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

Estimating cumulative infection rate of COVID-19 after adjusting the dynamic zero-COVID policy in China

Background

At the end of 2022, China adjusted its coronavirus disease 2019 (COVID-19) prevention and control strategy. How this adjustment affected the cumulative infection rate is debated, and how second booster dose vaccination affected the pandemic remains unclear.

Methods

We collected COVID-19 case data for China's mainland from December 7, 2022, to January 7, 2023, reported by the World Health Organization. We also collected cumulative infection rate data from five large-scale population-based surveys. Next, we developed a dynamic transmission compartment model to characterize the COVID-19 pandemic and to estimate the cumulative infection rate. In addition, we estimated the impact of second booster vaccination on the pandemic by examining nine scenarios with different vaccination coverages (0%, 20%, and 40%) and vaccine effectiveness (30%, 50%, and 70%).

Results

By January 7, 2023, when COVID-19 was classified as a Class B infectious disease, the cumulative infection rate of the Omicron variant nationwide had reached 84.11% (95% confidence interval [CI]: 78.13%-90.08%). We estimated that the cumulative infection rates reached 50.50% (95% CI: 39.58%-61.43%), 56.15% (95% CI: 49.05%-67.22%), 73.82% (95% CI: 64.63%-83.02%), 75.76% (95% CI: 67.02%-84.50%), and 84.99% (95% CI: 79.45%-90.53%) on December 19, 20, 25, and 26, 2022, and on January 15, 2023, respectively. These results are similar to those of the population survey conducted on the corresponding dates, that is 46.93%, 61%, 63.52%, 74%, and 84.7%, respectively. In addition, we estimated that by January 7, 2023, the cumulative infection rate decreased to 29.55% (64.25%) if vaccination coverage and the effectiveness of second booster vaccination were 40% (20%) and 70% (30%), respectively.

Conclusion

We estimate that, in late 2022, the cumulative infection rate was approximately 84% and that second booster vaccination before the policy adjustment was effective in reducing this rate.

A graph-theoretic framework for integrating mobility data into mathematical epidemic models

Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models. The recently introduced synthetic network model, which is an SIRS type model based on a non-linear transmission rate, effectively decouples the underlying population network structure from the epidemiological parameters of disease, and has been shown to produce superior fits to multi-wave epidemics. However, inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual. An alternate source of data that can inform the network alone has the potential to improve overall modeling results. Aggregate cell phone mobility data, which record daily numbers of visits to points of interest, provide a proxy for the number of contacts that people establish during their visits. In this paper, we link the contact rate from an epidemic model to the total number of contacts formed in the population. Inferring the latter from Google Community Mobility Reports data, we develop an integrated epidemic model whose transmission adapts to population mobility. This model is illustrated on the first four waves of the COVID-19 pandemic.

An updated review of pulmonary radiological features of acute and chronic COVID-19

PURPOSE OF REVIEW

Significant progress has been made in our understanding of the acute and chronic clinical and radiological manifestations of coronavirus-19 (COVID-19). This article provides an updated review on pulmonary COVID-19, while highlighting the key imaging features that can identify and distinguish acute COVID-19 pneumonia and its chronic sequelae from other diseases.

RECENT FINDINGS

Acute COVID-19 pneumonia typically presents with manifestations of organizing pneumonia on computed tomography (CT). In cases of severe disease, patients clinically progress to acute respiratory distress syndrome, which manifests as diffuse alveolar damage on CT. The most common chronic imaging finding is ground-glass opacities, which commonly resolves, as well as subpleural bands and reticulation. Pulmonary fibrosis is an overall rare complication of COVID-19, with characteristic features, including architectural distortion, and traction bronchiectasis.

SUMMARY

Chest CT can be a helpful adjunct tool in both diagnosing and managing acute COVID-19 pneumonia and its chronic sequelae. It can identify high-risk cases and guide decision-making, particularly in cases of severe or complicated disease. Follow-up imaging can detect persistent lung abnormalities associated with long COVID and guide appropriate management.

The immunopathogenesis of SARS-CoV-2 infection: Overview of lessons learned in the first 5 years

This review provides a broad overview of lessons learned in the five years since COVID-19 was identified. It is a bimodal disease, starting with an initially virus-driven phase, followed by resolution or ensuing inappropriate immune activation causing severe inflammation that is no longer strictly virus dependent. Humoral immunity is beneficial for preventing or attenuating the early stage, without benefit once the later stage begins. Neutralizing antibodies elicited by natural infection or vaccination are short-lived and highly vulnerable to viral sequence variation. By contrast, cellular immunity, particularly the CD8+ T cell arm, has a role in preventing or attenuating severe disease, is far less susceptible to viral variation, and is longer-lived than antibodies. Finally, an ill-defined phenomenon of prolonged symptoms after acute infection, termed "long COVID," is poorly understood but may involve various immunologic defects that are hyperactivating or immunosuppressive. Remaining issues include needing to better understand the immune dysregulation of severe disease to allow more tailored therapeutic interventions, developing antibody strategies that cope with the viral spike sequence variability, prolonging vaccine efficacy, and unraveling the mechanisms of long COVID to design therapeutic approaches.

Observed Mask Wearing and Presence of SARS-CoV-2 in School Wastewater, San Diego County, CA, 2022

Objectives. To test the association between directly observed school masking behaviors and the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in school wastewater. Methods. We randomly sampled a subset of schools participating in a translational study on the effectiveness of passive wastewater surveillance in nonresidential K‒12 settings in San Diego County. Trained observers conducted biweekly systematic observations of masking behaviors between March 2 and May 27, 2022. Results. The proportion of individuals observed masking was a significant predictor of detecting SARS-CoV-2 in school wastewater (adjusted odds ratio = 0.91; 95% confidence interval = 0.85, 0.99; P = .034). For every 10% increase in the percentage of observed individuals who were fully masked, the odds of detecting SARS-CoV-2 in school wastewater decreased by nearly 10%. Conclusions. Masking does not need to be perfect to be effective. Mask mandates are unlikely to be reimplemented in US schools, but interventions that encourage moderate increases in masking may have an important role to play in improving children's health and decreasing the spread of COVID-19 and other respiratory diseases. (Am J Public Health. 2025;115(4):519-527. https://doi.org/10.2105/AJPH.2024.307925).

Testing-isolation interventions will likely be insufficient to contain future novel disease outbreaks

Rapid identification and isolation of infected individuals with diagnostic testing plays a critical role in combating invasions of novel human pathogens. Unfortunately, unprepared health agencies may struggle to meet the massive testing capacity demands imposed by an outbreaking novel pathogen, potentially resulting in a failure of epidemic containment as occurred with COVID-19. Despite the critical importance of understanding the likelihood of such an outcome, it remains unclear how the particular characteristics of a novel disease will impact the magnitude of resource constraints on controllability. Specifically, is the failure of testing-isolation unique to COVID-19, or is this a likely outcome across the spectrum of disease traits that may constitute future epidemics? Here, using a generalized mathematical model parameterized for seven different human diseases and variants, we show that testing-isolation strategies will typically fail to contain epidemic outbreaks at practicably achievable testing capacities. From this analysis, we identify three key disease characteristics that govern controllability under resource constraints; the basic reproduction number, mean latent period, and non-symptomatic transmission index. Interactions among these characteristics play prominent roles in both explaining controllability differences among diseases and enhancing the efficacy of testing-isolation in combination with transmission-reduction measures. This study provides broad guidelines for managing controllability expectations during future novel disease invasions, describing which classes of diseases are most amenable to testing-isolation strategies alone and which will necessitate additional transmission-reduction measures like social distancing.

Management of SARS-CoV-2 Infection-Clinical Practice Guidelines of the Polish Association of Epidemiologists and Infectiologists, for 2025

The first Polish recommendations for the management of COVID-19 were published by the Polish Society of Epidemiologists and Infectiologists (PTEiLChZ) on 31 March 2020, and the last three years ago. The emergence of new SARS-CoV-2 variants, a different course of the disease, as well as new knowledge about therapies and vaccines, requires updating diagnostic, therapeutic, and prophylactic guidelines. Despite the reduction in the threat associated with COVID-19, there is a risk of another epidemic caused by coronaviruses, which was an additional reason for developing a new version of the guidelines. In preparing these recommendations, the Delphi method was used, reaching a consensus after three survey cycles. Compared to the 2022 version, the names of the individual stages of the disease have been changed, adapting them to the realities of clinical practice, and attention was paid to the differences observed in immunosuppressed patients and in children. Some previously recommended drugs have been discontinued, including monoclonal antibodies. In addition, general principles of vaccination were presented, as well as issues related to the post-COVID syndrome.

Prognosis of patients with acute myocardial infarction in the setting of COVID-19: A French nationwide observational study

BACKGROUND

The prognosis of patients with acute myocardial infarction (AMI) in the setting of coronavirus disease 2019 (COVID-19) remains uncertain.

AIMS

To evaluate patients' prognosis after an AMI concomitant with COVID-19.

METHODS

This retrospective nationwide observational cohort study was based on the French administrative hospital discharge database. Primary outcomes were incidences of all-cause death, cardiovascular death, heart failure (HF), recurrence of AMI, ischaemic stroke, incident atrial fibrillation (AF), ventricular tachycardia/ventricular fibrillation (VT/VF) and cardiac arrest. Patients with AMI and COVID-19 were matched to those without COVID-19 (using propensity score matching techniques) to account for differences between the two populations.

RESULTS

A total of 288,408 patients hospitalized for AMI in France from March 2020 to January 2023 were included; 26,879 had a COVID-19-positive test between 15 days before to 5 days after admission. Patients with COVID-19 were older, more frequently had diabetes mellitus and obesity but less frequently smoked. They more frequently had non-ST-segment elevation myocardial infarction presentation and more often had lung disease. After matching, patients with COVID-19 had higher risks of all-cause death (hazard ratio [HR] 1.255; 95% confidence interval [CI] 1.203-1.308; P<0.0001), HF (HR 1.205; 95% CI 1.159-1.254; P<0.0001), ischaemic stroke (HR 1.237; 95% CI 1.084-1.411; P=0.002), incident AF (HR 1.160; 95% CI 1.070-1.258; P=0.0003) and VT/VF (1.360; 95% CI 1.200-1.540; P<0.0001). Surprisingly, cardiovascular death risk was lower in patients with COVID-19 (HR 0.932; 95% CI 0.879-0.988; P=0.02) as a result of competition with non-cardiovascular death. No statistical difference was found for cardiac arrest or recurrent AMI.

CONCLUSION

In this French nationwide cohort study, AMI in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) increased all-cause death incidence compared to non-infected AMI, but this poorer prognosis was not due to cardiovascular death. Further investigations are needed to elucidate the aetiologies of death.

Mathematical Assessment of Wastewater-Based Epidemiology to Predict SARS-CoV-2 Cases and Hospitalizations in Miami-Dade County

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county's wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model's prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model's projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function-the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

Flexible Bayesian estimation of incubation times

The incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps the planning of public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We developed a new Bayesian methodology using Laplacian-P-splines that provides a semiparametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on coronavirus disease 2019, Middle East respiratory syndrome, and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.

Influence of easing COVID-19 strategies following downgrading of the national infectious disease category on COVID-19 occurrence among hospitalized patients in Japan

PURPOSE

We aimed to evaluate the influence of easing COVID-19 preventive measures following its downgrading to a common infectious disease on COVID-19 occurrence among hospitalized patients.

METHODS

Nosocomial occurrence of COVID-19 was compared between periods with national infectious disease category 5 versus the preceding category 2 equivalent. Changes in the revised manual included a shorter duration of work restrictions for infected health care practitioners (HCPs); no work restriction for HCPs exposed to SARS-CoV-2 with a negative test on days 1, 3 and 5; discontinuation of universal pre-admission screening; and pre-emptive isolation of patients without screening. Wearing an N95 mask and face shield was required in procedure/care with moderate-to high-risk contact.

RESULTS

Although the mean monthly number of infected HCPs increased from 8.1 to 12.7 in the category 5 period (p = 0.034) and that of pre-admission screening decreased to one-fourth, the COVID-19 incidence in hospitalized patients remained similar between the two study periods (1.60 ± 5.59/month versus 1.40 ± 2.63/month, p = 0.358). Clusters, defined as ≥3 COVID-19 patients on the ward, were experienced twice in the preceding period and only once in the category 5 period. The index cases causing nosocomial SARS-CoV-2 transmission mostly involved rehabilitation therapists in the preceding period; five of six index cases were patients in the category 5 period. Following the expanded indication for N95 masks, neither SARS-CoV-2 transmission to patients nor transmission from infected patients was observed in HCPs for 1 year.

CONCLUSION

With sustained, enhanced standard precautions, easing prevention strategies could limit nosocomial SARS-CoV-2 infections.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

An integrated airborne transmission risk assessment model for respiratory viruses: short- and long-range contributions

This study presents an advanced airborne transmission risk assessment model that integrates both short- and long-range routes in the spread of respiratory viruses, building upon the CERN Airborne Model for Indoor Risk Assessment (CAiMIRA) and aligned with the new World Health Organization (WHO) terminology. Thanks to a two-stage exhaled jet approach, the model accurately simulates short-range exposures, thereby improving infection risk predictions across diverse indoor settings. Key findings reveal that in patient wards, the short-range viral dose is 10-fold higher than the long-range component, highlighting the critical role of close proximity interactions. Implementation of FFP2 respirators resulted in a remarkable 13-fold reduction in viral dose, underscoring the effectiveness of personal protective equipment (PPE). Additionally, the model demonstrated that an 8 h exposure in a poorly ventilated office can equate to the risk of a 15 min face-to-face, mask-less interaction, emphasizing the importance of physical distancing and source control. We also found in high-risk or low-occupancy settings, that secondary transmission is driven more by overall epidemic trends than by the presence of individual superspreaders. Monte Carlo simulations across various scenarios, including classrooms and offices, validate the model's robustness in optimizing infection prevention strategies. These findings support targeted interventions for short- and long-range exposure to reduce airborne transmission.

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.

Molnupiravir clinical trial simulation suggests that polymerase chain reaction underestimates antiviral potency against SARS-CoV-2

Molnupiravir is an antiviral medicine that induces lethal copying errors during SARS-CoV-2 RNA replication. Molnupiravir reduced hospitalization in one pivotal trial by 50% and had variable effects on reducing viral RNA levels in three separate trials. We used mathematical models to simulate these trials and closely recapitulated their virologic outcomes. Model simulations suggest lower antiviral potency against pre-omicron SARS-CoV-2 variants than against omicron. We estimate that in vitro assays underestimate in vivo potency 7-8 fold against omicron variants. Our model suggests that because polymerase chain reaction detects molnupiravir mutated variants, the true reduction in non-mutated viral RNA is underestimated by ~0.5 log10 in the two trials conducted while omicron variants dominated. Viral area under the curve estimates differ significantly between non-mutated and mutated viral RNA. Our results reinforce past work suggesting that in vitro assays are unreliable for estimating in vivo antiviral drug potency and suggest that virologic endpoints for respiratory virus clinical trials should be catered to the drug mechanism of action.

Environmental, climatic, socio-economic factors and non-pharmacological interventions: A comprehensive four-domain risk assessment of COVID-19 hospitalization and death in Northern Italy

INTRODUCTION

Up to now, studies on environmental, climatic, socio-economic factors, and non-pharmacological interventions (NPI) show diverse associations, often contrasting, with COVID-19 spread or severity. Most studies used large-scale, aggregated data, with limited adjustment for individual factors, most of them focused on viral spread than severe outcomes. Moreover, evidence simultaneously evaluating variables belonging to different exposure domains is scarce, and none analysing their collective impact on an individual level.

METHODS

Our population-based retrospective cohort study aimed to assess the comprehensive role played by exposure variables belonging to four different domains, environmental, climatic, socio-economic, and non-pharmacological interventions (NPI), on individual COVID-19-related risk of hospitalization and death, analysing data from all patients (no. 68472) tested positive to a SARS-CoV-2 swab in Modena Province (Northern Italy) between February 2020 and August 2021. Using adjusted Cox proportional hazard models, we estimated the risk of severe COVID-19 outcomes, investigating dose-response relationships through restricted cubic spline modelling for hazard ratios.

RESULTS

Several significant associations emerged: long-term exposure to air pollutants (NO2, PM10, PM2.5) was linked to hospitalization risk in a complex way and showed an increased risk for death; while humidity was inversely associated; temperature showed a U-shaped risk; wind speed showed a linear association with both outcomes. Precipitation increased hospitalization risk but decreased mortality. Socio-economic and NPI indices showed clear linear associations, respectively negative and positive, with both outcomes.

CONCLUSIONS

Our findings offer insights for evidence-based policy decisions, improving precision healthcare practices, and safeguarding public health in future pandemics. Refinement of pandemic response plans by healthcare authorities could benefit significantly.

Dynamics of SARS-CoV-2 lineages in children and adults in 2021 and 2022

PURPOSE

We aimed to describe SARS-CoV-2 lineages and diversity in children and adults in Estonia and similarity to travel-related cases and neighbouring countries.

METHODS

SARS-CoV-2 sequences in 2021-2022 from a nationwide study were included. The proportion of predominant lineages in Estonian regions and among travel-related cases was described by multinomial logistic regression. Simpson's indices of diversity were compared using linear regression. Dynamics of Bray-Curtis dissimilarity was described by applying fuzzy clustering to non-metrical dimensional scaling results.

RESULTS

A total of 2,630 sequences from children (<15 years) and 23,031 from adults (≥15 years) were included. The increase in the proportion of Alpha/Delta/Omicron BA.1/BA.2 lineages was delayed in smaller regions (by 3.5-27.5 days). The proportion of Alpha/Delta/Omicron BA.1 increased earlier among travel-related (n = 4,654) than non-travel-related cases (10.5 days). Diversity was lower in non-travel-related than travel-related cases until Delta period by 0.066. Dynamics of lineages and diversity were similar in adults and children. Similarity of lineages was delayed compared to Finland during Alpha/Omicron BA.1/BA.2 periods and different from all neighbouring countries during Delta period.

CONCLUSION

SARS-CoV-2 lineages in children and adults were similar. Differences between regions and travel-related cases and varying similarity to neighbouring countries suggest the importance of mobility in the spread.

Unraveling metabolic signatures in SARS-CoV-2 variant infections using multiomics analysis

Introduction

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, notably delta and omicron, has significantly accelerated the global pandemic, worsening conditions worldwide. However, there is a lack of research concerning the molecular mechanisms related to immune responses and metabolism induced by these variants.

Methods

Here, metabolomics combined with transcriptomics was performed to elucidate the immunometabolic changes in the lung of hamsters infected with delta and omicron variants.

Results

Both variants caused acute inflammation and lung pathology in intranasally infected hamsters. Principal component analysis uncovered the delta variant significantly altered lung metabolite levels between the pre- and post-infection states. Additionally, metabolic pathways determined by assessment of metabolites and genes in lung revealed significant alterations in arginine biosynthesis, glutathione metabolism, and tryptophan metabolism upon infection with both variants and closely linked to inflammatory cytokines, indicating immune activation and oxidative stress in response to both variants. These metabolic changes were also evident in the serum, validating the presence of systemic alterations corresponding to those identified in lung. Notably, the delta variant induced a more robust metabolic regulation than the omicron variant.

Discussion

The study suggests that multi-omics is a valuable approach for understanding immunometabolic responses to infectious diseases, and providing insights for effective treatment strategies.

The comprehensive SARS-CoV-2 'hijackome' knowledge base

The continuous evolution of SARS-CoV-2 has led to the emergence of several variants of concern (VOCs) that significantly affect global health. This study aims to investigate how these VOCs affect host cells at proteome level to better understand the mechanisms of disease. To achieve this, we first analyzed the (phospho)proteome changes of host cells infected with Alpha, Beta, Delta, and Omicron BA.1 and BA.5 variants over time frames extending from 1 to 36 h post infection. Our results revealed distinct temporal patterns of protein expression across the VOCs, with notable differences in the (phospho)proteome dynamics that suggest variant-specific adaptations. Specifically, we observed enhanced expression and activation of key components within crucial cellular pathways such as the RHO GTPase cycle, RNA splicing, and endoplasmic reticulum-associated degradation (ERAD)-related processes. We further utilized proximity biotinylation mass spectrometry (BioID-MS) to investigate how specific mutation of these VOCs influence viral-host protein interactions. Our comprehensive interactomics dataset uncovers distinct interaction profiles for each variant, illustrating how specific mutations can change viral protein functionality. Overall, our extensive analysis provides a detailed proteomic profile of host cells for each variant, offering valuable insights into how specific mutations may influence viral protein functionality and impact therapeutic target identification. These insights are crucial for the potential use and design of new antiviral substances, aiming to enhance the efficacy of treatments against evolving SARS-CoV-2 variants.

Trends of common laboratory biomarkers after SARS-CoV-2 infection

BACKGROUND

Most studies that explore the long-term effects of COVID-19 are based on subjectively reported symptoms, while laboratory-measured biomarkers are mainly examined in studies of relatively small cohorts. This study investigates the long-term effects of SARS-CoV-2 infection on common laboratory biomarkers.

METHODS

We utilized a retrospective cohort of SARS-CoV-2 infected individuals and rigorously matched controls based on demographic and clinical characteristics, examining 63 common laboratory biomarkers. Additional lab-specific cohorts were matched with an additional criterion of baseline biomarker values. Differences in biomarkers over a 12-month follow-up were analyzed using standardized mean difference-in-differences.

RESULTS

The general cohort included 361,061 matched pairs, with 26M laboratory results. The effects on most biomarkers lasted 1-4 months and were consistent with anticipated changes after acute viral infections. Some biomarkers presented prolonged effects, consistent across the general and lab-specific cohorts. One group of such findings included a 7-8 month decrease in WBC counts, mainly driven by decreased counts of neutrophils, monocytes, and basophils. Potassium levels were decreased for 3-5 months. Vaccinated individuals' data suggested potentially smaller effects on WBCs, but cohort sizes limited this analysis.

CONCLUSIONS

This study explores SARS-CoV-2 infection effects on common laboratory biomarkers, characterizing the direction and duration of these effects on the largest infected cohort to date. The effects of most biomarkers resolve in the first months following infection. The most notable longer-lasting effects involved the immune system. Further research is required to characterize the magnitude of these effects among specific individuals.

A novel comparison framework for epidemiological strategies applied to age-based restrictions versus horizontal lockdowns

During an epidemic, such as the COVID-19 pandemic, policy-makers are faced with the decision of implementing effective, yet socioeconomically costly intervention strategies, such as school and workplace closure, physical distancing, etc. In this study, we propose a rigorous definition of epidemiological strategies. In addition, we develop a scheme for comparing certain epidemiological strategies, with the goal of providing policy-makers with a tool for their systematic comparison. Then, we put the suggested scheme to the test by employing an age-based epidemiological compartment model introduced in Bitsouni et al. (2024), coupled with data from the literature, in order to compare the effectiveness of age-based and horizontal interventions. In general, our findings suggest that these two are comparable, mainly at a low or medium level of intensity.

Modelling the effectiveness of antiviral treatment strategies to prevent household transmission of acute respiratory viruses

Households are a major driver of transmission of acute respiratory viruses, such as SARS-CoV-2 or Influenza. Until now antiviral treatments have mostly been used as a curative treatment in symptomatic individuals. During an outbreak, more aggressive strategies involving pre- or post-exposure prophylaxis (PrEP or PEP) could be employed to further reduce the risk of severe disease but also prevent transmission to household contacts. In order to understand the effectiveness of such strategies and the factors that may modulate them, we developed a multi-scale model that follows the infection at both the individual-level (viral dynamics) and the population-level (transmission dynamics) in households. Using a simulation study we explored different antiviral treatment strategies, evaluating their effectiveness on reducing the transmission risk and the virological burden in households for a range of virus characteristics (e.g., secondary attack rate-SAR, or time to peak viral load). We found that when the index case can be identified and treated before symptom onset, both transmission and virological burden are reduced by > 75% for most SAR values and time to peak viral load, with minimal benefit to treat additionally household contacts. While treatment initiated after index symptom onset does not reduce the risk of transmission, it can still reduce the virological burden in the household, a proxy for severe disease and subsequent transmission risk outside the household. In that case optimal strategies involve treatment of both index case and household contacts as PEP, with efficacy > 50% when peak viral load occurs after symptom onset, and 30-50% otherwise. In all the considered cases, antiviral treatment strategies were optimal for SAR ranging 20-60%, and for larger household sizes. This study highlights the opportunity of antiviral drug-based interventions in households during an outbreak to minimize viral transmission and disease burden.

Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model's potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.

An Analysis of Factors Contributing to Household Transmission of COVID-19 Using Data from Active Epidemiological Investigations Performed in the Setagaya Ward of Tokyo, Japan

An active epidemiological investigation of COVID-19 cases in the Setagaya Ward of Tokyo revealed that household transmission was the main route of spread. This study aimed to identify factors associated with household transmission in patients diagnosed with COVID-19 and cohabitants, during the wild-type (WT) (December 2020) and Alpha-variant (AV) (May 2021) epidemics. Factors in index cases significantly associated with household transmission of both WT and AV virus, included more than 3 days from onset to diagnosis (WT: risk ratio [RR] 1.44, 95% confidence interval [CI] 1.16-1.79; AV: RR 1.66, 95% CI 1.32-2.08), and a household size of three or more (WT: RR 1.37, 95% CI 1.10-1.72; AV: RR 1.29, 95% CI 1.05-1.59). Household transmission of WT was also significantly associated with the index case being aged ≥65 years (RR 2.39, 95% CI 1.26-4.54) and symptomatic at diagnosis (RR 3.05, 95% CI 1.22-7.63). Among cohabitants, factors associated with household transmission included being the spouse/partner of the index case (WT: RR 1.68, 95% CI 1.34-2.10; AV: RR 1.97, 95% CI 1.59-2.43) and at least 3 days from onset to diagnosis of the index case (WT: RR 1.48, 95% CI 1.21-1.82; AV: RR 1.86, 95% CI 1.52-2.28). Early diagnosis and isolation are effective for preventing household transmission.

Can self-testing be enhanced to hasten safe return of healthcare workers in pandemics? Random order, open label trial using two manufacturers' SARS-CoV-2 lateral flow devices concurrently and nested viral culture study

BACKGROUND

Covid-19 healthcare worker testing, isolation and quarantine policies had to balance risks to patients from the virus and from staff absence. The emergence of the Omicron variant led to dangerous levels of key-worker absence globally. We evaluated whether using two manufacturers' lateral flow tests (LFTs) concurrently improved SARS-CoV-2 Omicron detection significantly and was acceptable to hospital staff. In a nested study, to understand risks of return to work after a 5-day isolation/quarantine period, we examined virus culture 5-7 days after positive test or significant exposure.

METHODS

Fully-vaccinated Liverpool (UK) University Hospitals staff participated (February-May 2022) in a random-order, open-label trial testing whether dual LFTs improved SARS-CoV-2 detection, and whether dual swabbing was acceptable to users. Participants used nose-throat swab Innova and nose-only swab Orient Gene LFTs in daily randomised order for 10 days. A user-experience questionnaire was administered on exit. Selected participants gave swabs for viral culture on days 5-7 after symptom onset or first positive test. Cultures were considered positive if cytopathic effect was apparent or SARS-CoV-2 N gene sub-genomic RNA was detected.

RESULTS

Two hundred and twenty-six individuals reported 1466 pairs of LFT results. Tests disagreed in 127 cases (8.7%). Orient Gene was more likely (78 cf. 49; OR: 2.1, 1.1-4.1; P = 0.03) to be positive. If Innova was swabbed second, it was less likely to agree with a positive Orient Gene result (OR: 2.7, 1.3-5.2; P = 0.005); swabbing first with Innova made no significant difference (OR: 1.1, 0.5-2.3; P = 0.85). Orient Gene positive Innova negative result-pairs became more frequent over time (OR: 1.2, 1.1-1.3; P < 0.001). Of individuals completing the exit questionnaire, 90.7% reported dual swabbing was easy, 57.1% said it was no barrier to their daily routine and 65.6% preferred dual testing. Respondents had more confidence in dual versus single test results. Viral cultures from days 5-7 were positive for 6/31 (19.4%, 7.5%-37.5%) and indeterminate for 11/31 (35.5%, 19.2%-54.6%) LFT-positive participants, indicating they were likely still infectious.

CONCLUSIONS

Dual brand testing increased LFT detection of SARS-CoV-2 antigen by a small but meaningful margin and was acceptable to hospital workers. Viral cultures demonstrated that policies recommending safe return to work ~ 5 days after Omicron infection/exposure were flawed. Key-workers should be prepared for dynamic self-testing protocols in future pandemics.

TRIAL REGISTRATION

https://www.isrctn.com/ISRCTN47058442 (26 January 2022).

Within-host dynamics of antiviral treatment with remdesivir for SARS-CoV-2 infection

The effectiveness of antiviral treatment with remdesivir against COVID-19 has been investigated in clinical trials suggesting earlier recovery. However, this effect seems to be rather modest. In this study, we tracked the clinical course of SARS-CoV-2 infections in 369 COVID-19 individuals across a spectrum of illness severities, including both untreated individuals and individuals who received antiviral treatment with remdesivir. Moreover, using a process-based mathematical model, we quantified and analysed the within-host infection dynamics of a total of 88 individuals, of which 69 were untreated and 19 antiviral-treated individuals. For untreated individuals, we found that those hospitalized exhibit lower levels of early immune response and higher cumulative viral loads than those who were not. For treated individuals, we found that those who died were on average hospitalized later after symptom onset than those who survived, underscoring the importance of early medical intervention for severe COVID-19. Finally, our model estimates a rather limited antiviral activity of remdesivir. Our results provide valuable insights into the clinical course of COVID-19 during antiviral treatment with remdesivir and suggest the need for alternative treatment regimens.

Modeling the Impact of Ensitrelvir on SARS-CoV-2 Dynamics and Its Application for Assessment of Transmission Mitigation of Patients with COVID-19

INTRODUCTION

Mathematical modeling can provide quantitative understanding of the viral dynamics and viral reduction effects of drugs and enable simulations of the dynamics in various scenarios. In this study, a drug effect model of ensitrelvir was developed to describe the viral reduction effect. Using the model, we also estimated the impact of treatment with ensitrelvir on the reduction in the number of infected patients at the population level in Japan.

METHODS

The drug effect model of ensitrelvir was developed based on a viral dynamic model for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a population pharmacokinetic model of ensitrelvir using 10,477 samples of viral load from 1447 patients with coronavirus disease 2019 (COVID-19) in a phase 2/3 study. It was assumed that the drug effect on SARS-CoV-2 promoted the viral clearance depending on the free plasma concentrations. We estimated the impact of ensitrelvir treatment on the reduction in the number of infected patients at the population level in Japan using the susceptible-infectious-recovered-susceptible (SIRS) model including transmission mitigation.

RESULTS

The viral reduction effect of ensitrelvir was characterized as a promotion of viral clearance depending on the plasma ensitrelvir concentrations using the Emax model. The maximum reduction effect was considered to depend on the time from symptom onset to treatment. The maximum transmission mitigation effect was observed when treatment was initiated within 12-24 h of symptom onset, and secondary infections could be reduced by administering ensitrelvir as soon as possible after symptom onset.

CONCLUSION

The viral reduction by ensitrelvir could be characterized based on the viral dynamics, and the dynamics could be estimated using the drug effect model. Furthermore, the drug effect on population level transmission based on the dynamics could be estimated. Thus, the simulation could be conducted for various conditions.

Risk factors for COVID-19 transmission in England: a multilevel modelling study using routine contact tracing data

Contact tracing for COVID-19 in England operated from May 2020 to February 2022. The clinical, demographic and exposure information collected on cases and their contacts offered a unique opportunity to study secondary transmission. We aimed to quantify the relative impact of host factors and exposure settings on secondary COVID-19 transmission risk using 550,000 sampled transmission links between cases and their contacts. Links, or 'contact episodes', were established where a contact subsequently became a case, using an algorithm accounting for incubation period, setting, and contact date. A mixed-effects logistic regression model was used to estimate adjusted odds of transmission. Of sampled episodes, 8.7% resulted in secondary cases. Living with a case (71% episodes) was the most significant risk factor (aOR = 2.6, CI = 1.9-3.6). Other risk factors included unvaccinated status (aOR = 1.2, CI = 1.2-1.3), symptoms, and older age (66-79 years; aOR = 1.4, CI = 1.4-1.5). Whilst global COVID-19 strategies emphasized protection outside the home, including education, travel, and gathering restrictions, this study evidences the relative importance of household transmission. There is a need to reconsider the contribution of household transmission to future control strategies and the requirement for effective infection control within households.

Designing a model to estimate the burden of COVID-19 in Iran

The novel coronavirus disease 2019 (COVID-19) is the latest evidence of an epidemic disease resulting in an extraordinary number of infections and claimed several lives, along with extensive economic and social consequences. In response to the emergency situation, countries introduced different policies to address the situation, with different levels of efficacy. This paper outlines the protocol for developing a model to analyze the burden of COVID-19 in Iran and the effect of policies on the incidence and cumulative death of the disease. The importance of the model lies in the fact that no study, according to the authors' best knowledge, tried to quantify the impact of the disease on Iran society and the impact of various implemented interventions on disease control. Based on a systematic review of COVID-19 prediction models and expert interviews, we developed a system dynamics model that not only includes an epidemic part but also considers the impact of various policies implemented by the Ministry of Health. The epidemic model estimates the incidence and mortality of COVID-19 in Iran. The model also intends to evaluate the effect of implemented policies on these outcomes. The model reflects the continuum of COVID-19 infection and care in Iran (of which some of its elements are unique) and key activities and decisions in delivering care. The model is calibrated and validated using data published by the Ministry of Health of Iran. Finally, the study aims to provide evidence of the impact of interventions intended to curb COVID-19 in Iran. Insights provided by the model will be necessary for controlling either future waves of the disease or similar future pandemics.

Genomic profiling and spatial SEIR modeling of COVID-19 transmission in Western New York

The COVID-19 pandemic has prompted an unprecedented global effort to understand and mitigate the spread of the SARS-CoV-2 virus. In this study, we present a comprehensive analysis of COVID-19 in Western New York (WNY), integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Recovered (SEIR) computational model. The integration of genomic and spatial data enables a multi-faceted exploration of the factors influencing the transmission patterns of COVID-19, including genetic variations in the viral genomes, population density, and movement dynamics in New York State (NYS). Our genomic analyses provide insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage, at region-specific resolutions, while our population analyses provide models for SARS-CoV-2 lineage transmission. Together, our findings shed light on localized dynamics of the pandemic, revealing potential cross-county transmission networks. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more comprehensive understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, including guiding targeted interventions and resource allocations to control the spread of similar viruses.

Qualitative Study on Emotional Experiences and Coping Strategies in Patients With COVID-19 During the Early Stage of Wuhan Crisis

Objective: In the early stage of COVID-19 pandemic from December 2019 to March 2020, COVID-19 patients endured huge mental stress combined with constant physiological suffering. We aimed to summarize the emotional experiences of patients with COVID-19 during the early stages of the Wuhan crisis and present the coping strategies they used during the extreme time. Methods: We did a qualitative study using an empirical phenomenology approach. COVID-19 patients with recovery and near discharge were recruited from the Dawu County Hospital of Traditional Chinese Medicine in Hubei province using purposive sampling. Semistructured face-to-face interviews were conducted by frontline medical staff and recorded by video and audio, then transcribed by two researchers separately. The Haase adapted version of the Colaizzi method was used to analyze the transcriptional data. Results: We included 18 adult survivors of COVID-19 (33% female, 67% male) within the age range of 27-83 (mean age 48), and the average duration of isolation was 31.17 days. In conjunction with clinical data, we meticulously delved into the emotional trajectory of each survivor, spanning from the onset of illness through the phases of decline, improvement, and eventual recovery. Three theme categories were obtained from data analysis, including negative emotions and sources, coping strategies, and positive emotions and sources. COVID-19 patients adopted self-management strategies and received support from different roles when confronting high level of negative emotions. Conclusions: Early survivors of COVID-19 experienced both negative and positive emotional experiences. Anxiety and other negative emotions were originated from both collective and individual concerns. The influence of the emotion sources differed at each stage of the disease. Patients coped with these stressors using external supports and self-adjustment. Still, comprehensive and targeted psychological services are needed.

Comparative 60-day effectiveness of bivalent versus monovalent mRNA vaccines in Shelby County: a population-level analysis

Background

Two monovalent mRNA vaccines, available in December 2020, were demonstrated to have high efficacy against both the original SARS-CoV-2 strain and variants circulating through the summer and into the fall of 2021. In the context of the Omicron/BA.1 variant, which was predominant from late fall 2021 into winter of 2022 in the United States, and subsequent Omicron subvariants that have been predominant thereafter, vaccine effectiveness of the monovalent mRNA vaccine option is attenuated.

Objectives

We aim to investigate the relative effectiveness of the bivalent booster compared to the monovalent booster against SARS-CoV-2 infection in the 60 days following administration in Shelby County, TN.

Design

This observational population-based cohort study utilizes COVID-19 surveillance data to identify adults who were vaccinated with a monovalent booster dose between August 1, 2022 and August 30, 2022 or a bivalent booster dose between September 1, 2022 and September 30, 2022. Both groups were followed for COVID-19 status for 60 days from their administration date.

Methods

We calculated incidence rates with 95% confidence intervals and propensity-adjusted hazard ratios with 95% confidence intervals of COVID-19 diagnosis in the 60 days following administration of the booster dose between the bivalent group and the monovalent group. Stratified analysis was conducted by age group (18-34, 35-64, and 65+ years old).

Results

The incidence of reported SARS-CoV-2 infection was substantially higher for those who received the monovalent booster, across age groups. Overall, we observed a 51% lower hazard of infection during the study period among those who received the bivalent booster, compared to the monovalent booster.

Conclusion

These results support and extend prior findings that the bivalent booster dose may be more effective in preventing infection against the Omicron sub-variants of SARS-CoV-2.

Cost effectiveness analysis comparing varying booster intervals of vaccination policies to address COVID-19 situation in Thailand, 2023

The COVID-19 booster immunization policy is cost-effective, but evidence on additional booster doses and appropriate strategies is scarce. This research compared the cost-effectiveness of annual, twice-a-year, and biennial booster dose policies. We performed stochastic modeling using compartmental susceptible-exposed-infectious-recovered models and a system dynamic model. We evaluated four policy scenarios: (1) hypothetical no-booster immunization policy; (2) twice-a-year vaccination policy; (3) annual vaccination policy; and (4) biennial vaccination policy. In addition, we conducted a one-way sensitivity analysis by adjusting R0 from 1.8 to 3.0 in all scenarios (epidemic stage) and by decreasing the vaccination cost by 50% at the end of the first year to reflect the current policy direction to enhance domestic vaccine production. Compared to non-booster policies, all three booster strategies reduced the number of cases, hospital admissions, and severe infections remarkably. Without a booster, total cases would reach 16,220,615 (95% confidence interval [CI] 6,726,550-29,661,112) by day 1,460, whereas, with a twice-a-year booster, the total cases would reach 597,901 (95% CI 526,230-694,458) in the same period. Even though the no booster scenario exhibited the lowest cost by approximately the first 500 days, by day 1,460 the biennial booster scenario demonstrated the lowest cost at 72.0 billion baht (95% CI 68.6-79.4 billion). The most cost-saving policy was the biennial booster scenario. The annual booster scenario also stood as a cost-effective option for most outcomes. In the epidemic stage and in an assumption where the vaccination costs dropped, all booster policies became more cost-effective or cost-saving compared with the main assumption. This study underscores the significance of the COVID-19 vaccine booster policy. Implementing policies should take into consideration cost-effectiveness, feasibility, and public communication.

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.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic

Background

During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations.

Methods

The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including "date, test result, population density, area, latitude, longitude, district, and state" to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System.

Findings

The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate β approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100.

Interpretation

The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas.

Funding

This study is Funded by Indian Council of Medical Research (ICMR).

SARS-CoV-2 Transmission during High-Altitude Field Studies

Grimm, Mirjam, Lucie Ziegler, Annina Seglias, Maamed Mademilov, Kamila Magdieva, Gulzada Mirzalieva, Aijan Taalaibekova, Simone Suter, Simon R. Schneider, Fiona Zoller, Vera Bissig, Lukas Reinhard, Meret Bauer, Julian Müller, Tanja L. Ulrich, Arcangelo F. Carta, Patrick R. Bader, Konstantinos Bitos, Aurelia E. Reiser, Benoit Champigneulle, Damira Ashyralieva, Philipp M. Scheiwiller, Silvia Ulrich, Talant M. Sooronbaev, Michael Furian, and Konrad E. Bloch. SARS-CoV-2 Transmission during High-Altitude Field Studies. High Alt Med Biol. 25:197-204, 2024. Background: Throughout the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, virus transmission during clinical research was of concern. Therefore, during high-altitude field studies performed in 2021, we took specific COVID-19 precautions and investigated the occurrence of SARS-CoV-2 infection. Methods: From May to September 2021, we performed studies in patients with chronic obstructive pulmonary disease (COPD) and in healthy school-age children in Kyrgyzstan in high-altitude facilities at 3,100 m and 3,250 m and at 760 m. The various implemented COVID-19 safety measures included systematic SARS-CoV-2 rapid antigen testing (RAT). Main outcomes were SARS-CoV-2-RAT-positive rate among participants and staff at initial presentation (prevalence) and SARS-CoV-2-RAT-positive conversion during and within 10 days after studies (incidence). Results: Among 338 participants and staff, SARS-CoV-2-RAT-positive prevalence was 15 (4.4%). During mean ± SD duration of individual study participation of 3.1 ± 1.0 day and within 10 days, RAT-positive conversion occurred in 1/237(0.4%) participants. Among staff working in studies for 31.5 ± 29.3 days, SARS-CoV-2-RAT-positive conversion was 11/101(10.9%). In all 338 individuals involved in the studies over the course of 15.6 weeks, the median SARS-CoV-2-RAT-positive incidence was 0.00%/week (quartiles 0.00; 0.64). Over the same period, the median background incidence among the total Kyrgyz population of 6,636 million was 0.06%/week (0.03; 0.11), p = 0.013 (Wilcoxon rank sum test). Conclusions: Taking precautions by implementing specific safety measures, SARS-CoV-2 transmission during clinical studies was very rare, and the SARS-CoV-2 incidence among participants and staff was lower than that in the general population during the same period. The results are reassuring and may help in decision-making on the conduct of clinical research in similar settings.

Risk of nosocomial coronavirus disease 2019: comparison between single- and multiple-occupancy rooms

BACKGROUND

There is an ongoing controversy regarding whether single-occupancy rooms are superior to multiple-occupancy rooms in terms of infection prevention. We investigated whether treatment in a multiple-occupancy room is associated with an increased incidence of nosocomial coronavirus disease 2019 (COVID-19) compared with treatment in a single-occupancy room.

METHODS

In this retrospective cohort study, every hospitalization period of adult patients aged ≥ 18 years at a tertiary hospital in Korea from January 1, 2022, to December 31, 2022, was analyzed. If COVID-19 was diagnosed more than 5 days after hospitalization, the case was classified as nosocomial. We estimated the association between the number of patients per room and the risk of nosocomial COVID-19 using a Cox proportional hazards regression model.

RESULTS

In total, 25,143 hospitalizations per room type were analyzed. The incidence rate of nosocomial COVID-19 increased according to the number of patients per room; it ranged from 3.05 to 38.64 cases per 10,000 patient-days between single- and 6-bed rooms, respectively. Additionally, the hazard ratios of nosocomial COVID-19 showed an increasing trend according to the number of patients per room, ranging from 0.14 (95% confidence interval 0.001-1.03) to 2.66 (95% confidence interval 1.60-4.85) between single- and 6-bed rooms, respectively.

CONCLUSIONS

We demonstrated that the incidence of nosocomial COVID-19 increased according to the number of patients per room. To reduce nosocomial infections by respiratory viruses, the use of multiple-occupancy rooms should be minimized.

Impact of the 2022 African Cup of Nations mass-gathering event on the COVID-19 epidemic in Garoua, Cameroon

BACKGROUND

The 2022 African Cup of Nations (AFCON) took place in Cameroon from January 9th to February 5th, 2022, including Garoua in the north. We aimed to measure the impact of this event on the local COVID-19 epidemic given the implementation of a preventive strategy based on a health pass.

METHODS

All players, staff and fans involved in the AFCON event were screened with PCR tests. Symptomatic cases were also continuously monitored in the general population and screened for variants of concern. Daily numbers of confirmed cases were compared to neighboring countries numbers retrieved from a public domain source.

RESULTS

In total, 1479 and 2481 tests were performed in the general population and on asymptomatic AFCON attendees, respectively. From the latter, 12.5% were PCR-positive; 97% were infected with Omicron, with no significant difference compared to the passive program (G-test, P value = 0.162). Surveillance indicated the AFCON did not increase the number of symptomatic PCR-positive cases in the general population compared to neighboring countries.

CONCLUSIONS

Though the COVID-19 epidemic was fueled by asymptomatic cases infected with the Omicron variant at the time, the non-therapeutic preventive measures implemented for AFCON mitigated an increase in the epidemic in the local population.

Immunogenicity, clinical efficacy, and safety of the sinopharm (BBIBP-CorV) SARS-CoV-2 vaccine among people with multiple sclerosis receiving disease-modifying therapies: a prospective cohort study

BACKGROUND

To investigate the safety (adverse events [AEs] and post-vaccination multiple sclerosis [MS] activity within 6 weeks), clinical efficacy (protection against coronavirus disease 2019 [COVID-19]), and vaccine-induced humoral immunogenicity (SARS-CoV-2 neutralizing antibody, anti-nucleocapsid IgG, and anti-spike IgG) of the Sinopharm (BBIBP-CorV) vaccine among people with MS (PwMS) receiving different disease-modifying therapies (DMTs).

METHODS

This prospective cohort study was conducted between November 2021 and May 2022. PwMS were followed for six months after the 2nd dose of vaccination. Antibody responses were measured 2-16 weeks after the 2nd dose injection. Multivariate logistic regression was employed to assess the impact of each DMT on dichotomous antibody responses, adjusting for age, sex, MS phenotype, expanded disability status scale, disease duration, and vaccination-antibody titration interval.

RESULTS

Among the 261 screened PwMS, 209 (aged 38.23 ± 9.73 years, female: 70.8%; relapsing-remitting MS: 80.4%) were included. The frequencies of experiencing non-serious AEs and post-vaccination MS activity were 66.0% and 4.8%, respectively. Breakthrough COVID-19 infection was observed in 14.8% of the PwMS. A subcohort of 125 PwMS was assessed for antibody responses. Positive neutralizing antibodies, anti-nucleocapsid IgG, and anti-spike IgG were detected in 36.8%, 35.2%, and 52.0% of the PwMS, respectively. Multivariate regression indicated a 96% (OR: 0.04 [95% CI: 0.00, 0.51], P = 0.013), 93% (OR: 0.07 [0.01, 0.64], P = 0.019), and 89% (OR: 0.11 [0.01, 0.96], P = 0.045) reduced odds of positive neutralizing antibody, anti-nucleocapsid IgG, and anti-spike IgG, respectively, among fingolimod-receivers. Additionally, anti-CD20s-receivers had 88% (OR: 0.12 [0.02, 0.85], P = 0.034) lower odds of being positive for anti-nucleocapsid IgG.

CONCLUSIONS

BBIBP-CorV appeared to be well tolerated in PwMS, with promising clinical efficacy. However, a suboptimal humoral response was observed in PwMS receiving fingolimod and anti-CD20s. Future research should investigate the relationship between humoral responses and the frequency and severity of COVID-19 infection across various DMTs.

Delayed Mucosal Antiviral Responses Despite Robust Peripheral Inflammation in Fatal COVID-19

BACKGROUND

While inflammatory and immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in peripheral blood are extensively described, responses at the upper respiratory mucosal site of initial infection are relatively poorly defined. We sought to identify mucosal cytokine/chemokine signatures that distinguished coronavirus disease 2019 (COVID-19) severity categories, and relate these to disease progression and peripheral inflammation.

METHODS

We measured 35 cytokines and chemokines in nasal samples from 274 patients hospitalized with COVID-19. Analysis considered the timing of sampling during disease, as either the early (0-5 days after symptom onset) or late (6-20 days after symptom onset) phase.

RESULTS

Patients that survived severe COVID-19 showed interferon (IFN)-dominated mucosal immune responses (IFN-γ, CXCL10, and CXCL13) early in infection. These early mucosal responses were absent in patients who would progress to fatal disease despite equivalent SARS-CoV-2 viral load. Mucosal inflammation in later disease was dominated by interleukin 2 (IL-2), IL-10, IFN-γ, and IL-12p70, which scaled with severity but did not differentiate patients who would survive or succumb to disease. Cytokines and chemokines in the mucosa showed distinctions from responses evident in the peripheral blood, particularly during fatal disease.

CONCLUSIONS

Defective early mucosal antiviral responses anticipate fatal COVID-19 but are not associated with viral load. Early mucosal immune responses may define the trajectory of severe COVID-19.

Diagnostic Accuracy of the Abbott BinaxNOW COVID-19 Antigen Card Test, Puerto Rico

BACKGROUND

The COVID-19 pandemic underscored the need for rapid and accurate diagnostic tools. In August 2020, the Abbott BinaxNOW COVID-19 Antigen Card test became available as a timely and affordable alternative for SARS-CoV-2 molecular testing, but its performance may vary due to factors including timing and symptomatology. This study evaluates BinaxNOW diagnostic performance in diverse epidemiological contexts.

METHODS

Using RT-PCR as reference, we assessed performance of the BinaxNOW COVID-19 test for SARS-CoV-2 detection in anterior nasal swabs from participants of two studies in Puerto Rico from December 2020 to May 2023. Test performance was assessed by days post symptom onset, collection strategy, vaccination status, symptomatology, repeated testing, and RT-PCR cycle threshold (Ct) values.

RESULTS

BinaxNOW demonstrated an overall sensitivity of 84.1% and specificity of 98.8%. Sensitivity peaked within 1-6 days after symptom onset (93.2%) and was higher for symptomatic (86.3%) than asymptomatic (67.3%) participants. Sensitivity declined over the course of infection, dropping from 96.3% in the initial test to 48.4% in testing performed 7-14 days later. BinaxNOW showed 99.5% sensitivity in participants with low Ct values (≤ 25) but lower sensitivity (18.2%) for participants with higher Cts (36-40).

CONCLUSIONS

BinaxNOW demonstrated high sensitivity and specificity, particularly in early-stage infections and symptomatic participants. In situations where test sensitivity is crucial for clinical decision-making, nucleic acid amplification tests are preferred. These findings highlight the importance of considering clinical and epidemiological context when interpreting test results and emphasize the need for ongoing research to adapt testing strategies to emerging SARS-CoV-2 variants.

Hits and Misses: Digital Contact Tracing in a Pandemic

Traditional contact tracing is one of the most powerful weapons people have in the battle against a pandemic, especially when vaccines do not yet exist or do not afford complete protection from infection. But the effectiveness of contact tracing hinges on its ability to find infected people quickly and obtain accurate information from them. Therefore, contact tracing inherits the challenges associated with the fallibilities of memory. Against this backdrop, digital contact tracing is the "dream scenario"-an unobtrusive, vigilant, and accurate recorder of danger that should outperform manual contact tracing on every dimension. There is reason to celebrate the success of digital contact tracing. Indeed, epidemiologists report that digital contact tracing probably reduced the incidence of COVID-19 cases by at least 25% in many countries, a feat that would have been hard to match with its manual counterpart. Yet there is also reason to speculate that digital contact tracing delivered on only a fraction of its potential because it almost completely ignored the relevant psychological science. We discuss the strengths and weaknesses of digital contact tracing, its hits and misses in the COVID-19 pandemic, and its need to be integrated with the science of human behavior.

Impact of COVID-19 on healthcare-associated infections and antimicrobial use in Italy, 2022

BACKGROUND

It is unknown whether COVID-19 patients are at higher risk due to demographic and clinical characteristics associated with higher COVID-19 infection risk and severity of infection, or due to the disease and its management.

AIM

To assess the impact of COVID-19 on healthcare-associated infection (HAI) transmission and antimicrobial use (AMU) prevalence during the later stages of the pandemic.

METHODS

A point-prevalence survey (PPS) was conducted among 325 acute care hospitals of 19 out of 21 Regions of Italy, during November 2022. Non-COVID-19 patients were matched to COVID-19 patients according to age, sex, and severity of underlying conditions. HAI and AMU prevalence were calculated as the percentage of patients with at least one HAI or prescribed at least one antimicrobial over all included patients, respectively.

FINDINGS

In total, 60,403 patients were included, 1897 (3.14%) of which were classified as COVID-19 patients. Crude HAI prevalence was significantly higher among COVID-19 patients compared to non-COVID-19 patients (9.54% vs 8.01%; prevalence rate ratio (PRR): 1.19; 95% confidence interval (CI): 1.04-1.38; P < 0.05), and remained higher in the matched sample; however, statistical significance was not maintained (odds ratio (OR): 1.25; 95% CI: 0.99-1.59; P = 0.067). AMU prevalence was significantly higher among COVID-19 patients prior to matching (46.39% vs 41.52%; PRR: 1.21; 95% CI: 1.11-1.32; P < 0.001), and significantly lower after matching (OR: 0.77; 95% CI: 0.66-0.89; P < 0.001).

CONCLUSION

COVID-19 patients could be at higher HAI risk due to underlying clinical conditions and the intensity of healthcare needs. Further efforts should be dedicated to antimicrobial stewardship among COVID-19 patients.

Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Vaccination Shapes Within-Host SARS-CoV-2 Diversity of Omicron BA.2.2 Breakthrough Infection

BACKGROUND

Low-frequency intrahost single-nucleotide variants of SARS-CoV-2 have been recognized as predictive indicators of selection. However, the impact of vaccination on the intrahost evolution of SARS-CoV-2 remains uncertain at present.

METHODS

We investigated the genetic variation of SARS-CoV-2 in individuals who were unvaccinated, partially vaccinated, or fully vaccinated during Shanghai's Omicron BA.2.2 wave. We substantiated the connection between particular amino acid substitutions and immune-mediated selection through a pseudovirus neutralization assay or by cross-verification with the human leukocyte antigen-associated T-cell epitopes.

RESULTS

In contrast to those with immunologic naivety or partial vaccination, participants who were fully vaccinated had intrahost variant spectra characterized by reduced diversity. Nevertheless, the distribution of mutations in the fully vaccinated group was enriched in the spike protein. The distribution of intrahost single-nucleotide variants in individuals who were immunocompetent did not demonstrate notable signs of positive selection, in contrast to the observed adaptation in 2 participants who were immunocompromised who had an extended period of viral shedding.

CONCLUSIONS

In SARS-CoV-2 infections, vaccine-induced immunity was associated with decreased diversity of within-host variant spectra, with milder inflammatory pathophysiology. The enrichment of mutations in the spike protein gene indicates selection pressure exerted by vaccination on the evolution of SARS-CoV-2.

Two biases in incubation time estimation related to exposure

BACKGROUND

Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature.

METHODS

We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution.

RESULTS

Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile.

CONCLUSION

We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.

An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.

Incubation Period and Serial Interval of Mpox in 2022 Global Outbreak Compared with Historical Estimates

Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.