Temporal dynamics in viral shedding and transmissibility of COVID-19

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices

Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response

The epidemiological characteristics of SARS-CoV-2 transmission have changed over the pandemic due to emergence of new variants. A decrease in the generation or serial intervals would imply a shortened transmission timescale and, hence, outbreak response measures would need to expand at a faster rate. However, there are challenges in measuring these intervals. Alongside epidemiological changes, factors like varying delays in outbreak response, social contact patterns, dependence on the growth phase of an outbreak, and effects of exposure to multiple infectors can also influence measured generation or serial intervals. To guide real-time interpretation of variant data, we simulated concurrent changes in the aforementioned factors and estimated the statistical power to detect a change in the generation and serial interval. We compared our findings to the reported decrease or lack thereof in the generation and serial intervals of different SARS-CoV-2 variants. Our study helps to clarify contradictory outbreak observations and informs the required sample sizes under certain outbreak conditions to ensure that future studies of generation and serial intervals are adequately powered.

Findings from a discontinued clinical trial of favipiravir in high-risk patients with early-onset COVID-19

INTRODUCTION

Favipiravir terminates severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication. Accordingly, early administration of favipiravir to SARS-CoV-2-infected coronavirus disease 2019 (COVID-19) patients may be expected to suppress disease progression.

METHODS

A randomized double-blind placebo-controlled trial was conducted to demonstrate efficacy of favipiravir in reducing disease progression in patients with mild COVID-19. The participants were unvaccinated patients with comorbidities and at risk of progression to severe disease. Patients were enrolled within 72 h of disease onset and randomized to receive either favipiravir (1800 mg/dose on Day 1 followed by 800 mg/dose) or matching placebo twice daily for 10 days. The primary endpoint was the proportion of patients requiring oxygen therapy within 28 days of randomization.

RESULTS

The trial was discontinued after enrolling 84 patients due to slower than anticipated enrollment caused by rapid uptake of SARS-CoV-2-vaccines and the emergence of the Omicron variant. Results from the 84 patients demonstrated no significant difference in all clinical outcomes. In post-hoc analyses, favipiravir treatment showed higher efficacy in patients within 48 h of onset. No deaths or severe adverse events were documented in the favipiravir group. Plasma concentrations of favipiravir from Day 2 onward were maintained above 40 μg/mL.

CONCLUSIONS

Conducting clinical trials for pathogens like SARS-CoV-2 that rapidly accumulate mutations leading to altered disease characteristics carries significant risks unless it can be done in a short period. Therefore, it would be important to prepare the comprehensive clinical trial platform that can appropriately and promptly evaluate drugs even under a pandemic.

Three dimensional analysis of the exhalation flow in the proximity of the mouth

The human exhalation flow is characterized in this work from the three-dimensional velocimetry results obtained by using the stereo particle image velocimetry (SPIV) measurement technique on the flow emitted from a realistic airway model. For this purpose, the transient exhalation flow through the mouth of a person performing two different breaths corresponding to two metabolic rates, standing relaxed (SR) and walking active (WA), is emulated and studied. To reproduce the flow realistically, a detailed three-dimensional model obtained from computed tomography measurements on real subjects is used. To cope with the variability of the experimental data, a subsequent analysis of the results is performed using the TR-PIV (time resolved particle image velocimetry) technique. Exhalation produces a transient jet that becomes a puff when flow emission ends. Three-dimensional vector fields of the jet velocity are obtained in five equally spaced transverse planes up to a distance of Image 1 from the mouth at equally spaced time instants Image 2 which will be referred to as phases (φ), from the beginning to the end of exhalation. The time evolution during exhalation of the jet area of influence, the velocity field and the jet air entrainment have been characterized for each of the jet cross sections. The importance of the use of realistic airway models for the study of this type of flow and the influence of the metabolic rate on its development are also analyzed. The results obtained contribute to the characterization of the human exhalation as a pathway of the transmission of pathogens such as SARS-CoV-2 virus.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

The New Normal: Delayed Peak SARS-CoV-2 Viral Loads Relative to Symptom Onset and Implications for COVID-19 Testing Programs

BACKGROUND

Early in the coronavirus disease 2019 (COVID-19) pandemic, peak viral loads coincided with symptom onset. We hypothesized that in a highly immune population, symptom onset might occur earlier in infection, coinciding with lower viral loads.

METHODS

We assessed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A viral loads relative to symptom duration in symptomatic adults (≥16 years) presenting for testing in Georgia (4/2022-4/2023; Omicron variant predominant). Participants provided symptom duration and recent testing history. Nasal swabs were tested by Xpert Xpress SARS-CoV-2/Flu/RSV assay and cycle threshold (Ct) values recorded. Nucleoprotein concentrations in SARS-CoV-2 polymerase chain reaction (PCR)-positive samples were measured by single molecule array. To estimate hypothetical antigen rapid diagnostic test (Ag RDT) sensitivity on each day after symptom onset, percentages of individuals with Ct value ≤30 or ≤25 were calculated.

RESULTS

Of 348 newly-diagnosed SARS-CoV-2 PCR-positive individuals (65.5% women, median 39.2 years), 317/348 (91.1%) had a history of vaccination, natural infection, or both. By both Ct value and antigen concentration measurements, median viral loads rose from the day of symptom onset and peaked on the fourth/fifth day. Ag RDT sensitivity estimates were 30.0%-60.0% on the first day, 59.2%-74.8% on the third day, and 80.0%-93.3% on the fourth day of symptoms.In 74 influenza A PCR-positive individuals (55.4% women; median 35.0 years), median influenza viral loads peaked on the second day of symptoms.

CONCLUSIONS

In a highly immune adult population, median SARS-CoV-2 viral loads peaked around the fourth day of symptoms. Influenza A viral loads peaked soon after symptom onset. These findings have implications for ongoing use of Ag RDTs for COVID-19 and influenza.

In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission

Many hospitals have stopped or are considering stopping universal admission testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We discuss reasons why admission testing should still be part of a layered system to prevent hospital-acquired SARS-CoV-2 infections during times of significant community transmission. These include the morbidity of SARS-CoV-2 in vulnerable patients, the predominant contribution of presymptomatic and asymptomatic people to transmission, the high rate of transmission between patients in shared rooms, and data suggesting surveillance testing is associated with fewer nosocomial infections. Preferences of diverse patient populations, particularly the hardest-hit communities, should be surveyed and used to inform prevention measures. Hospitals' ethical responsibility to protect patients from serious infections should predominate over concerns about costs, labor, and inconvenience. We call for more rigorous data on the incidence and morbidity of nosocomial SARS-CoV-2 infections and more research to help determine when to start, stop, and restart universal admission testing and other prevention measures.

Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study

BACKGROUND

Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures.

OBJECTIVE

This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses.

METHODS

We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020.

RESULTS

The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong.

CONCLUSIONS

Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Utility of routine post-admission testing for SARS-CoV-2 in a rehabilitation facility

Asymptomatic screening for SARS-CoV-2 is recommended in healthcare settings during periods of increased incidence, yet studies in rehabilitation settings are lacking. Routine weekly post-admission asymptomatic testing in a rehabilitation facility offered marginal gain beyond syndromic and targeted unit testing and was not associated with a reduced risk of healthcare-associated COVID-19.

An intranasal live-attenuated SARS-CoV-2 vaccine limits virus transmission

The development of effective SARS-CoV-2 vaccines has been essential to control COVID-19, but significant challenges remain. One problem is intramuscular administration, which does not induce robust mucosal immune responses in the upper airways-the primary site of infection and virus shedding. Here we compare the efficacy of a mucosal, replication-competent yet fully attenuated virus vaccine, sCPD9-ΔFCS, and the monovalent mRNA vaccine BNT162b2 in preventing transmission of SARS-CoV-2 variants B.1 and Omicron BA.5 in two scenarios. Firstly, we assessed the protective efficacy of the vaccines by exposing vaccinated male Syrian hamsters to infected counterparts. Secondly, we evaluated transmission of the challenge virus from vaccinated and subsequently challenged male hamsters to naïve contacts. Our findings demonstrate that the live-attenuated vaccine (LAV) sCPD9-ΔFCS significantly outperformed the mRNA vaccine in preventing virus transmission in both scenarios. Our results provide evidence for the advantages of locally administered LAVs over intramuscularly administered mRNA vaccines in preventing infection and reducing virus transmission.

Fukuchi-Manabe Score for Infection Control Measures During the Very Early COVID-19 Pandemic Period When Access to Reverse Transcription-Polymerase Chain Reaction Testing Was Poor in Japan: A Single-Center Observational Prospective Cohort Study

BACKGROUND

During the early stages of the COVID-19 pandemic in Japan, access to reverse transcription-polymerase chain reaction (RT-PCR) testing was limited. All patients with COVID-19 required hospitalization, and isolation of suspected COVID-19 patients had not yet been implemented. With the recently acquired evidence on COVID-19, it is important to develop a risk stratification system for isolation rooms in the context of limited resources for better resource management.

OBJECTIVE

This study aimed to develop and validate a COVID-19 risk-scoring strategy, the Fukuchi-Manabe score, to safely stratify and manage isolation rooms, personal protective equipment (PPE), and RT-PCR testing in the context of limited RT-PCR testing and a short supply of PPE.

METHODS

This single-center prospective study consecutively enrolled suspected COVID-19 adult inpatients between March 1 and August 31, 2020. The primary and secondary outcomes were a positive RT-PCR test and the occurrence of nosocomial infections during the study period, respectively. Factors related to patient history, symptoms, chest computed tomography findings, and laboratory data suggestive of COVID-19 were scored, totaled, and divided into four categories ("probable," "possible," "less likely," and "non-suspicious") based on the likelihood of COVID-19. Sensitivity, specificity, and positive and negative predictive values were evaluated for each probability category.

FINDINGS

Twenty of 224 inpatients were positive on the RT-PCR test, including 18 "probable" patients (90.0%), one "possible" patient, and one "less likely" patient. The area under the curve (AUC) (95% confidence interval: 0.841-0.977), sensitivity, and specificity were 0.909, 90.0%, and 80.4%, respectively. The positive and negative predictive values and accuracy for the "probable" category were 0.90, 0.80, and 0.82, respectively. The mean and standard deviation of AUCs, validated by bootstrap analysis, were 0.910±0.034. No nosocomial infections were observed.

CONCLUSION

The Fukuchi-Manabe score will be helpful when other novel pathogens emerge in the future before the availability of genetic testing methods.

Intestinal immunological events of acute and resolved SARS-CoV-2 infection in non-human primates

SARS-CoV-2 infection has been associated with intestinal mucosal barrier damage, leading to microbial and endotoxin translocation, heightened inflammatory responses, and aggravated disease outcomes. This study aimed to investigate the immunological mechanisms associated with impaired intestinal barrier function. We conducted a comprehensive analysis of gut damage and inflammation markers and phenotypic characterization of myeloid and lymphoid populations in the ileum and colon of SARS-CoV-2-exposed macaques during both the acute and resolved infection phases. Our findings revealed a significant accumulation of terminally differentiated and activated CD4+ and CD8+ T cells, along with memory B cells, within the gastrointestinal tract up to 43 days after exposure to SARS-CoV-2. This robust infection-induced immune response was accompanied by a notable depletion of plasmacytoid dendritic cells, myeloid dendritic cells, and macrophages, particularly affecting the colon during the resolved infection phase. Additionally, we identified a population of CX3CR1Low inflammatory macrophages associated with intestinal damage during active viral replication. Elevated levels of immune activation and gut damage markers, and perturbation of macrophage homeostasis, persisted even after the resolution of the infection, suggesting potential long-term clinical sequelae. These findings enhance our understanding of gastrointestinal immune pathology following SARS-CoV-2 infection and provide valuable information for developing and testing medical countermeasures.

Benthic foraminifera as bioindicators of marine pollution: A bibliometric approach to unravel trends, patterns and perspectives

Benthic foraminifera, single-celled marine organisms, are known for their wide distribution, high abundance and species diversity, test (i.e., shell) preservation in the sedimentary (e.g., historical) record, and sensitivity to environmental changes. Because of these characteristics, they have been widely used as bioindicators in environmental monitoring and, more recently, as Biological Quality Elements (BQEs) in the Ecological Quality Status (EcoQS) evaluation. The global scientific literature on benthic foraminifera as bioindicators was gathered from the Scopus database (overall 966 papers from 1973 to 2022) and explored with scientometric software. The outcomes highlight that the investigation of benthic foraminiferal response to pollutants started over 50 years ago. Indeed, not only the number of published documents has recently peaked (i.e., 2021 and 2022) but there has been also a growth in the percentages of papers falling within the Decision Sciences category that deals with the application of foraminiferal indices for the EcoQS assessment.

Comparison of control and transmission of COVID-19 across epidemic waves in Hong Kong: an observational study

BACKGROUND

Hong Kong contained COVID-19 for two years but experienced a large epidemic of Omicron BA.2 in early 2022 and endemic transmission of Omicron subvariants thereafter. We reflected on pandemic preparedness and responses by assessing COVID-19 transmission and associated disease burden in the context of implementation of various public health and social measures (PHSMs).

METHODS

We examined the use and impact of pandemic controls in Hong Kong by analysing data on more than 1.7 million confirmed COVID-19 cases and characterizing the temporal changes non-pharmaceutical and pharmaceutical interventions implemented from January 2020 through to 30 December 2022. We estimated the daily effective reproductive number (Rt) to track changes in transmissibility and effectiveness of community-based measures against infection over time. We examined the temporal changes of pharmaceutical interventions, mortality rate and case-fatality risks (CFRs), particularly among older adults.

FINDINGS

Hong Kong experienced four local epidemic waves predominated by the ancestral strain in 2020 and early 2021 and prevented multiple SARS-CoV-2 variants from spreading in the community before 2022. Strict travel-related, case-based, and community-based measures were increasingly tightened in Hong Kong over the first two years of the pandemic. However, even very stringent measures were unable to contain the spread of Omicron BA.2 in Hong Kong. Despite high overall vaccination uptake (>70% with at least two doses), high mortality was observed during the Omicron BA.2 wave due to lower vaccine coverage (42%) among adults ≥65 years of age. Increases in antiviral usage and vaccination uptake over time through 2022 was associated with decreased case fatality risks.

INTERPRETATION

Integrated strict measures were able to reduce importation risks and interrupt local transmission to contain COVID-19 transmission and disease burden while awaiting vaccine development and rollout. Increasing coverage of pharmaceutical interventions among high-risk groups reduced infection-related mortality and mitigated the adverse health impact of the pandemic.

FUNDING

Health and Medical Research Fund.

High Doses of Colchicine Act As "Silver Bullets" Against Severe COVID-19

A 48-year-old patient with a weight of 120 kg with type 2 diabetes mellitus, hypertension, and gout was hospitalized on the third day of the COVID-19 diagnosis. His general condition is relatively good, oxygen saturation is 89%. Despite starting standard treatment, on the seventh day from the onset of symptoms, the patient deteriorated sharply (oxygen saturation dropped to 74%). The negative development of the disease is interrupted with a loading dose of colchicine of 6 mg. This is a typical case of the life-saving effect of high but safe doses of colchicine in high-risk COVID-19 patients.

Building a Simple Model to Assess the Impact of Case Investigation and Contact Tracing for Sexually Transmitted Diseases: Lessons From COVID-19

Introduction

During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention developed a simple spreadsheet-based tool to help state and local public health officials assess the performance and impact of COVID-19 case investigation and contact tracing in their jurisdiction. The applicability and feasibility of building such a tool for sexually transmitted diseases were assessed.

Methods

The key epidemiologic differences between sexually transmitted diseases and respiratory diseases (e.g., mixing patterns, incubation period, duration of infection, and the availability of treatment) were identified, and their implications for modeling case investigation and contact tracing impact with a simple spreadsheet tool were remarked on. Existing features of the COVID-19 tool that are applicable for evaluating the impact of case investigation and contact tracing for sexually transmitted diseases were also identified.

Results

Our findings offer recommendations for the future development of a spreadsheet-based modeling tool for evaluating the impact of sexually transmitted disease case investigation and contact tracing efforts. Generally, we advocate for simplifying sexually transmitted disease-specific complexities and performing sensitivity analyses to assess uncertainty. The authors also acknowledge that more complex modeling approaches might be required but note that it is possible that a sexually transmitted disease case investigation and contact tracing tool could incorporate features from more complex models while maintaining a user-friendly interface.

Conclusions

A sexually transmitted disease case investigation and contact tracing tool could benefit from the incorporation of key features of the COVID-19 model, namely its user-friendly interface. The inherent differences between sexually transmitted diseases and respiratory viruses should not be seen as a limitation to the development of such tool.

Detection of SARS-CoV-2 in aerosols in long term care facilities and other indoor spaces with known COVID-19 outbreaks

Coronavirus outbreaks are likely to occur in crowded and congregate indoor spaces, and their effects are most severe in vulnerable long term care facilities (LTCFs) residents. Public health officers benefit from tools that allow them to control COVID-19 outbreaks in vulnerable settings such as LTCFs, but which could be translated in the future to control other known and future virus outbreaks. This study aims to develop and test a methodology based on detection of SARS-CoV-2 in aerosol samples collected with personal pumps that could be easily implemented by public health officers. The proposed methodology was used to investigate the levels of SARS-CoV-2 in aerosol in indoor settings, mainly focusing on LTCFs, suffering COVID-19 outbreaks, or in the presence of known COVID-19 cases, and targeting the initial days after diagnosis. Aerosol samples (N = 18) were collected between November 2020 and March 2022 in Castelló (Spain) from LTCFs, merchant ships and a private home with recently infected COVID-19 cases. Sampling was performed for 24-h, onto 47 mm polytetrafluoroethylene (PTFE) and quartz filters, connected to personal pumps at 2 and 4 L/min respectively. RNA from filters was extracted and SARS-CoV-2 was determined by detection of regions N1 and N2 of the nucleocapsid gene alongside the E gene using RT-PCR technique. SARS-CoV-2 genetic material was detected in 87.5% samples. Concentrations ranged ND-19,525 gc/m3 (gene E). No genetic traces were detected in rooms from contacts that were isolated as a preventative measure. Very high levels were also measured at locations with poor ventilation. Aerosol measurement conducted with the proposed methodology provided useful information to public health officers and contributed to manage and control 12 different COVID-19 outbreaks. SARS-CoV-2 was detected in aerosol samples collected during outbreaks in congregate spaces. Indoor aerosol sampling is a useful tool in the early detection and management of COVID-19 outbreaks and supports epidemiological investigations.

Trajectory of COVID-19 response and management strategy in China: scientific rationale driven strategy adjustments

The pneumonia caused by novel coronavirus SARS-CoV-2 infection in early December 2019, which was later named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO), rapidly spread across the world. China has made extraordinary efforts to this unprecedented pandemic, put its response and control at a very high level of infectious disease management (Category B but with measures for Category A), given top priority to the people and their lives, and balanced the pandemic control and socio-economic development. After more than three years' fighting against this disease, China downgraded the management of COVID-19 to Category B infectious disease on January 8, 2023 and the WHO declared the end of public health emergency on May 5, 2023. However, the ending of pandemic does not mean that the disease is no longer a health threat. Experiences against COVID-19 from China and the whole world should be learned to prepare well for the future public health emergencies. This article gives a systematic review of the trajectory of COVID-19 development in China, summarizes the critical policy arrangements and provides evidence for the adjustment during policy making process, so as to share experiences with international community and contribute to the global health for all humanity.

Source of SARS-CoV-2 infection: results from a series of 584,846 cases in France from October 2020 to August 2022

BACKGROUND

We aimed to study the source of infection for recently SARS-CoV-2-infected individuals from October 2020 to August 2022 in France.

METHODS

Participants from the nationwide ComCor case-control study who reported recent SARS-CoV-2 infection were asked to document the source and circumstances of their infection through an online questionnaire. Multivariable logistic regression was used to identify the factors associated with not identifying any source of infection.

RESULTS

Among 584,846 adults with a recent SARS-CoV-2 infection in France, 46.9% identified the source of infection and an additional 22.6% suspected an event during which they might have become infected. Known and suspected sources of infection were household members (30.8%), extended family (15.6%), work colleagues (15.0%), friends (11.0%), and possibly multiple/other sources (27.6%). When the source of infection was known, was not a household member, and involved a unique contact (n = 69,788), characteristics associated with transmission events were indoors settings (91.6%), prolonged (> 15 min) encounters (50.5%), symptomatic source case (64.9%), and neither the source of infection nor the participant wearing a mask (82.2%). Male gender, older age, lower education, living alone, using public transportation, attending places of public recreation (bars, restaurants, nightclubs), public gatherings, and cultural events, and practicing indoor sports were all independently associated with not knowing the source of infection.

CONCLUSION

Two-thirds of infections were attributed to interactions with close relatives, friends, or work colleagues. Extra-household indoor encounters without masks were commonly reported and represented avoidable circumstances of infection.

TRIAL REGISTRATION

ClinicalTrials.gov registration number: NCT04607941.

Cost-Effectiveness of Universal Asymptomatic Preoperative SARS-CoV-2 Polymerase Chain Reaction Screening: A Cost-Utility Analysis

BACKGROUND

An early report has shown the clinical benefit of the asymptomatic preoperative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) screening test, and some clinical guidelines recommended this test. However, the cost-effectiveness of asymptomatic screening was not evaluated. We aimed to investigate the cost-effectiveness of universal preoperative screening of asymptomatic patients for SARS-CoV-2 using polymerase chain reaction (PCR) testing.

METHODS

We evaluated the cost-effectiveness of asymptomatic screening using a decision tree model from a payer perspective, assuming that the test-positive rate was 0.07% and the screening cost was 8500 Japanese yen (JPY) (approximately 7601 US dollars [USD]). The input parameter was derived from the available evidence reported in the literature. A willingness-to-pay threshold was set at 5 000 000 JPY/quality-adjusted life-year (QALY).

RESULTS

The incremental cost of 1 death averted was 74 469 236 JPY (approximately 566 048 USD) and 291 123 368 JPY/QALY (approximately 2 212 856 USD/QALY), which was above the 5 000 000 JPY/QALY willingness-to-pay threshold. The incremental cost-effectiveness ratio fell below 5 000 000 JPY/QALY only when the test-positive rate exceeded 0.739%. However, when the probability of developing a postoperative pulmonary complication among SARS-CoV-2-positive patients was below 0.22, asymptomatic screening was never cost-effective, regardless of how high the test-positive rate became.

CONCLUSIONS

Asymptomatic preoperative universal SARS-CoV-2 PCR screening is not cost-effective in the base case analysis. The cost-effectiveness mainly depends on the test-positive rate, the frequency of postoperative pulmonary complications, and the screening costs; however, no matter how high the test-positive rate, the cost-effectiveness is poor if the probability of developing postoperative pulmonary complications among patients positive for SARS-CoV-2 is sufficiently reduced.

Increased circulation of human adenovirus in 2023: an investigation of the circulating genotypes, upper respiratory viral loads, and hospital admissions in a large academic medical center

IMPORTANCE

The circulation of human adenoviruses (HAdV) increased in 2023. In this manuscript, we show that HAdV-B3 was predominant in 2023 in a cohort characterized by the Johns Hopkins Hospital System. We also show that HAdV-B3 was associated with an increase in viral loads in respiratory samples and provide a correlation with the clinical presentations and outcomes.

Plasmon-Enhanced Digital Fluoroimmunoassay for Subfemtomolar Detection of Protein Biomarkers

Rapid and accurate quantification of low-abundance protein biomarkers in biofluids can transform the diagnosis of a range of pathologies, including infectious diseases. Here, we harness ultrabright plasmonic fluors as "digital nanolabels" and demonstrate the detection and quantification of subfemtomolar concentrations of human IL-6 and SARS-CoV-2 alpha and variant proteins in clinical nasopharyngeal swab and saliva samples from COVID-19 patients. The resulting digital plasmonic fluor-linked immunosorbent assay (digital p-FLISA) enables detection of SARS-CoV-2 nucleocapsid protein, both in solution and in live virions. Digital p-FLISA outperforms the "gold standard" enzyme-linked immunosorbent assay (ELISA), having a nearly 7000-fold lower limit-of-detection, and outperforms a commercial antigen test, having over 5000-fold improvement in analytical sensitivity. Detection and quantification of very low concentrations of target proteins holds potential for early detection of pathological conditions, treatment monitoring, and personalized medicine.

Transmission characteristics and inactivated vaccine effectiveness against transmission of the SARS-CoV-2 Omicron BA.2 variant in Shenzhen, China

We conducted a retrospective cohort study to evaluate the transmission risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 variant and the effectiveness of inactivated COVID-19 vaccine boosters in Shenzhen during a BA.2 outbreak period from 1 February to 21 April 2022. A total of 1,248 individuals were infected with the BA.2 variant, and 7,855 close contacts were carefully investigated. The risk factors for the high secondary attack rate of SARS-CoV-2 infection were household contacts [adjusted odds ratio (aOR): 1.748; 95% confidence interval (CI): 1.448, 2.110], younger individuals aged 0-17 years (aOR: 2.730; 95% CI: 2.118, 3.518), older persons aged ≥60 years (aOR: 1.342; 95% CI: 1.135, 1.588), women (aOR: 1.442; 95% CI: 1.210, 1.718), and the subjects exposed to the post-onset index cases (aOR: 8.546; 95% CI: 6.610, 11.050), respectively. Compared with the unvaccinated and partially vaccinated individuals, a relatively low risk of secondary attack was found for the individuals who received booster vaccination (aOR: 0.871; 95% CI: 0.761, 0.997). Moreover, a high transmission risk was found for the index cases aged ≥60 years (aOR: 1.359; 95% CI: 1.132, 1.632), whereas a relatively low transmission risk was observed for the index cases who received full vaccination (aOR: 0.642; 95% CI: 0.490, 0.841) and booster vaccination (aOR: 0.676; 95% CI: 0.594, 0.770). Compared with full vaccination, booster vaccination of inactivated COVID-19 vaccine showed an effectiveness of 24.0% (95% CI: 7.0%, 37.9%) against BA.2 transmission for the adults ≥18 years and 93.7% (95% CI: 72.4%, 98.6%) for the adults ≥60 years, whereas the effectiveness was 51.0% (95% CI: 21.9%, 69.3%) for the individuals of 14 days to 179 days after booster vaccination and 51.2% (95% CI: 37.5%, 61.9%) for the non-household contacts. The estimated mean values of the generation interval, serial interval, incubation period, latent period, and viral shedding period were 2.7 days, 3.2 days, 2.4 days, 2.1 days, and 17.9 days, respectively. In summary, our results confirmed that the main transmission route of Omicron BA.2 subvariant was household contact, and booster vaccination of the inactivated vaccines was relatively effective against BA.2 subvariant transmission in older people.

Personal protective measures and settings on the risk of SARS-COV-2 community transmission: a case-control study

Background

During the SARS-CoV-2 pandemic, nurses of primary health care has been an important role in Spain. Even so, the data obtained in the tracing have been scarcely used to investigate the possible mechanisms of transmission. Few studies focused on community transmission, evaluating the effectiveness of individual protective measures and exposure environment. The main aim of the study was to evaluate the association between individual protective measures and SARS-CoV-2 transmission in the community and to compare secondary attack rates in different exposure settings.

Methods

A case-control study from contact tracing of SARS-CoV-2 index patients. COVID-19 contact tracing was led by nurses at the COVID-19 Coordinating Centre in Majorca (Spain). During the systematic tracing, additional information for this study was collected from the index patient (social-demographic variables, symptoms, the number of close contacts). And also, the following variables from their close contacts: contact place, ventilation characteristics mask-wearing, type of mask, duration of contact, shortest distance, case-contact relationship, household members, and handwashing, the test result for SARS-CoV-2 diagnostic. Close contacts with a positive test for SARS-CoV-2 were classified as "cases" and those negative as "controls."

Results

A total of 1,778 close contacts from 463 index patients were identified. No significant differences were observed between the sexes but between age groups. Overall Secondary Attack Rate (SAR) was 24.0% (95% CI: 22.0-26.0%), 36.9% (95% CI: 33.2-40.6%) in closed spaces without ventilation and 50.7% (95% CI: 45.6-55.8%) in exposure time > 24 h. A total of 49.2% of infections occurred among household members. Multivariate logistic regression analysis showed that open-air setting (OR 0.43, 95% CI: 0.27-0.71), exposure for less than 1 h (OR 0.19, 95% CI: 0.11-0.32), and wearing a mask (OR 0.49, 95% CI: 0.28-0.85) had a protective effect transmission of SARS-CoV-2 in the community.

Conclusion

Ventilation of the space, mask-wearing and shorter exposure time were associated with a lower risk of transmission in the community. The data obtained allowed an assessment of community transmission mechanisms and could have helped to improve and streamline tracing by identifying close contacts at higher risk.

Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe

BACKGROUND

Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic.

METHODS

Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method.

FINDINGS

The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations.

IMPLICATIONS

We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics.

LIMITATIONS

The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.

Predictors of later COVID-19 test seeking

Background

Delays in COVID-19 testing may increase the risk of secondary household and community transmission. Little is known about what patient characteristics and symptom profiles are associated with delays in test seeking.

Methods

We conducted a retrospective cohort study of all symptomatic patients diagnosed with COVID-19 and assessed in a COVID Expansion to Outpatients (COVIDEO) virtual care program between March 2020 and June 2021. The primary outcome was later test seeking more than 3 days from symptom onset. Multivariable logistic regression was used to examine predictors of later testing including patient characteristics and symptoms (30 individual symptoms or 7 symptom clusters).

Results

Of 5,363 COVIDEO patients, 4,607 were eligible and 2,155/4,607 (46.8%) underwent later testing. Older age was associated with increased odds of late testing (adjusted odds ratio [aOR] 1.007/year; 95% CI 1.00 to 1.01), as was history of recent travel (aOR 1.4; 95% CI 1.01 to 1.95). Health care workers had lower odds of late testing (aOR 0.50; 95% CI 0.39 to 0.62). Late testing was associated with symptoms in the cardiorespiratory (aOR 1.2; 95% CI 1.05, 1.36), gastrointestinal (aOR = 1.2; 95% CI 1.04, 1.4), neurological (aOR 1.1; 95% CI 1.003, 1.3) and psychiatric (aOR 1.3; 95% CI 1.1, 1.5) symptom clusters. Among individual symptoms, dyspnea, anosmia, dysgeusia, sputum, and anorexia were associated with late testing; pharyngitis, myalgia, and headache were associated with early testing.

Conclusion

Certain patient characteristics and symptoms are associated with later testing, and warrant further efforts to encourage earlier testing to minimize transmission.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.

Quantifying the presymptomatic transmission of COVID-19 in the USA

The emergence of many presymptomatic hidden transmission events significantly complicated the intervention and control of the spread of COVID-19 in the USA during the year 2020. To analyze the role that presymptomatic infections play in the spread of this disease, we developed a state-level metapopulation model to simulate COVID-19 transmission in the USA in 2020 during which period the number of confirmed cases was more than in any other country. We estimated that the transmission rate (i.e., the number of new infections per unit time generated by an infected individual) of presymptomatic infections was approximately 59.9% the transmission rate of reported infections. We further estimated that {at any point in time the} average proportion of infected individuals in the presymptomatic stage was consistently over 50% of all infected individuals. Presymptomatic transmission was consistently contributing over 52% to daily new infections, as well as consistently contributing over 50% to the effective reproduction number from February to December. Finally, non-pharmaceutical intervention targeting presymptomatic infections was very effective in reducing the number of reported cases. These results reveal the significant contribution that presymptomatic transmission made to COVID-19 transmission in the USA during 2020, as well as pave the way for the design of effective disease control and mitigation strategies.

Remdesivir for the Treatment of COVID-19: A Narrative Review

Despite the wide availability of effective vaccines, COVID-19 continues to be an infectious disease of global importance. Remdesivir is a broad-spectrum antiviral and was the first US Food and Drug Administration-approved treatment for COVID-19. In clinical guidelines, remdesivir is currently the only recommended antiviral for use in hospitalized patients with COVID-19, with or without a supplemental oxygen requirement. It is also recommended for nonhospitalized patients with COVID-19 and hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who are at high risk of progression to severe disease. This narrative review explores the evidence for remdesivir across various clinical outcomes and evolution of clinical guidelines through a survey over time of randomized controlled trials, observational studies, and meta-analyses. Remdesivir, compared to standard of care, appears to improve survival and disease progression in a variety of patient populations with COVID-19 across a spectrum of disease severity and SARS-CoV-2 variant periods. Remdesivir also appears to improve time to clinical recovery, increase rate of recovery, and reduce time on supplemental oxygen and readmission rates. More recent large, real-world studies further support the early use of remdesivir in a range of patient populations, including those with immunocompromising conditions.

Fatal COVID-19 Infection in Two Children with STAT1 Gain-of-Function

While SARS-CoV-2 infection causes a mild disease in most children, SARS-CoV-2 infection may be lethal in a few of them. In the defense against SARS-CoV-2, type I interferons are key players, and several studies have identified a defective or neutralized interferon response as the cause of overwhelming viral infection. However, inappropriate, untimely, or excessive interferon production may also be detrimental to the host. Here, we describe two patients with STAT1 gain-of-function (GOF), a known type I interferonopathy, who died of COVID-19. Whole-exome sequencing and interferon-gamma-activated sequence (GAS) and interferon-sensitive responsive element (ISRE) reporter assay were performed to identify and characterize STAT1 variants. Patient 1 developed hemophagocytic lymphohistiocytosis (HLH) in the context of COVID-19 infection and died in less than a week at the age of 4 years. Patient 2 developed a high fever, cough, and hypoxemia and succumbed to COVID-19 pneumonia at the age of 5 years. Two heterozygous missense variants, p.E563Q and p.K344E, in STAT1 were identified. Functional validation by reporter assay and immunoblot confirmed that both variants are gain-of-function (GOF). GOF variants transiently expressing cells exhibited enhanced upregulation of downstream genes, including ISG15, MX1, and OAS1, in response to IFN-α stimulation. A catastrophic course with HLH or acute respiratory failure is thought to be associated with inappropriate immunoregulatory mechanisms to handle SARS-CoV-2 in STAT1 GOF. While most patients with inborn errors of immunity who developed COVID-19 seem to handle it well, these cases suggest that patients with STAT1-GOF might be at risk of developing fatal complications due to SARS-CoV-2.

Amino acid catabolite markers for early prognostication of pneumonia in patients with COVID-19

Effective early-stage markers for predicting which patients are at risk of developing SARS-CoV-2 infection have not been fully investigated. Here, we performed comprehensive serum metabolome analysis of a total of 83 patients from two cohorts to determine that the acceleration of amino acid catabolism within 5 days from disease onset correlated with future disease severity. Increased levels of de-aminated amino acid catabolites involved in the de novo nucleotide synthesis pathway were identified as early prognostic markers that correlated with the initial viral load. We further employed mice models of SARS-CoV2-MA10 and influenza infection to demonstrate that such de-amination of amino acids and de novo synthesis of nucleotides were associated with the abnormal proliferation of airway and vascular tissue cells in the lungs during the early stages of infection. Consequently, it can be concluded that lung parenchymal tissue remodeling in the early stages of respiratory viral infections induces systemic metabolic remodeling and that the associated key amino acid catabolites are valid predictors for excessive inflammatory response in later disease stages.

A Randomized Controlled Trial to Study the Transmission of SARS-CoV-2 and Other Respiratory Viruses During Indoor Clubbing Events (ANRS0066s ITOC Study)

BACKGROUND

In the context of the circulation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.617.2 (Delta) variant, vaccination re-authorized mass indoor gatherings. The "Indoor Transmission of coronavirus disease 2019 (COVID-19)" (ITOC) trial (ClinicalTrials.gov, NCT05311865) aimed to assess the risk of transmission of SARS-CoV-2 and other respiratory viruses during an indoor clubbing event among participants fully vaccinated against COVID-19.

METHODS

ITOC, a randomized controlled trial in the Paris region (France), enrolled healthy volunteers aged 18-49 years, fully vaccinated against COVID-19, with no comorbidities or symptoms, randomized 1:1 to be interventional group "attendees" or control "non-attendees." The intervention was a 7-hour indoor event in a nightclub at full capacity, with no masking, prior SARS-CoV-2 test result, or social distancing required. The primary outcome measure was the number of reverse transcriptase-polymerase chain reaction (RT-PCR)-determined SARS-CoV-2-positive subjects using self-collected saliva 7 days post-gathering in the per-protocol population. Secondary endpoints focused on 20 other respiratory viruses.

RESULTS

Healthy participants (n = 1216) randomized 2:1 by blocks up to 10 815 attendees and 401 non-attendees, yielding 529 and 287 subjects, respectively, with day-7 saliva samples. One day-7 sample from each group was positive. Looking at all respiratory viruses together, the clubbing event was associated with an increased risk of infection of 1.59 (95% CI, 1.04-2.61).

CONCLUSIONS

In the context of low Delta variant of concern circulation, no evidence of SARS-CoV-2 transmission among asymptomatic and vaccinated participants was found, but the risk of other respiratory virus transmission was higher. Clinical Trials Registration. ClinicalTrials.gov, NCT05311865.

An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic

The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed "within-host" and adheres to the susceptible-infected-recovered (SIR) process, referred to as "between-host." Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.

Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR

Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in regions of low prevalence, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Here, we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to December 2022 during the third, fourth, fifth, sixth, seventh, and eighth waves of the COVID-19 outbreak in Japan. Viral RNA was below the limit of detection in all samples during the third and fourth waves. However, by counting the number of positive replicates in qPCR of each sample, we found that the positive ratio to all replicates in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during the third, fourth, and fifth waves. Time-step analysis indicated that, for 2 days either side of symptom onset, COVID-19 patients excreted in their feces large amounts of virus that wastewater surveillance could detect. We also demonstrated that the viral genome copy number in wastewater, as estimated from the positive ratio of SARSA-CoV-2 RNA, was correlated with the number of clinically confirmed cases. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence.

The increase in SARS-CoV-2 lineages during 2020-2022 in a state in the Brazilian Northeast is associated with a number of cases

SARS-CoV-2 has caused a high number of deaths in several countries. In Brazil, there were 37 million confirmed cases of COVID-19 and 700,000 deaths caused by the disease. The population size and heterogeneity of the Brazilian population should be considered in epidemiological surveillance due to the varied tropism of the virus. As such, municipalities and states must be factored in for their unique specificities, such as socioeconomic conditions and population distribution. Here, we investigate the spatiotemporal dispersion of emerging SARS-CoV-2 lineages and their dynamics in each microregion from Sergipe state, northeastern Brazil, in the first 3 years of the pandemic. We analyzed 586 genomes sequenced between March 2020 and November 2022 extracted from the GISAID database. Phylogenetic analyses were carried out for each data set to reconstruct evolutionary history. Finally, the existence of a correlation between the number of lineages and infection cases by SARS-CoV-2 was evaluated. Aracaju, the largest city in northeastern Brazil, had the highest number of samples sequenced. This represented 54.6% (320) of the genomes, and consequently, the largest number of lineages identified. Studies also analyzed the relationship between mean lineage distributions and mean monthly infections, daily cases, daily deaths, and hospitalizations of vaccinated and unvaccinated patients. For this, a correlation matrix was created. Results revealed that the increase in the average number of SARS-CoV-2 variants was related to the average number of SARS-CoV-2 cases in both unvaccinated and vaccinated individuals. Thus, our data indicate that it is necessary to maintain epidemiological surveillance, especially in capital cities, since they have a high rate of circulation of resident and non-resident inhabitants, which contributes to the dynamics of the virus.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

On-site airborne pathogen detection for infection risk mitigation

Human-infecting pathogens that transmit through the air pose a significant threat to public health. As a prominent instance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic has affected the world in an unprecedented manner over the past few years. Despite the dissipating pandemic gloom, the lessons we have learned in dealing with pathogen-laden aerosols should be thoroughly reviewed because the airborne transmission risk may have been grossly underestimated. From a bioanalytical chemistry perspective, on-site airborne pathogen detection can be an effective non-pharmaceutic intervention (NPI) strategy, with on-site airborne pathogen detection and early-stage infection risk evaluation reducing the spread of disease and enabling life-saving decisions to be made. In light of this, we summarize the recent advances in highly efficient pathogen-laden aerosol sampling approaches, bioanalytical sensing technologies, and the prospects for airborne pathogen exposure measurement and evidence-based transmission interventions. We also discuss open challenges facing general bioaerosols detection, such as handling complex aerosol samples, improving sensitivity for airborne pathogen quantification, and establishing a risk assessment system with high spatiotemporal resolution for mitigating airborne transmission risks. This review provides a multidisciplinary outlook for future opportunities to improve the on-site airborne pathogen detection techniques, thereby enhancing the preparedness for more on-site bioaerosols measurement scenarios, such as monitoring high-risk pathogens on airplanes, weaponized pathogen aerosols, influenza variants at the workplace, and pollutant correlated with sick building syndromes.

Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure

Digital contact tracing and notification were initially hailed as promising strategies to combat SARS-CoV-2; however, in most jurisdictions, they did not live up to their promise. To avert a given transmission event, both parties must have adopted the technology, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in the effective reproduction number Rt would require an 80% success rate at each of these 6 points of failure. Here, we review the 6 failure rates experienced by a variety of digital contact tracing and contact notification schemes, including Singapore's TraceTogether, India's Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, for example, that the narrative be framed in terms of user autonomy rather than user privacy, and that tracing/notification apps be multifunctional and integrated with testing, manual contact tracing, and the gathering of critical scientific data.

Positive association of SARS-CoV-2 RNA concentrations in wastewater and reported COVID-19 cases in Singapore - A study across three populations

Wastewater testing of SARS-CoV-2 has been adopted globally and has shown to be a useful, non-intrusive surveillance method for monitoring COVID-19 trends. In Singapore, wastewater surveillance has been widely implemented across various sites and has facilitated timely COVID-19 management and response. From April 2020 to February 2022, SARS-CoV-2 RNA concentrations in wastewater monitored across three populations, nationally, in the community, and in High Density Living Environments (HDLEs) were aggregated into indices and compared with reported COVID-19 cases and hospitalisations. Temporal trends and associations of these indices were compared descriptively and quantitatively, using Poisson Generalised Linear Models and Generalised Additive Models. National vaccination rates and vaccine breakthrough infection rates were additionally considered as confounders to shedding. Fitted models quantified the temporal associations between the indices and cases and COVID-related hospitalisations. At the national level, the wastewater index was a leading indicator of COVID-19 cases (p-value <0.001) of one week, and a contemporaneous association with hospitalisations (p-value <0.001) was observed. At finer levels of surveillance, the community index was observed to be contemporaneously associated with COVID-19 cases (p-value <0.001) and had a lagging association of 1-week in HDLEs (p-value <0.001). These temporal differences were attributed to differences in testing routines for different sites during the study period and the timeline of COVID-19 progression in infected persons. Overall, this study demonstrates the utility of wastewater surveillance in understanding underlying COVID-19 transmission and shedding levels, particularly for areas with falling or low case ascertainment. In such settings, wastewater surveillance showed to be a lead indicator of COVID-19 cases. The findings also underscore the potential of wastewater surveillance for monitoring other infectious diseases threats.

Ultrasensitive lateral-flow assays via plasmonically active antibody-conjugated fluorescent nanoparticles

Lateral-flow assays (LFAs) are rapid and inexpensive, yet they are nearly 1,000-fold less sensitive than laboratory-based tests. Here we show that plasmonically active antibody-conjugated fluorescent gold nanorods can make conventional LFAs ultrasensitive. With sample-to-answer times within 20 min, plasmonically enhanced LFAs read out via a standard benchtop fluorescence scanner attained about 30-fold improvements in dynamic range and in detection limits over 4-h-long gold-standard enzyme-linked immunosorbent assays, and achieved 95% clinical sensitivity and 100% specificity for antibodies in plasma and for antigens in nasopharyngeal swabs from individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Comparable improvements in the assay's performance can also be achieved via an inexpensive portable scanner, as we show for the detection of interleukin-6 in human serum samples and of the nucleocapsid protein of SARS-CoV-2 in nasopharyngeal samples. Plasmonically enhanced LFAs outperform standard laboratory tests in sensitivity, speed, dynamic range, ease of use and cost, and may provide advantages in point-of-care diagnostics.

Time warping between main epidemic time series in epidemiological surveillance

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.

Estimating the instantaneous reproduction number (Rt) by using particle filter

Background

Monitoring the transmission of coronavirus disease 2019 (COVID-19) requires accurate estimation of the effective reproduction number (R t ). However, existing methods for calculating R t may yield biased estimates if important real-world factors, such as delays in confirmation, pre-symptomatic transmissions, or imperfect data observation, are not considered.

Method

To include real-world factors, we expanded the susceptible-exposed-infectious-recovered (SEIR) model by incorporating pre-symptomatic (P) and asymptomatic (A) states, creating the SEPIAR model. By utilizing both stochastic and deterministic versions of the model, and incorporating predetermined time series of R t , we generated simulated datasets that simulate real-world challenges in estimating R t . We then compared the performance of our proposed particle filtering method for estimating R t with the existing EpiEstim approach based on renewal equations.

Results

The particle filtering method accurately estimated R t even in the presence of data with delays, pre-symptomatic transmission, and imperfect observation. When evaluating via the root mean square error (RMSE) metric, the performance of the particle filtering method was better in general and was comparable to the EpiEstim approach if perfectly deconvolved infection time series were provided, and substantially better when R t exhibited short-term fluctuations and the data was right truncated.

Conclusions

The SEPIAR model, in conjunction with the particle filtering method, offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies. This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.

A rapid, low-cost, and highly sensitive SARS-CoV-2 diagnostic based on whole-genome sequencing

Early detection of SARS-CoV-2 infection is key to managing the current global pandemic, as evidence shows the virus is most contagious on or before symptom onset. Here, we introduce a low-cost, high-throughput method for diagnosing and studying SARS-CoV-2 infection. Dubbed Pathogen-Oriented Low-Cost Assembly & Re-Sequencing (POLAR), this method amplifies the entirety of the SARS-CoV-2 genome. This contrasts with typical RT-PCR-based diagnostic tests, which amplify only a few loci. To achieve this goal, we combine a SARS-CoV-2 enrichment method developed by the ARTIC Network (https://artic.network/) with short-read DNA sequencing and de novo genome assembly. Using this method, we can reliably (>95% accuracy) detect SARS-CoV-2 at a concentration of 84 genome equivalents per milliliter (GE/mL). The vast majority of diagnostic methods meeting our analytical criteria that are currently authorized for use by the United States Food and Drug Administration with the Coronavirus Disease 2019 (COVID-19) Emergency Use Authorization require higher concentrations of the virus to achieve this degree of sensitivity and specificity. In addition, we can reliably assemble the SARS-CoV-2 genome in the sample, often with no gaps and perfect accuracy given sufficient viral load. The genotypic data in these genome assemblies enable the more effective analysis of disease spread than is possible with an ordinary binary diagnostic. These data can also help identify vaccine and drug targets. Finally, we show that the diagnoses obtained using POLAR of positive and negative clinical nasal mid-turbinate swab samples 100% match those obtained in a clinical diagnostic lab using the Center for Disease Control's 2019-Novel Coronavirus test. Using POLAR, a single person can manually process 192 samples over an 8-hour experiment at the cost of ~$36 per patient (as of December 7th, 2022), enabling a 24-hour turnaround with sequencing and data analysis time. We anticipate that further testing and refinement will allow greater sensitivity using this approach.

Modelling the spatial spread of COVID-19 in a German district using a diffusion model

In this study, we focus on modeling the local spread of COVID-19 infections. As the pandemic continues and new variants or future pandemics can emerge, modelling the early stages of infection spread becomes crucial, especially as limited medical data might be available initially. Therefore, our aim is to gain a better understanding of the diffusion dynamics on smaller scales using partial differential equation (PDE) models. Previous works have already presented various methods to model the spatial spread of diseases, but, due to a lack of data on regional or even local scale, few actually applied their models on real disease courses in order to describe the behaviour of the disease or estimate parameters. We use medical data from both the Robert-Koch-Institute (RKI) and the Birkenfeld district government for parameter estimation within a single German district, Birkenfeld in Rhineland-Palatinate, during the second wave of the pandemic in autumn 2020 and winter 2020-21. This district can be seen as a typical middle-European region, characterized by its (mainly) rural nature and daily commuter movements towards metropolitan areas. A basic reaction-diffusion model used for spatial COVID spread, which includes compartments for susceptibles, exposed, infected, recovered, and the total population, is used to describe the spatio-temporal spread of infections. The transmission rate, recovery rate, initial infected values, detection rate, and diffusivity rate are considered as parameters to be estimated using the reported daily data and least square fit. This work also features an emphasis on numerical methods which will be used to describe the diffusion on arbitrary two-dimensional domains. Two numerical optimization techniques for parameter fitting are used: the Metropolis algorithm and the adjoint method. Two different methods, the Crank-Nicholson method and a finite element method, which are used according to the requirements of the respective optimization method are used to solve the PDE system. This way, the two methods are compared and validated and provide similar results with good approximation of the infected in both the district and the respective sub-districts.

Monitoring SARS-CoV-2 Using Infoveillance, National Reporting Data, and Wastewater in Wales, United Kingdom: Mixed Methods Study

BACKGROUND

The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.

OBJECTIVE

This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.

METHODS

Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.

RESULTS

Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.

CONCLUSIONS

Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.

Reproduction number projection for the COVID-19 pandemic

The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.

The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number $\mathcal{R}_{0}$ R 0 for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.

The Imperial College London (ICL) group’s estimate of $\mathcal{R}_{0}$ R 0 for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t-test or not epidemiologically significant.

The SARS-CoV2 Delta variant’s $\mathcal{R}_{0}$ R 0 is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.

Management of a large outbreak of COVID-19 at a British Army training centre: lessons for the future

INTRODUCTION

The COVID-19 pandemic has posed major challenges for infection control within training centres, both civilian and military. Here we present a narrative review of an outbreak that occurred at the Royal Military Academy Sandhurst (RMAS) in January-March 2021, in the context of the circulating, highly transmissible SARS-CoV-2 variant B.1.1.7.

METHODS

Testing for SARS-CoV-2 was performed using a combination of reverse transcriptase PCR and Lateral Flow Devices (LFDs). Testing and isolation procedures were conducted in line with a pre-established symptom stratification system. Genomic sequencing was performed on 10 sample isolates.

RESULTS

By the end of the outbreak, 185 cases (153 Officer Cadets, 32 permanent staff) had contracted confirmed COVID-19. This represented 15% of the total RMAS population. This resulted in 0 deaths and 0 hospitalisations, but due to necessary isolation procedures did represent an estimated 12 959 person-days of lost training. 9 of 10 (90%) of sequenced isolates had a reportable lineage. All of those reported were found to be the Alpha lineage B.1.1.7.

CONCLUSIONS

We discuss the key lessons learnt from the after-action review by the Incident Management Team. These include the importance of multidisciplinary working, the utility of sync matrices to monitor outbreaks in real time, issues around Officer Cadets reporting symptoms, timing of high-risk training activities, infrastructure and use of LFDs. COVID-19 represents a vital learning opportunity to minimise the impact of potential future pandemics, which may produce considerably higher morbidity and mortality in military populations.