Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

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.

Surveillance of SARS-CoV-2 in wastewater by quantitative PCR and digital PCR: a case study in Shijiazhuang city, Hebei province, China

In this study, we reported the first long-term monitoring of SARS-CoV-2 in wastewater in Mainland China from November 2021 to October 2023. The city of Shijiazhuang was employed for this case study. We developed a triple reverse transcription droplet digital PCR (RT-ddPCR) method using triple primer-probes for simultaneous detection of the N1 gene, E gene, and Pepper mild mottle virus (PMMoV) to achieve accurate quantification of SARS-CoV-2 RNA in wastewater. Both the RT-ddPCR method and the commercial multiplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) method were implemented for the detection of SARS-CoV-2 in wastewater in Shijiazhuang City over a 24-month period. Results showed that SARS-CoV-2 was detected for the first time in the wastewater of Shijiazhuang City on 10 November 2022. The peak of COVID-19 cases occurred in the middle of December 2022, when the concentration of SARS-CoV-2 in the wastewater was highest. The trend of virus concentration increases and decreases forming a "long-tailed" shape in the COVID-19  outbreak and recession cycle. The results indicated that both multiplex RT-ddPCR and RT-qPCR are effective in detecting SARS-CoV-2 in wastewater, but RT-ddPCR is capable of detecting low concentrations of SARS-CoV-2 in wastewater which is more efficient. The SARS-CoV-2 abundance in wastewater is correlated to clinical data, outlining the public health utility of this work.HighlightsFirst long-term monitoring of SARS-CoV-2 in wastewater in Mainland ChinaCOVID-19 outbreak was tracked in Shijiazhuang City from outbreak to containmentWastewater was monitored simultaneously using RT-ddPCR and RT-qPCR methodsTriple primer-probe RT-ddPCR detects N1 and E genes of SARS-CoV-2 and PMMoV.

Rapid SARS-CoV-2 surveillance using clinical, pooled, or wastewater sequence as a sensor for population change

The COVID-19 pandemic has highlighted the critical role of genomic surveillance for guiding policy and control. Timeliness is key, but sequence alignment and phylogeny slow most surveillance techniques. Millions of SARS-CoV-2 genomes have been assembled. Phylogenetic methods are ill equipped to handle this sheer scale. We introduce a pangenomic measure that examines the information diversity of a k-mer library drawn from a country's complete set of clinical, pooled, or wastewater sequence. Quantifying diversity is central to ecology. Hill numbers, or the effective number of species in a sample, provide a simple metric for comparing species diversity across environments. The more diverse the sample, the higher the Hill number. We adopt this ecological approach and consider each k-mer an individual and each genome a transect in the pangenome of the species. Structured in this way, Hill numbers summarize the temporal trajectory of pandemic variants, collapsing each day's assemblies into genome equivalents. For pooled or wastewater sequence, we instead compare days using survey sequence divorced from individual infections. Across data from the UK, USA, and South Africa, we trace the ascendance of new variants of concern as they emerge in local populations well before these variants are named and added to phylogenetic databases. Using data from San Diego wastewater, we monitor these same population changes from raw, unassembled sequence. This history of emerging variants senses all available data as it is sequenced, intimating variant sweeps to dominance or declines to extinction at the leading edge of the COVID-19 pandemic.

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 the disease-free and endemic states calculated and evaluated 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.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

A combined neural ODE-Bayesian optimization approach to resolve dynamics and estimate parameters for a modified SIR model with immune memory

We propose a novel hybrid approach that integrates Neural Ordinary Differential Equations (NODEs) with Bayesian optimization to address the dynamics and parameter estimation of a modified time-delay-type Susceptible-Infected-Removed (SIR) model incorporating immune memory. This approach leverages a neural network to produce continuous multi-wave infection profiles by learning from both data and the model. The time-delay component of the SIR model, expressed through a convolutional integral, results in an integro-differential equation. To resolve these dynamics, we extend the NODE framework, employing a Runge-Kutta solver, to handle the challenging convolution integral, enabling us to fit the data and learn the parameters and dynamics of the model. Additionally, through Bayesian optimization, we enhance prediction accuracy while focusing on long-term dynamics. Our model, applied to COVID-19 data from Mexico, South Africa, and South Korea, effectively learns critical time-dependent parameters and provides accurate short- and long-term predictions. This combined methodology allows for early prediction of infection peaks, offering significant lead time for public health responses.

Implementation of an Immunoassay Based on the MVA-T7pol-Expression System for Rapid Identification of Immunogenic SARS-CoV-2 Antigens: A Proof-of-Concept Study

The emergence of hitherto unknown viral pathogens presents a great challenge for researchers to develop effective therapeutics and vaccines within a short time to avoid an uncontrolled global spread, as seen during the coronavirus disease 2019 (COVID-19) pandemic. Therefore, rapid and simple methods to identify immunogenic antigens as potential therapeutical targets are urgently needed for a better pandemic preparedness. To address this problem, we chose the well-characterized Modified Vaccinia virus Ankara (MVA)-T7pol expression system to establish a workflow to identify immunogens when a new pathogen emerges, generate candidate vaccines, and test their immunogenicity in an animal model. By using this system, we detected severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) nucleoprotein (N)-, and spike (S)-specific antibodies in COVID-19 patient sera, which is in line with the current literature and our observations from previous immunogenicity studies. Furthermore, we detected antibodies directed against the SARS-CoV-2-membrane (M) and -ORF3a proteins in COVID-19 patient sera and aimed to generate recombinant MVA candidate vaccines expressing either the M or ORF3a protein. When testing our candidate vaccines in a prime-boost immunization regimen in humanized HLA-A2.1-/HLA-DR1-transgenic H-2 class I-/class II-knockout mice, we were able to demonstrate M- and ORF3a-specific cellular and humoral immune responses. Hence, the established workflow using the MVA-T7pol expression system represents a rapid and efficient tool to identify potential immunogenic antigens and provides a basis for future development of candidate vaccines.

Acute kidney injury in coronavirus disease: a comparative study of the two waves in Brazil

BACKGROUND

Magalhães et al. demonstrated that the incidence of acute kidney injury was high in hospitalized patients with COVID-19 and that the second wave was associated with greater severity; however, the mortality rates were similar between the two periods. This may reflect both the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic to provide greater support to their patients.

BACKGROUND

◼ Renal involvement was frequent in patients with COVID-19 and related to worse outcomes.

BACKGROUND

◼ Diuretic use, mechanical ventilation, proteinuria, hematuria, age, and creatine phosphokinase and D-dimer levels were risk factors for acute kidney injury.

BACKGROUND

◼ Acute kidney injury, mechanical ventilation, elevated SOFA Score, and elevated ATN-ISS were associated with mortality.

BACKGROUND

◼ The second wave was associated with greater severity; however, the mortality rates were similar between the two periods.

BACKGROUND

◼ This may reflect the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic.

OBJECTIVE

This study aimed to evaluate the incidence of acute kidney injury in hospitalized Brazilian patients with COVID-19 and identify the risk factors associated with its development and prognosis during the two waves of the disease.

METHODS

We performed a prospective cohort study of hospitalized patients with COVID-19 at a public university hospital in São Paulo from March 2020 to May 2021.

RESULTS

Of 887 patients hospitalized with COVID-19, 54.6% were admitted to the intensive care unit. The incidence of acute kidney injury was 48.1%, and the overall mortality rate was 38.9%. Acute kidney replacement therapy was indicated for 58.8% of the patients. The factors associated with acute kidney injury were diuretic use (odds ratio [OR] 2.2, 95%CI= 1.2-4.1, p=0.01), mechanical ventilation (OR= 12.9, 95%CI= 4.3-38.2, p<0.0001), hematuria(OR= 2.02, 95%CI= 1.1-3.5, p<0.0001), chronic kidney disease (OR= 2.6, 95%CI= 1.2-5.5, p=0.009), age (OR= 1.03, 95%CI= 1.01-1.07, p=0.02), and elevated creatine phosphokinase (OR= 1.02, 95%CI= 1.01-1.07, p=0.02) and D-dimer levels (OR= 1.01, 95%CI= 1.01-1.09, p<0.0001). Mortality was higher among those with acute kidney injury (OR= 1.12, 95%CI= 1.02-2.05, p=0.01), elevated Sequential Organ Failure Assessment Scores (OR= 1.35, 95%CI= 1.1-1.6, p=0.007), elevated Acute Tubular Necrosis-Injury Severity Score (ATN-ISS; (OR= 96.4, 95%CI= 4.8-203.1, p<0.0001), and who received mechanical ventilation (OR= 12.9, 95%CI= 4.3-38.2, p<0.0001). During the second wave, the number of cases requiring mechanical ventilation (OR= 1.57, 95%CI= 1.01-2.3, p=0.026), with proteinuria (OR= 1.44, 95%CI= 1.01-2.1, p=0.04), and with higher ATN-ISS Scores (OR= 40.9, 95%CI= 1.7-48.1, p=0.04) was higher than that during the first wave.

CONCLUSION

Acute kidney injury was frequent in hospitalized patients with COVID-19, and the second wave was associated with greater severity. However, mortality rates were similar between the two periods, which may reflect both the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic to provide greater support to their patients.

REGISTRY OF CLINICAL TRIALS

RBR-62y3h7.

Optimal time-dependent SUC model for COVID-19 pandemic in India

In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India.

A six-compartment model for COVID-19 with transmission dynamics and public health strategies

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

A control measure for epidemic spread based on the susceptible-infectious-susceptible (SIS) model

When an epidemic occurs in a network, finding the important links and cutting them off is an effective measure for preventing the spread of the epidemic. Traditional methods that remove important links easily lead to a disconnected network, inevitably incurring high costs arising from quarantining individuals or communities in a real-world network. In this study, we combine the clustering coefficient and the eigenvector to identify the important links using the susceptible-infectious-susceptible (SIS) model. The results show that our approach can improve the epidemic threshold while maintaining the connectivity of the network to control the spread of the epidemic. Experiments on multiple real-world and synthetic networks of varying sizes, demonstrate the effectiveness and scalability of our approach.

Bacterial co-infection in COVID-19: a call to stay vigilant

Co-infection with diverse bacteria is commonly seen in patients infected with the novel coronavirus, SARS-CoV-2. This type of co-infection significantly impacts the occurrence and development of novel coronavirus infection. Bacterial co-pathogens are typically identified in the respiratory system and blood culture, which complicates the diagnosis, treatment, and prognosis of COVID-19, and even exacerbates the severity of disease symptoms and increases mortality rates. However, the status and impact of bacterial co-infections during the COVID-19 pandemic have not been properly studied. Recently, the amount of literature on the co-infection of SARS-CoV-2 and bacteria has gradually increased, enabling a comprehensive discussion on this type of co-infection. In this study, we focus on bacterial infections in the respiratory system and blood of patients with COVID-19 because these infection types significantly affect the severity and mortality of COVID-19. Furthermore, the progression of COVID-19 has markedly elevated the antimicrobial resistance among specific bacteria, such as Klebsiella pneumoniae, in clinical settings including intensive care units (ICUs). Grasping these resistance patterns is pivotal for the optimal utilization and stewardship of antibiotics, including fluoroquinolones. Our study offers insights into these aspects and serves as a fundamental basis for devising effective therapeutic strategies. We primarily sourced our articles from PubMed, ScienceDirect, Scopus, and Google Scholar. We queried these databases using specific search terms related to COVID-19 and its co-infections with bacteria or fungi, and selectively chose relevant articles for inclusion in our review.

A two-variant model of SARS-COV-2 transmission: estimating the characteristics of a newly emerging strain

BACKGROUND

The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses.

METHODS

This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference.

RESULTS

The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15-0.29) and transmission rate of 5.0 (95% CI 2.4-6.6) for the Omicron variant.

CONCLUSIONS

The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.

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).

The impact of COVID-19 on Italian dentists: A cross-sectional survey on 2443 participants

OBJECTIVE

To evaluate the impact of COVID-19 pandemic among a sample of Italian dentists in terms of infection, strategies for infection control, organization of the dental clinic, attitude, and behavior.

MATERIAL AND METHODS

This was a cross-sectional survey. The sample consisted of 8000 Italian dentists selected among 63,375 using a computerized random sampling method. An electronic informed consent had to be signed. The questionnaire categories were on demographic, infection risk management, organization, and dentists' attitude and behavior. Geographic macro-areas were used for subgroup analysis.

RESULTS

Among 8000 invited dentists, 2443 agreed to participate to the survey (30.6%). Mean age was 51.2 years, women were 34.5%. A total of 6.1% self-reported COVID-19 experience and higher rate of infection was reported in north Italy compared to the south (p < 0.05). FFP2/FFP3 respirators (97.1%) and visors (97.4%) were used by almost all dentists. While, natural ventilation and mouthwashes were the most frequent approaches used to reduce the infection risk. Most of the dentists reported positive attitude, nevertheless 83.6% felt an increased responsibility.

CONCLUSION

The self-reported COVID-19 prevalence was 6.1% with some differences among geographic areas. COVID 19 had a deep impact on preventive strategies, dental office organization, and behavior within this sample.

COVID-19 in Brazilian Pediatric Patients: A Retrospective Cross-Sectional Study with a Predictive Model for Hospitalization

BACKGROUND

This study was conducted to ascertain the most frequent symptoms of COVID-19 infection at first consultation in a pediatric cohort and to devise a predictive model for hospitalization.

METHODS

This is a retrospective cross-sectional study of 1028 Brazilian patients aged <18 years with SARS-CoV-2 infection in a single reference hospital in the first year of the pandemic. Clinical, demographic, laboratory, and disease spectrum data were analyzed via multivariate logistic regression modeling to develop a predictive model of factors linked to hospitalization.

RESULTS

The majority of our cohort were schoolchildren and adolescents, with a homogeneous distribution concerning sex. At first consultation, most patients presented with fever (64.1%) and respiratory symptoms (63.3%). We had 204 admitted patients, including 11 with Pediatric Multisystem Inflammatory Syndrome. Increased D-dimer levels were associated with comorbidities (p = 0.018). A high viral load was observed in patients within the first two days of symptoms (p < 0.0001). Our predictive model included respiratory distress, number and type of specific comorbidities, tachycardia, seizures, and vomiting as factors for hospitalization.

CONCLUSIONS

Most patients presented with mild conditions with outpatient treatment. However, understanding predictors for hospitalization can contribute to medical decisions at the first patient visit.

Underreporting of Cases in the COVID-19 Outbreak of Borriana (Spain) during Mass Gathering Events in March 2020: A Cross-Sectional Study

Determining the number of cases of an epidemic is the first function of epidemiological surveillance. An important underreporting of cases was observed in many locations during the first wave of the COVID-19 pandemic. To estimate this underreporting in the COVID-19 outbreak of Borriana (Valencia Community, Spain) in March 2020, a cross-sectional study was performed in June 2020 querying the public health register. Logistic regression models were used. Of a total of 468 symptomatic COVID-19 cases diagnosed in the outbreak through anti-SARS-CoV-2 serology, 36 cases were reported (7.7%), resulting in an underreporting proportion of 92.3% (95% confidence interval [CI], 89.5-94.6%), with 13 unreported cases for every reported case. Only positive SARS-CoV-2 polymerase chain reaction cases were predominantly reported due to a limited testing capacity and following a national protocol. Significant factors associated with underreporting included no medical assistance for COVID-19 disease, with an adjusted odds ratio [aOR] of 10.83 (95% CI 2.49-47.11); no chronic illness, aOR = 2.81 (95% CI 1.28-6.17); middle and lower social classes, aOR = 3.12 (95% CI 1.42-6.85); younger age, aOR = 0.97 (95% CI 0.94-0.99); and a shorter duration of illness, aOR = 0.98 (95% CI 0.97-0.99). To improve the surveillance of future epidemics, new approaches are recommended.

A modeling framework for the analysis of the SARS-CoV2 transmission dynamics

Despite the progress in medical data collection the actual burden of SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under-reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths. We adopt this framework and assume that the dynamics generating the total number of infections can be described by a continuous time transmission model expressed through a system of nonlinear ordinary differential equations where the transmission rate is modeled as a diffusion process allowing to reveal both the effect of control strategies and the changes in individuals behavior. We develop this flexible Bayesian tool in Stan and study 3 pairs of European countries, estimating the time-varying reproduction number (R t $$ {R}_t $$ ) as well as the true cumulative number of infected individuals. As we estimate the true number of infections we offer a more accurate estimate ofR t $$ {R}_t $$ . We also provide an estimate of the daily reporting ratio and discuss the effects of changes in mobility and testing on the inferred quantities.

Changes in Technical Equipment and Patient Perspectives Navigating Towards Enhanced Digitalization in Breast Cancer Across Pre-COVID-19 and Early COVID-19 Eras

INTRODUCTION

The potential benefits of eHealth support in enhancing patient care, satisfaction, and cancer outcomes are well-established; however, its integration into routine care has been gradual. The emergence of the COVID-19 pandemic in 2020 dramatically affected cancer patients, imposing multifaceted challenges that impede traditional doctor-patient interactions. Consequently, there has been a surge in the adoption of eHealth for supporting oncological therapies. This study investigates the fundamental prerequisites for transitioning to a more digitally oriented routine care, focusing on the availability of appropriate technical equipment and the cultivation of a positive mindset towards eHealth among breast cancer patients.

PATIENTS AND METHODS

In 2013, 2016, and 2020, breast cancer patients participated in surveys utilizing a comprehensive paper questionnaire encompassing 29 inquiries about their health status, technical equipment, and attitudes toward digital therapy support.

RESULTS

A total of 959 patients participated in the interviews. Comparative analyses between the 2013, 2016, and 2020 surveys revealed a widespread increase in internet access and device ownership across various age groups. By 2020, 3 quarters of patients were utilizing the internet for health-related topics. Notably, there has been a considerable improvement in patients' personal attitudes towards eHealth and their expectations for future digital therapy support.

DISCUSSION

Over the seven years spanned by the surveys, there has been a substantial positive shift in the attitudes of breast cancer patients towards eHealth, accompanied by a marked improvement in their technical equipment. This study reveals that the essential prerequisites for digital therapy support now appear to be prevalent among breast cancer patients.

Quantifying suicide contagion at population scale

The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.

Predictability of human mobility during the COVID-19 pandemic in the United States

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

COVID-19 risk perceptions, self-efficacy, and prevention behaviors among California undergraduate students

OBJECTIVE

College students play a major role in the transmission of SARS-CoV-2, the viral agent responsible for COVID-19. We aim to understand risk perceptions, self-efficacy, and adoption of prevention behaviors in this population to inform prevention strategies.

PARTICIPANTS

Undergraduate students attending a large public university.

METHODS

A convenience sample of students were surveyed (April-June 2020). Participants self-reported risk perceptions, perceived risk of contracting COVID-19, self-efficacy, and prevention behavior engagement.

RESULTS

A total of 1,449 students were included in the analysis. The majority were women (71.2%) and aged 18-24 (86.6%). Freshmen had the lowest risk and threat perceptions, as did men; men also had lower self-efficacy. Women engaged significantly more in prevention behaviors compared to men.

CONCLUSIONS

Perceived risk of contracting COVID-19 was low, but overall adoption of prevention behaviors was high due to local mandates. Freshmen men were identified as having the greatest need for changing perceptions and behaviors.

Randomized Controlled Trial on the Effects of Home-Based Breathing Exercises on Respiratory Function and Fatigue in COVID-19-Cured Young Patients

This study investigates the effects of home-based Kakao Healthcare breathing exercises and stretching on respiratory function and fatigue in COVID-19-cured patients. A total of 35 participants performed four movements of home-based breathing exercises and five respiratory muscle stretching exercises four times a week for four weeks. Respiratory function was measured using forced vital capacity(FVC), forced expiratory volume in one second(FEV1), FEV1/FVC ratio, and peak expiratory flow(PEF). Fatigue was assessed using the Fatigue Severity Scale (FSS). Data analysis was performed using independent-sample and paired-sample t-tests in SPSS 24, with the significance level set at p < 0.05. After four weeks of home-based Kakao Healthcare breathing exercises, there were significant increases in respiratory FVC, FEV1, FEV1/FVC, and PEF function values observed in the training group (T.G.) after the exercise intervention (p < 0.05). Such an increase was observed when comparing these values with their corresponding pre-exercise measurements. In contrast, there were no statistically significant differences in respiratory function outcomes before and after exercise in the control group (C.G.) (p > 0.05). The FSS scores were statistically significant within the training group (T.G.) (p > 0.05). The 4-week Kakao Healthcare breathing exercise scheme was found to be capable of improving some respiratory functions in COVID-19-recovered patients, but it showed no significant improvement in fatigue levels.

Nanoscale hyperthermia mesostructures for sustainable antimicrobial design

Sustainability is critical in addressing global challenges posed by prolonged pandemics that impact health, economies, and the environment. Here, we introduce a molecular engineering approach for thermoregulated antimicrobial management inspired by firewalking rituals. The study uses in situ spectroscopy and multi-scale modeling to validate a hierarchical design. Efficient light-to-thermal energy conversion is achieved by engineering the molecular band structure. Rapid nanoscale hyperthermia is facilitated through thermal engineering. This approach significantly reduces the half-life of pathogens such as Escherichia coli, influenza A, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to 1.4 min while maintaining a low perceived temperature on human skin. Standard disease infection and epidemic models show this technology's potential to flatten outbreak curves and delay peak infection rates, which is crucial during the early stages of pandemics when developing vaccines and antiviral drugs takes time. The scalable manufacturing and broad antimicrobial applicability hold great promise for controlling emerging infectious diseases and diverse bioprotective applications.

Insights into SARS-CoV-2 Surveillance among Prison Populations in Mato Grosso do Sul, Brazil, in 2022

This study examines the epidemiological and genomic characteristics, along with the transmission dynamics, of SARS-CoV-2 within prison units I and II in Campo Grande, Mato Grosso do Sul, Brazil. Conducted between May and October 2022, it reveals how the virus spreads in the confined settings of prisons, emphasizing the roles of overcrowded cells, frequent transfers, and limited healthcare access. The research involved 1927 participants (83.93% of the total prison population) and utilized nasopharyngeal swabs and RT-qPCR testing for detection. Contact tracing monitored exposure within cells. Out of 2108 samples, 66 positive cases were identified (3.13%), mostly asymptomatic (77.27%), with the majority aged 21-29 and varying vaccination statuses. Next-generation sequencing generated 28 whole genome sequences, identifying the Omicron variant (subtypes BA.2 and BA.5) with 99% average coverage. Additionally, the study seeks to determine the relationship between immunization levels and the incidence of SARS-CoV-2 cases within this enclosed population. The findings underscore the necessity of comprehensive control strategies in prisons, including rigorous screening, isolation protocols, vaccination, epidemiological monitoring, and genomic surveillance to mitigate disease transmission and protect both the incarcerated population and the broader community.

Inference on spatiotemporal dynamics for coupled biological populations

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference owing to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been proposed that enables computationally tractable likelihood-based inference for high-dimensional partially observed stochastic dynamic models of metapopulation systems. We use this algorithm to build a statistically principled data analysis workflow for metapopulation systems. Via a case study of COVID-19, we show how this workflow addresses the limitations of previous approaches. The COVID-19 pandemic provides a situation where mathematical models and their policy implications are widely visible, and we revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model weaknesses, leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on 23 January 2020 in China was more effective than previously thought.

Modeling relaxed policies for discontinuation of methicillin-resistant Staphylococcus aureus contact precautions

OBJECTIVE

To evaluate the economic costs of reducing the University of Virginia Hospital's present "3-negative" policy, which continues methicillin-resistant Staphylococcus aureus (MRSA) contact precautions until patients receive 3 consecutive negative test results, to either 2 or 1 negative.

DESIGN

Cost-effective analysis.

SETTINGS

The University of Virginia Hospital.

PATIENTS

The study included data from 41,216 patients from 2015 to 2019.

METHODS

We developed a model for MRSA transmission in the University of Virginia Hospital, accounting for both environmental contamination and interactions between patients and providers, which were derived from electronic health record (EHR) data. The model was fit to MRSA incidence over the study period under the current 3-negative clearance policy. A counterfactual simulation was used to estimate outcomes and costs for 2- and 1-negative policies compared with the current 3-negative policy.

RESULTS

Our findings suggest that 2-negative and 1-negative policies would have led to 6 (95% CI, -30 to 44; P < .001) and 17 (95% CI, -23 to 59; -10.1% to 25.8%; P < .001) more MRSA cases, respectively, at the hospital over the study period. Overall, the 1-negative policy has statistically significantly lower costs ($628,452; 95% CI, $513,592-$752,148) annually (P < .001) in US dollars, inflation-adjusted for 2023) than the 2-negative policy ($687,946; 95% CI, $562,522-$812,662) and 3-negative ($702,823; 95% CI, $577,277-$846,605).

CONCLUSIONS

A single negative MRSA nares PCR test may provide sufficient evidence to discontinue MRSA contact precautions, and it may be the most cost-effective option.

Evaluation of menstrual irregularities following COVID-19 infection or vaccination: The impact of COVID anxiety and associated risk factors

There has been significant interest in the changes in menstrual cycles following coronavirus disease 2019 (COVID-19) infection or vaccination. This study aimed to investigate the evidence for such changes and their potential risk factors. We used a descriptive study design and gathered data by sharing an online survey questionnaire on social media platforms. The questionnaire included questions regarding sociodemographic factors, menstrual cycle changes, and COVID-19 anxiety. The study population comprised women aged 18 to 49 years from Izmir. All data analyses were performed using Statistical Package for the Social Sciences 21.0. The risk factors influencing menstrual irregularities were determined after the COVID binary logistic regression analysis, including univariate and multivariate models. Among the 465 participants, those with an associate's degree had a significantly higher risk of menstrual irregularities than those with a high school diploma (P = .012). Anxiety scores emerged as a significant risk factor for menstrual cycle irregularities (P = .026). However, neither COVID-19 infection nor vaccination resulted in significant changes in the menstrual cycle characteristics (P > .05). Other sociodemographic variables, such as age, body mass index, and smoking, were not significantly associated with menstrual cycle changes(P > .05). The study findings suggest that educational level and anxiety may play a role in menstrual irregularities, whereas COVID-19 infection or vaccination itself may not directly affect menstrual cycle.

Persistent Vascular Complications in Long COVID: The Role of ACE2 Deactivation, Microclots, and Uniform Fibrosis

Angiotensin-converting enzyme 2 (ACE2), a key regulator in vasoregulation and the renin-angiotensin system, is hypothesized to be downregulated in patients with COVID-19, leading to a cascade of cardiovascular complications. This deactivation potentially results in increased blood pressure and vessel injury, contributing to the formation and persistence of microclots in the circulation. Herein, we propose a hypothesis regarding the prolonged vascular complications observed in long COVID, focusing on the role of ACE2 deactivation and/or shedding, the persistence of microclots, and the unique pattern of fibrosis induced by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Furthermore, we propose that the distinctive, uniform fibrosis associated with COVID-19, which is challenging to detect through conventional X-ray imaging, exacerbates vascular injury and impairs oxygenation. The persistence of these microclots and the unique fibrosis pattern are suggested as key factors in the extended duration of vascular complications post-COVID-19 infection, regardless of the initial disease severity. Moreover, plasma ACE2 activity has the potential to serve as prognostic or diagnostic biomarkers for monitoring disease severity and managing long COVID symptoms. Elucidating the role of ACE2 deactivation and the consequent events is vital for understanding the long-term effects of COVID-19. The experimental verification of this hypothesis through in vitro studies, clinical longitudinal studies, and advanced imaging techniques could yield significant insights into the pathophysiological mechanisms underlying long COVID, thereby improving the management of patients, particularly those with cardiovascular complications.

Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economical burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number ([Formula: see text]) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.

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).

Clinical characteristics and the risk factors for the exacerbation of symptoms in patients with inflammatory bowel disease during the COVID-19 pandemic

Background

In 2023, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant caused a large-scale outbreak of coronavirus disease 2019 (COVID-19) in China. It is not clear the risk factors that lead to the exacerbation of symptoms in patients with inflammatory bowel disease (IBD) after COVID-19 infection. Our study aims to find out the risk factors for the exacerbation of IBD-related symptoms in IBD patients with COVID-19 infection and to provide guidance for the clinical management of IBD.

Methods

This is a retrospective, observational study. The online questionnaire was distributed to conduct a survey to collect demographic, clinical, and IBD related characteristics in IBD patients. Univariate and multivariate regression analyses were conducted to assess the independent effects.

Results

In total, 534 cases of IBD patients were analyzed in our study. Among them, 466 (87.3%) cases diagnosed with COVID-19, 160 (34.3%) cases experienced exacerbation of IBD symptoms, and 84 (18.0%) patients opted for medication discontinuation. Male sex (OR 2.04, 95% CI 1.34-3.49, p = 0.001), and the decrease in body mass index (BMI) (OR 0.93, 95% CI 0.87-1.00, p = 0.035) were positively correlated with the exacerbation of IBD symptoms. Furthermore, the medication discontinuation (OR 2.60, 95% CI 1.58-4.30, p < 0.001) was strongly positively correlated with the exacerbation of IBD symptoms. No significant association was seen between age, comorbidities, smoking, disease activity, vaccination, therapy for COVID-19 and the worsening of IBD symptoms.

Conclusion

This study confirms that the infection rate of COVID-19 in China IBD patients was comparable to the general population. Male sex, the decrease in BMI and medication discontinuation are significant risk factors for the exacerbation of IBD-related symptoms in IBD patients with COVID-19 infection.

Estimating protection afforded by prior infection in preventing reinfection: applying the test-negative study design

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, but the underestimate was considerable only when > 50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI, 93.6-98.6) and 85.5% (95% CI, 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic

The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control

This study introduces a novel SI2HR model, where "I2" denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November-December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Magnitude and determinants of excess total, age-specific and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project

INTRODUCTION

To examine the impact of the COVID-19 pandemic on mortality, we estimated excess all-cause mortality in 24 countries for 2020 and 2021, overall and stratified by sex and age.

METHODS

Total, age-specific and sex-specific weekly all-cause mortality was collected for 2015-2021 and excess mortality for 2020 and 2021 was calculated by comparing weekly 2020 and 2021 age-standardised mortality rates against expected mortality, estimated based on historical data (2015-2019), accounting for seasonality, and long-term and short-term trends. Age-specific weekly excess mortality was similarly calculated using crude mortality rates. The association of country and pandemic-related variables with excess mortality was investigated using simple and multilevel regression models.

RESULTS

Excess cumulative mortality for both 2020 and 2021 was found in Austria, Brazil, Belgium, Cyprus, England and Wales, Estonia, France, Georgia, Greece, Israel, Italy, Kazakhstan, Mauritius, Northern Ireland, Norway, Peru, Poland, Slovenia, Spain, Sweden, Ukraine, and the USA. Australia and Denmark experienced excess mortality only in 2021. Mauritius demonstrated a statistically significant decrease in all-cause mortality during both years. Weekly incidence of COVID-19 was significantly positively associated with excess mortality for both years, but the positive association was attenuated in 2021 as percentage of the population fully vaccinated increased. Stringency index of control measures was positively and negatively associated with excess mortality in 2020 and 2021, respectively.

CONCLUSION

This study provides evidence of substantial excess mortality in most countries investigated during the first 2 years of the pandemic and suggests that COVID-19 incidence, stringency of control measures and vaccination rates interacted in determining the magnitude of excess mortality.

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

BACKGROUND

Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.

OBJECTIVE

This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.

METHODS

A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.

RESULTS

Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.

CONCLUSION

The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

A study of the attenuation stage of a global infectious disease

Introduction

Differences in control measures and response speeds between regions may be responsible for the differences in the number of infections of global infectious diseases. Therefore, this article aims to examine the decay stage of global infectious diseases. We demonstrate our method by considering the first wave of the COVID-19 epidemic in 2020.

Methods

We introduce the concept of the attenuation rate into the varying coefficient SEIR model to measure the effect of different cities on epidemic control, and make inferences through the integrated adjusted Kalman filter algorithm.

Results

We applied the varying coefficient SEIR model to 136 cities in China where the total number of confirmed cases exceeded 20 after the implementation of control measures and analyzed the relationship between the estimated attenuation rate and local factors. Subsequent analysis and inference results show that the attenuation rate is significantly related to the local annual GDP and the longitude and latitude of a city or a region. We also apply the varying coefficient SEIR model to other regions outside China. We find that the fitting curve of the average daily number of new confirmed cases simulated by the variable coefficient SEIR model is consistent with the real data.

Discussion

The results show that the cities with better economic development are able to control the epidemic more effectively to a certain extent. On the other hand, geographical location also affected the effectiveness of regional epidemic control. In addition, through the results of attenuation rate analysis, we conclude that China and South Korea have achieved good results in controlling the epidemic in 2020.

Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States

The novel coronavirus (COVID-19) pandemic, first identified in Wuhan China in December 2019, has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. There have been more than half a billion human infections and more than 6 million deaths globally attributable to COVID-19. Although treatments and vaccines to protect against COVID-19 are now available, people continue being hospitalized and dying due to COVID-19 infections. Real-time surveillance of population-level infections, hospitalizations, and deaths has helped public health officials better allocate healthcare resources and deploy mitigation strategies. However, producing reliable, real-time, short-term disease activity forecasts (one or two weeks into the future) remains a practical challenge. The recent emergence of robust time-series forecasting methodologies based on deep learning approaches has led to clear improvements in multiple research fields. We propose a recurrent neural network model named Fine-Grained Infection Forecast Network (FIGI-Net), which utilizes a stacked bidirectional LSTM structure designed to leverage fine-grained county-level data, to produce daily forecasts of COVID-19 infection trends up to two weeks in advance. We show that FIGI-Net improves existing COVID-19 forecasting approaches and delivers accurate county-level COVID-19 disease estimates. Specifically, FIGI-Net is capable of anticipating upcoming sudden changes in disease trends such as the onset of a new outbreak or the peak of an ongoing outbreak, a skill that multiple existing state-of-the-art models fail to achieve. This improved performance is observed across locations and periods. Our enhanced forecasting methodologies may help protect human populations against future disease outbreaks.

Who acquires infection from whom? A sensitivity analysis of transmission dynamics during the early phase of the COVID-19 pandemic in Belgium

Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial in studying infectious disease dynamics. Compartmental models represent a popular approach to address the problem, dividing the population of interest into a discrete and finite number of states depending on, for example, individuals' age and stage of infection. We study the corresponding linearised system whose operator, in the context of a discrete-time model, equates to a square matrix known as the next generation matrix. Performing formal perturbation analysis of the entries of the aforementioned matrix, we derive indices to quantify the age-specific variation of its dominant eigenvalue (i.e., the reproduction number) and explore the relevant epidemiological information we can derive from the eigenstructure of the matrix. The resulting method enables the assessment of the impact of age-related population heterogeneity on virus transmission. In particular, starting from an age-structured SEIR model, we demonstrate the use of this approach for COVID-19 dynamics in Belgium. We analyse the early stages of the SARS-CoV-2 spread, with particular attention to the pre-pandemic framework and the lockdown lifting phase initiated as of May 2020. Our results, influenced by our assumption on age-specific susceptibility and infectiousness, support the hypothesis that transmission was only influenced to a small extent by children in the age group [0,18) and adults over 60 years of age during the early phases of the pandemic and up to the end of July 2020.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.

Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha

SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.

Reconciling the efficacy and effectiveness of masking on epidemic outcomes

During the COVID-19 pandemic, mask wearing in public settings has been a key control measure. However, the reported effectiveness of masking has been much lower than laboratory measures of efficacy, leading to doubts on the utility of masking. Here, we develop an agent-based model that comprehensively accounts for individual masking behaviours and infectious disease dynamics, and test the impact of masking on epidemic outcomes. Using realistic inputs of mask efficacy and contact data at the individual level, the model reproduces the lower effectiveness as reported in randomized controlled trials. Model results demonstrate that transmission within households, where masks are rarely used, can substantially lower effectiveness, and reveal the interaction of nonlinear epidemic dynamics, control measures and potential measurement biases. Overall, model results show that, at the individual level, consistent masking can reduce the risk of first infection and, over time, reduce the frequency of repeated infection. At the population level, masking can provide direct protection to mask wearers, as well as indirect protection to non-wearers, collectively reducing epidemic intensity. These findings suggest it is prudent for individuals to use masks during an epidemic, and for policymakers to recognize the less-than-ideal effectiveness of masking when devising public health interventions.

Impact of removing the healthcare mask mandate on hospital-acquired COVID-19 rates

BACKGROUND

Mandatory mask-wearing policies were one of several measures employed to reduce hospital-acquired SARS-CoV-2 infection throughout the pandemic. Many nations have removed healthcare mask mandates, but there remains a risk of new SARS-CoV-2 variants or epidemics of other respiratory viruses.

AIM

To demonstrate the impact of removing the healthcare mask mandate.

METHODS

SARS-CoV-2 infections were analysed in a large teaching hospital for 40 weeks in 2022 using a controlled interrupted time-series design. The intervention was the removal of a staff/visitor surgical mask-wearing policy for the most wards at week 26 (intervention group) with a subset of specific wards retaining the mask policy (control group). The hospital-acquired SARS-CoV-2 infection rate was adjusted by the underlying community infection rate.

FINDINGS

In the context of a surge in SARS-CoV-2 infection, removal of the mask mandate for staff/visitors was not associated with a statistically significant change in the rate of nosocomial SARS-CoV-2 infection in the intervention group (incidence rate ratio: 1.105; 95% confidence interval: 0.523-2.334; P = 0.79) and there was no post-intervention trend (1.013; 0.932-1.100; P = 0.76) to suggest a delayed effect. The control group also showed no immediate or delayed change in infection rate.

CONCLUSION

No evidence was found that removal of a staff/visitor mask-wearing policy had a significant effect on the rate of hospital-acquired SARS-CoV-2 infection. This does not demonstrate that masks were ineffective through the pandemic, but provides some objective evidence to justify the removal of healthcare mask mandates once there was widespread immunity and reduced disease severity.

High pre-Delta and early-Omicron SARS-CoV-2 seroprevalence detected in dried blood samples from Kinshasa (D.R. Congo)

Studies on the impact of the COVID-19 pandemic in sub-Saharan Africa have yielded varying results, although authors universally agree the real burden surpasses reported cases. The primary objective of this study was to determine SARS-CoV-2 seroprevalence among patients attending Monkole Hospital in Kinshasa (D.R. Congo). The secondary objective was to evaluate the analytic performance of two chemiluminescence platforms: Elecsys® (Roche) and VirClia® (Vircell) on dried blood spot samples (DBS). The study population (N = 373) was recruited in two stages: a mid-2021 blood donor cohort (15.5% women) and a mid-2022 women cohort. Crude global seroprevalence was 61% (53.9%-67.8%) pre-Delta in 2021 and 90.2% (84.7%-94.2%) post-Omicron in 2022. Anti-spike (S) antibody levels significantly increased from 53.1 (31.8-131.3) U/mL in 2021 to 436.5 (219.3-950.5) U/mL in 2022 and were significantly higher above 45 years old in the 2022 population. Both platforms showed good analytic performance on DBS samples: sensitivity was 96.8% for IgG (antiN/S) (93.9%-98.5%) and 96.0% (93.0%-98.0%) for anti-S quantification. These results provide additional support for the notion that exposure to SARS-CoV-2 is more widespread than indicated by case-based surveillance and will be able to guide the pandemic response and strategy moving forward. Likewise, this study contributes evidence to the reliability of DBS as a tool for serological testing and diagnosis in resource-limited settings.