Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020

The interaction between population age structure and policy interventions on the spread of COVID-19

COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi - four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.

Dynamical analysis and numerical assessment of the 2019-nCoV virus transmission with optimal control

In this article, we discuss the qualitative analysis and develop an optimal control mechanism to study the dynamics of the novel coronavirus disease (2019-nCoV) transmission using an epidemiological model. With the help of a suitable mathematical model, health officials often can take positive measures to control the infection. To develop the model, we assume two disease transmission sources (humans and reservoirs) keeping in view the characteristics of novel coronavirus transmission. We formulate the model to study the temporal dynamics and determine an optimal control mechanism to minimize the infected population and control the spreading of the novel coronavirus disease propagation. In addition, to understand the significance of each model parameter, we compute the threshold quantity and perform the sensitivity analysis of the basic reproductive number. Based on the temporal dynamics of the model and sensitivity analysis of the threshold parameter, we develop a control mechanism to identify the best control policy for eradicating the disease. We then conduct numerical experiments using large-scale numerical simulations to validate the theoretical findings.

Hydrological and physicochemical parameters associated with SARS-CoV-2 and pepper mild mottle virus wastewater concentrations for a large-combined sewer system

During COVID-19, surveillance of SARS-CoV-2 in wastewater has been a promising tool for tracking viral infection at the community level. However, in addition to the shedding rates within the community, SARS-CoV-2 concentrations in raw wastewater are influenced by several environmental factors. This study investigated the effects of wastewater characteristics on the viral quantification of SARS-CoV-2 and pepper mild mottle virus (PMMoV) for a large wastewater system with combined sewers. Principal component analysis illustrated that water temperature negatively correlates with SARS-CoV-2 and PMMoV in wastewater, but flow rate and EC are highly correlated with SARS-CoV-2 in spring and winter. The normalization using EC enhanced the correlation with clinical data compared to normalization using pH, flow rate, and raw SARS-CoV-2. The normalization using PMMoV reduced the correlation with clinical data. Multiple linear and random forest (RF) applied to predict the concentrations of SARS-CoV-2 in wastewater, given the confirmed cases and physicochemical parameters. RF regression was the best model to predict SARS-CoV-2 in wastewater (R2=0.8), with the most important variables being the confirmed cases followed by water temperature. RF model is a potent predictor of the presence of SARS-CoV-2 in wastewater. This enhances the degree of reliability between community outbreaks and SARS-CoV-2 monitoring.

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.

The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses. The review begins by discussing the impact of a pandemic on emerging countries worldwide, elaborating on the critical significance of AI in epidemiological modelling, bringing data-driven decision-making, and enabling forecasting, mitigation and response to the pandemic. In epidemiology, AI-driven epidemiological models like SIR (Susceptible-Infectious-Recovered) and SIS (Susceptible-Infectious-Susceptible) are applied to predict the spread of disease, preventing outbreaks and optimising vaccine distribution. The review also demonstrates how Machine Learning (ML) algorithms and predictive analytics improve our knowledge of disease propagation patterns. The collaborative aspect of AI in vaccine discovery and clinical trials of various vaccines is emphasised, focusing on constructing AI-powered surveillance networks. Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. The review also focuses on screening, forecasting, contact tracing and monitoring the virus-causing pandemic. It advocates for sustained research, real-world implications, ethical application and strategic integration of AI technologies to strengthen our collective ability to face and alleviate the effects of global health issues.

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

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

DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.

COVID-19 pandemic impact on mental and professional cognition: A questionnaire survey on a sample of GP trainees and GPs

Background

Since the outbreak of 2019 coronavirus disease (COVID-19), general practitioners (GPs) have been working in the frontline under psychological and physical pressure. This study aims to evaluate the psychological health, career prospective, attitudes toward educational mode changes, and knowledge about COVID-19.

Methods

An online anonymous questionnaire survey was carried out on GP trainees and GPs from June 2022 to September 2022. The survey mainly consisted of four parts: 1) general information; 2) level of knowledge about COVID-19; 3) psychological and physical health impact; and 4) changes in professional perception.

Results

The total knowledge score of 43 GP trainees and 38 GPs was 334 and 283, respectively, without significant difference (z = -0.839, P = 0.402). There was no statistical difference between the scores of GP trainees and GPs for each subindicator of mental and physical disorders. Eleven GP trainees and four GPs had severe psychological disorder subindexes. Severe somatization disorder subindexes were found in eight GP trainees and five GPs. Also, 67.44% of GP trainees and 52.63% of GPs had a positive attitude toward GP career. Moreover, 62.79% of GP trainees and 52.63% of GPs considered the epidemic had no impact on their professional cognition. Among GP trainees, 62.8% and 32.6% considered the epidemic had no or slight impact on their academic activities, respectively. Also, 53.5% and 44.2% of GP trainees partially and fully approved online teaching, respectively. The most popular forms were live and recorded courses.

Conclusions

COVID-19 pandemic had no noticeable impact on their physical and mental health and their attitude toward GP career.

Factors affecting COVID-19 vaccine uptake in populations with higher education: insights from a cross-sectional study among university students in Malawi

BACKGROUND

The Coronavirus disease-2019 (COVID-19) vaccines were rolled out in many countries; however, sub-optimal COVID-19 vaccine uptake remains a major public health concern globally. This study aimed at assessing the factors that affected the uptake, hesitancy, and resistance of the COVID-19 vaccine among university undergraduate students in Malawi, a least developed country in Africa.

METHODS

A descriptive cross-sectional study design was conducted using an online semi-structured questionnaire. A total of 343 University undergraduate students in Blantyre participated in this study after obtaining ethical clearance. Data was exported from Survey Monkey to Microsoft Excel version-21 for cleaning and was analysed using SPSS version-29. Descriptive statistics, including percentages, were performed to define the sample characteristics. Pearson Chi-square and Fisher's exact test were performed to identify significant relationships between vaccine uptake and demographics. A 95% confidence interval was set, and a p-value of < 0.05 was considered statistically significant.

RESULTS

Of the 343 participants, 43% were vaccinated. Among the vaccinated, the majority (47.3%, n = 69/146) received Johnson & Johnson vaccine followed by AstraZeneca (46.6%, n = 68/146). The commonly reported reason for vaccine acceptance was 'to protect me against getting COVID-19' (49%); whereas vaccine hesitancy was attributed to 'lack of knowledge (34%), and concerns about vaccine safety (25%).

CONCLUSIONS

This study found that adequate knowledge about benefits and safety of COVID-19 vaccine could potentially increase uptake. Lack of credible information or misinformation contributed to vaccine hesitancy. The findings provide insights for design of strategies to increase future vaccine uptake and reduce determinants of vaccine hesitancy. To reduce vaccination hesitancy in any population with or without higher education, we recommend that institutions entrusted with vaccine management must optimise health messaging, and reduce mis-information and dis-information.

Killer-cell immunoglobulin-like receptor polymorphism is associated with COVID-19 outcome: Results of a pilot observational study

The pathogenesis of COVID-19 warrants unravelling. Genetic polymorphism analysis may help answer the variability in disease outcome. To determine the role of KIR and HLA polymorphisms in susceptibility, progression, and severity of SARS-CoV-2 infection, 458 patients and 667 controls enrolled in this retrospective observational study from April to December 2020. Mild/moderate and severe/death study groups were established. HLA-A, -B, -C, and KIR genotyping were performed using the Lifecodes® HLA-SSO and KIR-SSO kits on the Luminex® 200™ xMAP fluoroanalyser. A probability score using multivariate binary logistic regression analysis was calculated to estimate the likelihood of severe COVID-19. ROC analysis was used to calculate the best cut-off point for predicting a worse clinical outcome with high sensitivity and specificity. A p ≤ 0.05 was considered statistically significant. KIR AA genotype protected positively against severity/death from COVID-19. Furthermore, KIR3DL1, KIR2DL3 and KIR2DS4 genes protected patients from severe forms of COVID-19. KIR Bx genotype, as well as KIR2DL2, KIR2DS2, KIR2DS3 and KIR3DS1 were identified as biomarkers of severe COVID-19. Our logistic regression model, which included clinical and KIR/HLA variables, categorised our cohort of patients as high/low risk for severe COVID-19 disease with high sensitivity and specificity (Se = 94.29%, 95% CI [80.84-99.30]; Sp = 84.55%, 95% CI [79.26-88.94]; OR = 47.58, 95%CI [11.73-193.12], p < 0.0001). These results illustrate an association between KIR/HLA ligand polymorphism and different COVID-19 outcomes and remarks the possibility of use them as a surrogate biomarkers to detect severe patients in possible future infectious outbreaks.

SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..

Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%-12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%-5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting.

Spatiotemporal heterogeneity of the association between short-term exposure to carbon monoxide and COVID-19 incidence: A multistage time-series study in the continental United States

Background

Previous research has established carbon monoxide (CO) as a significant air pollutant contributing to coronavirus disease 2019 (COVID-19) transmission. The spatiotemporal heterogeneity in the relationship between short-duration CO exposure and COVID-19 incidence remain underexplored. Investigating such heterogeneity plays a crucial role in designing region-specific cost-effective public health policies, exploring the reasons for heterogeneity, and understanding the temporal trends in the association between CO and an emerging infectious disease such as COVID-19.

Methods

The 49 states of the continental United States (U.S.) were examined in this study. Initially, we developed time-series generalized additive models (GAMs) for each state to assess the preliminary correlation between daily COVID-19 cases and short-term CO exposure from April 1, 2020, to December 31, 2021. Subsequently, the correlations were compiled utilizing Leroux-prior-based conditional autoregression (LCAR) to achieve a smoothed spatial distribution. Finally, we integrated a time-varying component into the GAM and LCAR to analyze temporal correlations and illuminate the factors contributing to spatiotemporal heterogeneity.

Results

Our analysis revealed that, across the 49 states, a 10-ppb increase in CO concentration was associated with a 1.33 % (95%CI: 0.86%-1.81 %) increase in COVID-19 cases on average. Furthermore, spatial variability was noted, with weaker correlations observed in the central and southeastern regions, stronger associations in the northeastern regions, and negligible associations in the western regions. Temporally, the correlation was not significant from April 2020 to June 2021, but began to increase steadily thereafter until the end of 2021. Additionally, vaccination and temperature were determined to be potential causes contributing to the heterogeneity, indicating stronger positive associations in areas with higher vaccination rates and temperatures.

Conclusion

The findings of this study underscore the importance of monitoring CO pollution in the central and northeastern US, especially in the aftermath of the pandemic.

The effects of killer cell immunoglobulin-like receptor (KIR) genes on susceptibility to severe COVID-19 in the Iranian population

BACKGROUND

Variations in the innate and adaptive immune response systems are linked to variations in the severity of COVID-19. Natural killer cell (NK) function is regulated by sophisticated receptor system including Killer-cell immunoglobulin-like receptor (KIR) family. We aimed to investigate the impact of possessing certain KIR genes and genotypes on COVID19 severity in Iranians. KIR genotyping was performed on 394 age/sex matched Iranians with no underlying conditions who developed mild and severe COVID- 19. The presence and/or absence of 11 KIR genes were determined using the PCR with sequence specific primers (PCR-SSP).

RESULTS

Patients with mild symptoms had higher frequency ofKIR2DS1 (p = 0.004) and KIR2DS2 (p = 0.017) genes compared to those with severe disease. While KIR3DL3 and deleted variant of KIR2DS4 occurred more frequently in patients who developed a severe form of the disease. In this study, a significant increase of and B haplotype was observed in the Mild group compared to the Severe group (respectively, p = 0.002 and p = 0.02). Also, the prevalence of haplotype A was significantly higher in the Severe group than in the Mild group (p = 0.02).

CONCLUSIONS

These results suggest that the KIR2DS1, KIR2DS, and B haplotype maybe have a protective effect against COVID-19 severity. The results also suggest the inhibitory gene KIR2DL3 and haplotype A are risk factors for the severity of COVID-19.

Evaluating the impact of double dose vaccination on SARS-CoV-2 spread through optimal control analysis

This paper presents a new nonlinear epidemic model for the spread of SARS-CoV-2 that incorporates the effect of double dose vaccination. The model is analyzed using qualitative, stability, and sensitivity analysis techniques to investigate the impact of vaccination on the spread of the virus. We derive the basic reproduction number and perform stability analysis of the disease-free and endemic equilibrium points. The model is also subjected to sensitivity analysis to identify the most influential model parameters affecting the disease dynamics. The values of the parameters are estimated with the help of the least square curve fitting tools. Finally, the model is simulated numerically to assess the effectiveness of various control strategies, including vaccination and quarantine, in reducing the spread of the virus. Optimal control techniques are employed to determine the optimal allocation of resources for implementing control measures. Our results suggest that increasing the vaccination coverage, adherence to quarantine measures, and resource allocation are effective strategies for controlling the epidemic. The study provides valuable insights into the dynamics of the pandemic and offers guidance for policymakers in formulating effective control measures.

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.

Limited impact of lifting universal masks on SARS-CoV-2 transmission in schools: The crucial role of outcome measurements

Amid the COVID-19 pandemic, education systems globally implemented protective measures, notably mandatory mask wearing. As the pandemic's dynamics changed, many municipalities lifted these mandates, warranting a critical examination of these policy changes' implications. This study examines the effects of lifting mask mandates on COVID-19 transmission within Massachusetts school districts. We first replicated previous research that utilized a difference-in-difference (DID) model for COVID-19 incidence. We then repeated the DID analysis by replacing the outcome measurement with the reproductive number (Rt ), reflecting the transmissibility. Due to the data availability, the Rt we estimated only measures the within school transmission. We found a similar result in the replication using incidence with an average treatment effect on treated (ATT) of 39.1 (95% CI: 20.4 to 57.4) COVID-19 cases per 1,000 students associated with lifting masking mandates. However, when replacing the outcome measurement to Rt , our findings suggest that no significant association between lifting mask mandates and reduced Rt (ATT: 0.04, 95% CI: -0.09 to 0.18), except for the first 2 weeks postintervention. Moreover, we estimated Rt below 1 at 4 weeks before lifting mask mandates across all school types, suggesting nonsustainable transmission before the implementation. Our reanalysis suggested no evidence of lifting mask mandates in schools impacted the COVID-19 transmission in the long term. Our study highlights the importance of examining the transmissibility outcome when evaluating interventions against transmission.

Genetic features of SARS-CoV-2 Alpha, Delta, and Omicron variants and their association with the clinical severity of COVID-19 in Vietnam

Objectives

We investigated the genetic variations in the Alpha, Delta, and Omicron variants of SARS-CoV-2 and their association with clinical status and treatment outcomes in patients with COVID-19.

Methods

MiSeq was used to sequence the Alpha, Delta, and Omicron genomes, and MEGA 6.6 was used to define the nucleotide variations. We determined the association between clinical severity and treatment outcomes for the SARS-CoV-2 variants.

Results

The BA.1.1 and BA.2 lineages of the Omicron variant had 57-59 mutations, which is 2-2.7-fold higher than that of the B.1.1.7 (Alpha), B.1.617.2, and AY.57 (Delta) lineages. We found distinct mutations in SARS-CoV-2: five in Alpha (C26305T, G26558T, G7042T, C14120T, and C27509T); seven in Delta (C26408T, C1403T, C5184T, C9891T, T11418C, C11514T, and C22227T); and three in Omicron (C26408T, C8991T, and C25810T). Patients with the Delta variant had a severe rate of 23.8%, a critical rate of 53.7%, and a mortality rate of 38.9%, which were significantly higher than those with the Omicron and Alpha variants.

Conclusions

The Alpha, Delta, and Omicron variants in this study had genetic diversity and differed from the strains reported in other countries, with the Delta variant producing significantly more clinical severity and mortality than the Alpha and Omicron variants.

Natural evidence of coronaviral 2'-O-methyltransferase activity affecting viral pathogenesis via improved substrate RNA binding

Previous studies through targeted mutagenesis of K-D-K-E motif have demonstrated that 2'-O-MTase activity is essential for efficient viral replication and immune evasion. However, the K-D-K-E catalytic motif of 2'-O-MTase is highly conserved across numerous viruses, including flaviviruses, vaccinia viruses, coronaviruses, and extends even to mammals. Here, we observed a stronger 2'-O-MTase activity in SARS-CoV-2 compared to SARS-CoV, despite the presence of a consistently active catalytic center. We further identified critical residues (Leu-36, Asn-138 and Ile-153) which served as determinants of discrepancy in 2'-O-MTase activity between SARS-CoV-2 and SARS-CoV. These residues significantly enhanced the RNA binding affinity of 2'-O-MTase and boosted its versatility toward RNA substrates. Of interest, a triple substitution (Leu36 → Ile36, Asn138 → His138, Ile153 → Leu153, from SARS-CoV-2 to SARS-CoV) within nsp16 resulted in a proportional reduction in viral 2'-O-methylation and impaired viral replication. Furthermore, it led to a significant upregulation of type I interferon (IFN-I) and proinflammatory cytokines both in vitro and vivo, relying on the cooperative sensing of melanoma differentiation-associated protein 5 (MDA5) and laboratory of genetics and physiology 2 (LGP2). In conclusion, our findings demonstrated that alterations in residues other than K-D-K-E of 2'-O-MTase may affect viral replication and subsequently influence pathogenesis. Monitoring changes in nsp16 residues is crucial as it may aid in identifying and assessing future alteration in viral pathogenicity resulting from natural mutations occurring in nsp16.

Using early detection data to estimate the date of emergence of an epidemic outbreak

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.

Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables

Objectives

Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.

Methods

Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified.

Results

Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury.

Conclusion

Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

Constrained numerical deconvolution using orthogonal polynomials

In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature. This extension avoids the need for external optimization solvers, making it particularly suitable for applications requiring constraints on inputs and outputs. We demonstrate the effectiveness of the proposed method through two practical applications: the estimation of COVID-19 curves and the study of mavoglurant, an experimental drug. Our results show that the extended method presents a sum of squared residuals in the same order of magnitude of that of the original Cutler's method and other widely known unconstrained deconvolution techniques, but obtains instead physically plausible solutions, correcting the errors introduced by the alternative methods considered, as illustrated in our case studies.

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

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

Effect of Hospital-associated SARS-CoV-2 Infections in Cardiac Surgery: A Multicenter Study

BACKGROUND

The effect of hospital-associated SARS-CoV-2 infections in cardiac surgery patients remains poorly investigated, and current data are limited to small case series with conflicting results.

METHODS

A multicenter European collaboration was organized to analyze the outcomes of patients who tested positive with hospital-associated SARS-CoV-2 infection after cardiac surgery. The study investigators hypothesized that early infection could be associated with worse postoperative outcomes; hence 2 groups were considered: (1) an early hospital-associated SARS-CoV-2 infection group comprising patients who had a positive molecular test result ≤7 days after surgery, with or without symptoms; and (2) a late hospital-associated SARS-CoV-2 infection group comprising patients whose test positivity occurred >7 days after surgery, with or without symptoms. The primary outcome was 30-day mortality. Secondary outcomes included all-cause mortality or morbidity at early follow-up and SARS-CoV-2-related hospital readmission.

RESULTS

A total of 87 patients were included in the study. Of those, 30 were in the early group and 57 in the late group. Overall, 30-day mortality was 8%, and in-hospital mortality was 11.5%. The reintubation rate was 11.4%. Early infection was significantly associated with higher mortality (adjusted OR, 26.6; 95% CI, 2, 352.6; P < .01) when compared with the late group. At 6-month follow-up, survival probability was also significantly higher in the late infection group: 91% (95% CI, 83%, 98%) vs 75% (95% CI, 61%, 93%) in the early infection group (P = .036). Two patients experienced COVID-19-related rehospitalization.

CONCLUSIONS

In this multicenter analysis, hospital-associated SARS-CoV-2 infection resulted in higher than expected postoperative mortality after cardiac surgery, especially in the early infection group.

COVID-19: A threat to the respiratory system

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), causes acute coronavirus disease-19 (COVID-19) that has emerged on a pandemic level. Coronaviruses are well-known to have a negative impact on the lungs and cardiovascular system. SARS-CoV-2 induces a cytokine storm that primarily targets the lungs, causing widespread clinical disorders, including COVID-19. Although, SARS-CoV-2 positive individuals often show no or mild upper respiratory tract symptoms, severe cases can progress to acute respiratory distress syndrome (ARDS). Novel CoV-2 infection in 2019 resulted in viral pneumonia as well as other complications and extrapulmonary manifestation. ARDS is also linked to a higher risk of death. Now, it is essential to develop our perception of the long term sequelae coronavirus infection for the identification of COVID-19 survivors who are at higher risk of developing the chronic lung fibrosis. This review study was planned to provide an overview of the effects of SARS-CoV-2 infection on various parts of the respiratory system such as airways, pulmonary vascular, lung parenchymal and respiratory neuromuscular system as well as the potential mechanism of the ARDS related respiratory complications including the lung fibrosis in patients with severe COVID-19.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Background

Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19.

Methods

The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results

In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion

Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Susceptible-Infected-Susceptible type COVID-19 spread with collective effects

Many models developed to forecast and attempt to understand the COVID-19 pandemic are highly complex, and few take collective behavior into account. As the pandemic progressed individual recurrent infection was observed and simpler susceptible-infected type models were introduced. However, these do not include mechanisms to model collective behavior. Here, we introduce an extension of the SIS model that accounts for collective behavior and show that it has four equilibria. Two of the equilibria are the standard SIS model equilibria, a third is always unstable, and a fourth where collective behavior and infection prevalence interact to produce either node-like or oscillatory dynamics. We then parameterized the model using estimates of the transmission and recovery rates for COVID-19 and present phase diagrams for fixed recovery rate and free transmission rate, and both rates fixed. We observe that regions of oscillatory dynamics exist in both cases and that the collective behavior parameter regulates their extent. Finally, we show that the system exhibits hysteresis when the collective behavior parameter varies over time. This model provides a minimal framework for explaining oscillatory phenomena such as recurring waves of infection and hysteresis effects observed in COVID-19, and other SIS-type epidemics, in terms of collective behavior.

A deep-SIQRV epidemic model for COVID-19 to access the impact of prevention and control measures

The coronavirus (COVID-19) has mutated into several variants, and evidence says that new variants are more transmissible than existing variants. Even with full-scale vaccination efforts, the theoretical threshold for eradicating COVID-19 appears out of reach. This article proposes an artificial intelligence(AI) based intelligent prediction model called Deep-SIQRV(Susceptible-Infected-Quarantined-Recovered-Vaccinated) to simulate the spreading of COVID-19. While many models assume that vaccination provides lifetime protection, we focus on the impact of waning immunity caused by the conversion of vaccinated individuals back to susceptible ones. Unlike existing models, which assume that all coronavirus-infected individuals have the same infection rate, the proposed model considers the various infection rates to analyze transmission laws and trends. Next, we consider the influence of prevention and control strategies, such as media marketing and law enforcement, on the spread of the epidemic. We employed the PAN-LDA model to extract features from COVID-19-related discussions on social media and online news articles. Moreover, the Long Short Term Memory(LSTM) model and Evolution Strategies(ES) are used to optimize transmission rates of infection and other model parameters, respectively. The experimental results on epidemic data from various Indian states demonstrate that persons infected with coronavirus had a more significant infection rate within four to nine days after infection, which corresponds to the actual transmission laws of the epidemic. The experimental results show that the proposed model has good prediction ability and obtains the Mean Absolute Percentage Error(MAPE) of 0.875%, 0.965%, 0.298%, and 0.215% for the next eight days in Maharashtra, Kerala, Karnataka, and Delhi, respectively. Our findings highlight the significance of using vaccination data, COVID-19-related posts, and information generated by the government's tremendous efforts in the prediction calculation process.

"Low-risk groups" deserve more attention than "high-risk groups" in imported COVID-19 cases

Objective

To estimate the optimal quarantine period for inbound travelers and identify key risk factors to provide scientific reference for emerging infectious diseases.

Methods

A parametric survival analysis model was used to calculate the time interval between entry and first positive nucleic acid test of imported cases in Guangzhou, to identify the influencing factors. And the COVID-19 epidemic risk prediction model based on multiple risk factors among inbound travelers was constructed.

Results

The approximate 95th percentile of the time interval was 14 days. Multivariate analysis found that the mean time interval for inbound travelers in entry/exit high-risk occupations was 29% shorter (OR 0.29, 95% CI 0.18-0.46, p < 0.0001) than that of low-risk occupations, those from Africa were 37% shorter (OR 0.37, 95% CI 0.17-0.78, p = 0.01) than those from Asia, those who were fully vaccinated were 1.88 times higher (OR 1.88, 95% CI 1.13-3.12, p = 0.01) than that of those who were unvaccinated, and those in other VOC periods were lower than in the Delta period. Decision tree analysis showed that a combined entry/exit low-risk occupation group with Delta period could create a high indigenous epidemic risk by 0.24.

Conclusion

Different strata of imported cases can result in varying degrees of risk of indigenous outbreaks. "low-risk groups" with entry/exit low-risk occupations, fully vaccinated, or from Asia deserve more attention than "high-risk groups."

A bifurcation analysis and model of Covid-19 transmission dynamics with post-vaccination infection impact

SARS COV-2 (Covid-19) has imposed a monumental socio-economic burden worldwide, and its impact still lingers. We propose a deterministic model to describe the transmission dynamics of Covid-19, emphasizing the effects of vaccination on the prevailing epidemic. The proposed model incorporates current information on Covid-19, such as reinfection, waning of immunity derived from the vaccine, and infectiousness of the pre-symptomatic individuals into the disease dynamics. Moreover, the model analysis reveals that it exhibits the phenomenon of backward bifurcation, thus suggesting that driving the model reproduction number below unity may not suffice to drive the epidemic toward extinction. The model is fitted to real-life data to estimate values for some of the unknown parameters. In addition, the model epidemic threshold and equilibria are determined while the criteria for the stability of each equilibrium solution are established using the Metzler approach. A sensitivity analysis of the model is performed based on the Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCCs) approaches to illustrate the impact of the various model parameters and explore the dependency of control reproduction number on its constituents parameters, which invariably gives insight on what needs to be done to contain the pandemic effectively. The foregoing notwithstanding, the contour plots of the control reproduction number concerning some of the salient parameters indicate that increasing vaccination coverage and decreasing vaccine waning rate would remarkably reduce the value of the reproduction number below unity, thus facilitating the possible elimination of the disease from the population. Finally, the model is solved numerically and simulated for different scenarios of disease outbreaks with the findings discussed.

Using a time-varying LCAR-based strategy to investigate the spatiotemporal pattern of association between short-term exposure to particulate matter and COVID-19 incidence: a case study in the continental USA

Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 μm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 μg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

BACKGROUND

After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern.

METHODS

We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2.

RESULTS

Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics).

CONCLUSIONS

Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

The public health impact of Paxlovid COVID-19 treatment in the United States

The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario R e ∼ 1.2 treating 20% of symptomatic cases would be life and cost saving, leading to an estimated 0.26 (95% CrI: 0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI: 1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI: 2.62, 122.63) billion reduction in health- and treatment-related costs. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.

Symptomatic SARS-CoV-2 Episodes and Health-Related Quality of Life

BACKGROUND

Understanding the physical and mental health needs of the population through evidence-based research is a priority for informing health policy. During the COVID-19 pandemic, population wellbeing dramatically dropped. The relationship between experiences of symptomatic illness episodes and health-related quality of life has been less documented.

OBJECTIVE

This study analysed the association between symptomatic COVID-19 illness and health-related quality of life.

METHODS

The analyses drew from a cross-sectional analysis of data from a national digital symptoms' surveillance survey conducted in the UK in 2020. We identified illness episodes using symptoms and test results data and we analysed validated health-related quality of life outcomes including health utility scores (indexed on a 0-1 cardinal scale) and visual analogue scale (VAS) scores (0-100 scale) generated by the EuroQoL's EQ-5D-5L measure. The econometric model controlled for respondents' demographic and socioeconomic characteristics, comorbidities, social isolation measures, and regional and time fixed effects.

RESULTS

The results showed that the experience of common SARS-CoV-2 symptoms was significantly associated with poorer health-related quality of life across all EQ-5D-5L dimensions of mobility, self-care, usual activities, pain/discomfort and anxiety/depression, a decrement in utility score of - 0.13 and a decrement in the EQ-VAS score of - 15. The findings were robust to sensitivity analyses and restrictive test results-based definitions.

CONCLUSION

This evidence-based study highlights the need for targeting of interventions and services towards those experiencing symptomatic episodes during future waves of the pandemic and helps to quantify the benefits of SARS-CoV-2 treatment in terms of health-related quality of life.

Nowcasting the 2022 mpox outbreak in England

In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time.

The spread of coronavirus disease 2019 (COVID-19) via staff work and household networks in residential aged-care services in Victoria, Australia, May-October 2020

OBJECTIVE

Morbidity and mortality from coronavirus disease 2019 (COVID-19) have been significant among elderly residents of residential aged-care services (RACS). To prevent incursions of COVID-19 in RACS in Australia, visitors were banned and aged-care workers were encouraged to work at a single site. We conducted a review of case notes and a social network analysis to understand how workplace and social networks enabled the spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) among RACS.

DESIGN

Retrospective outbreak review.

SETTING AND PARTICIPANTS

Staff involved in COVID-19 outbreaks in RACS in Victoria, Australia, May-October 2020.

METHODS

The Victorian Department of Health COVID-19 case and contact data were reviewed to construct 2 social networks: (1) a work network connecting RACS through workers and (2) a household network connecting to RACS through households. Probable index cases were reviewed to estimate the number and size (number of resident cases and deaths) of outbreaks likely initiated by multisite work versus transmission via households.

RESULTS

Among 2,033 cases linked to an outbreak as staff, 91 (4.5%) were multisite staff cases. Forty-three outbreaks were attributed to multisite work and 35 were deemed potentially preventable had staff worked at a single site. In addition, 99 staff cases were linked to another RACS outbreak through their household contacts, and 21 outbreaks were attributed to staff-household transmission.

CONCLUSIONS

Limiting worker mobility through single-site policies could reduce the chances of SARS-CoV-2 spreading from one RACS to another. However, initiatives that reduce the chance of transmission via household networks would also be needed.

The role of modelling and analytics in South African COVID-19 planning and budgeting

BACKGROUND

The South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead.

METHODS

Our tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary.

RESULTS

Given the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely.

CONCLUSION

The SACMC's models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout.

A pandemic momentum index to manage the spread of COVID-19

Quantifying the spreading power of a pandemic like COVID-19 is important for the early implementation of early restrictions on social mobility and other interventions to slow its spread. This work aims to quantify the power of spread, defining a new indicator, the pandemic momentum index. It is based on the analogy between the kinematics of disease spread and the kinematics of a solid in Newtonian mechanics. This index, I PM , is useful for assessing the risk of spread. Based on the evolution of the pandemic in Spain, a decision-making scheme is proposed that allows early responses to the spread and decreases the incidence of the disease. This index has been calculated retrospectively for the pandemic in Spain, and a counterfactual analysis shows that if the decision-making scheme had been used as a guide, the most significant decisions on restrictions would have been advanced: the total number of confirmed cases of COVID-19 would have been much lower during the period studied, with a significant reduction in the total number of cases: 83 % (sd = 2.6). The results of this paper are consistent with the numerous studies on the pandemic that concluded that the early implementation of restrictions is more important than their severity. Early response slows the spread of the pandemic by applying less severe mobility restrictions, reducing the number of cases and deaths, and doing less damage to the economy.

Evaluating the frequency of neurological symptoms in COVID-19 patients: A cross-sectional study

BACKGROUND AND AIMS

Due to the recent emergence of COVID-19, the exact pathology of this disease has not been determined. Therefore, this study evaluated the frequency of neurological symptoms in patients with COVID-19.

METHODS

This cross-sectional study was conducted on 2200 in patients with COVID-19 who were selected from an educational hospital in Sanandaj, Iran, from April 2020 to March 2021. The research samples were selected by census, all patients with COVID-19 were admitted to the hospital. The data collection tool was a checklist of the studied variables (dizziness, headache, and impaired consciousness) prepared by the researchers based on the specialists' opinions. The researcher completed these checklists based on the patients' hospitalization records. The data were analyzed by descriptive and analytical statistical tests using SPSS Software Version 20. The quantitative variables were compared using the independent t-test. The χ 2 test was also used to compare qualitative variables. A p Value of less than 0.05 was considered statistically significant.

RESULTS

The mean age of the patients was 57.41 years old, of whom 53.1% were male. The average blood oxygen level of the patients was 88.10%, and most disease symptoms were related to shortness of breath and cough, with a frequency of 24.3%. In addition, 20.8% of patients needed hospitalization in intensive care unit. The highest frequency of central and peripheral nervous system manifestations was related to headache, ageusia (loss of sense of taste), hyposmia (A decreased sense of smell and anosmia (The complete loss of smell). Finally, 15.3% of patients died, and 84.7% recovered. The analytical findings showed a significant relationship between the disease outcome and patients' dizziness, consciousness disorder, seizure and ageusia. There was a significant relationship between gender and headache in patients. There was a significant difference between the mean age and oxygen level with central and peripheral nervous system manifestations (dizziness, headache, impaired consciousness, smell disorder) and the disease outcome in patients.

CONCLUSION

The pathophysiology of COVID-19 virus infection involving the central nervous system is not fully understood. Neurological symptoms of this virus include delirium, headache, decreased level of consciousness, and seizures. Identifying the symptoms and mechanisms of neurological complications of COVID-19 is necessary for proper screening and complete treatment because a patient infected by COVID-19 may not show respiratory failure signs but may be a carrier. A complete and accurate knowledge of the symptoms and complications of this infection for proper screening of patients to prevent transmission and spread of this disease is critically needed.

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection

T cell responses precede antibody and may provide early control of infection. We analyzed the clonal basis of this rapid response following SARS-COV-2 infection. We applied T cell receptor (TCR) sequencing to define the trajectories of individual T cell clones immediately. In SARS-COV-2 PCR+ individuals, a wave of TCRs strongly but transiently expand, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains but not with circulating human coronaviruses. Many expanding CDR3s were present at high frequency in pre-pandemic repertoires. Early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the preinfection naive repertoire. High-frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection.

Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2

BACKGROUND AND OBJECTIVE

Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease.

MATERIAL AND METHOD

This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection.

RESULT

Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection.

CONCLUSION

There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.

Estimating the impact of COVID-19 vaccine inequities: a modeling study

Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54-94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6-50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15-70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh

Background

Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.

Methodology

In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.

Result

The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.

Conclusion

Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Estimate the incubation period of coronavirus 2019 (COVID-19)

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

COVID-19 related risk factors and their association with non-syndromic orofacial clefts in five Arab countries: a case-control study

BACKGROUND

The environmental etiology of non-syndromic orofacial clefts (NSOFCs) is still under research. The aim of this case-control study is to assess COVID-19 associated factors that may be related to the risk of NSOFC in five Arab countries. These factors include COVID-19 infection, COVID-19 symptoms, family member or friends infected with COVID-19, stress, smoking, socioeconomic status and fear of COVID-19.

METHODS

The study took place in governmental hospitals in five Arab countries from November 2020 to November 2021. Controls are matched in the month of delivery and site of recruitment. A clinical examination was carried out using LASHAL classification. Maternal exposure to medication, illnesses, supplementation, COVID-19 infection during their pregestation and 1st trimester periods were evaluated using a validated questionnaire. Maternal exposure to stress was assessed using the Life Events scale, fear of covid-19 scale, family member or friend affected with covid-19 infection, pregnancy planning and threatened abortion.

RESULTS

The study recruited 1135 infants (386 NSOFC and 749 controls). Living in urban areas, maternal exposure to medications 3-months pregestation, maternal exposure to any of the prenatal life events and maternal fear of COVID-19 significantly increased the risk of having a child with NSOFC. On the other hand, mothers exposed to supplementation 3-months pregestation, mothers suspected of having COVID-19 infection, family members or friends testing positive with COVID-19 significantly decreased the risk of having a child with NSOFC.

CONCLUSIONS

This study suggests that NSOFC may be associated with maternal exposure to lifetime stress and COVID-19 fear in particular, with no direct effect of the COVID-19 infection itself. This highlights the importance of providing psychological support for expecting mothers during stressful events that affect populations such as the COVID-19 pandemic, in addition to the usual antenatal care.