Comparison of culture-competent virus shedding duration of SARS-CoV-2 Omicron variant in regard to vaccination status: A prospective cohort study.

Previous studies have shown that fully vaccinated patients with SARS-CoV-2 Delta variants has shorter viable viral shedding period compared to unvaccinated or partially vaccinated patients. However, data about effects of vaccination against the viable viral shedding period in patients with SARS-CoV-2 Omicron variants were limited. We compared the viable viral shedding period of SARS-CoV-2 omicron variant regard to vaccination status. Saliva samples were obtained daily from patients with SARS-CoV-2 Omicron variant, and genomic assessments and virus culture was performed to those samples. We found no difference in viable viral shedding period between fully vaccinated and not or partially vaccinated, nor between 1st boostered vs non-boostered patients with SARS-CoV-2 Omicron variant.

Comparison of Secondary Attack Rate and Viable Virus Shedding between Patients with SARS-CoV-2 Delta and Omicron Variants: A Prospective Cohort Study.

BACKGROUNDS: There are limited data comparing the transmission rates and kinetics of viable virus shedding of the Omicron variant to those of the Delta variant. We compared these rates in hospitalized patients infected with Delta and Omicron variants. METHODS: We prospectively enrolled adult patients with COVID-19 admitted to a tertiary care hospital in South Korea between September 2021 and May 2022. Secondary attack rates were calculated by epidemiologic investigation, and daily saliva samples were collected to evaluate viral shedding kinetics. Genomic and subgenomic SARS-CoV-2 RNA was measured by PCR, and virus culture was performed from daily saliva samples. RESULTS: A total of 88 patients with COVID-19 who agreed to daily sampling and were interviewed, were included. Of the 88 patients, 48 (59%) were infected with Delta, and 34 (41%) with Omicron; a further five patients gave undetectable or inconclusive RNA PCR results and one was suspected of being co-infected with both variants. Omicron group had a higher secondary attack rate (31% [38/124]) versus 7% [34/456], p<0.001). Survival analysis revealed that shorter viable virus shedding period was observed in Omicron variant compared with Delta variant (median 4 days, IQR [1 -7], vs. 8.5 days, IQR [5 - 12 days], p<0.001). Multivariable analysis revealed that moderate-to-critical disease severity (HR 1.96), and immunocompromised status (HR 2.17) were independent predictors of prolonged viral shedding, whereas completion of initial vaccine series or 1st booster-vaccinated status (HR 0.49), and Omicron infection (HR 0.44) were independently associated with shorter viable virus shedding. CONCLUSION: Patients with Omicron infections had higher transmission rates but shorter periods of transmissible virus shedding than those with Delta infections. This article is protected by copyright. All rights reserved.

Viral dynamics of Omicron and Delta SARS-CoV-2 variants with implications for timing of release from isolation: a longitudinal cohort study.

BACKGROUND: In January 2022, United States guidelines shifted to recommend isolation for 5 days from symptom onset, followed by 5 days of mask wearing. However, viral dynamics and variant and vaccination impact on culture conversion are largely unknown. METHODS: We conducted a longitudinal study on a university campus, collecting daily anterior nasal swabs for at least 10 days for RT-PCR and culture, with antigen rapid diagnostic testing (RDT) on a subset. We compared culture positivity beyond day 5, time to culture conversion, and cycle threshold trend when calculated from diagnostic test, from symptom onset, by SARS-CoV-2 variant, and by vaccination status. We evaluated sensitivity and specificity of RDT on days 4-6 compared to culture. RESULTS: Among 92 SARS-CoV-2 RT-PCR positive participants, all completed the initial vaccine series, 17 (18.5%) were infected with Delta and 75 (81.5%) with Omicron. Seventeen percent of participants had positive cultures beyond day 5 from symptom onset with the latest on day 12. There was no difference in time to culture conversion by variant or vaccination status. For 14 sub-study participants, sensitivity and specificity of day 4-6 RDT were 100% and 86% respectively. CONCLUSIONS: The majority of our Delta- and Omicron-infected cohort culture-converted by day 6, with no further impact of booster vaccination on sterilization or cycle threshold decay. We found that rapid antigen testing may provide reassurance of lack of infectiousness, though guidance to mask for days 6-10 is supported by our finding that 17% of participants remained culture positive after isolation.

Calibrating deterministic compartmental models of infection dynamics using neural network and data sampling approaches

Pandemics caused by the new coronavirus has spread globally with a strong contagion rate and death rate. In this paper the deterministic SEIR model is calibrated with Metropolis Hasting algorithm, physics-informed neural network (PINN) and latin hypercube sampling (LHS) method to identify the optimal hyper parameters of SEIR model and to forecast the dynamics of COVID-19 incidence in Saint-Petersburg, Russia, its retrospective analysis and evaluation of the effectiveness of control measures. © 2022 IEEE.

Agent-based simulations for protecting nursing homes with prevention and vaccination strategies.

Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.

Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak.

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation.

A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread.

Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible-exposed-infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments.

A systems biology approach to COVID-19 progression in population.

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation.

BACKGROUND: Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. OBJECTIVE: We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. METHODS: "COVOID" (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population "lockdowns" enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. RESULTS: COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. CONCLUSIONS: COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws.

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world.

This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called SARS-CoV-2 Severe Acute Respiratory Syndrome, corona virus n.2. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focussed also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior.

The pandemic infection of SARS-CoV-2 presents analogies with the behavior of chemical reactors. Susceptible population (A), active infected population (B), recovered cases (C) and deaths (D) can be assumed to be molecules of chemical compounds and their dynamics seem well aligned with those of composition and conversions in chemical syntheses. Thanks to these analogies, it is possible to generate pandemic predictive models based on chemical and physical considerations and regress their kinetic parameters, either globally or locally, to predict the peak time, entity and end of the infection with certain reliability. These predictions can strongly support the emergency plans decision making process. The model predictions have been validated with data from Chinese provinces that already underwent complete infection dynamics. For all the other countries, the evolution is re-regressed and re-predicted every day, updating a pandemic prediction database on Politecnico di Milano's webpage based on the real-time available data.