fakeeh Hospital during the COVID-19 pandemic.

Methods

The cross-sectional survey was conducted during the peak period of COVID 19 among 367 nurses recruited from Dr. Soliman Fakeeh Hospital. The online survey was used with the snowballing sampling technique to collect the participants' socio-demographic data and assess their psychological status using DASS-21; in addition, the major traumatic event was assessed by Impact of Event Scale-Revised, and self-efficacy was evaluated.

Results

67.7% of the 367 respondents experienced moderate or severe psychological problem; 46.1% reported moderate to severe depressive symptoms; 48.0% moderate to severe anxiety symptoms; and 48.4% moderate to severe stress levels. The psychological status and influence of a major traumatic event and self-efficacy were statistically significant different among nurses according to age, gender, working experience, marital status, working in a COVID 19 unit or with suspected cases.

Conclusions

Nurses had a significantly increased risk of developing psychological problems, negatively impacted by the major traumatic event and poor self-efficacy.

Seroprevalence of SARS-CoV-2 Antibodies in Africa: A Systematic Review and Meta-Analysis

A reliable estimate of SARS-CoV-2-specific antibodies is increasingly important to track the spread of infection and define the true burden of the ongoing COVID-19 pandemic. A systematic review and a meta-analysis were conducted with the objective of estimating the seroprevalence of SARS-CoV-2 infection in Africa. A systematic search of the PubMed, Scopus, Web of Science and Google Scholar electronic databases was conducted. Thirty-five eligible studies were included. Using meta-analysis of proportions, the overall seroprevalence of anti-SARS-CoV-2 antibodies was calculated as 16% (95% CI 13.1-18.9%). Based on antibody isotypes, 14.6% (95% CI 12.2-17.1%) and 11.5% (95% CI 8.7-14.2%) were seropositive for SARS-CoV-2 IgG and IgM, respectively, while 6.6% (95% CI 4.9-8.3%) were tested positive for both IgM and IgG. Healthcare workers (16.3%) had higher seroprevalence than the general population (11.7%), blood donors (7.5%) and pregnant women (5.7%). The finding of this systematic review and meta-analysis (SRMA) may not accurately reflect the true seroprevalence status of SARS-CoV-2 infection in Africa, hence, further seroprevalence studies across Africa are required to assess and monitor the growing COVID-19 burden.

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Background

The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts.

Methods

We applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R₀). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R₀, β and γ.

Results

We observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R₀ values ranged from 1.32 (95% CI, 1.17–1.49) in Rwanda to 8.52 (95% CI: 3.73–14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases.

Conclusion

The current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R₀ is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07510-3.

Survival analysis based on body mass index in patients with Covid-19 admitted to the intensive care unit of Amir Al-Momenin Hospital in Arak - 2021

INTRODUCTION

The potential risk of obesity on the severity of COVID-19 has been proposed. The main purpose of this study was to investigate the effect of BMI on the survival rate of COVID-19 patients admitted to the ICU.

METHODS & MATERIALS

Patients with COVID-19 admitted to ICU were included. Gender, height, weight, BMI, age, underlying disease status, prescribed drugs and nutritional supplements, and clinical and laboratory parameters at the beginning of admission were recorded. Death or discharge from the ICU and the days elapsed to these events were also reviewed and recorded. Data analysis was performed using the Cox regression model.

RESULTS

assessing 193 patients showed that BMI was not related to the survival rate even after adjusting for other potential confounding variables. It was shown that arterial oxygen saturation and taking Famotidine were the significant factors determining the time to event in these patients.

CONCLUSION

The BMI at the time of ICU admission has no effect on survival rate and time to event in COVID-19 infected patients admitted to ICU.

Estimating the effect of non-pharmaceutical interventions on US SARS-CoV-2 infections in the first year of the pandemic

SARS-CoV-2 emerged in late 2019 as a zoonotic infection of humans, and proceeded to cause a worldwide pandemic of historic magnitude. Here, we use a simple epidemiological model and consider the full range of initial estimates from published studies for infection and recovery rates, seasonality, changes in mobility, the effectiveness of masks and the fraction of people wearing them. Monte Carlo simulations are used to simulate the progression of possible pandemics and we show a match for the real progression of the pandemic during 2020 with an R 2 of 0.91. The results show that the combination of masks and changes in mobility avoided approximately 248.3 million (σ = 31.2 million) infections in the US before vaccinations became available.