A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning.

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Spectrum of Serious Neurological and Psychiatric Adverse Events in Indian COVID-19 Vaccine Recipients: A Systematic Review of Case Reports and Case Series.

Indian data regarding serious neurological and psychiatric adverse events, following coronavirus disease 2019 (COVID-19) vaccination, are lacking. We, therefore, systematically evaluated cases of post-vaccinal serious neurological and psychiatric adverse reactions published from India. A systematic review of cases published from India, which were archived in PubMed, Scopus, and Google Scholar databases, was performed; pre-print databases along with ahead-of-print contents were searched in addition. Retrieved articles, as on June 27, 2022, were evaluated following PRISMA guidelines. EndNote 20 web tool was used to make a PRISMA flow chart. Individual patients' data were compiled in a tabular form. The protocol of the systematic review was registered with PROSPERO (CRD42022324183). A total of 64 records describing 136 instances of serious neurological and psychiatric adverse events were identified. More than 50% (36/64) reports were from the following four states, namely, Kerala, Uttar Pradesh, New Delhi, and West Bengal. The mean age of persons developing these complications was 44.89 ± 15.77 years. In the majority, adverse events occurred within 2 weeks of administration of the first dose of COVISHIELD vaccine. Immune-mediated central nervous system (CNS) disorders were identified in 54 instances. Guillain-Barre syndrome and other immune-mediated peripheral neuropathies were reported in 21 cases. Post-vaccinal herpes zoster was recorded in 31 vaccine recipients. Psychiatric adverse events were recorded in six patients. In Indian recipients of COVID-19 vaccine, a variety of serious neurological complications were reported. The overall risk appears minuscule. Immune-mediated central and peripheral neuronal demyelinations were the most frequently reported post-vaccinal adverse events. A large number of cases of herpes zoster have also been reported. Immune-mediated disorders responded well to immunotherapy.

Internet-of-medical-things integrated point-of-care biosensing devices for infectious diseases: Toward better preparedness for futuristic pandemics.

Microbial pathogens have threatened the world due to their pathogenicity and ability to spread in communities. The conventional laboratory-based diagnostics of microbes such as bacteria and viruses need bulky expensive experimental instruments and skilled personnel which limits their usage in resource-limited settings. The biosensors-based point-of-care (POC) diagnostics have shown huge potential to detect microbial pathogens in a faster, cost-effective, and user-friendly manner. The use of various transducers such as electrochemical and optical along with microfluidic integrated biosensors further enhances the sensitivity and selectivity of detection. Additionally, microfluidic-based biosensors offer the advantages of multiplexed detection of analyte and the ability to deal with nanoliters volume of fluid in an integrated portable platform. In the present review, we discussed the design and fabrication of POCT devices for the detection of microbial pathogens which include bacteria, viruses, fungi, and parasites. The electrochemical techniques and current advances in this field in terms of integrated electrochemical platforms that include mainly microfluidic- based approaches and smartphone and Internet-of-things (IoT) and Internet-of-Medical-Things (IoMT) integrated systems have been highlighted. Further, the availability of commercial biosensors for the detection of microbial pathogens will be briefed. In the end, the challenges while fabrication of POC biosensors and expected future advances in the field of biosensing have been discussed. The integrated biosensor-based platforms with the IoT/IoMT usually collect the data to track the community spread of infectious diseases which would be beneficial in terms of better preparedness for current and futuristic pandemics and is expected to prevent social and economic losses.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

Perioperative Anaesthetic challenges during the management of Mucormycosis in a recovered SARS-CoV-2 Patient: A Case report

Mucormycosis is a progressive, opportunistic fungal infection with high risk of mortality. Rampant use of steroids in the treatment coronavirus disease 2019 creates a fertile environment for mucor growth. Perioperative challenges for the anesthesiologist in a patient having post-coronavirus disease mucormycosis include increased risks of arterial and venous thromboembolism, poor glycemic control and myocardial dysfunction, adrenal insufficiency from corticosteroid use, pulmonary dysfunction, and residual neuromuscular weakness. So, a complete biochemical workup of renal functions, hypothalamic–pituitary–adrenal axis, electrolyte, coagulation profile, optimization of blood glucose, and pulmonary function should be done.

Severe COVID-19-associated hyperinflammatory syndrome versus classic hemophagocytic lymphohistiocytosis: similarities, differences, and the way forward.

The hyperinflammatory immune response in severe COVID-19 infection shares features with secondary hemophagocytic lymphohistiocytosis (sHLH) in the form of fever, cytopenia, elevated inflammatory markers, and high mortality. There are contrasting opinions regarding utility of HLH 2004 or HScore in the diagnosis of severe COVID-19-related hyperinflammatory syndrome (COVID-HIS). This was a retrospective study of 47 patients of severe COVID-19 infection, suspected to have COVID-HIS and 22 patients of sHLH to other illnesses, to evaluate the diagnostic utility and limitations of HLH 2004 and/or HScore in context to COVID-HIS and to also evaluate the utility of Temple criteria for predicting severity and outcome in COVID-HIS. Clinical findings, hematological, and biochemical parameters along with the predictor of mortality were compared between two groups. Only 6.4% (3/47) of cases fulfilled ≥5/8 HLH 2004 criteria and only 40.52% (19/47) of patients showed HScore >169 in COVID-HIS group. 65.9% (31/47) satisfied the Temple criteria in COVID-HIS as compared with 40.9% (9/22) in the non-COVID group (p = 0.04). Serum ferritin (p = 0.02), lactate dehydrogenase (p = 0.02), direct bilirubin (p = 0.02), and C-reactive protein (p = 0.03) were associated with mortality in COVID-HIS. Both HScore and HLH-2004 criteria perform poorly for identifying COVID-HIS. Presence of bone marrow hemophagocytosis may help to identify about one-third of COVID-HIS missed by the Temple Criteria.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network.

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Blockchain for COVID-19: a comprehensive review.

The rampant and sudden outbreak of the SARS-CoV-2 coronavirus also called COVID-19 and its uncontrollable spread have led to a global crisis. COVID-19 is a highly contagious disease and the only way to fight with it is to follow social distancing and Non-Pharmaceutical Interventions (NPIs). Moreover, this virus is increasing exponentially day-by-day and a huge amount of data from this disease is also generated at the fast pace. So, there is a need to store, manage, and analyze this huge amount of data efficiently to get meaningful insights from it, which further helps medical professionals to tackle this global pandemic situation. Moreover, this data is to be passed through an open channel, i.e., the Internet, which opens the doors for the intruders to perform some malicious activities. Blockchain (BC) emerges as a technology that can manage the data in an efficient, transparent manner and also preserve the privacy of all the stakeholders. It can also aid in transaction authorization and verification in the supply chain or payments. Motivated by these facts, in this paper, we present a comprehensive review on the adoption of BC to tackle COVID-19 situations. We also present a case study on BC-based digital vaccine passports and analyzed its complexity. Finally, we analyzed the research challenges and future directions in this emerging area.

T-cell dysfunction as a potential contributing factor in post-COVID-19 mucormycosis.

BACKGROUND: The second wave of COVID-19 in India was followed by large number of mucormycosis cases. Indiscriminate use of immunosuppressive drugs, underlying diseases such as diabetes, cancers, or autoimmune diseases was thought to be the cause. However, the mortality was not as high as that seen in non-COVID mucormycosis. OBJECTIVE: To study the detailed characteristics of T-cells for evaluating the underlying differences in the T-cell immune dysfunction in post-COVID and non-COVID mucor patients. MATERIAL AND METHOD: The study included histopathologically confirmed cases of mucor (13 post-COVID, 13 non-COVID) and 15 healthy individuals (HI). Expression of T-cell activation (CD44, HLADR, CD69, CD38) and exhaustion (CTLA, PD-1, LAG-3 and TIM-3) markers was evaluated by flow cytometry. RESULTS: All cases showed significant depletion of T-cells compared to HI. Both post-COVID and non-COVID groups showed increased activation and exhaustion as compared to HI. Non-COVID mucor group showed significant activation of CD4+ T cells for HLADR and CD38 (p = .025, p = .054) and marked T-cell exhaustion in form of expression of LAG-3 on both CD4+ T and CD8+ T cells in comparison with post-COVID patients (p = .011, p = .036). Additionally, co-expression of PD-1 & LAG-3 and LAG-3 & TIM-3 on CD8+ T cells was statistically significant in non-COVID mucor patients (p = .016, p = .027). CONCLUSION: Immunosuppression in non-COVID mucor showed pronounced exhaustion of T-cells in comparison to post-COVID mucor cases implicating T-cell immune dysfunction is much more severe in non-COVID mucor which are in a state of continuous activation followed by extreme exhaustion leading to poorer outcome.

A case of ischemic stroke and transient thrombocytopenia in a young female following adenoviral vector-based COVID-19 vaccination: Was the association incidental or causal?

Since March 2021, cases with unusual clots, particularly cerebral venous sinus thrombosis and splanchnic vein thrombosis, have been reported worldwide following adenoviral vector-based coronavirus disease 2019 (COVID-19) vaccination. This entity has been termed vaccine-induced thrombotic thrombocytopenia (VITT). We report a 23-year-old healthy female who developed seizures, altered sensorium, and left hemiparesis, 20 days after receiving the first dose of adenoviral vector-based COVID-19 vaccine "Covishield™.” The patient had transient thrombocytopenia. The D-dimer level was 2460 ng/mL. Magnetic resonance imaging (MRI) demonstrated occlusion of M2 segment of the middle cerebral artery and cerebral infarction. Platelet factor-4 antibodies level was normal. Treatment with aspirin and antiepileptic drugs resulted in a remarkable recovery. This is the first Indian case report of ischemic stroke and transient thrombocytopenia following SARS-CoV-2 ChAdOx1 nCoV-19 vaccination. Our case had clinical features consistent with the diagnosis of probable VITT. Familiarity with VITT is crucial because timely treatment with non-heparin anticoagulants and intravenous immunoglobulin improves the outcome.

Mechanistic investigation of SARS-CoV-2 main protease to accelerate design of covalent inhibitors.

Targeted covalent inhibition represents one possible strategy to block the function of SARS-CoV-2 Main Protease (MPRO), an enzyme that plays a critical role in the replication of the novel SARS-CoV-2. Toward the design of covalent inhibitors, we built a covalent inhibitor dataset using deep learning models followed by high throughput virtual screening of these candidates against MPRO. Two top-ranking inhibitors were selected for mechanistic investigations-one with an activated ester warhead that has a piperazine core and the other with an acrylamide warhead. Specifically, we performed a detailed analysis of the free energetics of covalent inhibition by hybrid quantum mechanics/molecular mechanics simulations. Cleavage of a fragment of the non-structured protein (NSP) from the SARS-CoV-2 genome was also simulated for reference. Simulations show that both candidates form more stable enzyme-inhibitor (E-I) complexes than the chosen NSP. It was found that both the NSP fragment and the activated ester inhibitor react with CYS145 of MPRO in a concerted manner, whereas the acrylamide inhibitor follows a stepwise mechanism. Most importantly, the reversible reaction and the subsequent hydrolysis reaction from E-I complexes are less probable when compared to the reactions with an NSP fragment, showing promise for these candidates to be the base for efficient MPRO inhibitors.

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Prognostic value of elevated cardiac and inflammatory biomarkers in patients with severe COVID-19: a single-center, retrospective study

Background The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 in India has been declared a public health emergency. Many patients with COVID-19 experience cardiac injury. Patients with COVID-19 admitted to the intensive care unit (ICU) with acute myocardial injury showed increased high-sensitivity troponin levels. Abnormal troponin levels may indicate myocardial injury and are commonly associated with COVID-19. Methods We conducted a retrospective observational study of 44 patients with severe COVID-19 in ICU during the second wave. The primary end point of our retrospective study was 28-day mortality, and the time of ICU admission was designated as day 0. We extracted and analyzed cardiac biomarkers, such as creatine kinase (CK), creatine kinase-MB (CK-MB), B-type natriuretic peptide (BNP), and high-sensitivity cardiac troponin I (hs-cTnI), and various inflammatory markers such as C-reactive protein (CRP) level, interleukin 6 (IL-6), d-dimer, ferritin, lactate dehydrogenase, IL-6, and procalcitonin in patients with severe COVID-19 at ICU admission and 72 hours after ICU admission from our electronic medical record system. Results The best cutoff of BNP were 326.8 and 398.5 pg/mL, CK were 195.95 and 180.12 U/L, CK-MB were 112.10 and 108.5 U/L, and hs-cTnI were 0.035 and 0.025 ng/mL, at ICU admission and 72 hours after ICU admission for predicting 28-day mortality among nonsurvivors. Conclusion In patients with severe COVID-19, CK and hs-cTnI may be considered effective and valuable predictive cardiac biomarkers among nonsurvivors and predict poor prognosis.

Determinants of stroke in patients with rhino cerebral mucormycosis seen during the second wave of COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Rhino cerebral mucormycosis is an uncommon opportunistic infection of the nasal sinuses and brain, and a group of saprophytic fungi causes it. During the second wave of COVID-19, India witnessed an unprecedented number of patients with rhino cerebral mucormycosis. Invasion of the cavernous sinus and occlusion of the internal carotid artery in many cases resulted in a stroke. The study aimed to assess the clinical and neuroimaging predictors of stroke in patients with rhino cerebral mucormycosis. We also evaluated the predictors of death in these patients at 90 days. METHODS: A prospective study was performed at a tertiary care centre in India between July 2021 and September 2021. We enrolled consecutive microbiologically confirmed patients of rhino cerebral mucormycosis. All patients underwent neuroimaging of the brain. Treatment comprised of anti-fungal drugs and endoscopic nasal/sinus debridement. We followed the patients for 90 days and assessed the predictors of stroke and mortality RESULTS: Forty-four patients with rhino cerebral mucormycosis were enrolled. At inclusion, in 24 patients, the RT-PCR test for SARS-COV-2 was negative. Diabetes mellitus was the most frequent (72.7 %) underlying risk factor; in most, diabetes mellitus was recently discovered. At inclusion or subsequent follow-up, stroke was seen in 11 (25 %) patients. Only seven patients had hemiparesis. Imaging revealed internal carotid artery occlusion in 17 (38.6 %) patients. Hypertension, corticosteroid use, and cavernous sinus thrombosis were independent predictors of stroke. Nine (20.5 %) died during follow-up, and stroke was an independent predictor of death. CONCLUSION: Stroke indicated poor prognosis among rhino cerebral mucormycosis patients encountered during the second wave of the COVID-19 epidemic.

Does Long-term Oxygen Therapy and Noninvasive Ventilation Predispose Rhino-orbital-cerebral Mucormycosis in COVID-19 Patients?

How to cite this article: Kumar A, Kumar A, Kumar N, Kumar A, Sinha C, Singh PK. Does Long-term Oxygen Therapy and Noninvasive Ventilation Predispose Rhino-orbital-cerebral Mucormycosis in COVID-19 Patients? Indian J Crit Care Med 2022;26(9):1063-1064.

An AI-based disease detection and prevention scheme for COVID-19☆

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches. Graphical

An AI-based disease detection and prevention scheme for COVID-19.

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.