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

A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.

Secondary haemophagocytic lymphohistiocytosis in COVID-19: correlation of the autopsy findings of bone marrow haemophagocytosis with HScore

BACKGROUND

Secondary haemophagocytic lymphohistiocytosis (sHLH) is characterised by a hyper activation of immune system that leads to multiorgan failure. It is suggested that excessive immune response in patients with COVID-19 could mimic this syndrome. Some COVID-19 autopsy studies have revealed the presence of haemophagocytosis images in bone marrow, raising the possibility, along with HScore parameters, of sHLH.

AIM

Our objective is to ascertain the existence of sHLH in some patients with severe COVID-19.

METHODS

We report the autopsy histological findings of 16 patients with COVID-19, focusing on the presence of haemophagocytosis in bone marrow, obtained from rib squeeze and integrating these findings with HScore parameters. CD68 immunohistochemical stains were used to highlight histiocytes and haemophagocytic cells. Clinical evolution and laboratory parameters of patients were collected from electronic clinical records.

RESULTS

Eleven patients (68.7%) displayed moderate histiocytic hyperplasia with haemophagocytosis (HHH) in bone marrow, three patients (18.7%) displayed severe HHH and the remainder were mild. All HScore parameters were collected in 10 patients (62.5%). Among the patients in which all parameters were evaluable, eight patients (80%) had an HScore >169. sHLH was not clinically suspected in any case.

CONCLUSIONS

Our results support the recommendation of some authors to use the HScore in patients with severe COVID-19 in order to identify those who could benefit from immunosuppressive therapies. The presence of haemophagocytosis in bone marrow tissue, despite not being a specific finding, has proved to be a very useful tool in our study to identify these patients.

Spatialized epidemiological forecasting applied to Covid-19 pandemic at departmental scale in France

In this paper, we present a spatialized extension of a SIR model that accounts for undetected infections and recoveries as well as the load on hospital services. The spatialized compartmental model we introduce is governed by a set of partial differential equations (PDEs) defined on a spatial domain with complex boundary. We propose to solve the set of PDEs defining our model by using a meshless numerical method based on a finite difference scheme in which the spatial operators are approximated by using radial basis functions. Such an approach is reputed as flexible for solving problems on complex domains. Then we calibrate our model on the French department of Isère during the first period of lockdown, using daily reports of hospital occupancy in France. Our methodology allows to simulate the spread of Covid-19 pandemic at a departmental level, and for each compartment. However, the simulation cost prevents from online short-term forecast. Therefore, we propose to rely on reduced order modeling to compute short-term forecasts of infection number. The strategy consists in learning a time-dependent reduced order model with few compartments from a collection of evaluations of our spatialized detailed model, varying initial conditions and parameter values. A set of reduced bases is learnt in an offline phase while the projection on each reduced basis and the selection of the best projection is performed online, allowing short-term forecast of the global number of infected individuals in the department. The original approach proposed in this paper is generic and could be adapted to model and simulate other dynamics described by a model with spatially distributed parameters of the type diffusion-reaction on complex domains. Also, the time-dependent model reduction techniques we introduced could be leveraged to compute control strategies related to such dynamics.

Investigation of a COVID-19 outbreak on the Charles de Gaulle aircraft carrier, March to April 2020: a retrospective cohort study

Background

SARS-CoV-2 emergence was a threat for armed forces. A COVID-19 outbreak occurred on the French aircraft carrier Charles de Gaulle from mid-March to mid-April 2020.

Aim

To understand how the virus was introduced, circulated then stopped circulation, risk factors for infection and severity, and effectiveness of preventive measures.

Methods

We considered the entire crew as a cohort and collected personal, clinical, biological, and epidemiological data. We performed viral genome sequencing and searched for SARS-CoV-2 in the environment.

Results

The attack rate was 65% (1,148/1,767); 1,568 (89%) were included. The male:female ratio was 6.9, and median age was 29 years (IQR: 24–36). We examined four clinical profiles: asymptomatic (13.0%), non-specific symptomatic (8.1%), specific symptomatic (76.3%), and severe (i.e. requiring oxygen therapy, 2.6%). Active smoking was not associated with severe COVID-19; age and obesity were risk factors. The instantaneous reproduction rate (R_t) and viral sequencing suggested several introductions of the virus with 4 of 5 introduced strains from within France, with an acceleration of R_t when lifting preventive measures. Physical distancing prevented infection (adjusted OR: 0.55; 95% CI: 0.40–0.76). Transmission may have stopped when the proportion of infected personnel was large enough to prevent circulation (65%; 95% CI: 62–68).

Conclusion

Non-specific clinical pictures of COVID-19 delayed detection of the outbreak. The lack of an isolation ward made it difficult to manage transmission; the outbreak spread until a protective threshold was reached. Physical distancing was effective when applied. Early surveillance with adapted prevention measures should prevent such an outbreak.

The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of “total-evidence” analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth–death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth–death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.]

On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data

Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely, a new modified flexible Weibull extension (NMFWE) distribution for modeling the mortality rate of COVID-19 patients. The introduced model is obtained by modifying the flexible Weibull extension model. The maximum likelihood estimators of the NMFWE model are obtained. The evaluation of the estimators of the NMFWE model is assessed in a simulation study. The flexibility and applicability of the NMFWE model are established by taking two datasets representing the mortality rates of COVID-19-infected persons in Mexico and Canada. For predictive modeling, we consider two pure statistical models and two machine learning (ML) algorithms. The pure statistical models include the autoregressive moving average (ARMA) and non-parametric autoregressive moving average (NP-ARMA), and the ML algorithms include neural network autoregression (NNAR) and support vector regression (SVR). To evaluate their forecasting performance, three standard measures of accuracy, namely, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are calculated. The findings demonstrate that ML algorithms are very effective at predicting the mortality rate data.

Microfluidics-Based Point-of-Care Testing (POCT) Devices in Dealing with Waves of COVID-19 Pandemic: The Emerging Solution

Recent advances in microfluidics-based point-of-care testing (POCT) technology such as paper, array, and beads have shown promising results for diagnosing various infectious diseases. The fast and timely detection of viral infection has proven to be a critical step for deciding the therapeutic outcome in the current COVID-19 pandemic, which in turn not only enhances the patient survival rate but also reduces the disease-associated comorbidities. In the present scenario, rapid, noninvasive detection of the virus using low cost and high throughput microfluidics-based POCT devices embraces the advantages over existing diagnostic technologies, for which a centralized lab facility, expensive instruments, sample pretreatment, and skilled personnel are required. Microfluidic-based multiplexed POCT devices can be a boon for clinical diagnosis in developing countries that lacks a centralized health care system and resources. The microfluidic devices can be used for disease diagnosis and exploited for the development and testing of drug efficacy for disease treatment in model systems. The havoc created by the second wave of COVID-19 led several countries' governments to the back front. The lack of diagnostic kits, medical devices, and human resources created a huge demand for a technology that can be remotely operated with single touch and data that can be analyzed on a phone. Recent advancements in information technology and the use of smartphones led to a paradigm shift in the development of diagnostic devices, which can be explored to deal with the current pandemic situation. This review sheds light on various approaches for the development of cost-effective microfluidics POCT devices. The successfully used microfluidic devices for COVID-19 detection under clinical settings along with their pros and cons have been discussed here. Further, the integration of microfluidic devices with smartphones and wireless network systems using the Internet-of-things will enable readers for manufacturing advanced POCT devices for remote disease management in low resource settings.

Inclusive health: modeling COVID-19 in correctional facilities and communities

Background

Mass incarceration, commonly associated with overcrowding and inadequate health resources for incarcerated people, creates a fertile environment for the spread of the coronavirus disease 2019 (COVID-19) in U.S. correctional facilities. The exact role that correctional facilities play in enhancing COVID-19 spread and enabling community re-emergence of COVID-19 is unknown.

Methods

We constructed a novel stochastic model of COVID-19 transmission to estimate the impact of correctional facilities, specifically jails and state prisons, for enhancing disease transmission and enabling disease re-emergence in local communities. Using our model, we evaluated scenarios of testing and quarantining infected incarcerated people at 0.0, 0.5, and 1.0 times the rate that occurs for infected people in the community for population sizes of 5, 10, and 20 thousand people.

Results

Our results illustrate testing and quarantining an incarcerated population of 800 would reduce the probability of a major outbreak in the local community. In addition, testing and quarantining an incarcerated population would prevent between 10 to 2640 incidences of COVID-19 per year, and annually save up to 2010 disability-adjusted life years, depending on community size.

Conclusions

Managing COVID-19 in correctional facilities is essential to mitigate risks to community health, and thereby stresses the importance of improving the health standards of incarcerated people.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13313-7.

Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images

Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of obtaining such information. However, a reliable model for accurately mapping the water clarity of global lakes (reservoirs) is still lacking due to the high optical complexity of lake waters. In this study, by using gated recurrent units (GRU) layers instead of full-connected layers from Artificial Neural Networks (ANNs) to capture the efficient sequence information of in-situ datasets, we propose a novel and transferrable hybrid deep-learning-based recurrent model (DGRN) to map the water clarity of global lakes with Landsat 8 Operational Land Imager (OLI) images. We trained and further validated the model using 1260 pairs of in-situ measured water clarity and surface reflectance of Landsat 8 OLI images with Google Earth Engine. The model was subsequently utilized to construct the global pattern of temporal and spatial changes in water clarity (lake area>10 km²) from 2014 to 2020. The results show that the model can estimate water clarity with good performance (R² = 0.84, MAE = 0.55, RMSE = 0.83, MAPE = 45.13%). The multi-year average of water clarity for global lakes (16,475 lakes) ranged from 0.0004 to 9.51 m, with an average value of 1.88 ± 1.24 m. Compared to the lake area, elevation, discharge, residence time, and the ratio of area to depth, water depth was the most important factor that determined the global spatial distribution pattern of water clarity. Water clarity of 15,840 global lakes between 2014 and 2020 remained stable (P ≥ 0.05); while there was a significant increase in 243 lakes (P < 0.05) and a decrease in 392 lakes (P < 0.05). However, water clarity in 2020 (COVID-19 period) showed a significant increase in most global lakes, especially in China and Canada, suggesting that the worldwide lockdown strategy due to COVID-19 might have improved water quality, espically water clarity, dueto the apparent reduction of anthropogenic activities.

Who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.

Improved methods for the detection and quantification of SARS-CoV-2 RNA in wastewater

Since the start of the COVID-19 pandemic, different methods have been used to detect the presence of genetic material of SARS-CoV-2 in wastewater. The use of wastewater for SARS-CoV-2 RNA detection and quantification showed different problems, associated to the complexity of the matrix and the lack of standard methods used to analyze the presence of an enveloped virus, such as coronavirus. Different strategies for the concentration process were selected to carry out the detection and quantification of SARS-CoV-2 RNA in wastewater: (a) aluminum hydroxide adsorption-precipitation, (b) pre-treatment with glycine buffer and precipitation with polyethylene-glycol (PEG) and (c) ultrafiltration (Centricon). Our results showed that the reduction of organic matter, using the pre-treatment with glycine buffer before the concentration with Centricon or aluminum hydroxide adsorption-precipitation, improved the recovery percentage of the control virus, Mengovirus (MgV) (8.37% ± 5.88 n = 43; 6.97% ± 6.51 n = 20, respectively), and the detection of SARS-CoV-2 in comparison with the same methodology without a pre-treatment. For the concentration with Centricon, the use of 100 mL of wastewater, instead of 200 mL, increased the MgV recovery, and allowed a positive detection of SARS-CoV-2 with N1 and N2 targets. The quantity of SARS-CoV-2 RNA detected in wastewater did not show a direct correlation with the number of confirmed cases, but the study of its upwards or downwards trend over time enabled the detection of an increase of epidemiological data produced in September 2020, January 2021 and April 2021.

The comparison of clinical characteristics between imported and native cases of COVID-19 in China

The study aimed to determine the trends in the manifestations and severity over the epidemic course of imported COVID-19 cases, with comparison to native cases. The clinical characteristics of imported and native Chinese COVID-19 cases included in the study were assessed and compared. The association was analyzed using Mann-Whitney U test for categorical variables, Kruskal-Wallis H test for continuous variables, and Spearman's correlation test for disease severity. A total of 247 imported patients were enrolled, with an average age of 29 years, and 41.3% were female. The imported patients were younger than the native patients (29 vs 47 years) and included a lower proportion of fever (44.1%), chills (5.3%), fatigue (10.1%), leukopenia (14.6%), lymphopenia (39.3%), elevated C-reactive protein (CRP) (7.3%), elevated D-dimer (16.3%), and pneumonia (65.6%). Among patients with moderate severity, imported cases had a lower proportion of fever (44.2%), dyspnea (8.3%), and increased CRP (7.7%) than native cases. COVID-19 infection was less severe in imported cases than that in native cases.

Covid-19 and its implications for the provision of gynecological services globally

Covid-19 took the world by surprise and has completely changed the way humans live and work. There is hardly an aspect of life that has not been affected. Whether social, economic, physical, psychological, cultural or religious, this pandemic has revolutionized every aspect of our lives and some of these changes are here to stay for the unforeseeable time. Although much has been written about the negative effects of Covid-19 on our social lives, some technological advances on COVID-19 have profoundly affected various aspects of our lives. These are mostly to do with how we communicate, deliver health services, innovate and investigate new preventative measures and treatments, travel and indeed influenced the carbon footprint of the planet. Although most of gynaecology is elective and was therefore not considered a priority in the early phases of COVI-19, there are considerable consequences of delaying treatment for some of these elective conditions. Of particular importance are infertility, pre-malignant conditions, chronic pelvic pain, sexual disorders and those affecting the psychological and social aspects of women and families. The pandemic forced a rethink of how healthcare is delivered with wide adoption of remote/virtual consultation and triaging of clinical presentations. The rapid development of immunization and drugs against the virus was met with doubts by a large proportion of the population with reluctance to accept these. Consequently, there remains unvaccinated portions of both low and high-risk populations, some of whom may be denied access to gynaecological care. On the other hand, some pregnant women who are frightened of the impact of vaccination on pregnancy put their own lives at risk. While significant progress has been made to combat the pandemic, lessons about healthcare delivery (face-to-face versus virtual), education of the end users and introduction of new technologies into the development of drugs and vaccines must be evaluated and improved moving forward not only during the ongoing epidemic but with future outbreaks.

Household COVID-19 secondary attack rate and associated determinants in Pakistan; A retrospective cohort study

BACKGROUND

COVID-19 household transmissibility remains unclear in Pakistan. To understand the dynamics of Severe Acute Respiratory Syndrome Coronavirus disease epidemiology, this study estimated Secondary Attack Rate (SAR) among household and close contacts of index cases in Pakistan using a statistical transmission model.

METHODOLOGY

A retrospective cohort study was conducted using an inclusive contact tracing dataset from the provinces of Punjab and Khyber-Pakhtunkhwa to estimate SAR. We considered the probability of an infected person transmitting the infection to close contacts regardless of residential addresses. This means that close contacts were identified irrespective of their relationship with the index case. We assessed demographic determinants of COVID-19 infectivity and transmissibility. For this purpose based on evolving evidence, and as CDC recommends fully vaccinated people get tested 5-7 days after close contact with a person with suspected or confirmed COVID-19. Therefore we followed the same procedure in the close contacts for secondary infection.

FINDINGS

During the study period from 15th May 2020 to 15th Jan 2021, a total of 339 (33.9%) index cases were studied from 1000 cases initially notified. Among close contact groups (n = 739), households were identified with an assumed mean incubation period of 8.2+4.3 days and a maximum incubation period of 15 days. SAR estimated here is among the household contacts. 117 secondary cases from 739 household contacts, with SAR 11.1% (95% CI 9.0-13.6). All together (240) SAR achieved was 32.48% (95% CI; 29.12-37.87) for symptomatic and confirmed cases. The potential risk factors for SAR identified here included; old age group (>45 years of age), male (gender), household members >5, and residency in urban areas and for index cases high age group. Overall local reproductive number (R) based on the observed household contact frequencies for index/primary cases was 0.9 (95% CI 0.47-1.21) in Khyber Pakhtunkhwa and 1.3 (95% CI 0.73-1.56) in Punjab.

CONCLUSIONS

SAR estimated here was high especially in the second phase of the COVID-19 pandemic in Pakistan. The results highlight the need to adopt rigorous preventive measures to cut the chain of viral transmission and prevent another wave of COVID-19.

Sampling Inspection Plan to Test Daily COVID-19 Cases Using Gamma Distribution under Indeterminacy Based on Multiple Dependent Scheme

The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.

PD-LAMP smartphone detection of SARS-CoV-2 on chip

In 2019 the COVID-19 pandemic, caused by SARS-CoV-2, demonstrated the urgent need for rapid, reliable, and portable diagnostics. The COVID-19 pandemic was declared in January 2020 and surges of the outbreak continue to reoccur. It is clear that early identification of infected individuals, especially asymptomatic carriers, plays a huge role in preventing the spread of the disease. The current gold standard diagnostic for SARS-CoV-2 is quantitative reverse transcription polymerase chain reaction (qRT-PCR) test based on the detection of the viral RNA. While RT-PCR is reliable and sensitive, it requires expensive centralized equipment and is time consuming (∼2 h or more); limiting its applicability in low resource areas. The FDA issued Emergency Use Authorizations (EUAs) for several COVID-19 diagnostics with an emphasis on point-of care (PoC) testing. Numerous RT-PCR and serological tests were approved for use at the point of care. Abbott's ID NOW, and Cue Health's COVID-19 test are of particular interest, which use isothermal amplification methods for rapid detection in under 20 min. We look to expand on the range of current PoC testing platforms with a new rapid and portable isothermal nucleic acid detection device. We pair reverse transcription loop mediated isothermal amplification (RT-LAMP) with a particle imaging technique, particle diffusometry (PD), to successfully detect SARS-CoV-2 in only 35 min on a portable chip with integrated heating. A smartphone device is used to image the samples containing fluorescent beads post-RT-LAMP and correlates decreased diffusivity to positive samples. We detect as little as 30 virus particles per μL from a RT-LAMP reaction in a microfluidic chip using a portable heating unit. Further, we can perform RT-LAMP from a diluted unprocessed saliva sample without RNA extraction. Additionally, we lyophilize SARS-CoV-2-specific RT-LAMP reactions that target both the N gene and the ORF1ab gene in the microfluidic chip, eliminating the need for cold storage. Our assay meets specific target product profiles outlined by the World Health Organization: it is specific to SARS-CoV-2, does not require cold storage, is compatible with digital connectivity, and has a detection limit of less than 35 × 10⁴ viral particles per mL in saliva. PD-LAMP is rapid, simple, and attractive for screening and use at the point of care.

Asymptomatic Transmissibility Calls for Implementing a Zero-COVID Strategy to End the Current Global Crisis

The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented global challenges. A zero-COVID strategy is needed to end the crisis, but there is a lack of biological evidence. In the present study, we collected available data on SARS, MERS, and COVID-19 to perform a comprehensive comparative analysis and visualization. The study results revealed that the fatality rate of COVID-19 is low, whereas its death toll is high compared to SARS and MERS. Moreover, COVID-19 had a higher asymptomatic rate. In particular, COVID-19 exhibited unique asymptomatic transmissibility. Further, we developed a foolproof operating software in Python language to simulate COVID-19 spread in Wuhan, showing that the cumulative cases of existing asymptomatic spread would be over 100 times higher than that of only symptomatic spread. This confirmed the essential role of asymptomatic transmissibility in the uncontrolled global spread of COVID-19, which enables the necessity of implementing the zero-COVID policy. In conclusion, we revealed the triggering role of the asymptomatic transmissibility of COVID-19 in this unprecedented global crisis, which offers support to the zero-COVID strategy against the recurring COVID-19 spread.

Clinical Manifestations of COVID-19 Patients with Comorbid and Non-comorbid at Dr. Soetomo Hospital, Surabaya

Background : Covid-19 has been declared a global health emergency. Reports of thousands of cases with morbidity and mortality continue to increase every day. The clinical course of patients with comorbidities influences the prognosis and progression of the covid-19 disease. Hypertension is the most common cormorbidity of covid-19 patients with long hospitalizations. Objective : This study aimed to determine the clinical differences between covid-19 patients cormobid and non cormobid . Methods : The study was conducted retrospectively through samples of medical records of inpatients for the period June 1, 2021 – August 31, 2021. The samples were divided into comorbid and non-cormobid groups; each totaling 130 medical records. The sample of the comorbid group was selected by simple random; while the non-cormobid group with the matching process. Data were analyzed using t-test and Wilcoxon. Results : The most common kormobid is hypertension with clinical manifestations of cough, fever, headache, runny nose, painful swallowing, anosmia, shortness of breath, nausea, vomiting and diarrhea. The average length of stay for patients with comorbidities was 21 days and without comorbidities 14 days. The test results showed that there were clinical differences between patients with comorbid and non-cormobid patients with p value = 0.0000 (p>0.05) and there was a difference in length of stay with p-value = 0.001 (p>0.05). Conclusion : The clinical difference between covid-19 patients comorbid and non cormobid lies in the symptoms of headache with a longer duration of treatment, which is 22 days. More intensive treatment and care is needed for covid-19 patients with comorbid hypertension. Keywords : clinical manifestation; covid-19; comorbid; length of treatment.

Challenges and recommendations for COVID-19 public health messaging: a Canada-wide qualitative study using virtual focus groups

OBJECTIVES

To understand Canadian's attitudes and current behaviours towards COVID-19 public health measures (PHM), vaccination and current public health messaging, to provide recommendations for a public health intervention.

DESIGN

Ten focus groups were conducted with 2-7 participants/group in December 2020. Focus groups were transcribed verbatim and analysed using content and inductive thematic analysis. The capability opportunity motivation behaviour Model was used as our conceptual framework.

SETTING

Focus groups were conducted virtually across Canada.

PARTICIPANTS

Participants were recruited from a pool of individuals who previously completed a Canada-wide survey conducted by our research team.

MAIN OUTCOME MEASURE

Key barriers and facilitators towards COVID-19 PHM and vaccination, and recommendations for public health messaging.

RESULTS

Several themes were identified (1) participants' desire to protect family and friends was the main facilitator for adhering to PHM, while the main barrier was inconsistent PHM messaging and (2) participants were optimistic that the vaccine offers a return to normal, however, worries of vaccine efficacy and effectiveness were the main concerns. Participants felt that current public health messaging is inconsistent, lacks transparency and suggested that messaging should include scientific data presented by a trustworthy source.

CONCLUSIONS

We suggest six public health messaging recommendations to increase adherence to PHM and vaccination (1) use an unbiased scientist as a spokesperson, (2) openly address any unknowns, (3) more is better when sharing data, (4) use personalised stories to reinforce PHM and vaccinations, (5) humanise the message by calling out contradictions and (6) focus on the data and keep politics out.

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

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

Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic

Due to the increasing number of COVID-19-infected and vaccinated individuals, radiologists continue to see patients with COVID-19 pneumonitis and recall pneumonitis, which could result in additional workups and false-positive results. Moreover, cancer patients undergoing immunotherapy may show therapy-related pneumonitis during imaging management. This is otherwise known as immune checkpoint inhibitor-related pneumonitis. Following on from this background, radiologists should seek to know their patients' COVID-19 infection and vaccination history. Knowing the imaging features related to COVID-19 infection and vaccination is critical to avoiding misleading results and alarmism in patients and clinicians.

Impact of air temperature and containment measures on mitigating the intrahousehold transmission of SARS-CoV-2: a data-based modelling analysis

OBJECTIVES

Air temperature has been considered a modifiable and contributable variable in COVID-19 transmission. Implementation of non-pharmaceutical interventions (NPIs) has also made an impact on COVID-19 transmission, changing the transmission pattern to intrahousehold transmission under stringent containment measures. Therefore, it is necessary to re-estimate the influence of air temperature on COVID-19 transmission while excluding the influence of NPIs.

DESIGN, SETTING AND PARTICIPANTS

This study is a data-based comprehensive modelling analysis. A stochastic epidemiological model, the ScEIQR model (contactable susceptible-exposed-infected-quarantined-removed), was established to evaluate the influence of air temperature and containment measures on the intrahousehold spread of COVID-19. Epidemic data on COVID-19, including daily confirmed cases, number of close contacts, etc, were collected from the National Health Commission of China.

OUTCOME MEASURES

The model was fitted using the Metropolis-Hastings algorithm with a cost function based on the least squares method. The LOESS (locally weighted scatterplot smoothing) regression function was used to assess the relationship between air temperature and rate of COVID-19 transmission within the ScEIQR model.

RESULTS

The ScEIQR model indicated that the optimal temperature for spread of COVID-19 peaked at 10℃ (50℉), ranging from 5℃ to 14℃ (41℉-57.2℉). In the fitted model, the fitted intrahousehold transmission rate (β') of COVID-19 was 10.22 (IQR 8.47-12.35) across mainland China. The association between air temperature and β' of COVID-19 suggests that COVID-19 might be seasonal. Our model also validated the effectiveness of NPIs, demonstrating that diminishing contactable susceptibility (Sc) and avoiding delay in diagnosis and hospitalisation (η) were more effective than contact tracing (κ and ρ).

CONCLUSIONS

We constructed a novel epidemic model to estimate the effect of air temperature on COVID-19 transmission beyond implementation of NPIs, which can inform public health strategy and predict the transmission of COVID-19.

How the nature of behavior change affects the impact of asymptomatic coronavirus transmission

SARS-CoV-2 has caused severe respiratory illnesses and deaths since late 2019 and spreads globally. While asymptomatic cases play a crucial role in transmitting COVID-19, they do not contribute to the observed prevalence, which drives behavior change during the pandemic. This study aims to identify the effect of the proportion of asymptomatic infections on the magnitude of an epidemic under behavior change scenarios by developing a compartmental mathematical model. In this interest, we discuss three different behavior change cases separately: constant behavior change, instantaneous behavior change response to the disease’s perceived prevalence, and piecewise constant behavior change response to government policies. Our results imply that the proportion of asymptomatic infections which maximizes the spread of the epidemic depends on the nature of the dominant force driving behavior changes.

Implementation of novel and conventional outbreak control measures in managing COVID-19 outbreaks in a large UK prison

Background

Outbreak control measures during COVID-19 outbreaks in a large UK prison consisted of standard (e.g., self-isolation) and novel measures, including establishment of: (i) reverse cohorting units for accommodating new prison admissions; (ii) protective isolation unit for isolating symptomatic prisoners, and (iii) a shielding unit to protect medically vulnerable prisoners.

Methods

Single-centre prospective longitudinal study (outbreak control study), implementing novel and traditional outbreak control measures to prevent a SARS-COV-2 outbreak. The prison held 977 prisoners and employed 910 staff at that start of the outbreak.

Results

120 probable and 25 confirmed cases among prisoners and staff were recorded between March and June 2020 during the first outbreak. Over 50% of initial cases among prisoners were on the two wings associated with the index case.

During the second outbreak, 182 confirmed cases were recorded after probable reintroduction from a staff member. Widespread testing identified 145 asymptomatic prisoners, 16.9% of the total prisoner cases. The cohorting units prevented re-infection from new prison admissions and the shielding unit had no COVID-19 infections linked to either outbreak.

Conclusions

Identifying and isolating infected prisoners, cohorting new admissions and shielding vulnerable individuals helped prevent uncontrollable spread of SARS-COV-2. These novel and cost-effective approaches can be implemented in correctional facilities globally.

An olfactory self-test effectively screens for COVID-19

Background

Key to curtailing the COVID-19 pandemic are wide-scale screening strategies. An ideal screen is one that would not rely on transporting, distributing, and collecting physical specimens. Given the olfactory impairment associated with COVID-19, we developed a perceptual measure of olfaction that relies on smelling household odorants and rating them online.

Methods

Each participant was instructed to select 5 household items, and rate their perceived odor pleasantness and intensity using an online visual analogue scale. We used this data to assign an olfactory perceptual fingerprint, a value that reflects the perceived difference between odorants. We tested the performance of this real-time tool in a total of 13,484 participants (462 COVID-19 positive) from 134 countries who provided 178,820 perceptual ratings of 60 different household odorants.

Results

We observe that olfactory ratings are indicative of COVID-19 status in a country, significantly correlating with national infection rates over time. More importantly, we observe indicative power at the individual level (79% sensitivity and 87% specificity). Critically, this olfactory screen remains effective in participants with COVID-19 but without symptoms, and in participants with symptoms but without COVID-19.

Conclusions

The current odorant-based olfactory screen adds a component to online symptom-checkers, to potentially provide an added first line of defense that can help fight disease progression at the population level. The data derived from this tool may allow better understanding of the link between COVID-19 and olfaction.

Modeling the SARS-CoV-2 parallel transmission dynamics: Asymptomatic and symptomatic pathways

Asymptomatic transmission of the coronavirus disease and the infected individual prediction has become very important in the COVID-19 outbreak study. The asymptomatic and symptomatic transmission studies are still ongoing to assess their impacts on disease monitoring and burden. However, there has been limited research on how asymptomatic and symptomatic transmissions together can affect the coronavirus disease outbreak. A mathematical model is therefore needed to be developed in order to assess the effect of these transmissions on the coronavirus disease dynamics. This paper develops a mathematical model concerning asymptomatic and symptomatic disease transmission processes in the COVID-19 outbreak. The model sensitivity has been analysed in terms of the variance of each parameter, and the local stability at two equilibrium points have been discussed in terms of the basic reproduction number (R0). It is found that the disease-free equilibrium gets stable for R0 < 1 whereas the endemic equilibrium becomes stable for R0 > 1 and unstable otherwise. The proportion of the effect of asymptomatic and symptomatic transmission rates on R0 is calculated to be approximately between 1 and 3. The results demonstrate that asymptomatic transmission has a significant impact compared to symptomatic transmission in the disease outbreak. Outcomes of this study will contribute to setting an effective control strategy for the COVID-19 outbreak.

Water-sanitation-health nexus in the Indus-Ganga-Brahmaputra River Basin: need for wastewater surveillance of SARS-CoV-2 for preparedness during the future waves of pandemic

The Indus-Ganga-Brahmaputra River Basin (IGBRB) is a trans-boundary river basin flowing through four major countries in South Asia viz., India, Pakistan, Bangladesh, and Nepal. Contamination of surface water by untreated or inadequately treated wastewater has been a huge problem for pathogenic microorganisms in economies in transition. Recent studies have reported that sewage surveillance can provide prior information of the outbreak data, because faeces can contain the novel coronavirus (SARS-CoV-2) shed by infected humans. Hence, in this study we geo-spatially mapped the COVID-19 hotspots during the peak time in the first and second wave of pandemic to demonstrate the need and usefulness of wastewater surveillance strategy in IGBRB during ongoing pandemic. Further we discussed the status of sanitation, health and hand-hygiene in the IGBRB along with characterization of the challenges posed by the pandemic in achieving the United Nations Sustainable Development Goals (UN-SDGs). Monthly Geographical Information System (GIS) mapping of COVID-19 hotspots in the IGBRB showed an increase in the spread along the direct sewage discharge points. The social inequalities expose the vulnerabilities of the urban poor in terms of the burden, risks and access to Water, Sanitation, and Hygiene (WASH) needs. Such an evidence-based image of the actual SARS-CoV-2 viral load in the community along the IGBRB can provide valuable insights and recommendations to deal with the future waves of COVID-19 pandemic in this region that can go a long way in achieving the UN-SDGs.

Improving correlation of wastewater SARS-CoV-2 gene copy numbers with COVID-19 public health cases using readily available biomarkers

The COVID-19 pandemic has highlighted the potential role that wastewater-based epidemiology can play in assessing aggregate community health. However, efforts to translate SARS-CoV-2 gene copy numbers obtained from wastewater samples into meaningful community health indicators are nascent. In this study, SARS-CoV-2 nucleocapsid (N) genes (N1 and N2) were quantified weekly using reverse transcriptase droplet digital PCR from two municipal wastewater treatment plants for seven months. Four biomarkers (ammonium, biological oxygen demand (BOD), creatinine, and human mitochondrial gene NADH dehydrogenase subunit 5) were quantified and used to normalize SARS-CoV-2 gene copy numbers. These were correlated to daily new case data and one-, two-, and three-week cumulative case data. Over the course of the study, the strongest correlations were observed with a one-day case data lag. However, early measurements were strongly correlated with a five-day case data lag. This indicates that in the early stages of the pandemic, the wastewater samples may have indicated active COVID-19 cases before clinical indications. Mitochondrial and creatinine normalization methods showed the strongest correlations throughout the study, indicating that human-specific biomarkers were better at normalizing wastewater data than ammonium or BOD. Granger causality tests supported this observation and showed that gene copies in wastewater could be predictive of new cases in a sewershed.

An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy: a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of τ days. We also implement a random testing strategy with a threshold over the population. We found that τ=7 is a good choice for the quarantine strategy since it reduces the infected population and, conveniently, it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, and the medical facilities to deal with the additional critical patients would be relatively low.

COVID-19 epidemic under the K-quarantine model: Network approach

The COVID-19 pandemic is still ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage but also efficiently prevents the spread of the disease. In this work, the spread of COVID-19 under local quarantine measures is modeled using the Susceptible-Exposed-Infected-Recovered model on complex networks. In this network approach, the links connected to infected and so isolated people are disconnected and then reinstated when they are released. These link dynamics leads to time-dependent reproduction number. Numerical simulations are performed on networks with reaction rates estimated from empirical data. The temporal pattern of the accumulated number of confirmed cases is then reproduced. The results show that a large number of asymptomatic infected patients are detected as they are quarantined together with infected patients. Additionally, possible consequences of the breakdowns of local quarantine measures and social distancing are considered.

Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic. Note to Practitioners-This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.

Functional nucleic acids as modular components against SARS-CoV-2: From diagnosis to therapeutics

Coronavirus Disease 2019 (COVID-19), which poses an extremely serious global impact on human public healthcare, represents a high transmission and disease-causing viral infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that is expanding at a rapid pace. Therefore, it is urgent for researchers to establish effective platforms for the assay and treatment of SARS-CoV-2. Functional nucleic acids (FNAs), comprising aptamers and nucleases, are of primary concern within the biological and medical communities owing of the distinctive properties of their target recognition and catalysis. This review will concentrate on the essential aspects of insights regarding FNAs and their technological expertise for the diagnostic and therapeutic utilization against COVID-19. We first offer a historical perspective of the COVID-19 pandemics, its clinical characteristics and potential biomarkers. Then, we briefly discuss the current diagnostic and therapeutic methodology towards COVID-19, highlighting the superiorities and existing shortcomings. After that, we introduce the key features of FNAs, and summarize recent progress of in vitro selection of FNAs for SARS-CoV-2 specific proteins and RNAs, followed by highlighting the general concept of translating FNAs into functional probes for diagnostic and therapeutic purposes. Then, we critically review the emerging FNAs-based diagnostic and therapeutic strategies that are fast, precise, efficient, and highly specific to fight COVID-19. Finally, we identify remaining challenges and offer future outlook of this emerging field.

Intestinal viral infections of nSARS-CoV2 in the Indian community: Risk of virus spread in India

In December 2019, novel severe acute respiratory syndrome coronavirus 2 (nSARS-CoV-2) virus outbreaks emerged from Wuhan, China, and spread all over the world, including India. Molecular diagnosis of Coronavirus Disease 2019 (COVID) 19 for densely and highly populated countries like India is time-consuming. A few reports have described the successful diagnosis of nSARS-CoV-2 virus from sewage and wastewater samples contaminated with fecal matter, suggesting the diagnosis of COVID 19 from the same to raise an alarm about the community transmission of virus for implementation of evacuation and lockdown strategies. So far, the association between the detection of virus and its concentration in stool samples with severity of the disease and the presence or absence of gastrointestinal symptoms have been rarely reported. We led the search utilizing multiple databases, specifically PubMed (Medline), EMBASE, and Google Scholar. We conducted a literature survey on gastrointestinal infection and the spread of this virus through fecal-oral transmission. Reports suggested that the existence and persistence of nSARS-CoV-2 in anal/rectal swabs and stool specimens for a longer period of time than in nasopharyngeal swabs provides a strong tenable outcome of gastrointestinal contamination and dissemination of this infection via potential fecal-oral transmission. This review may be helpful to conduct further studies to address the enteric involvement and excretion of nSARS-CoV-2 RNA in feces and control the community spread in both COVID-19 patients ahead of the onset of symptoms and in asymptomatic individuals through wastewater and sewage surveillance as an early indication of infection. The existence of the viral genome and active viral particle actively participate in genomic variations. Hence, we comprehended the enteric spread of different viruses amongst communities with special reference to nSARS-CoV-2.

Inhibition of SARS-CoV-2 Infection by Human Defensin HNP1 and Retrocyclin RC-101

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 is an enveloped virus responsible for the COVID-19 pandemic. The emergence of new potentially more transmissible and vaccine-resistant variants of SARS-CoV-2 is an ever-present threat. Thus, it remains essential to better understand innate immune mechanisms that can inhibit the virus. One component of the innate immune system with broad antipathogen, including antiviral, activity is a group of cationic immune peptides termed defensins. The ability of defensins to neutralize enveloped and non-enveloped viruses and to inactivate numerous bacterial toxins correlate with their ability to promote the unfolding of proteins with high conformational plasticity. We found that human neutrophil α-defensin HNP1 binds to SARS-CoV-2 Spike protein with submicromolar affinity that is more than 20 fold stronger than its binding to serum albumin. As such, HNP1, as well as a θ-defensin retrocyclin RC-101, both interfere with Spike-mediated membrane fusion, Spike-pseudotyped lentivirus infection, and authentic SARS-CoV-2 infection in cell culture. These effects correlate with the abilities of the defensins to destabilize and precipitate Spike protein and inhibit the interaction of Spike with the ACE2 receptor. Serum reduces the anti-SARS-CoV-2 activity of HNP1, though at high concentrations, HNP1 was able to inactivate the virus even in the presence of serum. Overall, our results suggest that defensins can negatively affect the native conformation of SARS-CoV-2 Spike, and that α- and θ-defensins may be valuable tools in developing SARS-CoV-2 infection prevention strategies.

What Is COVID 19 Teaching Us about Pulmonary Ultrasound?

In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement.

Rapid and Reliable Detection of SARS-CoV-2 Using Direct RT-LAMP

Background: The global pandemic coronavirus SARS-CoV-2 has a healthcare, social and economic burden. To limit the spread of the virus, the World Health Organization (WHO) urgently called for extensive screening of suspected individuals; thus, a quick, simple, and sensitive diagnostic assay is always in need. Methods: We applied reverse transcription-loop-mediated isothermal amplification (RT-LAMP) for the detection of SARS-CoV-2. The RT-LAMP method was optimized by evaluating two fluorescence amplification mixes and several reaction times, and results were compared to the standard real-time RT-PCR (rtRT-PCR). The assay was validated using 200 nasopharyngeal swabs collected in viral transport media (62 positive for SARS-CoV-2, and 138 negative for SARS-CoV-2 detected by the rtRT-PCR method). The samples were diluted 1:4 in diethylpyrocarbonate (DEPC)-treated water, utilized for RT-LAMP using different singleplex and multiplex sets of LAMP primers (N gene, S gene, and orf1ab gene), and incubated at 65 °C using real-time PCR 7500. Results: Our direct detection with the RT-LAMP protocol showed 100% concordance (sensitivity and specificity) with the standard protocol used for the detection of SARS-CoV-2 nucleic acid. Conclusions: In this study, we set up a rapid, simple, and sensitive RT-LAMP assay for the detection of SARS-CoV-2 in clinical samples. The assay is suitable for point of care detection in public hospitals, medical centers in rural areas, and in transportation hubs.

Analysis of Risk Factors for In-Hospital Death Due to COVID-19 in Patients Hospitalised at the Temporary Hospital Located at the National Stadium in Warsaw: A Retrospective Analysis

The outbreak of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has affected all aspects of social life and brought massive changes to the healthcare sector. The aim of this study was to identify the factors affecting the mortality of COVID-19 patients at a temporary hospital in Warsaw (Poland). The present study was conducted based on a retrospective analysis of the medical records of patients hospitalised at the temporary hospital located at the National Stadium in Warsaw between 1 March 2020 and 30 April 2021. The study included all cases of patients who were brought directly or transferred to the National Hospital from other hospitals for further treatment. With regard to comorbidities, the analysis found that five comorbidities—namely, diabetes (OR = 1.750, 95% CI: 1.009−2.444, p < 0.05), stroke history (OR = 2.408, 95% CI: 1.208−4.801, p < 0.05), renal failure (OR = 2.141, 95% CI: 1.052−4.356, p < 0.05), chronic obstructive pulmonary disease (OR = 2.044, 95% CI: 1.133−3.690, p < 0.05) and heart failure (OR = 1.930, 95% CI: 1.154−3.227, p < 0.05)—had a significant impact on the survival of COVID-19 patients. The analysis identified 14 factors that had a significant impact on the prognosis and mortality of the COVID-19 patients studied.

Increased risk for thromboembolic events from combination of a gynecologic malignancy with severe acute respiratory syndrome coronavirus 2 infection: a case report

PURPOSE

During the severe acute respiratory syndrome coronavirus 2 pandemic, several patient groups are at particular risk. Mortality is higher among cancer patients and may be increased further by thromboembolic events, which are more common in coronavirus 2019 patients according to recent publications. We discuss the association of gynecologic malignancies, Severe acute respiratory syndrome coronavirus 2, and thromboembolism by reporting a case study and summarizing available literature.

CASE REPORT

A 71-year-old Caucasian patient with ovarian cancer receiving first-line chemotherapy was diagnosed with deep vein thrombosis and pulmonary embolism. Routine screening revealed infection with severe acute respiratory syndrome coronavirus 2 in absence of specific symptoms. After uneventful recovery, oncologic treatment could be continued a few weeks later.

METHODS

We performed a systematic review of the literature on PubMed following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The search included articles ahead of print, published between 1 December 2019 and 1 June 2020. Cross-searches were conducted on all relevant articles.

RESULTS

We identified five articles meeting the defined criteria, including two retrospective studies, a review, a position paper, as well as a letter to the editor.

CONCLUSION

Cancer patients infected with severe acute respiratory syndrome coronavirus 2 have a relatively poor outcome, which may partially be due to a higher rate of thromboembolic events. Thromboprophylaxis is recommended, and scoring systems are helpful in early detection. In cancer patients with severe acute respiratory syndrome coronavirus 2, individual risk for thromboembolic events should be taken into account when considering interruption versus continuation of antitumoral therapy. However, further data and studies are required.

A network SIRX model for the spreading of COVID-19

Infectious diseases, such as the current COVID-19, have a huge economic and societal impact. The ability to model its transmission characteristics is critical to minimize its impact. In fact, predicting how fast an infection is spreading could be a major factor in deciding on the severity, extent and strictness of the applied mitigation measures, such as the recent lockdowns. Even though modelling epidemics is a well studied subject, usually models do not include quarantine or other social measures, such as those imposed in the recent pandemic. The current work builds upon a recent paper by Maier and Brockmann (2020), where a compartmental SIRX model was implemented. That model included social or individual behavioural changes during quarantine, by introducing state X , in which symptomatic quarantined individuals are not transmitting the infection anymore, and described well the transmission in the initial stages of the infection. The results of the model were applied to real data from several provinces in China, quite successfully. In our approach we use a Monte-Carlo simulation model on networks. Individuals are network nodes and the links are their contacts. We use a spreading mechanism from the initially infected nodes to their nearest neighbours, as has been done previously. Initially, we find the values of the rate constants (parameters) the same way as in Maier and Brockmann (2020) for the confirmed cases of a country, on a daily basis, as given by the Johns Hopkins University. We then use different types of networks (random Erdős-Rényi, Small World, and Barabási-Albert Scale-Free) with various characteristics in an effort to find the best fit with the real data for the same geographical regions as reported in Maier and Brockmann (2020). Our simulations show that the best fit comes with the Erdős-Rényi random networks. We then apply this method to several other countries, both for large-size countries, and small size ones. In all cases investigated we find the same result, i.e. best agreement for the evolution of the pandemic with time is for the Erdős-Rényi networks. Furthermore, our results indicate that the best fit occurs for a random network with an average degree of the order of〈 k 〉 ≈ 10-25, for all countries tested. Scale Free and Small World networks fail to fit the real data convincingly.

The changing epidemiology of SARS-CoV-2

We have come a long way since the start of the COVID-19 pandemic-from hoarding toilet paper and wiping down groceries to sending our children back to school and vaccinating billions. Over this period, the global community of epidemiologists and evolutionary biologists has also come a long way in understanding the complex and changing dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. In this Review, we retrace our steps through the questions that this community faced as the pandemic unfolded. We focus on the key roles that mathematical modeling and quantitative analyses of empirical data have played in allowing us to address these questions and ultimately to better understand and control the pandemic.

Vaccines alone are no silver bullets: a modeling study on the impact of efficient contact tracing on COVID-19 infection and transmission in Malaysia

BACKGROUND

The computer simulation presented in this study aimed to investigate the effect of contact tracing on coronavirus disease 2019 (COVID-19) transmission and infection in the context of rising vaccination rates.

METHODS

This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted.

RESULTS

At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases.

CONCLUSIONS

While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.

Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities

With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population. Two control measures are considered: selecting participants based on vaccine efficacy and restricting the event capacity. We build the model using agent-based modeling to capture the spatiotemporal crowd dynamics and utilize a genetic algorithm to assess the control strategies. This assessment is based on factors that are important for policymakers such as disease prevalence, vaccine diversity, and event capacity. A quantitative evaluation of vaccine diversity using the Simpson's Diversity Index is also provided. The Hajj ritual is used as a case study. We show that strategies that prioritized lowering the prevalence resulted in low event capacity but facilitated vaccine diversity. Moreover, strategies that prioritized diversity resulted in high infection rates. However, increasing the prioritization of participants with high vaccine efficacy significantly decreased the disease prevalence. Strategies that prioritized ritual capacity did not show clear trends.

A new approach for modeling COVID-19 death data

The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW.

Public Transport COVID-19-Safe: New Barriers and Policies to Implement Effective Countermeasures under User’s Safety Perspective

The COVID-19 emergency forced cities worldwide to adopt measures to restrict travel and implement new urban public transport solutions. The discontinuity and reduction of services made users recognize public transport systems as contamination vectors, and the decrease in the number of passengers can already be seen in several places. Thus, this study assessed the impact of the COVID-19 pandemic on urban public transport. We used hybrid choice models (HCMs) to identify the new barriers and potential solutions to increase users’ perception of safety, considering preexistent perceptions of public transportation quality. We used data from an online survey with users of public transportation in a metropolitan area in southern Brazil. We identified that the main barriers to using public transport during virus transmission are related to the system characteristics that force constant interaction with other passengers. Crowded vehicles and crowded stops/stations were considered the most detrimental factor in feeling safe while riding in the COVID-19 outbreak. Countermeasures that reduce the contact with other passengers—directly (limit the number of passengers in vehicles) or indirectly (operate with large vehicles)—and increase offers are possible solutions to make users feel safe while riding. The results of this research might help reduce passenger evasion and migration to more unsustainable transport modes.

Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population

Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.

Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) seroconversion and occupational exposure of employees at a Swiss university hospital: A large longitudinal cohort study

BACKGROUND

The dynamics of coronavirus disease 2019 (COVID-19) seroconversion of hospital employees are understudied. We measured the proportion of seroconverted employees and evaluated risk factors for seroconversion during the first pandemic wave.

METHODS

In this prospective cohort study, we recruited Geneva University Hospitals employees and sampled them 3 times, every 3 weeks from March 30 to June 12, 2020. We measured the proportion of seroconverted employees and determined prevalence ratios of risk factors for seroconversion using multivariate mixed-effects Poisson regression models.

RESULTS

Overall, 3,421 participants (29% of all employees) were included, with 92% follow-up. The proportion of seroconverted employees increased from 4.4% (95% confidence interval [CI], 3.7%-5.1%) at baseline to 8.5% [(95% CI, 7.6%-9.5%) at the last visit. The proportions of seroconverted employees working in COVID-19 geriatrics and rehabilitation (G&R) wards (32.3%) and non-COVID-19 G&R wards (12.3%) were higher compared to office workers (4.9%) at the last visit. Only nursing assistants had a significantly higher risk of seroconversion compared to office workers (11.7% vs 4.9%; P = .006). Significant risk factors for seroconversion included the use of public transportation (adjusted prevalence ratio, 1.59; 95% CI, 1.25-2.03), known community exposure to severe acute respiratory coronavirus virus 2 (2.80; 95% CI, 2.22-3.54), working in a ward with a nosocomial COVID outbreak (2.93; 95% CI, 2.27-3.79), and working in a COVID-19 G&R ward (3.47; 95% CI, 2.45-4.91) or a non-COVID-19 G&R ward (1.96; 95% CI, 1.46-2.63). We observed an association between reported use of respirators and lower risk of seroconversion (0.73; 95% CI, 0.55-0.96).

CONCLUSION

Additional preventive measures should be implemented to protect employees in G&R wards. Randomized trials on the protective effect of respirators are urgently needed.

The risk from SARS-CoV-2 to bat species in england and mitigation options for conservation field workers

The newly evolved coronavirus, SARS-CoV-2, which has precipitated a global COVID-19 pandemic among the human population, has been shown to be associated with disease in captive wild animals. Bats (Chiroptera) have been shown to be susceptible to experimental infection and therefore may be at risk from disease when in contact with infected people. Numerous conservation fieldwork activities are undertaken across the United Kingdom bringing potentially infected people into close proximity with bats. In this study, we analysed the risks of disease from SARS-CoV-2 to free-living bat species in England through fieldworkers undertaking conservation activities and ecological survey work, using a qualitative, transparent method devised for assessing threats of disease to free-living wild animals. The probability of exposure of bats to SARS-CoV-2 through fieldwork activities was estimated to range from negligible to high, depending on the proximity between bats and people during the activity. The likelihood of infection after exposure was estimated to be high and the probability of dissemination of the virus through bat populations medium. The likelihood of clinical disease occurring in infected bats was low, and therefore, the ecological, economic and environmental consequences were predicted to be low. The overall risk estimation was low, and therefore, mitigation measures are advisable. There is uncertainty in the pathogenicity of SARS-CoV-2 in bats and therefore in the risk estimation. Disease risk management measures are suggested, including the use of personal protective equipment, good hand hygiene and following the existing government advice. The disease risk analysis should be updated as information on the epidemiology of SARS-CoV-2 and related viruses in bats improves. The re-analysis may be informed by health surveillance of free-living bats.

COVID-19 and chronic fatigue syndrome: An endocrine perspective

Patients recovering from COVID-19 may have persistent debilitating symptoms requiring long term support through individually tailored cardiopulmonary and psychological rehabilitation programs. Clinicians need to be aware about the likely long-term complications and their diagnostic assessments to help identify any occult problems requiring additional help. Endocrinological evaluations should be considered as part of the armamentarium in the management of such individuals with diligent cognizance about the involvement of the hypothalamo-pituitary-adrenal (HPA) axis, adrenal and thyroid function. We here review the literature and potential pathophysiological mechanisms involved in and related to post COVID-19 symptoms with an emphasis on endocrine function.

SARS-CoV-2 genome quantification in wastewaters at regional and city scale allows precise monitoring of the whole outbreaks dynamics and variants spreading in the population

SARS-CoV-2 is a coronavirus causing a globalized outbreak called COVID-19. SARS-CoV-2 transmission is associated with inhalation of contaminated respiratory droplets and could causes severe complications. Until today several "waves" of infections have been observed despite implementation of strict health policies. Decisions for such sanitary measures are based on population health monitoring. Unfortunately, for COVID-19, a significant proportion of individuals are asymptomatic but play a role in the virus transmission. To overcome these limitations, several strategies were developed including genome quantification in wastewater that could allow monitoring of the health status of population, since shedding of SARS-CoV-2 in patient stool is frequent. Wastewater-based epidemiology (WBE) was established and several countries implemented this approach to allow COVID-19 outbreak monitoring. In France, the OBEPINE project performed a quantitative analysis of SARS-CoV-2 in raw wastewater samples collected from major wastewater treatment plants (WWTP) since March 2020. In the greater Paris area 1101 samples (507 for five WWTP and 594 for sewer) were collected. This 16 months monitoring allows us to observe the outbreak dynamics. Comparison of WBE indicators with health data lead to several important observation; the good level of correlation with incidence rates, the average 3 days lead time, and the sensitivity (WBE change when incidence is > to 7/100000 inhabitants). We also compared the local monitoring (city level) with the regional monitoring, to help cluster identification. Moreover, variants of concern (VOC) emerged due to the selection pressure. We developed a specific RT-qPCR method targeting the deletion H69-V70 in the spike protein, using this deletion as a proxy of the B.1.1.7 presence in the wastewater. With this data we demonstrate the predominant role played by this strain in the third wave. All these results allow a better description and understanding of the pandemic and highlight the role of such WBE indicators.

The non-pharmaceutical interventions may affect the advantage in transmission of mutated variants during epidemics: A conceptual model for COVID-19

As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that is difficult to be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in slowing or even containing the growth of the proportion of variants.