Forecasting emergence of COVID-19 variants of concern

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

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

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants.

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

OBJECTIVE

The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study.

METHODS

Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase.

RESULTS

This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power.

CONCLUSION

This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.

The emergence of a virus variant: dynamics of a competition model with cross-immunity time-delay validated by wastewater surveillance data for COVID-19

We consider the dynamics of a virus spreading through a population that produces a mutant strain with the ability to infect individuals that were infected with the established strain. Temporary cross-immunity is included using a time delay, but is found to be a harmless delay. We provide some sufficient conditions that guarantee local and global asymptotic stability of the disease-free equilibrium and the two boundary equilibria when the two strains outcompete one another. It is shown that, due to the immune evasion of the emerging strain, the reproduction number of the emerging strain must be significantly lower than that of the established strain for the local stability of the established-strain-only boundary equilibrium. To analyze the unique coexistence equilibrium we apply a quasi steady-state argument to reduce the full model to a two-dimensional one that exhibits a global asymptotically stable established-strain-only equilibrium or global asymptotically stable coexistence equilibrium. Our results indicate that the basic reproduction numbers of both strains govern the overall dynamics, but in nontrivial ways due to the inclusion of cross-immunity. The model is applied to study the emergence of the SARS-CoV-2 Delta variant in the presence of the Alpha variant using wastewater surveillance data from the Deer Island Treatment Plant in Massachusetts, USA.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Role of error catastrophe in transmission ability of virus

The role played by error catastrophe is explicitly taken into account in a mathematical formulation to analyze COVID-19 data. The idea is to combine the mathematical genetics formalism of the error catastrophe of mutations in virus gene loci with the standard model of epidemics, which lacks the explicit incorporation of the effect of mutation on the spreading of viruses. We apply this formalism to the case of SARS-CoV-2 virus. We assume the universality of the error catastrophe in the process of analyzing the data. This means that some basic parameter to describe the error catastrophe is independent of which group (country or city) we deal with. Concretely, we analyze Omicron variant data from South Africa and then analyze cases from Japan using the same value of the basic parameter derived in the South Africa analysis. The excellent fit between the two sets of data, one from South Africa and the other from Japan, using the common values of genetic parameters, justifies our assumption of the universality of these parameters.

In silico evaluation of Philippine Natural Products against SARS-CoV-2 Main Protease

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a novel strain of coronavirus first reported in December 2019 which rapidly spread throughout the world and was subsequently declared a pandemic by the World Health Organization (WHO) in March 2020. Although vaccines, as well as treatments, have been rapidly developed and deployed, these are still spread thin, especially in the developing world. There is also a continuing threat of the emergence of mutated variants which may not be as responsive to available vaccines and drugs. Accessible and affordable sources of antiviral drugs against SARS-CoV-2 offer wider options for the clinical treatment of populations at risk for severe COVID-19. Using in silico methods, this study identified potential inhibitors against the SARS-CoV-2 main protease (Mpro), the protease directly responsible for the activation of the viral replication enzyme, from a consolidated database of 1516 Philippine natural products. Molecular docking experiments, along with in silico ADME predictions, determined top ligands from this database with the highest potential inhibitory effects against Mpro. Molecular dynamic trajectories of the apo and diosmetin-7-O-b-D-glucopyranoside (DG) in complex with the protein predicted potential mechanisms of action for the ligand-by separating the Cys145-His41 catalytic dyad and by influencing the protein network through key intra-signaling residues within the Mpro binding site. These findings show the inhibitory potential of DG against the SARS-CoV-2 Mpro, and further validation is recommended through in vitro or in vivo experimentation.

Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development

The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.

Assessment of Anxiety, Depression, Work-Related Stress, and Burnout in Health Care Workers (HCWs) Affected by COVID-19: Results of a Case–Control Study in Italy

This study aims to investigate whether HCWs infected with COVID-19 may experience potential psychological consequences and a higher incidence of depression, anxiety, work-related stress, and burnout compared to non-infected HCWs. A case–control study with 774 participants was conducted comparing COVID-19-infected HCWs (cases) and non-infected HCWs (controls) from the Occupational Medicine Unit at the Teaching Hospital Policlinico Umberto I, who were administered the same questionnaire including Hospital Anxiety and Depression Scale, Copenhagen Burnout Inventory and Karasek’s Job Content Questionnaire. No differences in the levels of burnout and decision latitude were found between the two groups. Cases showed higher level of anxiety and job demand compared to controls. In contrast, levels of depression in the case group were significantly lower compared to the control group. The results are indicating the need for workplace health promotion activities based on stress and burnout management and prevention. Multiple organizational and work-related interventions can lower the impact of mental health-related issues in the COVID-19 pandemics, including the improvement of workplace infrastructures, as well as the adoption of correct and shared anti-contagion measures, which must include regular personal protective equipment supply, and the adoption of training programs that deal with mental health-related issues.