The time scale of asymptomatic transmission affects estimates of epidemic potential in the COVID-19 outbreak

Hybrid analysis with phylogeny and population modeling to estimate the recent founding date of a population: A case study in the origins of COVID-19 illustrates how a branching process approximation can simplify a hybrid analysis

The exact date of the primary infection in COVID-19 remains unknown. One influential article (Pekar et al. (2021)) estimated the date with a hybrid analysis combining epidemiological and phylogenetic methods. The phylogenetic methods analyzed 583 SARS-COV-2 complete genomes to estimate the sample tMRCA (time of the most recent common ancestor). Before igniting as an epidemic, however, COVID-19 may have had several population bottlenecks with only a single infected person, so the MRCA merely represents the last such bottleneck. Pekar et al. (2021) therefore used epidemiological methods to estimate the time from the primary infection to the sample MRCA. The hybrid method involved several arbitrary decisions, however, reflecting the fact that the epidemiological and phylogenetic analyses overlap at the sample MRCA and are generally probabilistically dependent. Towards removing the dependence, note that the start of an epidemic has a branching process approximation. Let the branching process have a single ancestor. If the branching process does not go extinct, define skeleton particles (individuals) to be particles whose lineages do not go extinct, and define the long-time MRCA as the earliest skeleton particle with at least two skeleton offspring. A linear phylogeny of skeleton particles therefore separates the ancestor from the long-time MRCA. Probabilistically, the linear phylogeny is a defective renewal process of skeleton particles, making the generation count geometrically distributed. Moreover, the terminology "long-time MRCA" is apt, because as time becomes arbitrarily large, the MRCA of the corresponding extant population approaches the long-time MRCA. Effectively, the focus on the long-time MRCA makes the forward epidemiological and backward phylogenetic analyses probabilistically independent. The present article can therefore confirm most of the epidemiological conclusions of the hybrid analysis of Pekar et al. (2021). Its use of branching process approximations also points the way to noticeable simplifications in the hybrid method.

Synthesis, biological evaluation and molecular docking studies of flavonol-3-O-β-D-glycoside as a potential inhibitor of SARS-CoV-2 main protease (3CLpro) in drug development for COVID-19

The COVID-19 pandemic began in March 2020 and has affected many countries and infected over a million people. It has had a serious impact on people's physical and mental health, daily life and the global economy. Today, many drugs show limited efficacy in the treatment of COVID-19 and studies to develop effective drugs continue. Here, we aim to the synthesise and characterise of the flavonol-3-O-glycoside derivatives, the following and evaluated molecular docking studies with antimicrobial activity, inhibition of SARS-CoV-2 main protease enzyme (3CLpro) and nuclease activity. Molecular docking simulations of the synthesized flavonol-3-O-glycoside derivatives, especially compounds 5a, 5d, 5h, 5i and 5m, showed a stronger interaction with SARS-CoV-2 3CLpro in the active site. Two compounds from the target compounds, 5h and 5m, were found to be specifically effective against M. smegmatis and yeasts. In particular, compounds 5a, 5d, 5h, 5i and 5m, which exhibited high activity against the SARS-CoV-2 main protease enzyme, were found to be effective at low concentrations. We determined the IC50 values for the compounds that showed an inhibitory effect as well as their nuclease activities, which further emphasising the potential of our results. Among these, compound 5d showed a significant competitive inhibitor of 3CLpro. Furthermore, nuclease activity studies identified compound 5d as the most potent. The above results suggest that the flavonol-3-O-glycoside derivatives could be promising new antiviral agents for the development of 3CLpro inhibitors to combat COVID-19.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Phylogenetic inference of inter-population transmission rates for infectious diseases

Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.

Hybrid-Capture Target Enrichment in Human Pathogens: Identification, Evolution, Biosurveillance, and Genomic Epidemiology

High-throughput sequencing (HTS) has revolutionised the field of pathogen genomics, enabling the direct recovery of pathogen genomes from clinical and environmental samples. However, pathogen nucleic acids are often overwhelmed by those of the host, requiring deep metagenomic sequencing to recover sufficient sequences for downstream analyses (e.g., identification and genome characterisation). To circumvent this, hybrid-capture target enrichment (HC) is able to enrich pathogen nucleic acids across multiple scales of divergences and taxa, depending on the panel used. In this review, we outline the applications of HC in human pathogens-bacteria, fungi, parasites and viruses-including identification, genomic epidemiology, antimicrobial resistance genotyping, and evolution. Importantly, we explored the applicability of HC to clinical metagenomics, which ultimately requires more work before it is a reliable and accurate tool for clinical diagnosis. Relatedly, the utility of HC was exemplified by COVID-19, which was used as a case study to illustrate the maturity of HC for recovering pathogen sequences. As we unravel the origins of COVID-19, zoonoses remain more relevant than ever. Therefore, the role of HC in biosurveillance studies is also highlighted in this review, which is critical in preparing us for the next pandemic. We also found that while HC is a popular tool to study viruses, it remains underutilised in parasites and fungi and, to a lesser extent, bacteria. Finally, weevaluated the future of HC with respect to bait design in the eukaryotic groups and the prospect of combining HC with long-read HTS.

Quantifying the presymptomatic transmission of COVID-19 in the USA

The emergence of many presymptomatic hidden transmission events significantly complicated the intervention and control of the spread of COVID-19 in the USA during the year 2020. To analyze the role that presymptomatic infections play in the spread of this disease, we developed a state-level metapopulation model to simulate COVID-19 transmission in the USA in 2020 during which period the number of confirmed cases was more than in any other country. We estimated that the transmission rate (i.e., the number of new infections per unit time generated by an infected individual) of presymptomatic infections was approximately 59.9% the transmission rate of reported infections. We further estimated that {at any point in time the} average proportion of infected individuals in the presymptomatic stage was consistently over 50% of all infected individuals. Presymptomatic transmission was consistently contributing over 52% to daily new infections, as well as consistently contributing over 50% to the effective reproduction number from February to December. Finally, non-pharmaceutical intervention targeting presymptomatic infections was very effective in reducing the number of reported cases. These results reveal the significant contribution that presymptomatic transmission made to COVID-19 transmission in the USA during 2020, as well as pave the way for the design of effective disease control and mitigation strategies.

Markov modeling and performance analysis of infectious diseases with asymptomatic patients

After over three years of COVID-19, it has become clear that infectious diseases are difficult to eradicate, and humans remain vulnerable under their influence in a long period. The presence of presymptomatic and asymptomatic patients is a significant obstacle to preventing and eliminating infectious diseases. However, the long-term transmission of infectious diseases involving asymptomatic patients still remains unclear. To address this issue, this paper develops a novel Markov process for infectious diseases with asymptomatic patients by means of a continuous-time level-dependent quasi-birth-and-death (QBD) process. The model accurately captures the transmission of infectious diseases by specifying several key parameters (or factors). To analyze the role of asymptomatic and symptomatic patients in the infectious disease transmission process, a simple sufficient condition for the stability of the Markov process of infectious diseases is derived using the mean drift technique. Then, the stationary probability vector of the QBD process is obtained by using RG-factorizations. A method of using the stationary probability vector is provided to obtain important performance measures of the model. Finally, some numerical experiments are presented to demonstrate the model's feasibility through analyzing COVID-19 as an example. The impact of key parameters on the system performance evaluation and the infectious disease transmission process are analyzed. The methodology and results of this paper can provide theoretical and technical support for the scientific control of the long-term transmission of infectious diseases, and we believe that they can serve as a foundation for developing more general models of infectious disease transmission.

Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

BACKGROUND

The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9-5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87-5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants.

METHODS

This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: ("serial interval" or "generation time"), ("Omicron" or "Delta"), and ("SARS-CoV-2" or "COVID-19"). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported.

RESULTS

There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3-5.8 days for Delta and 2.1-4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4-4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9-3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8-3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7-3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6-3.1) (three studies).

CONCLUSIONS

Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak

Compartmental models are commonly used in practice to investigate the dynamical response of infectious diseases such as the COVID-19 outbreak. Such models generally assume exponentially distributed latency and infectiousness periods. However, the exponential distribution assumption fails when the sojourn times are expected to distribute around their means. This study aims to derive a novel S (Susceptible)-E (Exposed)-P (Presymptomatic)-A (Asymptomatic)-D (Symptomatic)-C (Reported) model with arbitrarily distributed latency, presymptomatic infectiousness, asymptomatic infectiousness, and symptomatic infectiousness periods. The SEPADC model is represented by nonlinear Volterra integral equations that generalize ordinary differential equation-based models. Our primary aim is the derivation of a general relation between intrinsic growth rate r and basic reproduction number R 0 with the help of the well-known Lotka-Euler equation. The resulting r - R 0 equation includes separate roles of various stages of the infection and their sojourn time distributions. We show that R 0 estimates are considerably affected by the choice of the sojourn time distributions for relatively higher values of r. The well-known exponential distribution assumption has led to the underestimation of R 0 values for most of the countries. Exponential and delta-distributed sojourn times have been shown to yield lower and upper bounds of the R 0 values depending on the r values. In quantitative experiments, R 0 values of 152 countries around the world were estimated through our novel formulae utilizing the parameter values and sojourn time distributions of the COVID-19 pandemic. The global convergence, R 0 = 4.58 , has been estimated through our novel formulation. Additionally, we have shown that increasing the shape parameter of the Erlang distributed sojourn times increases the skewness of the epidemic curves in entire dynamics.

Intermediate levels of asymptomatic transmission can lead to the highest epidemic fatalities

Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse if asymptomatic individuals-unaware they are infected-transmit more than symptomatic individuals. Using an epidemic model, we show that intermediate levels of asymptomatic infection lead to the highest levels of epidemic fatalities when the decrease in symptomatic transmission, due either to individual behavior or mitigation efforts, is strong. We generalize this result to include presymptomatic transmission, showing that intermediate levels of nonsymptomatic transmission lead to the highest levels of fatalities. Finally, we extend our framework to illustrate how the intersection of asymptomatic spread and immunity profiles determine epidemic trajectories, including population-level severity, of future variants. In particular, when immunity provides protection against symptoms, but not against infections or deaths, epidemic trajectories can have faster growth rates and higher peaks, leading to more total deaths. Conversely, even modest levels of protection against infection can mitigate the population-level effects of asymptomatic spread.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics

Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).

COVID-19 Vaccination Rollout: Aspects of Hesitancy in South Africa

Across the globe, comprehensive COVID-19 vaccination programs have been rolled out. Naturally, it remains paramount for efficiency to ensure uptake. Hypothetical vaccine acceptability in South Africa was high prior to the availability of inoculation in August 2020-three-quarters stated intent to immunize nationally. However, 24 months on, less than one-third have finished their vaccination on a national average, and in the sprawling South Western Townships (Soweto), this figure remains troublingly low with as many as four in every five still hesitant. Medical anthropologists have recently portrayed how COVID-19's jumbled mediatization produces a 'field of suspicion' casting serious doubt on authorities and vaccines through misinformation and counterfactual claims, which fuels 'othering' and fosters hesitancy. It follows that intent to immunize cannot be used to predict uptake. Here, we take this conceptual framework one step further and illustrate how South African context-specific factors imbricate to amplify uncertainty and fear due the productive nature of communicability, which transforms othering into racialization and exacerbates existing societal polarizations. We also encounter Africanized forms of conspiracy theories and find their narrational roots in colonization and racism. Finally, we discuss semblances with HIV and how the COVID-19 pandemic's biomedicalization may inadvertently have led to vaccine resistance due to medical pluralism and cultural/spiritual practices endemic to the townships.

Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts

Severe acute respiratory coronavirus 2 (SARS-CoV-2) infections have been associated with substantial presymptomatic transmission, which occurs when the generation interval-the time between infection of an individual with a pathogen and transmission of the pathogen to another individual-is shorter than the incubation period-the time between infection and symptom onset. We collected a dataset of 257 SARS-CoV-2 transmission pairs in Japan during 2020 and jointly estimated the mean incubation period of infectors (4.8 days, 95 % CrI: 4.4-5.1 days), mean generation interval to when they infect others (4.3 days, 95 % credible interval [CrI]: 4.0-4.7 days), and the correlation (Kendall's tau: 0.5, 95 % CrI: 0.4-0.6) between these two epidemiological parameters. Our finding of a positive correlation and mean generation interval shorter than the mean infector incubation period indicates ample infectiousness before symptom onset and suggests that reliance on isolation of symptomatic COVID-19 cases as a focal point of control efforts is insufficient to address the challenges posed by SARS-CoV-2 transmission dynamics.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Cross-cultural validation of the Worries about COVID-19 and its consequences Scale (W-COV) in adolescents and young people

The pandemic context presents remarkable psychological challenges for adolescents and young adults. The aim of the present work was to construct and study the psychometric properties of a scale in Spanish language (W-COV) to measure their worries related to the pandemic. Participants were 5559 people aged between 14 and 25 years old (M = 19.05; SD = 3.28). Self-report data were collected using a cross-sectional and cross-cultural design. Participants were from 5 Spanish-speaking countries. Instruments were W-COV to assess worries about COVID-19 and its consequences; DASS-21 for anxiety, depression and stress; and SWLS for life satisfaction. Exploratory, confirmatory and multi-group factor analyses were conducted to determine the factorial structure of the W-COV and its measurement invariance (configural, metric, scalar and error variance). Correlational and regression analyses were also performed to study convergent and predictive validity. The results suggest that W-COV presents a bifactorial structure: (1) a general factor of worries about COVID-19; and (2) three different factors: worries about health, economic and psychosocial consequences from COVID-19. The internal reliability indices Cronbach's α and Omega were adequate. With respect to the invariance results, the instrument can be used interchangeably in the five countries considered, in both genders and in two different age groups (12-17 and 18-25). Regarding validity, W-COV factors were positively associated with anxiety, depression and stress, and negatively predicted life satisfaction. In conclusion, W-COV is a reliable and valid instrument for researchers and health care professionals to assess the psychological impact of the pandemic on mental health of young Ibero-Americans.

Simulation applications to support teaching and research in epidemiological dynamics

BACKGROUND

An understanding of epidemiological dynamics, once confined to mathematical epidemiologists and applied mathematicians, can be disseminated to a non-mathematical community of health care professionals and applied biologists through simple-to-use simulation applications. We used Numerus Model Builder RAMP ^Ⓡ (Runtime Alterable Model Platform) technology, to construct deterministic and stochastic versions of compartmental SIR (Susceptible, Infectious, Recovered with immunity) models as simple-to-use, freely available, epidemic simulation application programs.

RESULTS

We take the reader through simulations used to demonstrate the following concepts: 1) disease prevalence curves of unmitigated outbreaks have a single peak and result in epidemics that 'burn' through the population to become extinguished when the proportion of the susceptible population drops below a critical level; 2) if immunity in recovered individuals wanes sufficiently fast then the disease persists indefinitely as an endemic state, with possible dampening oscillations following the initial outbreak phase; 3) the steepness and initial peak of the prevalence curve are influenced by the basic reproductive value R₀, which must exceed 1 for an epidemic to occur; 4) the probability that a single infectious individual in a closed population (i.e. no migration) gives rise to an epidemic increases with the value of R₀>1; 5) behavior that adaptively decreases the contact rate among individuals with increasing prevalence has major effects on the prevalence curve including dramatic flattening of the prevalence curve along with the generation of multiple prevalence peaks; 6) the impacts of treatment are complicated to model because they effect multiple processes including transmission, recovery and mortality; 7) the impacts of vaccination policies, constrained by a fixed number of vaccination regimens and by the rate and timing of delivery, are crucially important to maximizing the ability of vaccination programs to reduce mortality.

CONCLUSION

Our presentation makes transparent the key assumptions underlying SIR epidemic models. Our RAMP simulators are meant to augment rather than replace classroom material when teaching epidemiological dynamics. They are sufficiently versatile to be used by students to address a range of research questions for term papers and even dissertations.

The African swine fever modelling challenge: Objectives, model description and synthetic data generation

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers' ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.

Heterogeneous adaptive behavioral responses may increase epidemic burden

Non-pharmaceutical interventions (NPIs) constitute the front-line responses against epidemics. Yet, the interdependence of control measures and individual microeconomics, beliefs, perceptions and health incentives, is not well understood. Epidemics constitute complex adaptive systems where individual behavioral decisions drive and are driven by, among other things, the risk of infection. To study the impact of heterogeneous behavioral responses on the epidemic burden, we formulate a two risk-groups mathematical model that incorporates individual behavioral decisions driven by risk perceptions. Our results show a trade-off between the efforts to avoid infection by the risk-evader population, and the proportion of risk-taker individuals with relaxed infection risk perceptions. We show that, in a structured population, privately computed optimal behavioral responses may lead to an increase in the final size of the epidemic, when compared to the homogeneous behavior scenario. Moreover, we find that uncertain information on the individuals' true health state may lead to worse epidemic outcomes, ultimately depending on the population's risk-group composition. Finally, we find there is a set of specific optimal planning horizons minimizing the final epidemic size, which depend on the population structure.

The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants

Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.

Modeling the COVID-19 pandemic: a primer and overview of mathematical epidemiology

Since the start of the still ongoing COVID-19 pandemic, there have been many modeling efforts to assess several issues of importance to public health. In this work, we review the theory behind some important mathematical models that have been used to answer questions raised by the development of the pandemic. We start revisiting the basic properties of simple Kermack-McKendrick type models. Then, we discuss extensions of such models and important epidemiological quantities applied to investigate the role of heterogeneity in disease transmission e.g. mixing functions and superspreading events, the impact of non-pharmaceutical interventions in the control of the pandemic, vaccine deployment, herd-immunity, viral evolution and the possibility of vaccine escape. From the perspective of mathematical epidemiology, we highlight the important properties, findings, and, of course, deficiencies, that all these models have.

Quarantine, physical distancing and social isolation measures in individuals potentially exposed to SARS-CoV-2 in community settings and health services: a scoping review

To provide a synthesis of diverse evidence on the impact of the non-therapeutic preventive measures, specifically quarantine, physical distancing and social isolation, on the control of COVID-19. A scoping review conducted in the PubMed, Embase, LILACS, CENTRAL and SCOPUS databases between 2019 and August 28th, 2020. The descriptors used were the following: "quarantine", "physical distancing", "social isolation", "COVID-19" and "SARS-Cov2". Studies that addressed the non-therapeutic preventive measures in people exposed to SARs-CoV-2 in community settings and health services were included. A total of 14,442 records identified through a database search were reduced to 346 studies and, after a standardized selection process, a total of 68 articles were selected for analysis. A total of 35 descriptive, cross-sectional or longitudinal observational studies were identified, as well as 3 reviews, in addition to 30 studies with mathematical modeling. The main intervention assessed was social distancing (56.6%), followed by lockdown (25.0%) and quarantine (18.4%). The main evidence analyzed points to the need for rapid responses to reduce the number of infections, deaths and hospital admissions, especially in intensive care unit beds.The current review revealed consistent reports that the quarantine, physical distancing and social isolation are effective strategies to contain spread of the new coronavirus.

Modelling preventive measures and their effect on generation times in emerging epidemics

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.

How the nature of behavior change affects the impact of asymptomatic coronavirus transmission

SARS-CoV-2 has caused severe respiratory illnesses and deaths since late 2019 and spreads globally. While asymptomatic cases play a crucial role in transmitting COVID-19, they do not contribute to the observed prevalence, which drives behavior change during the pandemic. This study aims to identify the effect of the proportion of asymptomatic infections on the magnitude of an epidemic under behavior change scenarios by developing a compartmental mathematical model. In this interest, we discuss three different behavior change cases separately: constant behavior change, instantaneous behavior change response to the disease’s perceived prevalence, and piecewise constant behavior change response to government policies. Our results imply that the proportion of asymptomatic infections which maximizes the spread of the epidemic depends on the nature of the dominant force driving behavior changes.

An Electrochemical Biosensing Platform for the SARS-CoV-2 Spike Antibody Detection Based on the Functionalised SARS-CoV-2 Spike Antigen Modified Electrode

We developed an electrochemical biosensing platform using gold-clusters, cysteamine, the spike protein of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antigen and bovine serum albumin on a glassy carbon electrode able to determine the SARS-CoV-2 spike antibody. The developed biosensor could detect 9.3 ag/mL of the SARS-CoV-2 spike antibody in synthetic media in 20 min in a linear range from 0.1 fg/mL to 10.0 pg/mL. The developed method demonstrated good selectivity in the presence of spike antigens from other viruses. Clinical samples consisting of gargle and mouthwash liquids were analyzed with both RT-PCR and the developed biosensor system to reveal the sensitivity and specificity of the proposed method. Moreover, the developed method was compared with the lateral flow immunoassay method in terms of sensitivity.

Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population

Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.

Inferring the reproduction number using the renewal equation in heterogeneous epidemics

Real-time estimation of the reproduction number has become the focus of modelling groups around the world as the SARS-CoV-2 pandemic unfolds. One of the most widely adopted means of inference of the reproduction number is via the renewal equation, which uses the incidence of infection and the generation time distribution. In this paper, we derive a multi-type equivalent to the renewal equation to estimate a reproduction number which accounts for heterogeneity in transmissibility including through asymptomatic transmission, symptomatic isolation and vaccination. We demonstrate how use of the renewal equation that misses these heterogeneities can result in biased estimates of the reproduction number. While the bias is small with symptomatic isolation, it can be much larger with asymptomatic transmission or transmission from vaccinated individuals if these groups exhibit substantially different generation time distributions to unvaccinated symptomatic transmitters, whose generation time distribution is often well defined. The bias in estimate becomes larger with greater population size or transmissibility of the poorly characterized group. We apply our methodology to Ebola in West Africa in 2014 and the SARS-CoV-2 in the UK in 2020-2021.

Comparative Analysis of Circulating Levels of SARS-CoV-2 Antibodies and Inflammatory Mediators in Healthcare Workers and COVID-19 Patients

Advances in knowledge of the pathophysiology of COVID-19 have been acquired; however, the host factors that could explain the mild and severe forms of the disease are not fully understood. Thus, we proposed to evaluate anti-SARS-CoV-2 antibodies and the inflammatory response of different groups of individuals, including healthcare workers (HCW), sick and dead COVID-19 patients and also recovered patients to contribute to this knowledge gap. Our objective is to relate the clinical evolution of these individuals with the level of detection and functionality of specific antibodies and with the production of inflammatory mediators. As main findings, IgA and IgG anti-SARS-CoV-2 were detected in asymptomatic HCW. IFN-γ and TNF-α levels were higher in symptomatic HCWs than patients with COVID-19 and those who died. Patients who died had higher levels of IL-6, IL-10, and CCL2/MCP-1. We found an imbalance between antiviral and pro-inflammatory mediators in the groups, in which IFN-γ and TNF-α seem to be more associated with protection and IL-6 and CCL2/MCP-1 with pathology. Our work is pioneering the Brazilian population and corroborates data from people from other countries.

Hidden fraction of Polish population immune to SARS-CoV-2 in May 2021

Population immunity (herd immunity) to SARS-CoV-2 derives from two sources: vaccinations or cases of infection with the virus. Infections can be diagnosed as COVID-19 and registered, or they can be asymptomatic, oligosymptomatic, or even full-blown but undiagnosed and unregistered when patients recovered at home. Estimation of population immunity to SARS-CoV-2 is difficult and remains a subject of speculations. Here we present a population screening for SARS-CoV-2 specific IgG and IgA antibodies in Polish citizens (N = 501) who had never been positively diagnosed with or vaccinated against SARS-CoV-2. Serum samples were collected in Wrocław (Lower Silesia) on 15th and 22nd May 2021. Sera from hospitalized COVID-19 patients (N = 22) or from vaccinated citizens (N = 14) served as positive controls. Sera were tested with Microblot-Array COVID-19 IgG and IgA (quantitative) that contain specific SARS-CoV-2 antigens: NCP, RBD, Spike S2, E, ACE2, PLPro protein, and antigens for exclusion cross-reactivity with other coronaviruses: MERS-CoV, SARS-CoV, HCoV 229E Np, HCoV NL63 Np. Within the investigated population of healthy individuals who had never been positively diagnosed with or vaccinated against SARS-CoV-2, we found that 35.5% (178 out of 501) were positive for SARS-CoV-2-specific IgG and 52.3% (262 out of 501) were positive for SARS-CoV-2-specific IgA; 21.2% of the investigated population developed virus-specific IgG or IgA while being asymptomatic. Anti-RBD IgG, which represents virus-neutralizing potential, was found in 25.6% of individuals (128 out of 501). These patients, though positive for anti-SARS-CoV-2 antibodies, cannot be identified in the public health system as convalescents due to undiagnosed infections, and they are considered unaffected by SARS-CoV-2. Their contribution to population immunity against COVID-19 should however be considered in predictions and modeling of the COVID-19 pandemic. Of note, the majority of the investigated population still lacked anti-RBD IgG protection (74.4%); thus vaccination against COVID-19 is still of the most importance for controlling the pandemic.

Asymptomatic infection and transmission of COVID-19 among clusters: systematic review and meta-analysis

OBJECTIVES

Countries throughout the world are experiencing COVID-19 viral load in their populations, leading to potential transmission and infectivity of asymptomatic COVID-19 cases. The current systematic review and meta-analysis aims to investigate the role of asymptomatic infection and transmission reported in family clusters, adults, children and health care workers, globally.

STUDY DESIGN

Systematic review and meta-analysis.

METHODS

An online literature search of PubMed, Google Scholar, medRixv and BioRixv was performed using standard Boolean operators and included studies published up to 17 August 2021. For the systematic review, case reports, short communications and retrospective studies were included to ensure sufficient asymptomatic COVID-19 transmission data were reported. For the quantitative synthesis (meta-analysis), participant data from a collection of cohort studies focusing on groups of familial clusters, adults, children and health care workers were included. Inconsistency among studies was assessed using I2 statistics. The data synthesis was computed using the STATA 16.0 software.

RESULTS

This study showed asymptomatic transmission among familial clusters, adults, children and health care workers of 15.72%, 29.48%, 24.09% and 0%, respectively. Overall, asymptomatic transmission was 24.51% (95% confidence interval [CI]: 14.38, 36.02) among all studied population groups, with a heterogeneity of I2 = 95.30% (P < 0.001). No heterogeneity was seen in the population subgroups of children and health care workers. The risk of bias in all included studies was assessed using the Newcastle Ottawa Scale.

CONCLUSIONS

For minimising the spread of COVID-19 within the community, this study found that following the screening of asymptomatic cases and their close contacts for chest CT scan (for symptomatic patients), even after negative nucleic acid testing, it is essential to perform a rigorous epidemiological history, early isolation, social distancing and an increased quarantine period (a minimum of 14-28 days). This systematic review and meta-analysis supports the notion of asymptomatic COVID-19 infection and person-to-person transmission and suggests that this is dependent on the varying viral incubation period among individuals. Children, especially those of school age (i.e. <18 years), need to be monitored carefully and follow mitigation strategies (e.g. social distancing, hand hygiene, wearing face masks) to prevent asymptomatic community transmission of COVID-19.

User experience of home-based AbC-19 SARS-CoV-2 antibody rapid lateral flow immunoassay test

The urgent need to scale up testing capacity during the COVID-19 pandemic has prompted the rapid development of point-of-care diagnostic tools such as lateral flow immunoassays (LFIA) for large-scale community-based rapid testing. However, studies of how the general public perform when using LFIA tests in different environmental settings are scarce. This user experience (UX) study of 264 participants in Northern Ireland aimed to gather a better understanding of how self-administered LFIA tests were performed by the general public at home. The UX performance was assessed via analysis of a post-test questionnaire including 30 polar questions and 11 7-point Likert scale questions, which covers the multidimensional aspects of UX in terms of ease of use, effectiveness, efficiency, accuracy and satisfaction. Results show that 96.6% of participants completed the test with an overall average UX score of 95.27% [95% confidence interval (CI) 92.71–97.83%], which suggests a good degree of user experience and effectiveness. Efficiency was assessed based on the use of physical resources and human support received, together with the mental effort of self-administering the test measured via NASA Task Load Index (TLX). The results for six TLX subscales show that the participants scored the test highest for mental demand and lowest for physical demand, but the average TLX score suggests that the general public have a relatively low level of mental workload when using LFIA self-testing at home. Five printed LFIA testing results (i.e. the ‘simulated’ results) were used as the ground truth to assess the participant’s performance in interpreting the test results. The overall agreement (accuracy) was 80.63% [95% CI 75.21–86.05%] with a Kappa score 0.67 [95% CI 0.58–0.75] indicating substantial agreement. The users scored lower in confidence when interpreting test results that were weak positive cases (due to the relatively low signal intensity in the test-line) compared to strong positive cases. The end-users also found that the kit was easier to use than they expected (p < 0.001) and 231 of 264 (87.5%) reported that the test kit would meet their requirements if they needed an antibody testing kit. The overall findings provide an insight into the opportunities for improving the design of self-administered SARS-CoV-2 antibody testing kits for the general public and to inform protocols for future UX studies of LFIA rapid test kits.

Estimating the Proportion of Asymptomatic COVID-19 Cases in an Italian Region with Intermediate Incidence during the First Pandemic Wave: An Observational Retrospective Study

Early in the COVID-19 pandemic, asymptomatic transmission represented an important challenge for controlling the spread of SARS-CoV-2 through the traditional public health strategies. Further understanding of the contribution of asymptomatic infections to SARS-CoV-2 transmission has been of crucial importance for pandemic control. We conducted a retrospective epidemiological study to characterize asymptomatic COVID-19 cases occurred in the Apulia region, Italy, during the first epidemic wave of COVID-19 outbreak (February 29-July 7, 2020). We analyzed data collected in a regional platform developed to manage surveillance activities, namely, investigation and follow-up of cases and contacts, contact tracing, and laboratory and clinical data collection. We included all asymptomatic cases that were laboratory-confirmed during the appropriate follow-up, defined as persons infected with SARS-CoV-2 who did not develop symptoms/clinical signs of the disease. Between February 29 and July 7, 2020, a total of 4,536 cases were diagnosed with COVID-19 among 193,757 tests performed. The group of persons with asymptomatic SARS-CoV-2 infection consisted of 903 cases; the asymptomatic proportion was 19.9% (95% CI: 18.8-21.1%); this decreased with increasing age (OR: 0.89, 95% CI: 0.83-0.96; p = 0.001), in individuals with underlying comorbidities (OR: 0.55, 95% CI: 0.41-0.73; p < 0.001), and in males (OR: 0.69, 95% CI: 0.54-0.87; p = 0.002). The median asymptomatic SARS-CoV-2 RNA positive period was 19 days (IQR: 14-31) and the cumulative proportion of persons with resolution of infection 14 days after the first positive PCR test was 74%. As the public health community is debating the question of whether asymptomatic and late spreaders could sustain virus transmission in the communities, such cases present unique opportunities to gain insight into SARS-CoV-2 adaptation to human host. This has important implications for future COVID-19 surveillance and prevention.

Electrochemical biosensing platform based on hydrogen bonding for detection of the SARS-CoV-2 spike antibody

Among the deadliest pandemics in history, coronavirus disease 2019 (COVID-19) has wreaked havoc on human lives, economies and public health systems worldwide. To temper its effects, diagnostic methods that are simple, rapid, inexpensive, accurate, selective and sensitive continue to be necessary. In our study, we developed an electrochemical biosensing platform based on gold clusters, mercaptoethanol, the spike protein of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antigen and bovine serum albumin-modified glassy carbon electrode able to detect the SARS-CoV-2 spike antibody. Moreover, during the detection of the SARS-CoV-2 spike antibody in spiked-real samples, the anodic signal of the produced biosensor at 0.85 V decreased as the amount of the SARS-CoV-2 spike antibody increased. Meanwhile, the recovery and relative standard deviation values for saliva and oropharyngeal swab samples were 97.73% and 3.35% and 102.43% and 4.63%, respectively. In 35 min, the biosensing platform could detect 0.03 fg/mL of the SARS-CoV-2 spike antibody in synthetic media and spiked-saliva or -oropharyngeal swab samples. The method thus issues a linear response to the SARS-CoV-2 spike antibody from 0.1 fg/mL to 10 pg/mL. The cross-reactivity studies with spike antigens of Middle East respiratory syndrome-coronavirus and influenza A and the antigen of pneumonia confirmed the excellent selectivity of the proposed method. The developed method was compared with the lateral flow immunoassay method in terms of sensitivity and it was found to be approximately 109 times more sensitive. Biosensing mechanism of the platform to the SARS-CoV-2 spike antibody.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2-the receptor of SARS-CoV-2 in human cells-and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8-3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

A rapid, ultrasensitive voltammetric biosensor for determining SARS-CoV-2 spike protein in real samples

The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to threaten public health systems all around the world. In controlling the viral outbreak, early diagnosis of COVID-19 is pivotal. This article describes a novel method of voltammetrically determining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with a newly designed sensor involving bovine serum albumin, SARS-CoV-2 spike antibody and a functionalised graphene oxide modified glassy carbon electrode (BSA/AB/f-GO/GCE) or screen-printed electrode (BSA/AB/f-GO/SPE). The oxidation reaction based on the antibody-antigen protein interaction was evaluated as a response to SARS-CoV-2 spike protein at -200 mV and 1430 mV with the BSA/AB/f-GO/SPE and BSA/AB/f-GO/GCE, respectively. The developed sensors, BSA/AB/f-GO/SPE and BSA/AB/f-GO/GCE, could detect 1 ag/mL of virus spike protein in synthetic, saliva and oropharyngeal swab samples in 5 min and 35 min, and both sensors demonstrated a dynamic response to the SARS-CoV-2 spike protein between 1 ag/mL and 10 fg/mL. Real-time polymerase chain reaction (RT-PCR), rapid antigen test and the proposed method were applied to saliva samples. When compared to RT-PCR, it was observed that the developed method had a 92.5% specificity and 93.3% sensitivity. Moreover, BSA/AB/f-GO/SPE sensor achieved 91.7% accuracy compared to 66.7% accuracy of rapid antigen test kit in positive samples. In view of these findings, the developed sensor provides great potential for the diagnosing of COVID-19 in real samples.

Antibody Responses to SARS-CoV-2 Infection-Comparative Determination of Seroprevalence in Two High-Throughput Assays versus a Sensitive Spike Protein ELISA

Robust assay development for SARS-CoV-2 serological testing requires assessment of asymptomatic and non-hospitalised individuals to determine if assays are sensitive to mild antibody responses. Our study evaluated the performance characteristics of two high-throughput SARS-CoV-2 IgG nucleocapsid assays (Abbott Architect and Roche) and The Binding Site (TBS) Anti-Spike IgG/A/M ELISA kit in samples from healthcare workers (HCWs). The 252 samples were collected from multi-site NHS trusts and analysed for SARS-CoV-2 serology. Assay performance was evaluated between these three platforms and ROC curves were used to redefine the Abbott threshold. Concordance between Abbott and TBS was 66%. Any discrepant results were analysed using Roche, which showed 100% concordance with TBS. Analysis conducted in HCWs within 58 days post-PCR result demonstrated 100% sensitivity for both Abbott and Roche. Longitudinal analysis for >100 days post-PCR led to sensitivity of 77.2% and 100% for Abbott and Roche, respectively. A redefined Abbott threshold (0.64) increased sensitivity to 90%, producing results comparable to TBS and Roche. The manufacturer's threshold set by Abbott contributes to lower sensitivity and elevated false-negative occurrences. Abbott performance improved upon re-optimisation of the cut-off threshold. Our findings provided evidence that TBS can be used as bespoke alternative for SARS-CoV-2 serology analysis where high-throughput platforms are not feasible on site.

Mechanistic modelling of COVID-19 and the impact of lockdowns on a short-time scale

BACKGROUND

To mitigate the spread of the COVID-19 coronavirus, some countries have adopted more stringent non-pharmaceutical interventions in contrast to those widely used. In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies also have been implemented, such as the total lockdown of fragmented regions, which are composed of sparsely and highly populated areas.

METHODS

In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host is infected if in close spatial proximity of the infectious host with an assigned transmission probability. Our focus is on a short-time scale (∼ 3 days), which is the average time lag time before an infected individual becomes infectious.

RESULTS

We find that the level of infection remains approximately constant with an increase in population diffusion, and also in the case of faster population dispersal (super-diffusion). Moreover, we demonstrate how the efficacy of imposing a lockdown depends heavily on how susceptible and infectious individuals are distributed over space.

CONCLUSION

Our results indicate that on a short-time scale, the type of movement behaviour does not play an important role in rising infection levels. Also, lock-down restrictions are ineffective if the population distribution is homogeneous. However, in the case of a heterogeneous population, lockdowns are effective if a large proportion of infectious carriers are distributed in sparsely populated sub-regions.

Asymptomatic individuals can increase the final epidemic size under adaptive human behavior

Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves-and be perceived by others-as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system's future state over a finite planning horizon. We found that individuals' risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps

Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.

COVID-19 Data Utilization in North Carolina: Qualitative Analysis of Stakeholder Experiences

Background

As the world faced the pandemic caused by the novel coronavirus disease 2019 (COVID-19), medical professionals, technologists, community leaders, and policy makers sought to understand how best to leverage data for public health surveillance and community education. With this complex public health problem, North Carolinians relied on data from state, federal, and global health organizations to increase their understanding of the pandemic and guide decision-making.

Objective

We aimed to describe the role that stakeholders involved in COVID-19–related data played in managing the pandemic in North Carolina. The study investigated the processes used by organizations throughout the state in using, collecting, and reporting COVID-19 data.

Methods

We used an exploratory qualitative study design to investigate North Carolina’s COVID-19 data collection efforts. To better understand these processes, key informant interviews were conducted with employees from organizations that collected COVID-19 data across the state. We developed an interview guide, and open-ended semistructured interviews were conducted during the period from June through November 2020. Interviews lasted between 30 and 45 minutes and were conducted by data scientists by videoconference. Data were subsequently analyzed using qualitative data analysis software.

Results

Results indicated that electronic health records were primary sources of COVID-19 data. Often, data were also used to create dashboards to inform the public or other health professionals, to aid in decision-making, or for reporting purposes. Cross-sector collaboration was cited as a major success. Consistency among metrics and data definitions, data collection processes, and contact tracing were cited as challenges.

Conclusions

Findings suggest that, during future outbreaks, organizations across regions could benefit from data centralization and data governance. Data should be publicly accessible and in a user-friendly format. Additionally, established cross-sector collaboration networks are demonstrably beneficial for public health professionals across the state as these established relationships facilitate a rapid response to evolving public health challenges.

Development of a high-sensitivity ELISA detecting IgG, IgA and IgM antibodies to the SARS-CoV-2 spike glycoprotein in serum and saliva

Detecting antibody responses during and after SARS-CoV-2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease. Scalable, sensitive and specific serological assays are essential to this process. The detection of antibody in hospitalized patients with severe disease has proven relatively straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable. We hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect. We systematically developed an ELISA, optimizing different antigens and amplification steps, in serum and saliva from non-hospitalized SARS-CoV-2-infected subjects. Using trimeric spike glycoprotein, rather than nucleocapsid, enabled detection of responses in individuals with low antibody responses. IgG1 and IgG3 predominate to both antigens, but more anti-spike IgG1 than IgG3 was detectable. All antigens were effective for detecting responses in hospitalized patients. Anti-spike IgG, IgA and IgM antibody responses were readily detectable in saliva from a minority of RT-PCR confirmed, non-hospitalized symptomatic individuals, and these were mostly subjects who had the highest levels of anti-spike serum antibodies. Therefore, detecting antibody responses in both saliva and serum can contribute to determining virus exposure and understanding immune responses after SARS-CoV-2 infection.

Estimating the quarantine failure rate for COVID-19

Quarantine is a crucial control measure in reducing imported COVID-19 cases and community transmissions. However, some quarantined COVID-19 patients may show symptoms after finishing quarantine due to a long median incubation period, potentially causing community transmissions. To assess the recommended 14-day quarantine policy, we develop a formula to estimate the quarantine failure rate from the incubation period distribution and the epidemic curve. We found that the quarantine failure rate increases with the exponential growth rate of the epidemic curve. We apply our formula to United States, Canada, and Hubei Province, China. Before the lockdown of Wuhan City, the quarantine failure rate in Hubei Province is about 4.1%. If the epidemic curve flattens or slowly decreases, the failure rate is less than 2.8%. The failure rate in US may be as high as 8.3%–11.5% due to a shorter 10-day quarantine period, while the failure rate in Canada may be between 2.5% and 3.9%. A 21-day quarantine period may reduce the failure rate to 0.3%–0.5%.

Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach

The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.

Modelling SARS-CoV-2 unreported cases in Italy: Analysis of serological survey and vaccination scenarios

OBJECTIVES

Aim of the present paper is the study of the large unreported component, characterizing the SARS-CoV-2 epidemic event in Italy, taking advantage of the Istat survey. Particular attention is devoted to the sensitivity and specificity of the serological test and their effects.

METHODS

The model satisfactory reproduces the data of the Italian survey showing a relevant predictive power and relegating in a secondary position models which do not include, in the simulation, the presence of asymptomatic groups. The corrections due to the serological test sensitivity (in particular those ones depending on the symptoms onset) are crucial for a realistic analysis of the unreported (and asymptomatic) components.

RESULTS

The relevant presence of an unreported component during the second pandemic wave in Italy is confirmed and the ratio of reported to unreported cases is predicted to be roughly 1:4 in the last months of year 2020. A method to correct the serological data on the basis of the antibody sensitivity is suggested and systematically applied. The asymptomatic component is also studied in some detail and its amount quantified. A model analyses of the vaccination scenarios is performed confirming the relevance of a massive campaign (at least 80000 immunized per day) during the first six months of the year 2021, to obtain important immunization effects within August/September 2021.

Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach

The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.

Electrochemical immunosensor platform based on gold-clusters, cysteamine and glutaraldehyde modified electrode for diagnosing COVID-19

Amid the global threat caused by the coronavirus disease 2019 (COVID-19) pandemic, developing sufficiently rapid, accurate, sensitive and selective methods of diagnosing both symptomatic and asymptomatic cases is essential to alleviating and controlling the pandemic’s effects. This article describes an electrochemical immunoassay platform developed to determine the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) spike antibody by using gold-clusters capped with cysteamine, glutaraldehyde, the spike protein of the SARS-CoV-2 antigen and bovine serum albumin on a glassy carbon electrode. The electrochemical oxidation signal of the antigen-based immunosensor at 0.9 V was used to detect the SARS-CoV-2 spike antibody. When saliva and oropharyngeal swab samples were analysed, the recovery and relative standard deviation values were 96.97%–101.99% and 4.99%–5.74%, respectively. The method’s limit of detection relative to the SARS-CoV-2 spike antibody in synthetic media and in saliva or oropharyngeal swab samples was 0.01 ag/mL, while the immunosensor’s linear response to the SARS-CoV-2 spike antibody varied from 0.1 to 1000 ag/mL. The cross-reactivity of the Middle East respiratory syndrome-coronavirus spike antigen was evaluated after being immobilised onto the functionalised gold-cluster based sensor, indicated that the good specifity of the produced immunosensor.

COVID-19 outbreak in a personal service setting in Kingston, Ontario, 2020

BACKGROUND

During the coronavirus disease 2019 (COVID-19) pandemic, Ontario created a three-phase reopening framework for the economy. Outbreaks were expected at each phase. One week after Phase Two of reopening in the provincial public health administration region of Kingston, Frontenac, Lennox and Addington (KFL&A), a positive case was reported after three weeks of zero new COVID-19 cases. The objective of this report is to describe this COVID-19 outbreak, linked to a personal service setting (PSS), and the public health response to contain the outbreak.

METHODS

The outbreak investigation included all COVID-19 cases in KFL&A between June 20, 2020 and July 3, 2020. Public health inspectors and nurses were rapidly deployed to inspect the PSS. A multimodal approach to high-volume testing involved fixed assessment centres, drive-through testing capacity and targeted testing at the outbreak site. Testing was conducted through a real-time polymerase chain reaction assay at the local Public Health Ontario laboratory.

RESULTS

Thirty-seven cases were associated with the outbreak: 38% through direct PSS exposure; 32% through household contact; and 30% through social and workplace contact. A superspreading event contributed to 38% of total cases. The majority of cases were in the low to mid-quintiles when analyzed for material deprivation. Testing rates increased four-fold compared to the prior baseline weeks in response to media attention and public health messaging, resulting in a low percent positivity.

CONCLUSION

The interplay of aggressive accessible testing, quick lab turnaround time, contact tracing within 24 hours of positive laboratory results as per provincial standards, frequent public communication, rapid inspections, mandatory self-isolation and face coverings were measures successful in halting the outbreak. Inspections or self-audits should be required at all PSSs prior to reopening and outbreak management must work with PSSs to reduce the possibility of superspreading events.

Assessing the spread of the novel coronavirus in the absence of mass testing

BACKGROUND

Assessing why the spread of the COVID-19 virus slowed down in many countries in March through to May of 2020 is of great significance. The relative role of restrictions on behaviour ("lockdowns") and of a natural slowing for other reasons is difficult to assess when mass testing was not widely done. This paper assesses the evolution of the spread of the COVID-19 virus over this period when there was no data on test results for a large, random sample of the population.

METHOD

We estimate a version of the susceptible-infected-recovered model applied to data on the numbers who were tested positive in several countries over the period when the virus spread very fast and then its spread slowed sharply. Up to the end of April 2020, test data came from non-random samples of populations who were overwhelmingly those who displayed symptoms. Using data from a period when the criteria used for testing (which was that people had clear symptoms) was relatively consistent is important in drawing out the message from test results. We use this data to assess two things: how large might be the group of those infected who were not recorded and how effective were lockdown measures in slowing the spread of the infection.

RESULTS

We find that to match data on daily new cases of the virus, the estimated model favours high values for the number of people infected but not recorded.

CONCLUSIONS

Our findings suggest that the infection may have spread far enough in many countries by April 2020 to have been a significant factor behind the fall in measured new cases. Government restrictions on behaviour-lockdowns-were only one factor behind slowing in the spread of the virus.