Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic

An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe. Unlike previous review studies, this review encompasses newly developed approaches such as the combined model and Hawkes model after the COVID-19 pandemic, providing a thorough evaluation of their current application status and development prospects for the first time. These models not only rely on conventional surveillance data but also incorporate information from various sources. We aim to provide valuable insights for enhancing global infectious disease surveillance and early warning systems, as well as informing future research in this field, by summarizing the underlying modeling concepts, algorithms, and application scenarios of each model.

The rules in co-infection of multiple viruses across diverse lineages in a fungal host

Viruses, ubiquitous non-cellular organisms, pose significant threats to human health and to the agricultural productivity of both livestock and crops. Emerging evidence indicates that multiple viruses can infect a single host, and viral co-infection can exert a profound influence on host physiology. However, our understanding of the prevalence of co-infection and the compatibility of phylogenetically distant viruses is still limited. In this study, we surveyed 406 field strains of the plant fungal pathogen Botrytis cinerea and identified 76 mycoviruses. Strikingly, 404 strains were co-infected with two or more viruses, with some harboring up to 25 viruses simultaneously. We discerned significant preference patterns among viruses in their host. Specifically, we identified "one-to-one" and "two-to-one" rules, wherein one or two viruses could be used to reliably predict the presence or absence of other viruses in the same host, and validated these predicted rules by using five B. cinerea strains. Furthermore, through the RNA-sequencing approach, we uncovered B. cinerea genes associated with the differences caused by different sets of co-infecting viruses. These are implicated in integral components of membrane, transmembrane transporter activity, autophagy pathways, mitophagy pathway, fatty acid biosynthetic process, sphingolipid metabolism, and glycosphingolipid biosynthesis. Our findings underscore the high prevalence of co-infection by multiple viruses in a fungal host within a population and highlight compatibility dynamics among phylogenetically diverse viruses. These insights contribute to our understanding of viral ecology and hold promise for informing strategies to manage viral diseases effectively.

IMPORTANCE

Viruses, pervasive threats to both humans and agriculture, often infect hosts concurrently, profoundly impacting physiology. Despite this, the prevalence and compatibility of co-infecting viruses remain poorly understood. In the study of 406 Botrytis cinerea strains, we discovered a striking phenomenon: 404 out of the 406 strains hosted multiple viruses, some with up to 25 at once. Through rigorous analysis, we unveiled distinct preference patterns among these viruses within hosts, identifying predictive co-infection rules validated by experimentation. Furthermore, we identified genes linked to these dynamics, shedding light on critical cellular processes involved in the regulation of the co-infection rules. These findings highlight the widespread nature of viral co-infection and offer insights crucial for effectively managing viral diseases.

Global containment policy duration and long-term epidemic progression: A target trial emulation using COVID-19 data from 2020 to 2022

OBJECTIVES

Global countries often apply containment policies (CPs) to combat infectious disease surges. Whether countries with longer cumulative duration of CPs are associated with slower long-term epidemic progression necessitates a thorough evaluation.

METHODS

We collected CP and COVID-19 data of 185 territories during 2020-2022, with a total of 23 CPs. Using the target trial emulation and cloning-censoring-weighting approaches, we assessed the effectiveness of CPs with different cumulative durations in delaying countries from reaching the 1% and 10% cumulative infection incidence end points (i.e. 10,000 and 100,000 COVID-19 cases per million population, respectively) over a 3-year observation period.

RESULTS

For reaching the 1% cumulative infection incidence, recommending closing workplaces and limiting gatherings to 10 people, each presented that a longer cumulative duration of those CPs is associated with a lower proportion of countries achieving this end point throughout 2020-2022. For reaching the 10% cumulative infection incidence, mandatory bans on public events and domestic movements, closing public transports, and screening and quarantining inbound tourists, each showed similar associations. Notably, long-lasting border bans upon high-risk regions are associated with a higher proportion of countries reaching the 10% cumulative infection incidence.

CONCLUSIONS

From the long-term perspective, we highlight CPs that warrant extending the duration to achieve slower epidemic progression. By contrast, our findings demonstrate the limited effectiveness of the ban on regions in slowing the long-term epidemic progression.

Adaptive human behavior and delays in information availability autonomously modulate epidemic waves

The recurrence of epidemic waves has been a hallmark of infectious disease outbreaks. Repeated surges in infections pose significant challenges to public health systems, yet the mechanisms that drive these waves remain insufficiently understood. Most prior models attribute epidemic waves to exogenous factors, such as transmission seasonality, viral mutations, or implementation of public health interventions. We show that epidemic waves can emerge autonomously from the feedback loop between infection dynamics and human behavior. Our results are based on a behavioral framework in which individuals continuously adjust their level of risk mitigation subject to their perceived risk of infection, which depends on information availability and disease severity. We show that delayed behavioral responses alone can lead to the emergence of multiple epidemic waves. The magnitude and frequency of these waves depend on the interplay between behavioral factors (delay, severity, and sensitivity of responses) and disease factors (transmission and recovery rates). Notably, if the response is either too prompt or excessively delayed, multiple waves cannot emerge. Our results further align with previous observations that adaptive human behavior can produce nonmonotonic final epidemic sizes, shaped by the trade-offs between various biological and behavioral factors-namely, risk sensitivity, response stringency, and disease generation time. Interestingly, we found that the minimal final epidemic size occurs on regimes that exhibit a few damped oscillations. Altogether, our results emphasize the importance of integrating social and operational factors into infectious disease models, in order to capture the joint evolution of adaptive behavioral responses and epidemic dynamics.

Protection conferred by SARS-CoV-2 infection across a spectrum of reinfection symptoms and severities

BACKGROUND

SARS-CoV-2 infection is associated with protection against reinfection. This study analysed this protection across different reinfection symptoms and severities, comparing the preomicron and omicron eras.

METHODS

A nationwide, matched, test-negative, case-control study was conducted in Qatar from 5 February 2020 to 12 March 2024. The preomicron analysis used a sample of 509 949 positive and 8 494 782 negative tests, while the omicron analysis included 682 257 positive and 6 904 044 negative tests. Data were sourced from Qatar's national databases for COVID-19 laboratory testing, vaccination, hospitalisation and death.

RESULTS

Effectiveness of preomicron infection against preomicron reinfection was estimated at 80.9% (95% CI: 79.1% to 82.6%) for asymptomatic reinfection, 87.5% (95% CI: 86.1% to 88.9%) for symptomatic reinfection, 97.8% (95% CI: 95.7% to 98.9%) for severe COVID-19 reinfection, 100.0% (95% CI: 97.5% to 100.0%) for critical COVID-19 reinfection and 88.1% (95% CI: 50.3% to 97.2%) for fatal COVID-19 reinfection. For omicron infection against omicron reinfection, the estimates were 46.4% (95% CI: 36.9% to 54.4%) for asymptomatic reinfection, 52.8% (95% CI: 44.4% to 60.0%) for symptomatic reinfection, 100.0% (95% CI: 55.4% to 100.0%) for severe COVID-19 reinfection, 100.0% (95% CI: 15.1% to 100.0%) for critical COVID-19 reinfection, and 75.2% (95% CI: -58.8% to 97.5%) for fatal COVID-19 reinfection. Effectiveness over time since previous infection showed no discernible decline in protection against all forms of reinfection in the preomicron era, but a rapid decline against asymptomatic and symptomatic reinfections in the omicron era.

CONCLUSIONS

A gradient of protection against reinfection is evident, with the highest protection observed against severe forms of COVID-19. Over time, this gradient becomes more pronounced, as protection against asymptomatic and symptomatic reinfections decreases, while protection against severe outcomes remains strong.

Optimal control analysis on the spread of COVID-19: Impact of contact transmission and environmental contamination

The study investigates the intricate dynamics of SARS-CoV-2 transmission, with a particular focus on both close-contact interactions and environmental factors. Using advanced mathematical modeling and epidemiological analysis, explored the effects of these transmission pathways on the spread of COVID-19. The equilibrium points for both disease-free and endemic states are calculated and evaluated to determine their global stability. Additionally, the basic reproduction number (R0) is derived to quantify the transmission potential of the virus. To ensure model accuracy, numerical simulations are performed using MATLAB, utilizing daily COVID-19 case data from India. Parameter values are sourced from existing literature, with certain parameters estimated through fitting the model to observed data. Crucially, the model incorporates environmental transmission factors, such as surface contamination and airborne spread. The inclusion of these factors provides a more comprehensive understanding of the virus's spread, demonstrating the importance of interventions like use of face masks, environmental sanitization, vaccine efficacy, availability of treatment resources underappreciated when focusing solely on direct human contact. A sensitivity analysis is conducted to assess the impact of different parameters on R0, with results visualized through heat maps to identify the most influential factors. Furthermore, Pontryagin's maximum principle is employed to develop an optimal control model, enabling the formulation of effective intervention strategies. By analysing both interpersonal and environmental transmission mechanisms, this study offers a more holistic framework for understanding SARS-CoV-2 transmission. The insights gained are critical for informing public health strategies, emphasizing the necessity of addressing both direct contact and environmental sources of infection to more effectively manage current and future outbreaks.

Development of a Fluorescence Polarization Assay for the SARS-CoV-2 Papain-like Protease

The COVID-19 pandemic has caused significant losses to the global community. Although effective vaccination and antiviral therapeutics provide primary defense, SARS-CoV-2 remains a public health threat, given the emerging resistant variants. The SARS-CoV-2 papain-like protease (PLpro) is essential for viral replication and is a promising drug target. We recently designed a series of biarylphenyl PLpro inhibitors with a representative lead Jun12682 showing potent antiviral efficacy in a SARS-CoV-2 infection mouse model. In this study, we designed a fluorescein-labeled biarylphenyl probe Jun12781 and used it to optimize a fluorescence polarization (FP) assay. The FP assay is suitable for high-throughput screening with a Z' factor of 0.69. In addition, we found a positive correlation between the FP binding affinity and the enzymatic inhibitory potency of PLpro inhibitors, suggesting that the FP assay is valid in characterizing the binding affinity of PLpro inhibitors.

Increasing child vaccination coverage can reduce influenza cases across age groups: An agent-based modeling study

OBJECTIVES

Availability of caregiver-administered nasal spray live attenuated influenza vaccine (LAIV) raises the potential for increased influenza vaccine uptake. Direct and indirect benefits (decreased influenza cases and hospitalizations) of increased uptake among school-age children may be realized across the age spectrum. We used an agent-based model to determine the extent to which increased vaccination of children might affect overall influenza epidemiology.

METHODS

The Framework for Reproducing Epidemiological Dynamics (FRED) uses a population based on the US census and accounts for individual characteristics to estimate the effect of changes in parameters including vaccine uptake, on outcomes. We modeled increases in vaccine uptake among school-age children 5-17 years old on influenza cases and hospitalizations by age group.

RESULTS

Increasing vaccination rates in school-aged children by 5%-15% decreased their symptomatic influenza cases by 3.2%-10.9%, and among all age groups by 3.3%-11.6%, corresponding to an estimated annual reduction in cases of 522,867-1,810,170 among school-age children and of 1,394,687-4,945,952 overall. Annual U.S. hospitalizations could decrease by as much as 49,977, with the greatest impact (23,258) in those ages 65 years and over.

CONCLUSIONS

The opportunity to increase vaccination coverage in school-age children using LAIV can have a positive impact across all ages.

Differential protection against SARS-CoV-2 reinfection pre- and post-Omicron

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved over short timescales, leading to the emergence of more transmissible variants such as Alpha and Delta1-3. The arrival of the Omicron variant marked a major shift, introducing numerous extra mutations in the spike gene compared with earlier variants1,2. These evolutionary changes have raised concerns regarding their potential impact on immune evasion, disease severity and the effectiveness of vaccines and treatments1,3. In this epidemiological study, we identified two distinct patterns in the protective effect of natural infection against reinfection in the Omicron versus pre-Omicron eras. Before Omicron, natural infection provided strong and durable protection against reinfection, with minimal waning over time. However, during the Omicron era, protection was robust only for those recently infected, declining rapidly over time and diminishing within a year. These results demonstrate that SARS-CoV-2 immune protection is shaped by a dynamic interaction between host immunity and viral evolution, leading to contrasting reinfection patterns before and after Omicron's first wave. This shift in patterns suggests a change in evolutionary pressures, with intrinsic transmissibility driving adaptation pre-Omicron and immune escape becoming dominant post-Omicron, underscoring the need for periodic vaccine updates to sustain immunity.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters. The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30-60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots. We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Modeling-based design of adaptive control strategy for the effective preparation of 'Disease X'

This study aims at exploring a general and adaptive control strategy to confront the rapid evolution of an emerging infectious disease ('Disease X'), drawing lessons from the management of COVID-19 in China. We employ a dynamic model incorporating age structures and vaccination statuses, which is calibrated using epidemic data. We therefore estimate the cumulative infection rate (CIR) during the first epidemic wave of Omicron variant after China relaxed its zero-COVID policy to be 82.9% (95% CI: 82.3%, 83.5%), with a case fatality rate (CFR) of 0.25% (95% CI: 0.248%, 0.253%). We further show that if the zero-COVID policy had been eased in January 2022, the CIR and CFR would have decreased to 81.64% and 0.205%, respectively, due to a higher level of immunity from vaccination. However, if we ease the zero-COVID policy during the circulation of Delta variant from June 2021, the CIR would decrease to 74.06% while the CFR would significantly increase to 1.065%. Therefore, in the face of a 'Disease X', the adaptive strategies should be guided by multiple factors, the 'zero-COVID-like' policy could be a feasible and effective way for the control of a variant with relative low transmissibility. However, we should ease the strategy as the virus matures into a new variant with much higher transmissibility, particularly when the population is at a high level of immunity.

Analysis of antibacterial drug use and bacterial resistance in psychiatric hospital in the epidemic

Analyze the use of antibacterial drugs and bacterial resistance in psychiatric hospital during the epidemic. Using the hospital information system and the National Antibacterial Drug Clinical Application Monitoring Network, we retrospectively collected data on the use of antibacterial drugs and bacterial resistance in psychiatric hospitals during the 2022 epidemic. During the 2022 epidemic, our hospital had an antibiotic use rate of 5.00%, a usage intensity of 3.07, a combined medication rate of 11.11%, a cumulative DDDs of 12,039.04, and antibiotic costs accounting for 3.95% of total drug costs. These are much lower than the levels in Jiangsu Province and nationwide. However, the rate of microbiological submission for antibacterial drug use was 77.78%, higher than that of Jiangsu Province and nationwide. The main antibiotics used in our hospital were third-generation cephalosporins, penicillins, and quinolone antibiotics, with the most commonly used being cefodizime, amoxicillin, and piperacillin-tazobactam. The results showed that Gram-negative bacteria mainly exhibited resistance to penicillins, cephalosporins, and quinolones, especially ampicillin, amoxicillin-clavulanic acid, ceftazidime, ceftriaxone, amikacin, and ciprofloxacin. Gram-positive bacteria mainly resisted penicillins, macrolides, and quinolones, especially penicillin, benzylpenicillin, erythromycin, levofloxacin, and ciprofloxacin. This study reveals a complex relationship between the rational use of antibacterial drugs and bacterial resistance in the psychiatric hospital. Although antimicrobial usage during the pandemic was generally appropriate, increased use in psychiatric settings correlated with rising bacterial resistance, thereby impacting treatment outcomes and patient prognosis. Therefore, it is recommended to enhance monitoring of bacterial resistance and regularly analyze resistance data to optimize antimicrobial use in psychiatric hospitals. This approach aims to ensure effective treatment while minimizing the development of resistant strains, ultimately improving the overall value of healthcare services.

Intersecting Memories of Immunity and Climate: Potential Multiyear Impacts of the El Niño-Southern Oscillation on Infectious Disease Spread

Climate and infectious diseases each present critical challenges on a warming planet, as does the influence of climate on disease. Both are governed by nonlinear feedbacks, which drive multi-annual cycles in disease outbreaks and weather patterns. Although climate and weather can influence infectious disease transmission and have spawned rich literature, the interaction between the independent feedbacks of these two systems remains less explored. Here, we demonstrate the potential for long-lasting impacts of El Niño-Southern Oscillation (ENSO) events on disease dynamics using two approaches: interannual perturbations of a generic SIRS model to represent ENSO forcing, and detailed analysis of realistic specific humidity data in an SIRS model with endemic coronavirus (HCoV-HKU1) parameters. Our findings reveal the importance of considering nonlinear feedbacks in susceptible population dynamics for predicting and managing disease risks associated with ENSO-related weather variations.

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states. Our proposed hybrid SEIRV-DNNs model can also be applied to other infectious diseases such as influenza and dengue, with some modifications to the compartments and parameters in the model to accommodate the related control measures. This approach will facilitate improving predictive modeling and optimizing public health intervention strategies.

Visual and High-Efficiency Secretion of SARS-CoV-2 Nanobodies with Escherichia coli

Nanobodies have gained attention as potential therapeutic and diagnostic agents for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to their ability to bind and neutralize the virus. However, rapid, scalable, and robust production of nanobodies for SARS-CoV-2 remains a crucial challenge. In this study, we developed a visual and high-efficiency biomanufacturing method for nanobodies with Escherichia coli by fusing the super-folder green fluorescent protein (sfGFP) to the N-terminus or C-terminus of the nanobody. Several receptor-binding domain (RBD)-specific nanobodies of the SARS-CoV-2 spike protein (S) were secreted onto the surface of E. coli cells and even into the culture medium, including Fu2, ANTE, mNb6, MR3-MR3, and n3113.1. The nanobodies secreted by E. coli retained equal activity as prior research, regardless of whether sfGFP was removed. Since some of the nanobodies bound to different regions of the RBD, we combined two nanobodies to improve the affinity. Fu2-sfGFP-ANTE was constructed to be bispecific for the RBD, and the bispecific nanobody exhibited significantly higher affinity than Fu2 (35.0-fold), ANTE (7.3-fold), and the combination of the two nanobodies (3.3-fold). Notably, Fu2-sfGFP-ANTE can be normally secreted into the culture medium and outer membrane. The novel nanobody production system enhances the efficiency of nanobody expression and streamlines the downstream purification process, enabling large-scale, cost-effective nanobody production. In addition, E. coli cells secreting the nanobodies on their surface facilitates screening and characterization of antigen-binding clones.

Household transmission of SARS-CoV-2 in five US jurisdictions: Comparison of Delta and Omicron variants

Households are a significant source of SARS-CoV-2 transmission, even during periods of low community-level spread. Comparing household transmission rates by SARS-CoV-2 variant may provide relevant information about current risks and prevention strategies. This investigation aimed to estimate differences in household transmission risk comparing the SARS-CoV-2 Delta and Omicron variants using data from contact tracing and interviews conducted from November 2021 through February 2022 in five U.S. public health jurisdictions (City of Chicago, Illinois; State of Connecticut; City of Milwaukee, Wisconsin; State of Maryland; and State of Utah). Generalized estimating equations were used to estimate attack rates and relative risks for index case and household contact characteristics. Data from 848 households, including 2,622 individuals (median household size = 3), were analyzed. Overall transmission risk was similar in households with Omicron (attack rate = 47.0%) compared to Delta variant (attack rate = 48.0%) circulation. In the multivariable model, a pattern of increased transmission risk was observed with increased time since a household contact's last COVID-19 vaccine dose in Delta households, although confidence intervals overlapped (0-3 months relative risk = 0.8, confidence interval: 0.5-1.2; 4-7 months relative risk = 1.3, 0.9-1.8; ≥8 months relative risk = 1.2, 0.7-1.8); no pattern was observed in Omicron households. Risk for household contacts of symptomatic index cases was twice that of household contacts of asymptomatic index cases (relative risk = 2.0, 95% confidence interval: 1.4-2.9), emphasizing the importance of symptom status, regardless of variant. Uniquely, this study adjusted risk estimates for several index case and household contact characteristics and demonstrates that few characteristics strongly dictate risk, likely reflecting the complexity of the biological and social factors which combine to impact SARS-CoV-2 transmission.

State-space modelling using wastewater virus and epidemiological data to estimate reported COVID-19 cases and the potential infection numbers

The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence of SARS-CoV-2 due to a decrease in reporting rates, leading to missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist in estimating potential infections, including asymptomatic and unreported infections. Understanding the COVID-19 situation hidden behind the reported cases is critical for decision-making when choosing appropriate social intervention measures. However, current models implicitly assume homogeneity in human behaviour, such as virus shedding patterns within the population, making it challenging to predict the emergence of new variants due to variant-specific transmission or shedding parameters. This can result in predictions with considerable uncertainty. In this study, we established a state-space model based on wastewater viral load to predict both reported cases and potential infection numbers. Our model using wastewater virus data showed high goodness-of-fit to COVID-19 case numbers despite the dataset including waves of two distinct variants. Furthermore, the model successfully provided estimates of potential infection, reflecting the superspreading nature of SARS-CoV-2 transmission. This study supports the notion that wastewater surveillance and state-space modelling have the potential to effectively predict both reported cases and potential infections.

Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%-60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

Gut microbiota in post-acute COVID-19 syndrome: not the end of the story

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has led to major global health concern. However, the focus on immediate effects was assumed as the tip of iceberg due to the symptoms following acute infection, which was defined as post-acute COVID-19 syndrome (PACS). Gut microbiota alterations even after disease resolution and the gastrointestinal symptoms are the key features of PACS. Gut microbiota and derived metabolites disorders may play a crucial role in inflammatory and immune response after SARS-CoV-2 infection through the gut-lung axis. Diet is one of the modifiable factors closely related to gut microbiota and COVID-19. In this review, we described the reciprocal crosstalk between gut and lung, highlighting the participation of diet and gut microbiota in and after COVID-19 by destroying the gut barrier, perturbing the metabolism and regulating the immune system. Therefore, bolstering beneficial species by dietary supplements, probiotics or prebiotics and fecal microbiota transplantation (FMT) may be a novel avenue for COVID-19 and PACS prevention. This review provides a better understanding of the association between gut microbiota and the long-term consequences of COVID-19, which indicates modulating gut dysbiosis may be a potentiality for addressing this multifaceted condition.

HealthPass: a contactless check-in and adaptive access control system for lowering cluster infection risk in public health crisis

Introduction

Ensuring effective measures against the spread of the virus is paramount for educational institutions and workplaces as they resume operations amidst the ongoing public health crisis. A touchless and privacy-conscious check-in procedure for visitor assessment is critical to safeguarding venues against potential virus transmission.

Methods

In our study, we developed an interaction-free entry system featuring anonymous visitors who voluntarily provide data. This system introduces an adaptable venue entry management mechanism that accounts for both visitors' potential risk and the venue's capacity, aiming to curb the risk of localized infections. We assess visitors' liability based on their voluntarily provided data through radar map analysis. Additionally, we evaluate the venue's situation by quantifying its risk from multiple dimensions. A queuing model is then employed to control visitor access adaptively based on visitors' liability and the venue's availability.

Results

Since May, our university campus has been the operational site for the implemented system, catering to the needs of visitors across distinct venues. Using real-world implementation, we conduct a series of simulation experiments and case studies to verify the effectiveness of the HealthPass system in lowering infection risks.

Discussion

The system has demonstrated its capacity to reduce infection risks by adapting visitor entry procedures based on individual risk factors and venue conditions. Our results suggest that the integration of a dynamic queuing model and real-time data analysis can effectively manage the flow of visitors while ensuring public health safety.

Social dilemma of nonpharmaceutical interventions: Determinants of dynamic compliance and behavioral shifts

In fighting infectious diseases posing a global health threat, ranging from influenza to Zika, nonpharmaceutical interventions (NPI), such as social distancing and face covering, remain mitigation measures public health can resort to. However, the success of NPI lies in sufficiently high levels of collective compliance, otherwise giving rise to recurrent infections that are not only driven by pathogen evolution but also changing vigilance in the population. Here, we show that compliance with each NPI measure can be highly dynamic and context-dependent during an ongoing epidemic, where individuals may prefer one to another or even do nothing, leading to intricate temporal switching behavior of NPI adoptions. By characterizing dynamic regimes through the perceived costs of NPI measures and their effectiveness in particular regarding face covering and social distancing, our work offers insights into overcoming barriers in NPI adoptions.

Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models' preliminary assessment about Omicron's transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.

Post-acute sequelae of COVID-19 in cancer patients: Two cohorts in UK and Hong Kong

BACKGROUND

Limited research exists on the risks and spectrum of complications in post-acute phase of COVID-19 in cancer patients. This study aimed to evaluate the post-acute effects of COVID-19 on different types of morbidities among cancer patients across two regions with different healthcare systems and dominant variants of COVID-19.

MATERIALS AND METHODS

Cancer patients with COVID-19 from the UK Biobank (UKB, n = 2230; March 16, 2020 to May 31, 2021; pre-Omicron-variants dominant) and electronic medical records in Hong Kong (HK cohort, n = 22,335; April 1, 2020 to October 31, 2022; Omicron-variant dominant) were included. Each COVID-19 case was randomly matched with up to 10 non-COVID-19 cancer patients based on age and sex. Follow-up lasted until 31 August 2021 for UKB and 23 January 2023 for HK. Inverse probability treatment weighting balanced cohort characteristics. Cox regression evaluated the association of COVID-19 with morbidities occurred 30 days post-infection.

RESULTS

Cancer patients with COVID-19 consistently showed significantly higher risk of major cardiovascular diseases (CVDs) [UKB: hazard ratio [HR] 1.8 (95% CI 1.3, 2.5); HK: HR 1.4 (95% CI 1.1, 1.8)], CVD death [UKB: HR 4.3 (95% CI 2.9, 6.2); HK: HR 1.7 (95% CI 1.3, 2.4)], and all-cause mortality [UKB: HR 4.7 (95% CI 4.0, 5.5); HK: HR 1.6 (95% CI 1.5, 1.7)] in both cohorts despite the difference in dominant variants. Cancer patients at advanced ages or severely infected had higher all-cause mortality risk. However, associations between COVID-19 and CVDs became insignificant for fully vaccinated patients.

CONCLUSION

COVID-19 infection is associated with increased risks of CVDs and mortality in cancer patients. Fully vaccination may reduce the post-acute effects of COVID-19 on CVDs. This information may guide effective pre-emptive measures to reduce COVID-19-related morbidities and mortality in cancer patients.

Predicting the impact of non-pharmaceutical interventions against COVID-19 on Mycoplasma pneumoniae in the United States

The introduction of non-pharmaceutical interventions (NPIs) against COVID-19 disrupted circulation of many respiratory pathogens and eventually caused large, delayed outbreaks, owing to the build up of the susceptible pool during the intervention period. In contrast to other common respiratory pathogens that re-emerged soon after the NPIs were lifted, longer delays (> 3 years) in the outbreaks of Mycoplasma pneumoniae (Mp), a bacterium commonly responsible for respiratory infections and pneumonia, have been reported in Europe and Asia. As Mp cases are continuing to increase in the US, predicting the size of an imminent outbreak is timely for public health agencies and decision makers. Here, we use simple mathematical models to provide robust predictions about a large Mp outbreak ongoing in the US. Our model further illustrates that NPIs and waning immunity are important factors in driving long delays in epidemic resurgence.

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals' perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals' behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model's outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

Revealing patterns of SARS-CoV-2 variant emergence and evolution using RBD amplicon sequencing of wastewater

OBJECTIVES

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study aimed to monitor the SARS-CoV-2 variant community dynamics and evolution using receptor-binding domain (RBD) amplicon sequencing of wastewater samples.

METHODS

We sequenced wastewater from El Paso, Texas, over 17 months, compared the sequencing data with clinical genome data, and performed biodiversity analysis to reveal SARS-CoV-2 variant dynamics and evolution.

RESULTS

We identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Comparison with clinical genome sequencing data revealed earlier detection of variants and identification of unreported outbreaks. Our results also showed strong consistency with clinical data for dominant variants at the local, state, and national levels. Alpha diversity analyses revealed significant seasonal variations, with the highest diversity observed in winter. By segmenting the outbreak into lag, growth, stationary, and decline phases, we found higher variant diversity during the lag phase, likely due to lower inter-variant competition preceding outbreak growth.

CONCLUSIONS

Our findings underscore the importance of low transmission periods in facilitating rapid mutation and variant evolution. Our approach, integrating RBD amplicon sequencing with wastewater surveillance, demonstrates effectiveness in tracking viral evolution and understanding variant emergence, thus enhancing public health preparedness.

Dual-strain dynamics of COVID-19 variants in India: Modeling, analysis, and implications for pandemic control

This study introduces a detailed compartmental model developed to understand the complex dynamics of COVID-19 transmission, focusing on the Delta and Omicron variants in India. The model tracks disease progression through different population compartments, considering factors like vaccination, time-dependent transmission, economic burden and COVID-19 death rates, loss of vaccine-induced immunity, and the transition of asymptomatic cases to recovery. The model is validated against established epidemiological knowledge and real-world data, emphasizing dynamic parameterization and accurate representation of immunity dynamics. The basic reproduction number for both variants is calculated, and sensitivity analysis for various parameters is conducted. Time-dependent parameters are estimated using the discrete inverse method. The study also explores the economic burden, impact of different types of masks, vaccine efficacy, and vaccine-induced immunity through numerical analysis.

Predicting pathogen mutual invasibility and co-circulation

Observations of pathogen community structure provide evidence for both the coexistence and replacement of related strains. Despite many studies of specific host-pathogen systems, a unifying framework for predicting the outcomes of interactions among pathogens has remained elusive. We address this gap by developing a pathogen invasion theory (PIT) based on modern ecological coexistence theory and testing the resulting framework against empirical systems. Across major human pathogens, PIT predicts near-universal mutual susceptibility of one strain to invasion by another strain. However, predicting co-circulation from mutual invasion also depends on the degree to which susceptible abundance is reduced below the invasion threshold by overcompensatory epidemic dynamics, and the time it takes for susceptibles to replenish. The transmission advantage of an invading strain and the strength and duration of immunity are key determinants of susceptible dynamics. PIT unifies existing ideas about pathogen co-circulation, offering a quantitative framework for predicting the emergence of novel pathogen strains.

Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic

The overall course of the COVID-19 pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.

Novel Multifunctional Meta-Surface Plasmon Resonance Chip Microplate for High-Throughput Molecular Screening

The utilization of surface plasmon resonance (SPR) sensors for real-time label-free molecular interaction analysis is already being employed in the fields of in vitro diagnostics and biomedicine. However, the widespread application of SPR technology is hindered by its limited detection throughput and high cost. To address this issue, this study introduces a novel multifunctional MetaSPR high-throughput microplate biosensor featuring 3D nanocups array structure, aiming to achieve high-throughput screening with a reduced cost and enhanced speed. Different types of MetaSPR sensors and analytical detection methods have been developed for accurate antibody subtype identification, epitope binding, affinity determination, antibody collocation, and quantitative detection， greatly promoting the screening and analysis of early-stage antibody drugs. The MetaSPR platform combined with nano-enhanced particles amplifies the detection signal and improves the detection sensitivity, making it more convenient, sensitive, and efficient than traditional ELISA. The findings demonstrate that the MetaSPR biosensor is a new practical technology detection platform that can improve the efficiency of biomolecular interaction studies with unlimited potential for new drug development.

Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics

The multiple immunity responses exhibited in the population and co-circulating variants documented during pandemics show a high potential to generate diverse long-term epidemiological scenarios. Transmission variability, immune uncertainties and human behaviour are crucial features for the predictability and implementation of effective mitigation strategies. Nonetheless, the effects of individual health incentives on disease dynamics are not well understood. We use a behavioural-immuno-epidemiological model to study the joint evolution of human behaviour and epidemic dynamics for different immunity scenarios. Our results reveal a trade-off between the individuals' immunity levels and the behavioural responses produced. We find that adaptive human behaviour can avoid dynamical resonance by avoiding large outbreaks, producing subsequent uniform outbreaks. Our forward-looking behaviour model shows an optimal planning horizon that minimizes the epidemic burden by balancing the individual risk-benefit trade-off. We find that adaptive human behaviour can compensate for differential immunity levels, equalizing the epidemic dynamics for scenarios with diverse underlying immunity landscapes. Our model can adequately capture complex empirical behavioural dynamics observed during pandemics. We tested our model for different US states during the COVID-19 pandemic. Finally, we explored extensions of our modelling framework that incorporate the effects of lockdowns, the emergence of a novel variant, prosocial attitudes and pandemic fatigue.

Epidemic curves and the profile of patients hospitalized by COVID-19 in a border region

OBJECTIVE

to describe the epidemic curves and analyze the epidemiological profile of patients hospitalized with COVID-19 in a triple border city.

METHOD

descriptive-quantitative. The population consisted of COVID-19 cases that required hospitalization, analyzing variables such as: age, gender, race/color, city where they lived, occupation, pregnant woman, institutionalized patient and evolution. Descriptive statistical analysis and analysis of variance and chi-square tests were used.

RESULTS

four epidemic curves were identified in the studied period. Among hospitalized cases, males predominated (55%). Cure was the most frequent outcome in curves 1, 2 and 4, but with no statistical difference (p = 0.2916). Curve 3 showed a higher frequency of deaths (41.70%) in relation to cures (38.77%). The mean ages were significantly different between the curves, with curve 4 having the lowest mean age.

CONCLUSION

it was concluded that the epidemic curves were influenced by different situations; unvaccinated population, easing of restrictive measures, reopening of the Brazil-Paraguay border, interruption of control actions, crowding of people and circulation of new variants of the disease. Through the epidemiological profile of hospitalized patients, it was concluded that being male, of mixed race/color, aged between 61 and 85 years, and being deprived of freedom were associated with hospitalization and the occurrence of death.

Beyond six feet: The collective behavior of social distancing

In a severe epidemic such as the COVID-19 pandemic, social distancing can be a vital tool to stop the spread of the disease and save lives. However, social distancing may induce profound negative social or economic impacts as well. How to optimize social distancing is a serious social, political, as well as public health issue yet to be resolved. This work investigates social distancing with a focus on how every individual reacts to an epidemic, what role he/she plays in social distancing, and how every individual's decision contributes to the action of the population and vice versa. Social distancing is thus modeled as a population game, where every individual makes decision on how to participate in a set of social activities, some with higher frequencies while others lower or completely avoided, to minimize his/her social contacts with least possible social or economic costs. An optimal distancing strategy is then obtained when the game reaches an equilibrium. The game is simulated with various realistic restraints including (i) when the population is distributed over a social network, and the decision of each individual is made through the interactions with his/her social neighbors; (ii) when the individuals in different social groups such as children vs. adults or the vaccinated vs. unprotected have different distancing preferences; (iii) when leadership plays a role in decision making, with a certain number of leaders making decisions while the rest of the population just follow. The simulation results show how the distancing game is played out in each of these scenarios, reveal the conflicting yet cooperative nature of social distancing, and shed lights on a self-organizing, bottom-up perspective of distancing practices.

Association of depressive symptoms with incidence and mortality rates of COVID-19 over 2 years among healthcare workers in 20 countries: multi-country serial cross-sectional study

BACKGROUND

Long-term deterioration in the mental health of healthcare workers (HCWs) has been reported during and after the COVID-19 pandemic. Determining the impact of COVID-19 incidence and mortality rates on the mental health of HCWs is essential to prepare for potential new pandemics. This study aimed to investigate the association of COVID-19 incidence and mortality rates with depressive symptoms over 2 years among HCWs in 20 countries during and after the COVID-19 pandemic.

METHODS

This was a multi-country serial cross-sectional study using data from the first and second survey waves of the COVID-19 HEalth caRe wOrkErS (HEROES) global study. The HEROES study prospectively collected data from HCWs at various health facilities. The target population included HCWs with both clinical and non-clinical roles. In most countries, healthcare centers were recruited based on convenience sampling. As an independent variable, daily COVID-19 incidence and mortality rates were calculated using confirmed cases and deaths reported by Johns Hopkins University. These rates represent the average for the 7 days preceding the participants' response date. The primary outcome was depressive symptoms, assessed by the Patient Health Questionnaire-9. A multilevel linear mixed model (LMM) was conducted to investigate the association of depressive symptoms with the average incidence and mortality rates.

RESULTS

A total of 32,223 responses from the participants who responded to all measures used in this study on either the first or second survey, and on both the first and second surveys in 20 countries were included in the analysis. The mean age was 40.1 (SD = 11.1), and 23,619 responses (73.3%) were from females. The 9323 responses (28.9%) were nurses and 9119 (28.3%) were physicians. LMM showed that the incidence rate was significantly and positively associated with depressive symptoms (coefficient = 0.008, standard error 0.003, p = 0.003). The mortality rate was significantly and positively associated with depressive symptoms (coefficient = 0.049, se = 0.020, p = 0.017).

CONCLUSIONS

This is the first study to show an association between COVID-19 incidence and mortality rates with depressive symptoms among HCWs during the first 2 years of the outbreak in multiple countries. This study's findings indicate that additional mental health support for HCWs was needed when the COVID-19 incidence and mortality rates increase during and after the early phase of the pandemic, and these findings may apply to future pandemics.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04352634.

Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2

The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained during Wave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic.

Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment.

Dynamics of anti-SARS-CoV-2 IgG antibody responses following breakthrough infection and the predicted protective efficacy: A longitudinal community-based population study in China

OBJECTIVES

To assess the dynamics of the anti-SARS-CoV-2 IgG antibody levels and their efficacy against COVID-19.

METHODS

We conducted a longitudinal serological analysis of 852 breakthrough COVID-19 infections among the community-based population in Yichang, China. Anti-SARS-CoV-2 IgG levels were measured by chemiluminescence at approximately 3, 4, and 9 months after infection. A linear mixed model predicted IgG antibody decline over 18 months. The effectiveness of antibodies in preventing symptomatic and severe infections was determined using an existing meta-regression model.

RESULTS

IgG antibodies slowly declined after breakthrough infections. Initially high at around 3 months (339.44 AU/mL, IQR: 262.78-382.95 AU/mL), levels remained significant at 9 months (297.74 AU/mL, IQR: 213.22-360.62 AU/mL). The elderly (≥60 years) had lower antibody levels compared to the young (<20 years) (P < 0.001). The protective efficacy of antibodies against symptomatic and severe infections was lower in the elderly (≥60 years) (78.34% and 86.33%) compared to the young (<20 years) (96.56% and 98.75%) after 1 year.

CONCLUSION

The study indicated a slow decline in anti-SARS-CoV-2 IgG antibodies, maintaining considerable efficacy for over 1 year. However, lower levels in the elderly suggest reduced protective effects, underscoring the need for age-specific vaccination strategies.

An SEIR network epidemic model with manual and digital contact tracing allowing delays

We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately notified and isolated. The early phase of the epidemic with manual and/or digital tracing is approximated by different multi-type branching processes, and three respective reproduction numbers are derived. The effectiveness of both contact tracing mechanisms is numerically quantified through the reduction of the reproduction number. This shows that app-using fraction plays an essential role in the overall effectiveness of contact tracing. The relative effectiveness of manual tracing compared to digital tracing increases if: more of the transmission occurs on the network, when the tracing delay is shortened, and when the network degree distribution is heavy-tailed. For realistic values, the combined tracing case can reduce R0 by 20%-30%, so other preventive measures are needed to reduce the reproduction number down to 1.2-1.4 for contact tracing to make it successful in avoiding big outbreaks.

Discovery of meisoindigo derivatives as noncovalent and orally available Mpro inhibitors: their therapeutic implications in the treatment of COVID-19

The progressive emergence of SARS-CoV-2 variants has necessitated the urgent exploration of novel therapeutic strategies to combat the COVID-19 pandemic. The SARS-CoV-2 main protease (Mpro) represents an evolutionarily conserved therapeutic target for drug discovery. This study highlights the discovery of meisoindigo (Mei), derived from the traditional Chinese medicine (TCM) Indigo naturalis, as a novel non-covalent and nonpeptidic Mpro inhibitor. Substantial optimizations and structure-activity relationship (SAR) studies, guided by a structure-based drug design approach, led to the identification of several Mei derivatives, including S5-27 and S5-28, exhibiting low micromolar inhibition against SARS-CoV-2 Mpro with high binding affinity. Notably, S5-28 provided significant protection against wild-type SARS-CoV-2 in HeLa-hACE2 cells, with EC50 up to 2.66 μM. Furthermore, it displayed favorable physiochemical properties and remarkable gastrointestinal and metabolic stability, demonstrating its potential as an orally bioavailable drug for anti-COVID-19 therapy. This research presents a promising avenue for the development of new antiviral agents, offering hope in the ongoing battle against COVID-19.

Reduction in ACE2 expression in peripheral blood mononuclear cells during COVID-19 - implications for post COVID-19 conditions

BACKGROUND

Severe COVID-19 is uncommon, restricted to 19% of the total population. In response to the first virus wave (alpha variant of SARS-CoV-2), we investigated whether a biomarker indicated severity of disease and, in particular, if variable expression of angiotensin converting enzyme 2 (ACE2) in blood might clarify this difference in risk and of post COVID -19 conditions (PCC).

METHODS

The IRB-approved study compared patients hospitalized with severe COVID-19 to healthy controls. Severe infection was defined requiring oxygen or increased oxygen need from baseline at admission with positive COVID-19 PCR. A single blood sample was obtained from patients within a day of admission. ACE2 RNA expression in blood cells was measured by an RT-PCR assay. Plasma ACE1 and ACE2 enzyme activities were quantified by fluorescent peptides. Plasma TIMP-1, PIIINP and MMP-9 antigens were quantified by ELISA. Data were entered into REDCap and analyzed using STATA v 14 and GraphPad Prism v 10.

RESULTS

Forty-eight patients and 72 healthy controls were recruited during the pandemic. ACE2 RNA expression in peripheral blood mononuclear cells (PBMC) was rarely detected acutely during severe COVID-19 but common in controls (OR for undetected ACE2: 12.4 [95% CI: 2.62-76.1]). ACE2 RNA expression in PBMC did not determine plasma ACE1 and ACE2 activity, suggesting alternative cell-signaling pathways. Markers of fibrosis (TIMP-1 and PIIINP) and vasculopathy (MMP-9) were additionally elevated. ACE2 RNA expression during severe COVID-19 often responded within hours to convalescent plasma. Analogous to oncogenesis, we speculate that potent, persistent, cryptic processes following COVID-19 (the renin-angiotensin system (RAS), fibrosis and vasculopathy) initiate or promote post-COVID-19 conditions (PCC) in susceptible individuals.

CONCLUSIONS

This work elucidates biological and temporal plausibility for ACE2, TIMP1, PIIINP and MMP-9 in the pathogenesis of PCC. Intersection of these independent systems is uncommon and may in part explain the rarity of PCC.

Broad-spectrum RNA antiviral inspired by ISG15 -/- deficiency

Type I interferons (IFN-I) are cytokines with potent antiviral and inflammatory capacities. IFN-I signaling drives the expression of hundreds of IFN-I stimulated genes (ISGs), whose aggregate function results in the control of viral infection. A few of these ISGs are tasked with negatively regulating the IFN-I response to prevent overt inflammation. ISG15 is a negative regulator whose absence leads to persistent, low-grade elevation of ISG expression and concurrent, self-resolving mild autoinflammation. The limited breadth and low-grade persistence of ISGs expressed in ISG15 deficiency are sufficient to confer broad-spectrum antiviral resistance. Inspired by ISG15 deficiency, we have identified a nominal collection of 10 ISGs that recapitulate the broad antiviral potential of the IFN-I system. The expression of the 10 ISG collection in an IFN-I non-responsive cell line increased cellular resistance to Zika, Vesicular Stomatitis, Influenza A (IAV), and SARS-CoV-2 viruses. A deliverable prophylactic formulation of this syndicate of 10 ISGs significantly inhibited IAV PR8 replication in vivo in mice and protected hamsters against a lethal SARS-CoV-2 challenge, suggesting its potential as a broad-spectrum antiviral against many current and future emerging viral pathogens.

One-Sentence Summary

Human inborn error of immunity-guided discovery and development of a broad-spectrum RNA antiviral therapy.

Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.

"The genie is out of the bottle": a qualitative study on the impact of COVID-19 on continuing professional development

BACKGROUND

The onset of the COVID-19 pandemic catalysed a monumental shift in the field of continuing professional development (CPD). Prior to this, the majority of CPD group-learning activities were offered in-person. However, the pandemic forced the field to quickly pivot towards more novel methods of learning and teaching in view of social distancing regulations. The purpose of this study was to obtain the perspectives of CPD leaders on the impact of the pandemic to elucidate trends, innovations, and potential future directions in the field.

METHODS

Semi-structured interviews were conducted between April-September 2022 with 23 CPD leaders from Canada and the USA. Interviews were audio-recorded, transcribed, and de-identified. A thematic analysis approach was used to analyse the data and generate themes.

RESULTS

Participants characterised COVID-19 as compelling widespread change in the field of CPD. From the interviews, researchers generated six themes pertaining to the impact of the pandemic on CPD: (1) necessity is the mother of innovation, (2) the paradox of flexibility and accessibility, (3) we're not going to unring the bell, (4) reimagining design and delivery, (5) creating an evaluative culture, and (6) a lifeline in times of turmoil.

CONCLUSION

This qualitative study discusses the impact of the pandemic on the field of CPD and leaders' vision for the future. Despite innumerable challenges, the pandemic created opportunities to reform design and delivery. Our findings indicate a necessity to maintain an innovative culture to best support learners, to improve the healthcare system, and to prepare for future emergencies.

Lessons should be learned: Why did we not learn from the Spanish flu?

COVID-19 has become a global pandemic that has affected millions of people worldwide. The disease is caused by the novel coronavirus that was first reported in Wuhan, China, in December 2019. The virus is highly contagious and can spread from person to person through respiratory droplets when an infected person coughs, sneezes, talks, or breathes. The symptoms of COVID-19 include fever, cough, and shortness of breath, and in severe cases, it can lead to respiratory failure, pneumonia, and death. The Spanish flu, caused by the H1N1 influenza virus, and the COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 are two of the most significant global health crises in history. While these two pandemics occurred almost a century apart and are caused by different types of viruses, there are notable similarities in their impact, transmission, and public health responses. Here are some key similarities between the Spanish flu and SARS-CoV-2. The Spanish flu pandemic of 1918-1919 stands as one of the deadliest pandemics in human history, claiming the lives of an estimated 50 million people worldwide. Its impact reverberated across continents, leaving behind a legacy of devastation and lessons that, unfortunately, seem to have been forgotten or ignored over time. Despite the advancements in science, medicine, and public health in the intervening century, humanity found itself facing a strikingly similar situation with the outbreak of the COVID-19 pandemic. Additionally, amidst the search for effective measures to combat COVID-19, novel approaches such as iodine complexes, such as Iodine-V has emerged as potential interventions, reflecting the ongoing quest for innovative solutions to mitigate the impact of pandemics. This raises the poignant question: why did we not learn from the Spanish flu?

A high throughput immuno-affinity mass spectrometry method for detection and quantitation of SARS-CoV-2 nucleoprotein in human saliva and its comparison with RT-PCR, RT-LAMP, and lateral flow rapid antigen test

OBJECTIVES

Many reverse transcription polymerase chain reaction (RT-PCR) methods exist that can detect SARS-CoV-2 RNA in different matrices. RT-PCR is highly sensitive, although viral RNA may be detected long after active infection has taken place. SARS-CoV-2 proteins have shorter detection windows hence their detection might be more meaningful. Given salivary droplets represent a main source of transmission, we explored the detection of viral RNA and protein using four different detection platforms including SISCAPA peptide immunoaffinity liquid chromatography-mass spectrometry (SISCAPA-LC-MS) using polyclonal capture antibodies.

METHODS

The SISCAPA-LC MS method was compared to RT-PCR, RT-loop-mediated isothermal amplification (RT-LAMP), and a lateral flow rapid antigen test (RAT) for the detection of virus material in the drool saliva of 102 patients hospitalised after infection with SARS-CoV-2. Cycle thresholds (Ct) of RT-PCR (E gene) were compared to RT-LAMP time-to-positive (TTP) (NE and Orf1a genes), RAT optical densitometry measurements (test line/control line ratio) and to SISCAPA-LC-MS for measurements of viral protein.

RESULTS

SISCAPA-LC-MS showed low sensitivity (37.7 %) but high specificity (89.8 %). RAT showed lower sensitivity (24.5 %) and high specificity (100 %). RT-LAMP had high sensitivity (83.0 %) and specificity (100.0 %). At high initial viral RNA loads (<20 Ct), results obtained using SISCAPA-LC-MS correlated with RT-PCR (R2 0.57, p-value 0.002).

CONCLUSIONS

Detection of SARS-CoV-2 nucleoprotein in saliva was less frequent than the detection of viral RNA. The SISCAPA-LC-MS method allowed processing of multiple samples in <150 min and was scalable, enabling high throughput.

Meaning and prediction of 'excess mortality': a comparison of Covid-19 and pre-Covid-19 mortality data in 31 Eurostat countries from 1965 to 2021

Determining 'excess mortality' makes it possible to compare the burden of disasters between countries and over time, and thus also to evaluate the success of mitigation measures. However, the debate on coronavirus disease 2019 (Covid-19) has exposed that calculations of excess mortalities vary considerably depending on the method and its specification. Moreover, it is often unclear what exactly is meant by 'excess mortality'. We define excess mortality as the excess over the number of deaths that would have been expected counter-factually, that is without the catastrophic event in question. Based on this definition, we use a very parsimonious calculation method, namely the linear extrapolation of death figures from previous years to determine the excess mortality during the Covid-19 pandemic. But unlike most other literature on this topic, we first evaluated and optimized the specification of our method using a larger historical data set in order to identify and minimize estimation errors and biases. The result shows that excess mortality rates in the literature are often inflated. Moreover, they would have exhibited considerable excess mortalities in the period before Covid-19, if this value had already been of public interest at that time. Three conclusions can be drawn from this study and its findings: (i) All calculation methods for current figures should first be evaluated against past figures. (ii) To avoid alarm fatigue, thresholds should be introduced which would differentiate between 'usual fluctuations' and 'remarkable excess'. (iii) Statistical offices could provide more realistic estimates.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Advancements in SARS-CoV-2 detection: Navigating the molecular landscape and diagnostic technologies

According to information from the World Health Organization, the world has experienced about 430 million cases of COVID-19, a world-wide health crisis caused by the SARS-CoV-2 virus. This outbreak, originating from China in 2019, has led to nearly 6 million deaths worldwide. As the number of confirmed infections continues to rise, the need for cutting-edge techniques that can detect SARS-CoV-2 infections early and accurately has become more critical. To address this, the Federal Drug Administration (FDA) has issued emergency use authorizations (EUAs) for a wide range of diagnostic tools. These include tests based on detecting nucleic acids and antigen-antibody reactions. The quantitative real-time reverse transcription PCR (qRT-PCR) assay stands out as the gold standard for early virus detection. However, despite its accuracy, qRT-PCR has limitations, such as complex testing protocols and a risk of false negatives, which drive the continuous improvement in nucleic acid and serological testing approaches. The emergence of highly contagious variants of the coronavirus, such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529), has increased the need for tests that can specifically identify these mutations. This article explores both nucleic acid-based and antigen-antibody serological assays, assessing the performance of recently approved FDA tests and those documented in scientific research, especially in identifying new coronavirus strains.