Department of Biological Sciences

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1922

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Subject: search.filters.subject.COVID-19

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    Modeling the effect of vaccinations, hospital beds, and treatments on the dynamics of infectious disease with delayed optimal control and sensitivity analysis
    (Springer, 2024-08) Dubey, Uma S.; Dubey, Balram
    Immunization plays a vital role in eradicating infectious diseases, typically requiring multiple doses at specific time intervals. This study focuses on developing and analyzing an infectious disease model governed by a six-dimensional system of ordinary differential equations, considering the impact of first and second vaccination doses along with hospital beds and treatment. The model’s qualitative behavior is analyzed, including conditions for positive solutions, the invariant region of the solution, equilibrium points, and their stability. When the basic reproduction number () is less than one (), the disease will be eradicated; conversely, an epidemic occurs when . Moreover, the transcritical bifurcation of the system is examined using the center manifold theory. Interestingly, backward bifurcation is discovered, and it indicates that the disease is not entirely eradicated even when . We have investigated different bifurcations like saddle-node, transcritical, and Hopf bifurcations of codimension 1, as well as Generalized-Hopf (GH), Cusp (CP), and Bogdanov–Takens (BT) bifurcations of codimension 2. Additionally, a delayed epidemiological model is explored, assuming a lag in vaccination among the susceptible population. A Hopf-bifurcation is observed near the endemic equilibrium point, linked to critical parameter values during the latent period. Moreover, the model is calibrated using the least-squares technique, incorporating coronavirus-infected case data and vaccination information from India and Italy’s mass vaccination program between March 1, 2021, and May 30, 2021. Global sensitivity analysis, utilizing the Partial Rank Correlation Coefficient (PRCC), identifies crucial parameters affecting threshold quantities after fitting the model. The study highlights the significance of critical parameters such as the effective transmission rate, rates of first and second-dose vaccinations, and recovery rate due to double-dose vaccination. Further, delayed optimal control measures are determined using Pontryagin’s maximal principle to mitigate infection, prevention, and treatment burdens. Numerical simulations are conducted to understand the effect of these delayed control measures on disease progression and demonstrate the insights obtained through analytical investigations. The study indicates that implementing all control strategies effectively reduces the disease burden among the population. Accurate estimation of vaccine efficacy is crucial for disease prevention, underlining the importance of well-planned vaccination strategies. Moreover, the numerical simulations validate all the theoretical findings, emphasizing the validity of this model in a real-world situation. Relying solely on vaccination might not swiftly or completely control the disease. Complementary pharmaceutical and non-pharmaceutical measures are necessary to combat the infection effectively. Further limitations on medical resources could lead to a backward bifurcation. Simulation results suggest that delaying the implementation of control measures could exacerbate epidemic situations.
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    COVID-19 infection and metabolic comorbidities: Mitigating role of nutritional sufficiency and drug – nutraceutical combinations of vitamin D
    (Elsevier, 2023-03) Deepa, P. R.; Tare, Meghana
    The vulnerability of human health is amplified in recent times with global increase in non-communicable diseases (due to lifestyle changes and environmental insults) and infectious diseases (caused by newer pathogens and drug-resistance strains). Clinical management of diseases is further complicated by disease severity caused by other comorbid factors. Drug-based therapy may not be the sole approach, particularly in scenarios like the COVID-19 pandemic, where there is no specific drug against SARS-CoV-2. Nutritional interventions are significant in armouring human populations in disease prevention, and as adjunctive therapy for disease alleviation. Amidst ongoing clinical trials to determine the efficacy of Vit. D against infections and associated complications, this review examines the pleiotropic benefits of nutritional adequacy of vitamin D (Vit. D) in combating viral infections (COVID-19), its severity and complications due to co-morbidities (obesity, diabetes, stroke and Kawasaki disease), based on research findings and clinical studies. Supplements of Vit. D in combination with other nutrients, and drugs, are suggested as promising preventive-health and adjunct-treatment strategies in the clinical management of viral infections with metabolic comorbidities.
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    A clustering and graph deep learning-based framework for COVID-19 drug repurposing
    (Elsevier, 2024-09) Agarwal, Vinti; Deepa, P.R.
    Drug repurposing (or repositioning) is the process of finding new therapeutic uses for drugs already approved by drug regulatory authorities (e.g., the Food and Drug Administration (FDA) and Therapeutic Goods Administration (TGA)) for other diseases. This involves analysing the interactions between different biological entities, such as drug targets (genes/proteins and biological pathways) and drug properties, to discover novel drug–target or drug–disease relations. Machine learning and deep learning models have successfully analysed complex heterogeneous data with applications in the biomedical domain, and have also been used for drug repurposing. This study presents a novel unsupervised machine learning framework that utilizes a graph-based autoencoder for multi-feature type clustering on heterogeneous drug data. The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19 (category A). The rest are systematically filtered to ensure the safety and efficacy of the treatment (category B). The framework solely relies on reported drug data, including its pharmacological properties, chemical/physical properties, interaction with the host, and efficacy in different publicly available COVID-19 assays. Our machine-learning framework revealed three clusters of interest and provided recommendations featuring the top 15 drugs for COVID-19 drug repurposing, which were shortlisted based on the predicted clusters that were dominated by category A drugs. Our framework can be extended to support other datasets and drug repurposing studies with the availability of our open-source code.