Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19

Sensitivity of Symptom-Based Screening for COVID-19 in Active Duty Basic Trainees

INTRODUCTION

Symptomatic Coronavirus Disease 2019 (COVID-19) screening has been a cornerstone of case identification during the pandemic. Despite the myriad of COVID-19 symptoms, symptom screens have primarily focused on symptoms of influenza-like illnesses such as fever, cough, and dyspnea. It is unknown how well these symptoms identify cases in a young, healthy military population. This study aims to evaluate the utility of symptom-based screening in identifying COVID-19 through three different COVID-19 waves.

MATERIALS AND METHODS

A convenience sample of 600 military trainees who arrived at Joint Base San Antonio-Lackland in 2021 and 2022 were included. Two hundred trainees with symptomatic COVID-19 before the emergence of the Delta variant (February-April 2021), when Delta variant was predominant (June-August 2021), and when Omicron was the predominant variant (January 2022) had their presenting symptoms compared. At each time point, the sensitivity of a screen for influenza-like illness symptoms was calculated.

RESULTS

Of the 600 symptomatic active duty service members who tested positive for COVID-19, the most common symptoms were sore throat (n = 385, 64%), headache (n = 334, 56%), and cough (n = 314, 52%). Although sore throat was the most prominent symptom during Delta (n = 140, 70%) and Omicron (n = 153, 77%), headache was the most common before Delta (n = 93, 47%). There were significant differences in symptoms by vaccination status; for example, ageusia was more common in patients who were not completely vaccinated (3% vs. 0%, P = .01). Overall, screening for fever, cough, or dyspnea had a 65% sensitivity with its lowest sensitivity in the pre-Delta cases (54%) and highest sensitivity in Omicron cases (78%).

CONCLUSIONS

In this descriptive cross-sectional study evaluating symptomatic military members with COVID-19, symptom prevalence varied based on predominant circulating COVID-19 variant as well as patients' vaccination status. As screening strategies evolve with the pandemic, changing symptom prevalence should be considered.

Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020

When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

Effectiveness of Point of Entry Health Screening Measures among Travelers in the Detection and Containment of the International Spread of COVID-19: A Review of the Evidence

COVID-19 remains a communicable disease with the capacity to cause substantial damage to health and health systems. Enhanced health screening at points of entry (POEs) is a public health measure implemented to support early detection, prevention and response to communicable diseases, such as COVID-19. The purpose of this study was to review the available evidence on the effectiveness of POE health screening in the detection and containment of the COVID-19 pandemic. This study was registered under PROSPERO and followed PRISMA guidelines in which the literature between 2019 and 2022 was retrieved from Scopus, PubMed, Web of Science, Global Health, CINAHL, Embase, Google Scholar and international organizations. A total of 33,744 articles were screened for eligibility, from which 43 met the inclusion criteria. The modeling studies predicted POE screening able to detect COVID-19 in a range of 8.8% to 99.6%, while observational studies indicated a detection rate of 2% to 77.9%, including variants of concern depending on the screening method employed. The literature also indicated these measures can delay onset of the epidemic by 7 to 32 days. Based on our review findings, if POE screening measures are implemented in combination with other public health interventions such as rapid tests, they may help detect and reduce the spread of COVID-19.

Preparing correctional settings for the next pandemic: a modeling study of COVID-19 outbreaks in two high-income countries

Introduction

Correctional facilities are high-priority settings for coordinated public health responses to the COVID-19 pandemic. These facilities are at high risk of disease transmission due to close contacts between people in prison and with the wider community. People in prison are also vulnerable to severe disease given their high burden of co-morbidities.

Methods

We developed a mathematical model to evaluate the effect of various public health interventions, including vaccination, on the mitigation of COVID-19 outbreaks, applying it to prisons in Australia and Canada.

Results

We found that, in the absence of any intervention, an outbreak would occur and infect almost 100% of people in prison within 20 days of the index case. However, the rapid rollout of vaccines with other non-pharmaceutical interventions would almost eliminate the risk of an outbreak.

Discussion

Our study highlights that high vaccination coverage is required for variants with high transmission probability to completely mitigate the outbreak risk in prisons.

Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences

Introduction

Quantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures.

Methods

Drawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends.

Results

Analysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies.

Discussion

Our findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

Relationship between clinical symptom profiles and COVID-19 infection status during Delta-dominant period versus Omicron-dominant period-analysis of real-world data collected in Hiroshima Prefecture, Japan

OBJECTIVES

The present study investigates the diagnosis and prediction of COVID-19 based on clinical symptoms, and corresponding difference between the Delta- and Omicron-dominant periods, using data collected at polymerase chain reaction (PCR) centers in Hiroshima Prefecture, Japan.

METHODS

Data was collected using a J-SPEED-style COVID-19 standard data collection form. The analysis was done in two directions: calculating the likelihood ratio that clinical symptoms will manifest in "infected" versus "non-infected" individuals and calculating the diagnostic odds ratio (OR) of infection for those who have symptoms compared to those without symptoms.

RESULTS

COVID-19 was more strongly associated with smell and taste disorders during the Delta-dominant period, and muscle pain during the Omicron-dominant period. An age-specific analysis of likelihood and diagnostic ORs found cold-like symptoms had the lowest ability to diagnose COVID-19, and the lowest predictability of COVID-19 with children during both periods. The likelihood and diagnostic ORs of other symptoms for COVID-19 were highest in adults and lowest in those over 65.

CONCLUSION

Symptoms are an important indicator of COVID-19, but the association between specific symptoms and COVID-19 is dependent on the dominant variant of the virus.

Clinical manifestations and outcomes of COVID-19 in maintenance hemodialysis patients of a high infectious epidemic country: a prospective cross-sectional study

Introduction

The outbreak of COVID-19 poses great challenges for patients on maintenance haemodialysis. Here, we reported the clinical characteristics and laboratory features of maintenance haemodialysis (MHD) patients with COVID-19 in Bangladesh.

Methods

Altogether, 67 MHD patients were enroled in the study from two dedicated tertiary-level hospitals for COVID-19 after the prospective cross-sectional execution of selection criteria. Data were collected from medical records and interviews. Different statistical analysis was carried out in the data analysis.

Results

The mean age was 55.0±9.9 years, with 40 males (59.7%). The mean dialysis duration was 23.4±11.5 months. The most common symptoms were fever (82.1%), cough (53.7%), and shortness of breath (55.2%), while the common comorbid condition was hypertension (98.5%), followed by diabetes (56.7%). Among MHD patients, 52.2% to 79.1% suffered from severe to critical COVID-19, 48 patients (71.6%) had 26-75% lung involvement on high resolution computed tomography of the chest, 23 patients (34.3%) did not survive, 20 patients (29.9%) were admitted to ICU, and nine patients (13.4%) needed mechanical ventilation. Patients who did not survive were significantly older (mean age: 63.0 vs. 50.86 years, P=0.0001), had significantly higher cardiovascular risk factors (69.6% vs. 43.2%, P=0.04), severe shortness of breath (82.6% vs. 40.9%, P=0.0001), and longer hospital stays (mean days: 17.9 vs. 13.0, P=0,0001) compared to the survivor group. The white blood cell count, C-reactive protein, lactate dehydrogenase, pro-calcitonin, and thrombocytopenia were significantly (P<0.0001) higher, while the albumin level was significantly lower (P=0.0001) in non-survivor compared to patients who survived.

Conclusion

Maintenance haemodialysis patients had severe to critical COVID-19 and had a higher risk of non-survival if they were older and had comorbidities such as hypertension and diabetes. Therefore, MHD patients with COVID-19 need close monitoring to improve their outcomes.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO₂ concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Effectiveness of mouthwashes on reducing SARS-CoV-2 viral load in oral cavity: a systematic review and meta-analysis

BACKGROUND

The risk of SARS-COV-2 transmission is relatively high during dental procedures. A study was conducted to investigate the effects of mouthwashes on SARS-COV-2 viral load reduction in the oral cavity.

METHODS

A systematic search was performed in PubMed, EMBASE, Scopus, Web of Science, and Cochrane library for relevant studies up to 20 July, 2022. Randomized and non-randomized clinical trial and quasi-experimental studies evaluating patients with Covid-19 infection (patients) who used mouthwashes (intervention) compared to the same patients before using the mouthwash (comparison) for reducing the SARS-COV-2 load or increasing the cycle threshold (Ct) value (outcome) were searched according to PICO components. Three independent reviewers conducted literature screening and data extraction. The Modified Downs and Black checklist was used for quality assessment. A meta-analysis was performed with a random effects model in the Revman 5.4.1software using the mean difference (MD) of cycle threshold (Ct) values.

RESULTS

Of 1653 articles, 9 with a high methodological quality were included. A meta-analysis indicated that 1% Povidone-iodine (PVP-I) was an effective mouthwash for reducing the SARS-COV-2 viral load [MD 3.61 (95% confidence interval 1.03, 6.19)]. Cetylpyridinium chloride (CPC) [MD 0.61 (95% confidence interval -1.03, 2.25)] and Chlorhexidine gluconate (CHX) [MD -0.04 95% confidence interval (-1.20, 1.12)] were not effective against SARS-COV-2.

CONCLUSION

Using mouthwashes containing PVP-I may be recommended for reducing the SARS-COV-2 viral load in the oral cavity of patients before and during dental procedures, while the evidence is not sufficient for such effects for CPC and CHX-containing mouthwashes.

Validation of the Health Consciousness Scale among the Czech Population

The COVID-19 pandemic has demonstrated the importance of maintaining good health. It became has become apparent that health consciousness is a crucial factor in promoting healthy lifestyles, disease prevention, and the overall well-being of individuals. A higher level of health consciousness is associated with healthy habits, better adherence to medical recommendations, and a higher quality of life. Therefore, health consciousness is a critical construct in health care that reflects the degree to which individuals care about their health. This study, which is based on a representative sample of the adult population (n = 1372), aims to validate the Health Consciousness Scale (HCS) to assess its reliability and validity, and evaluate the factor structure of the translated version of the scale in the Czech language. The validation of the HCS in the Czech context is a significant step forward and provides useful information for healthcare professionals, policymakers, and researchers. The findings of this study contribute to the understanding of health consciousness in the Czech population and provide unique information for the development and evaluation of health interventions aimed at promoting healthy behaviors and attitudes.

How to derive ethically appropriate recommendations for action? A methodology for applied ethics

Researchers in applied ethics, and some areas of bioethics particularly, aim to develop concrete and appropriate recommendations for action in morally relevant real-world situations. When proceeding from more abstract levels of ethical reasoning to such concrete recommendations, however, even with regard to the very same normative principle or norm, it seems possible to develop divergent or even contradictory recommendations for action regarding a certain situation. This may give the impression that such recommendations would be arbitrary and, hence, not well justified. Against this background, we, first, aim at showing that ethical recommendations for action, although being contingent in some sense, are not arbitrary if developed appropriately. For this purpose, we examine two types of contingencies arising in applied ethics reasoning based on recent examples of recommendations for action in the context of the COVID-19 pandemic. In doing so, we refer to a three-step model of ethical reasoning towards recommendations for actions. This, however, leaves open the question of how applied ethics may cope with contingent recommendations for action. Therefore, in a second step, we analyze the role of bridge principles for developing ethically appropriate recommendations for action, i.e., principles which connect normative claims with relevant empirical information to justify certain recommendations for action in a given morally relevant situation. Finally, we discuss some implications for reasoning and reporting in empirically informed ethics.

Enhancing global preparedness during an ongoing pandemic from partial and noisy data

As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.

Community incidence patterns drive the risk of SARS-CoV-2 outbreaks and alter intervention impacts in a high-risk institutional setting

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings.

Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents’ daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study’s findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents’ daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

A Strategy Formulation Framework for Efficient Screening during the Early Stage of a Pandemic

For viruses that can be transmitted by contacts of people, efficiently screening infected individuals is beneficial for controlling outbreaks rapidly and avoiding widespread diffusion, especially during the early stage of a pandemic. The process of virus transmission can be described as virus diffusion in complex networks such as trajectory networks. We propose a strategy formulation framework (SFF) for generating various screening strategies to identify influential nodes in networks. We propose two types of metrics to measure the nodes' influence and three types of screening modes. Then, we can obtain six combinations, i.e., six strategies. To verify the efficiencies of the strategies, we build a scenario model based on the multi-agent modelling. In this model, people can move according to their self-decisions, and a virtual trajectory network is generated by their contacts. We found that (1) screening people will have a better performance based on their contact paths if there is no confirmed case yet, and (2) if the first confirmed case has been discovered, it is better to screen people sequentially by their influences. The proposed SFF and strategies can provide support for decision makers, and the proposed scenario model can be applied to simulate and forecast the virus-diffusion process.

A Metasynthesis and Meta-analysis of the Impact and Diagnostic Safety of COVID-19 Symptom Agnostic Rapid Testing in Low- and Middle-Income Countries: Protocol for a Systematic Review

BACKGROUND

Amid all public health measures to contain COVID-19, the most challenging has been how to break the transmission chain. This has been even more challenging in low- and middle-income countries (LMICs). A public health emergency warrants a public health perspective, which comes down to prevention. Rapid mass testing has been advocated throughout the pandemic as a way to promptly deal with asymptomatic infections, but its usefulness in LMICs is yet to be fully understood.

OBJECTIVE

The study objectives of this paper are to (1) investigate the impact of the different rapid mass testing options for SARS-CoV-2 that have been delivered at point of care in LMICs and (2) evaluate the diagnostic safety (accuracy) of rapid mass testing for SARS-CoV-2 in LMICs.

METHODS

This review will systematically search records in PubMed, EBSCOhost, Cochrane library, Global Index Medicus COVID-19 Register, and Scopus. Records will be managed using Mendeley reference manager and SWIFT-Review. Risk of bias for randomized controlled trials will be assessed using the RoB 2 assessment tool, while nonrandomized interventions will be assessed using the tool developed by the Evidence Project. A narrative approach will be used to synthesize data under the first objective, and either a meta-analysis or synthesis without meta-analysis for the second objective. Tables, figures, and textual descriptions will be used to present findings. The overall body of evidence for the first objective will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach, and for the second objective using GRADE.

RESULTS

The screening of records has been finalized. We hope to finalize the synthesis by the end of February 2023 and to prepare the manuscript for publication by April 2023. The study will be reported in accordance with standard guidelines for the reporting of systematic reviews. Review results will be disseminated through conferences and their peer-reviewed publication in a relevant journal.

CONCLUSIONS

This review highlights the role of a preventive approach in infection control using rapid mass testing. It also flags the overriding need to involve users and providers in the evaluation of such tests in the settings for which they are intended. This will be the first review to the best of our knowledge to generate both qualitative and quantitative evidence regarding rapid mass testing specific to LMICs.

TRIAL REGISTRATION

PROSPERO CRD42022283776; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=283776.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/41132.

Characterization of COVID-19 cases in the early phase (March to July 2020) of the pandemic in Kenya

Background

Kenya detected the first case of COVID-19 on March 13, 2020, and as of July 30, 2020, 17 975 cases with 285 deaths (case fatality rate (CFR) = 1.6%) had been reported. This study described the cases during the early phase of the pandemic to provide information for monitoring and response planning in the local context.

Methods

We reviewed COVID-19 case records from isolation centres while considering national representation and the WHO sampling guideline for clinical characterization of the COVID-19 pandemic within a country. Socio-demographic, clinical, and exposure data were summarized using median and mean for continuous variables and proportions for categorical variables. We assigned exposure variables to socio-demographics, exposure, and contact data, while the clinical spectrum was assigned outcome variables and their associations were assessed.

Results

A total of 2796 case records were reviewed including 2049 (73.3%) male, 852 (30.5%) aged 30-39 years, 2730 (97.6%) Kenyans, 636 (22.7%) transporters, and 743 (26.6%) residents of Nairobi City County. Up to 609 (21.8%) cases had underlying medical conditions, including hypertension (n = 285 (46.8%)), diabetes (n = 211 (34.6%)), and multiple conditions (n = 129 (21.2%)). Out of 1893 (67.7%) cases with likely sources of exposure, 601 (31.8%) were due to international travel. There were 2340 contacts listed for 577 (20.6%) cases, with 632 contacts (27.0%) being traced. The odds of developing COVID-19 symptoms were higher among case who were aged above 60 years (odds ratio (OR) = 1.99, P = 0.007) or had underlying conditions (OR = 2.73, P < 0.001) and lower among transport sector employees (OR = 0.31, P < 0.001). The odds of developing severe COVID-19 disease were higher among cases who had underlying medical conditions (OR = 1.56, P < 0.001) and lower among cases exposed through community gatherings (OR = 0.27, P < 0.001). The odds of survival of cases from COVID-19 disease were higher among transport sector employees (OR = 3.35, P = 0.004); but lower among cases who were aged ≥60 years (OR = 0.58, P = 0.034) and those with underlying conditions (OR = 0.58, P = 0.025).

Conclusion

The early phase of the COVID-19 pandemic demonstrated a need to target the elderly and comorbid cases with prevention and control strategies while closely monitoring asymptomatic cases.

The COVID-19 Response in North America

In our Information Technology (IT) based societies, social media plays an important role in communications and social networks for COVID-19. This study explores social responses for COVID-19 in North America, which is the most severe continent affected by the COVID-19 pandemic. This study employs social network analysis for Twitter among the US, Canada, and Mexico. This study finds that the 3 countries show different characteristics of social networks for COVID-19. For example, the Prime Minister plays the second most important role in the Canadian networks, whereas the Presidents play the most significant role in them, in the US, and Mexico. WHO shows a pivotal effect on social networks of COVID-19 in Canada and the US, whereas it does not affect them in Mexico. Canadians are interested in COVID-19 apps, the American people criticize the president and administration as incompetent in terms of COVID-19, and the Mexican people search for COVID-19 cases and the pandemic in Mexico. This study shows that governments and disease experts should understand social networks and communications of social network services, to develop effective COVID-19 policies according to the characteristics of their country.

Prioritizing interventions for preventing COVID-19 outbreaks in military basic training

Like other congregate living settings, military basic training has been subject to outbreaks of COVID-19. We sought to identify improved strategies for preventing outbreaks in this setting using an agent-based model of a hypothetical cohort of trainees on a U.S. Army post. Our analysis revealed unique aspects of basic training that require customized approaches to outbreak prevention, which draws attention to the possibility that customized approaches may be necessary in other settings, too. In particular, we showed that introductions by trainers and support staff may be a major vulnerability, given that those individuals remain at risk of community exposure throughout the training period. We also found that increased testing of trainees upon arrival could actually increase the risk of outbreaks, given the potential for false-positive test results to lead to susceptible individuals becoming infected in group isolation and seeding outbreaks in training units upon release. Until an effective transmission-blocking vaccine is adopted at high coverage by individuals involved with basic training, need will persist for non-pharmaceutical interventions to prevent outbreaks in military basic training. Ongoing uncertainties about virus variants and breakthrough infections necessitate continued vigilance in this setting, even as vaccination coverage increases.

Optimized infection control practices augment the robust protective effect of vaccination for ESRD patients during a hemodialysis facility SARS-CoV-2 outbreak

BACKGROUND

While dialysis patients are at greater risk of serious SARS-CoV-2 complications, stringent infection prevention measures can help mitigate infection and transmission risks within dialysis facilities. We describe an outbreak of 14 cases diagnosed in a hospital-based outpatient ESRD facility over 13 days in the second quarter of 2021, and our coordinated use of epidemiology, viral genome sequencing, and infection control practices to quickly end the transmission cycle.

METHODS

Symptomatic patients and staff members were diagnosed by RT-PCR. Facility-wide screening utilized SARS-CoV-2 antigen tests. SARS-CoV-2 genome sequences were obtained from residual diagnostic specimens.

RESULTS

Of the 106 patients receiving dialysis in the facility, 10 were diagnosed with SARS-CoV-2 infection, as was 1 patient support person. Of 3 positive staff members, 2 were unvaccinated and had provided care for 6 and 4 of the affected patients, respectively. Sequencing demonstrated that all cases in the cluster shared an identical B.1.1.7./Alpha substrain. Attack rates were greatest among unvaccinated patients and staff. Vaccine effectiveness was 88% among patients.

CONCLUSIONS

Prompt recognition of an infection cluster and rapid intervention efforts successfully ended the outbreak. Alongside consistent adherence to core infection prevention measures, vaccination was highly effective in reducing disease incidence and morbidity in this vulnerable population.

Education during the COVID-19 pandemic: the perception of Italian dental and medical students

BACKGROUND

During COVID-19 pandemic many dental and medical students continued their education from home. From several studies it emerges that students prefer face-to-face teaching, due to the better interaction with teachers, the least number of distractions and due to the negative effect of social isolation on the psychological aspect. The aim of this study was to evaluate the perception of Italian dentistry and medicine students about teaching and training in this scenario.

METHODS

Two online questionnaires were sent to medical and dental students of all Italian universities to investigate the limits and advantages of distance learning and the experience regarding the internship. A chi-square test was used to assess the association between gender, year of the course and region of residence (P<0.05).

RESULTS

One thousand one hundred thirty-eight students answered the first survey, and 292 the second one. Most students preferred face-to-face teaching due to the difficult to maintain attention during online lessons. Many students reported that the online internship was not adequate for their education.

CONCLUSIONS

Distance learning disadvantages are the greater number of sources of distraction, low level of interaction, and absence of the relationship with the patient during the practical training. Furthermore, isolation has strongly affected psychosocial balance of the students. The advantages are the greater comfort, the reduction of transport costs, the decrease in pollution, and the possibility of reviewing the lessons. Most Italian students considered appropriate to provide distance learning during the pandemic but believed that the best teaching method was teaching in presence.

Assessment of clinical and virological outcomes of rural and urban populations: COVID-19

Objective

To assess the clinical and virological status in urban and rural populations.

Methods

A cross-sectional study was conducted in a tertiary care hospital, Postgraduate Institute of Medical Sciences, Rohtak for a period of six months. Upper respiratory tract (URT) specimens including nasopharyngeal and oropharyngeal swabs were collected from the patients and their contacts and processed by RT-PCR technique for COVID-19 detection. Further, clinical and virological response in both the population were assessed and compared.

Results

A total of 37,724 URT samples were tested, out of which 20,144 (53%) samples were from the rural population and 17,580 (47%) from the urban population. Out of the total samples from urban and rural population, COVID-19 positivity was 13.9% in urban population and 6.2% in rural population. Around 86% patients or contacts were asymptomatic in both the rural and urban population and rests were symptomatic 14%. Among the symptomatic patients, sore throat was seen as the most common presenting symptom (95-100%) followed by fever (80-83%), dry cough (55-61%), nasal discharge (18-23%), and breathlessness (3-5%) in both the rural and urban population.

Conclusion

Our outcomes provide novel facts that the COVID-19 epidemic severely affected both rural and urban populations but with few differences. In our study, positivity rate in case of urban population was 13.9% as compared to 6.2% in rural population. There are two foremost facets that contributed variation in positivity in both the population. First, better immune response in rural population as compared to urban population which can be due to the fact that rural people in India are more exposed to various pathogens during their early lifetime thus, improving their immune status. Second, factor could be elevated population densities in urban areas which can contribute to increased infectiousness thus higher positivity rate. In addition, people living in urban population have to commute more for their work and are exposed to more people throughout the day thus, having more possibility to get infection of COVID-19 as compared to the rural population. To the best of our knowledge, there are no studies conducted on COVID-19, among rural population of Haryana. Hence, this study will allow us to fill the gap in knowledge about the variation in contagion spread and immune response in both rural and urban populations.

Antecedents of attitude and their impact on behavioral intention in the staycation context

The year 2020 and 2021 have been decimated by the pandemic, leading to outbound vacations largely scrapped. Staycation, a typical domestic journal, has then been adopted by those who are tired of self-isolation for so long. This study aims to explore and assess the drivers exerting impact on attitude of tourists toward staycation and the interrelationship among the research constructs is also examined. A quantitative analysis is employed for evaluating the roles of reduced risk perception, benign envy, and perceived benefits as they exert the effect on attitude toward staycation. An online questionnaire survey was used, and a total of 213 samples were collected from target respondents in Hong Kong, which were still under lockdown at the time of the study. The results of the study showed that reduced risk perception, benign envy as well as perceived benefits will influence tourists' attitude toward staycation. The managerial and theoretical implications of the results are discussed based on the significant relationships identified in the study.

Clinical characteristics of patients with confirmed and asymptomatic SARS-CoV-2 infection in China

Objective

To examine the clinical characteristics of patients with asymptomatic novel coronavirus disease 2019 (COVID-19) and compare them with those of patients with mild disease.

Design

A retrospective cohort study.

Setting

Multiple medical centers in Wuhan, Hubei, China.

Participants

A total of 3,263 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between February 4, 2020, and April 15, 2020.

Main outcome measures

Patient demographic characteristics, medical history, vital signs, and laboratory and chest computed tomography (CT) findings.

Results

A total of 3,173 and 90 patients with mild and moderate, and asymptomatic COVID-19, respectively, were included. A total of 575 (18.2%) symptomatic patients and 4 (4.4%) asymptomatic patients developed the severe illness. All asymptomatic patients recovered; no deaths were observed in this group. The median duration of viral shedding in asymptomatic patients was 17 (interquartile range, 9.25–25) days. Patients with higher levels of ultrasensitive C-reactive protein (odds ratio [OR] = 1.025, 95% confidence interval [CI], 1.01–1.04), lower red blood cell volume distribution width (OR = 0.68, 95% CI 0.51–0.88), lower creatine kinase Isoenzyme(0.94, 0.89–0.98) levels, or lower lesion ratio (OR = 0.01, 95% CI 0.00–0.33) at admission were more likely than their counterparts to have asymptomatic disease.

Conclusions

Patients with younger ages and fewer comorbidities are more likely to be asymptomatic. Asymptomatic patients had similar laboratory characteristics and longer virus shedding time than symptomatic patients; screen and isolation during their infection were helpful to reduce the risk of SARS-CoV-2 transmission.

SARS-CoV-2 RT-PCR positivity in relation to clinical and demographic characteristics in residents of border quarantine centres, Khyber Pakhtunkhwa, Pakistan: a prospective cohort study

Background

As international travellers were the primary source of sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, border checkpoints became an important tool to isolate cases. We determined the period prevalence and SARS-CoV-2 reverse transcription polymerase chain reaction positivity in relation to clinical and demographic characteristics in healthy travellers quarantined at the Pakistan–Afghanistan border.

Methods

The study was conducted from 15 to 25 April 2020. Period prevalence was calculated and the association between positivity and individuals’ age, sex and occupation were assessed using χ² and Mantel–Haenszel tests. Logistic regression was used to calculate adjusted odds ratios (ORs) for each age group. Time-to-event (TTE) analysis was conducted to check the difference in positivity among various groups.

Results

In a total of 708 individuals, 71 tested positive (10%). Compared with those ≤20 y of age, the sex- and occupation-adjusted odds of testing positive were less among the older age group (41–60 y; OR 0.26, p=0.008). Taxi drivers had higher odds of testing positive (OR 4.08, p<0.001). Kaplan–Meier curves and hazard ratios (0.32, p<0.01) showed that the positivity period differed significantly across the pre-symptomatic vs asymptomatic group (26 vs 14 d).

Conclusions

The cases who were likely to acquire infection through occupational exposure largely remained asymptomatic. For effective control of transmission and the emergence of new variants, testing capacities should be revamped with effective isolation measures.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.

Prevalence of Malaria and Covid-19 in Febrile Patients in Lomé, Togo in 2020

Purpose

The objective of this study was to estimate the prevalence of malaria and Covid-19 by PCR and serological tests in febrile patients in Lomé.

Methods

A cross-sectional study was conducted from September 1 to October 31, 2020 in febrile patients ≥ 10 years in three health facilities in Lomé. Finger stick blood was collected to detect Plasmodium spp. using thin/thick smear and venous blood on EDTA tubes to test for malaria Histidin-Rich-Protein-2 antigen using rapid diagnostic tests (RDT) and SARS-CoV-2 antibodies specific immunoglobulin (Ig) M and G. Detection of SARS-CoV-2 in nasopharyngeal samples was performed by rRT-PCR using GeneXpert.

Results

A total of 243 participants (61.7% of female) with median age 28 years (IQR 18–41) were included in the study. Prevalence of malaria was 25.1%, 95% CI [19.8–31.0] and 30.4%, 95% CI [24.7–36.7] for thin/thick smear and rapid malaria test, respectively. Eighteen patients (7.4%, 95% CI [4.4–11.5]) were positive for SARS-CoV-2 and forty-two (17.3%, 95% CI [12.8–22.6]) were positive for IgM and/or IgG against SARS-CoV-2. SARS-CoV-2 IgM seroprevalence was significantly higher in malaria RDT positive participants (33.8% vs. 10.1%, p < 0.001).

Conclusion

This study confirms a possible cross-reactivity between Covid-19 and malaria in case of single use of rapid tests, suggesting a possible past contamination. In case of clinical signs related to Covid-19 in malaria-endemic areas, PCR screening should be requested in order to identify and isolate patients.

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.

Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification

In this article, COVID-19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID-19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images. The FGLHE technique and fuzzy stacking technique are combined together and forms into stacked dataset image. This stacked dataset are trained with AlexNet convolutional deep neural network model and the feature packages acquired via the models are processed by the anopheles search algorithm. Subsequently, the efficient features are combined and delivered to random forest (RF) classifier. The proposed approach is implemented in MATLAB. The proposed ADCNN-ASA-RFC provides 91.66%, 69.13%, 34.86%, and 70.13% higher accuracy, 79.13%, 60.33%, and 63.34% higher specificity and 77.13%, 58.45%, 25.86%, and 55.33%, higher sensitivity compared with existing algorithms. At last, the simulation outcomes demonstrate that the proposed system can be able to find the optimal solutions efficiently and accurately with COVID-19 diagnosis.

An Epidemic Model with Pro and Anti-vaccine Groups

Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective.

The Tragedy of Liberal Democratic Governance in the Face of Global Threats

In hindsight, the early response of liberal governments to the SARS-CoV-2 pandemic was chaotic and generally inefficient. Though one might be tempted to attribute these failures to the incompetence of certain political decision-makers, we propose another explanation. Global threats require a coordinated international response, which is only possible if the threat is perceived in the same way by all, and if government priorities are similar. The effectiveness of the response also relies on massive adhesion of citizens to the measures imposed, which in turn requires trust in government. Our hypothesis is that certain fundamental features of liberalism complicate such global and collective responses: neutrality of the state and primacy of the individual over collective society. Liberalism considers that institutions and public policy must not be designed to favor any specific conception of the common good. That which is best for all is usually determined by a "competition of opinions," which frequently leads to scientific expertise being considered as only one opinion among many. Liberalism also imposes strict respect for individual freedoms and private interests and tends to reject any form of collectivism or dictate imposed by the common good. In order to solve these structural problems and improve society's management of global threats, we make several proposals, such as the introduction of a minimal and consensual definition of the common good and the promotion of a health policy guided by One Health-like concepts. Overall, our analysis suggests that because political ideologies provide their own definitions of the common good and the place of scientific knowledge in the governance process and can thus affect the response to global threats, they should be urgently taken into consideration by public health experts.

A Report on Statistics of an Online Self-screening Platform for COVID-19 and Its Effectiveness in Iran

BACKGROUND

The most recent emerging infectious disease, coronavirus disease 2019 (COVID-19), is pandemic now. Iran is a country with community transmission of the disease. Telehealth tools have been proved to be useful in controlling public health disasters. We developed an online self-screening platform to offer a population-wide strategy to control the massive influx to medical centers.

METHODS

We developed a platform operating based on given history by participants, including sex, age, weight, height, location, primary symptoms and signs, and high risk past medical histories. Based on a decision-making algorithm, participants were categorized into four levels of suspected cases, requiring diagnostic tests, supportive care, not suspected cases. We made comparisons with Iran STEPs (STEPwise approach to Surveillance) 2016 study and data from the Statistical Centre of Iran to assess population representativeness of data. Also, we made a comparison with officially confirmed cases to investigate the effectiveness of the platform. A multilevel mixed-effects Poisson regression was used to check the association of visiting platform and deaths caused by COVID-19.

RESULTS

About 310 000 individuals participated in the online self-screening platform in 33 days. The majority of participants were in younger age groups, and males involved more. A significant number of participants were screened not to be suspected or needing supportive care, and only 10.4% of males and 12.0% of females had suspected results of COVID-19. The penetration of the platform was assessed to be acceptable. A correlation coefficient of 0.51 was calculated between suspected results and confirmed cases of the disease, expressing the platform's effectiveness.

CONCLUSION

Implementation of a proper online self-screening tool can mitigate population panic during wide-spread epidemics and relieve massive influx to medical centers. Also, an evidence-based education platform can help fighting infodemic. Noticeable utilization and verified effectiveness of such platform validate the potency of telehealth tools in controlling epidemics and pandemics.

Prevalence of the Clinical Symptoms and PCR Test Results on Patients With COVID-19 in South of Tehran

Background:

The first human case of COVID-19 was reported on 19 February 2020 in Iran, since then the number of infected cases has dramatically increased to about 1.800 million and about 62 000 deaths, also elderly people have accounted for the largest frequency of mortality.

Objective:

This study aims to evaluate the prevalence of Covid-19 RT-PCR positive among Iranian patients and to evaluate the most important clinical symptoms of identified patients based on age, gender, and their background diseases to be used as an aid for early diagnosis.

Methods:

During 9 months, swab samples of 11 034 patients’ nasopharynx and oropharynx secretions were obtained and were referred to the laboratory for Covid-19 RT-PCR test. In addition, the history of signs and symptoms from patients was recorded.

Results:

In total, 11 034 Covid-19 RT-PCR tests that performed, 3358 samples had positive results (30.4%). Headache was the most commonly reported, which was found in 42.5% of our cases. Fever was the second most common symptom among the patients studied, with a prevalence rate of 36.2%.

Conclusion:

In this study, almost 30% of symptomatic patients had positive Covid-19 RT-PCR test results. Headache was the most common symptom; also diarrhea and nausea were the least common symptoms among patients under this study.

Modeling nosocomial infection of COVID-19 transmission dynamics

COVID-19 epidemic has posed an unprecedented threat to global public health. The disease has alarmed the healthcare system with the harm of nosocomial infection. Nosocomial spread of COVID-19 has been discovered and reported globally in different healthcare facilities. Asymptomatic patients and super-spreaders are sough to be among of the source of these infections. Thus, this study contributes to the subject by formulating a S E I H R mathematical model to gain the insight into nosocomial infection for COVID-19 transmission dynamics. The role of personal protective equipment θ is studied in the proposed model. Benefiting the next generation matrix method, R 0 was computed. Routh-Hurwitz criterion and stable Metzler matrix theory revealed that COVID-19-free equilibrium point is locally and globally asymptotically stable whenever R 0 < 1 . Lyapunov function depicted that the endemic equilibrium point is globally asymptotically stable when R 0 > 1 . Further, the dynamics behavior of R 0 was explored when varying θ . In the absence of θ , the value of R 0 was 8.4584 which implies the expansion of the disease. When θ is introduced in the model, R 0 was 0.4229, indicating the decrease of the disease in the community. Numerical solutions were simulated by using Runge-Kutta fourth-order method. Global sensitivity analysis is performed to present the most significant parameter. The numerical results illustrated mathematically that personal protective equipment can minimizes nosocomial infections of COVID-19.

Predictive Ability of Factors in diagnosing COVID-19: Experiences from Qatar's Primary Care Settings

•

rt-PCR is considered the gold standard in diagnosing SARS-CoV2.

•

However, rt-PCR it has limitations.

•

A structured questionnaire based on clinical factors is a valid alternative.

•

Study aimed to identify factors in predicting SARS-CoV2 through targeted screening.

•

A scoring system was developed to predict a positive rt-PCR test.

Objective

The aim of this paper is to establish the predictive ability of demographic and clinical factors in diagnosing Coronavirus Disease 2019 (COVID-19) in Qatar's publicly funded primary care settings.

Methods

Reverse transcription polymerase chain reaction (rt-PCR) test and COVID-19 screening data (COVID-19 related factors) were extracted from electronic medical records for all individuals who visited a primary health care centre in Qatar between 15th March to 15th June 2020. Data analysis was undertaken to assess the validity of individual factors in predicting a positive rt-PCR test.

Results

Fever/history of fever [N= 1471 (54.7%); OR 4.6 (95% CI 4.16 - 5.08)], followed by cough [N=1020 (37.9%); OR 1.82 (95% CI 1.65 - 2)] and headache [N=372 (13.8%); OR 1.45 (95% CI 1.27 - 1.67)] were the most frequently reported clinical symptoms amongst individuals who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) infection by rt-PCR. Only five factors, fever/history of fever, cough, working/living in an area reporting local transmission, gender and headache (ranked according to predictive power), were found to be statistically significant. Fever/history of fever alone had a specificity of 79.2% and it gradually increased to 99.9% in combination with runny nose, cough, male gender and age ≥ 50.

Conclusions

The study identified predictive ability of factors in diagnosing COVID-19, individually and in combination. It proposes a scoring system for use in publicly funded primary care settings in Qatar without an rt-PCR test, thus enabling early isolation and treatment where necessary. Further similar studies are needed as newer variations of SARS-CoV2 are continuously emerging to ensure its accuracy.

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

BACKGROUND

A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry.

METHODS

Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening.

RESULTS

About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95-100) to 100% and specificity from 99% (95% CI 97-100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76-87) to 94% (95% CI 89-98) and specificity ranging from 76% (95% CI 70-82) to 92% (95% CI 88-96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only.

CONCLUSIONS

People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people.Trial Registration NCT04509713 (clinicaltrials.gov).

Lessons from past pandemics: a systematic review of evidence-based, cost-effective interventions to suppress COVID-19

BACKGROUND

In an unparalleled global response, during the COVID-19 pandemic, 90 countries asked 3.9 billion people to stay home. Yet other countries avoided lockdowns and focused on other strategies, like contact tracing. How effective and cost-effective are these strategies? We aimed to provide a comprehensive summary of the evidence on past pandemic controls, with a focus on cost-effectiveness.

METHODS

Following PRISMA guidelines, MEDLINE (1946 to April week 2, 2020) and EMBASE (1974 to April 17, 2020) were searched using a range of terms related to pandemic control. Articles reporting on the effectiveness or cost-effectiveness of at least one intervention were included.

RESULTS

We found 1653 papers; 62 were included. The effectiveness of hand-washing and face masks was supported by randomized trials. These measures were highly cost-effective. For other interventions, only observational and modelling studies were found. They suggested that (1) the most cost-effective interventions are swift contact tracing and case isolation, surveillance networks, protective equipment for healthcare workers, and early vaccination (when available); (2) home quarantines and stockpiling antivirals are less cost-effective; (3) social distancing measures like workplace and school closures are effective but costly, making them the least cost-effective options; (4) combinations are more cost-effective than single interventions; and (5) interventions are more cost-effective when adopted early. For 2009 H1N1 influenza, contact tracing was estimated to be 4363 times more cost-effective than school closure ($2260 vs. $9,860,000 per death prevented).

CONCLUSIONS AND CONTRIBUTIONS

For COVID-19, a cautious interpretation suggests that (1) workplace and school closures are effective but costly, especially when adopted late, and (2) scaling up as early as possible a combination of interventions that includes hand-washing, face masks, ample protective equipment for healthcare workers, and swift contact tracing and case isolation is likely to be the most cost-effective strategy.

Detection of SARS-CoV-2 by Canine Olfaction: A Pilot Study

Background

As the number of coronavirus disease 2019 (COVID-19) cases continue to surge worldwide and new variants emerge, additional accurate, rapid, and noninvasive screening methods to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed. The number of COVID-19 cases reported globally is >455 million, and deaths have surpassed 6 million. Current diagnostic methods are expensive, invasive, and produce delayed results. While COVID-19 vaccinations are proven to help slow the spread of infection and prevent serious illness, they are not equitably available worldwide. Almost 40% of the world’s population remains unvaccinated. Evidence suggests that SARS-CoV-2 virus–associated volatile organic compounds found in the breath, urine, and sweat of infected individuals can be detected by canine olfaction. Medical detection dogs may be a feasible, accurate, and affordable SARS-CoV-2 screening method.

Methods

In this double-blinded, case–control, validation study, we obtained sweat samples from inpatients and outpatients tested for SARS-CoV-2 by a polymerase chain reaction test. Medical detection dogs were trained to distinguish SARS-CoV-2-positive samples from SARS-CoV-2-negative samples using reward-based reinforcement.

Results

Samples were obtained from 584 individuals (6–97 years of age; 24% positive SARS-CoV-2 samples and 76% negative SARS-CoV-2 samples). In the testing phase, all dogs performed with high accuracy in detecting SARS-CoV-2. The overall diagnostic sensitivity was 98%, and specificity was 92%. In a follow-up phase, 1 dog screened 153 patients for SARS-CoV-2 in a hospital setting with 96% diagnostic sensitivity and 100% specificity.

Conclusions

Canine olfaction is an accurate and feasible method for diagnosis of SARS-CoV-2, including asymptomatic and presymptomatic infected individuals.

A Non-Uniform Continuous Cellular Automata for Analyzing and Predicting the Spreading Patterns of COVID-19

During the COVID-19 outbreak, modeling the spread of infectious diseases became a challenging research topic due to its rapid spread and high mortality rate. The main objective of a standard epidemiological model is to estimate the number of infected, suspected, and recovered from the illness by mathematical modeling. This model does not capture how the disease transmits between neighboring regions through interaction. A more general framework such as Cellular Automata (CA) is required to accommodate a more complex spatial interaction within the epidemiological model. The critical issue of modeling in the spread of diseases is how to reduce the prediction error. This research aims to formulate the influence of the interaction of a neighborhood on the spreading pattern of COVID-19 using a neighborhood frame model in a Cellular-Automata (CA) approach and obtain a predictive model for the COVID-19 spread with the error reduction to improve the model. We propose a non-uniform continuous CA (N-CCA) as our contribution to demonstrate the influence of interactions on the spread of COVID-19. The model has succeeded in demonstrating the influence of the interaction between regions on the COVID-19 spread, as represented by the coefficients obtained. These coefficients result from multiple regression models. The coefficient obtained represents the population’s behavior interacting with its neighborhood in a cell and influences the number of cases that occur the next day. The evaluation of the N-CCA model is conducted by root mean square error (RMSE) for the difference in the number of cases between prediction and real cases per cell in each region. This study demonstrates that this approach improves the prediction of accuracy for 14 days in the future using data points from the past 42 days, compared to a baseline model.

COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2

In controlling transmission of coronavirus disease 2019 (COVID-19), the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large-scale lockdown policies. Here, we develop and apply an individual-based model of COVID-19 to simulate case importation from managed quarantine under various vaccination scenarios. We then use the output of the individual-based model as input to a branching process model to assess community transmission risk. For parameters corresponding to the Delta variant, our results demonstrate that vaccination effectively counteracts the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.

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.

Factors affecting the mode choice behavior before and during COVID-19 pandemic in Pakistan

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.

Challenges for maintaining surgical care practices in the COVID-19 pandemic: an integrative review

OBJECTIVES

To present the knowledge produced on challenges of health services for maintaining surgical care practices in times of the COVID-19 pandemic.

METHODS

This is an integrative literature review, performed with descriptors 'Operating rooms' and 'Coronavirus Infections' in the MEDLINE/PubMed Central, IBECS, LILACS, BDENF, Coleta SUS, BIGG, BINACIS, SciELO, PubMed, Science Direct, and Cochrane Library databases.

RESULTS

Of the 405 studies analyzed, 27 met the inclusion criteria. The main challenges for surgical services during the pandemic were: (i) rearrangement of general practice in surgical units; (ii) administration and management of resources and elective surgeries; (iii) follow-up and control of preoperative patients to medium term; (iv) maintenance of patients' and health professionals' autonomy and mental health in this context; and (v) teaching health residents in the operating room.

CONCLUSIONS

For surgical care services, the challenges caused by managing the high demand of patients in need of care resulted in the transfer of own resources to other units and the consequent hiring of professionals to meet the demand for these services due to the damming of postponed elective surgeries. This knowledge will allow us to propose strategies in decision making in this scenario, considering the new waves that may arise from this disease.

Performance of Three Tests for SARS-CoV-2 on a University Campus Estimated Jointly with Bayesian Latent Class Modeling

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a 1-week period in October 2020. We found that reverse transcriptase PCR (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median, 92.3%; 95% credible interval [CrI], 73.2 to 99.6%) than RT-PCR testing of nasal samples performed at a commercial facility (median, 85.9%; 95% CrI, 54.7 to 99.4%). The reverse was true for specificity, although the specificity of saliva testing was still very high (median, 99.3%; 95% CrI, 98.3 to 99.9%). An antigen test was less sensitive and specific than both of the RT-PCR tests, although the sample sizes with this test were small and the statistical uncertainty was high. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting. IMPORTANCE Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been vitally important during the COVID-19 pandemic. There are a variety of methods for testing for this virus, and it is important to understand their accuracy in choosing which one might be best suited for a given application. To estimate the accuracy of three different testing methods, we used a data set collected at a university that involved testing the same samples with multiple tests. Unlike most other estimates of test accuracy, we did not assume that one test was perfect but instead allowed for some degree of inaccuracy in all testing methods. We found that molecular tests performed on saliva samples at a university facility were similarly accurate as molecular tests performed on nasal samples at a commercial facility. An antigen test appeared somewhat less accurate than the molecular tests, but there was high uncertainty about that.

Stem cell therapy for COVID-19 pneumonia

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is a highly contagious microorganism, and despite substantial investigation, no progress has been achieved in treating post-COVID complications. However, the virus has made various mutations and has spread around the world. Researchers have tried different treatments to reduce the side effects of the COVID-19 symptoms. One of the most common and effective treatments now used is steroid therapy to reduce the complications of this disease. Long-term steroid therapy for chronic inflammation following COVID-19 is harmful and increases the risk of secondary infection, and effective treatment remains challenging owing to fibrosis and severe inflammation and infection. Sometimes our immune system can severely damage ourselves in disease. In the past, many researchers have conducted various studies on the immunomodulatory properties of stem cells. This property of stem cells led them to modulate the immune system of autoimmune diseases like diabetes, multiple sclerosis, and Parkinson's. Because of their immunomodulatory properties, stem cell-based therapy employing mesenchymal or hematopoietic stem cells may be a viable alternative treatment option in some patients. By priming the immune system and providing cytokines, chemokines, and growth factors, stem cells can be employed to build a long-term regenerative and protective response. This review addresses the latest trends and rapid progress in stem cell treatment for Acute Respiratory Distress Syndrome (ARDS) following COVID-19.

First Long-Time Airborne Fungal Spores Study in Dublin, Ireland (1978–1980)

Ambient fungal spores within the atmosphere can contribute to a range of negative human, animal and plant health conditions and diseases. However, trends in fungal spore seasonality, species prevalence, and geographical origin have been significantly understudied in Ireland. Previously unpublished data from the late 1970s have recently been collected and analysed to establish historical fungal spore trends/characteristics for Dublin. Historical spore concentrations were largely dominated by Alternaria, Ascospores, Basidiospores, Botrytis, Cladosporium, Erysiphe and Rusts. The main fungal spore season for Dublin commenced in April with the fructification of Scopulariopsis and Ganoderma. However, the vast majority of other spore types did not reach peak spore release until late summer. The correlation between ambient spore concentration, and meteorological parameters was examined using Multivariable Regression Tree (MRT) analysis. The notable correlations found for fungal spore concentrations tended to involve temperature-based parameters. The use of a non-parametric wind regression was also employed to determine the potential geographical origin of ambient fungal spores. The impact of wind direction, and high windspeed on fungal spores was established, ultimately highlighting the importance of studying and monitoring fungal spores within Ireland, rather than attempting to rely on data from other regions, as most fungal spores collected in Dublin appeared to originate from within the island.

Airborne Fungal Spore Review, New Advances and Automatisation

Fungal spores make up a significant portion of Primary Biological Aerosol Particles (PBAPs) with large quantities of such particles noted in the air. Fungal particles are of interest because of their potential to affect the health of both plants and humans. They are omnipresent in the atmosphere year-round, with concentrations varying due to meteorological parameters and location. Equally, differences between indoor and outdoor fungal spore concentrations and dispersal play an important role in occupational health. This review attempts to summarise the different spore sampling methods, identify the most important spore types in terms of negative effects on crops and the public, the factors affecting their growth/dispersal, and different methods of predicting fungal spore concentrations currently in use.

Sampling bias minimization in disease frequency estimates

An accurate estimate of the number of infected individuals in any disease is crucial. Current estimates are mainly based on the fraction of positive samples or the total number of positive samples. However, both methods are biased and sensitive to the sampling depth. We here propose an alternative method to use the attributes of each sample to estimate the change in the total number of positive patients in the total population. We present a Bayesian estimator assuming a combination of condition and time-dependent probability of being positive, and mixed implicit-explicit solution for the probability of a person with conditions i at time t of being positive. We use this estimate to predict the total probability of being positive at a given day t. We show that these estimate results are smooth and not sensitive to the properties of the samples. Moreover, these results are a better predictor of future mortality.