How much can we rely on a scrambled COVID-19 vaccine? It is not the only way out!

The COVID-19 pandemic has engulfed the world in the blink of an eye and created a state of panic. Vaccine is one of the suggested ways to counter the menace. Smallpox was an ideal vaccine candidate yet, it took about 182 years to eradicate. Vaccines are associated with incidents of mishap like the 'Cutter incident'. COVID-19 vaccine has a political, commercial angle to it, with at least 62 vaccine candidates in the pipeline. Potential obstacles in development of vaccines are mutations in strains of SARS-CoV-2, lack of details of biological properties of the virus and incomplete information of pathogenicity, lack of suitable animal models. The potential side effects of the vaccine are eosinophilic infiltration, increased infectivity following challenge infections, antibody-dependent enhancement of infections. The general public has to stop anticipating that a vaccine is nearby and believe that for now, vaccine is face mask, hand wash, and social distancing and hope that either the virus goes weak or an effective vaccine arrives rather early than late. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.

Predicting the potential for zoonotic transmission and host associations for novel viruses.

Host-virus associations have co-evolved under ecological and evolutionary selection pressures that shape cross-species transmission and spillover to humans. Observed virus-host associations provide relevant context for newly discovered wildlife viruses to assess knowledge gaps in host-range and estimate pathways for potential human infection. Using models to predict virus-host networks, we predicted the likelihood of humans as hosts for 513 newly discovered viruses detected by large-scale wildlife surveillance at high-risk animal-human interfaces in Africa, Asia, and Latin America. Predictions indicated that novel coronaviruses are likely to infect a greater number of host species than viruses from other families. Our models further characterize novel viruses through prioritization scores and directly inform surveillance targets to identify host ranges for newly discovered viruses.

A QUANTITATIVE APPROACH TO ASSESS THE LIKELIHOOD OF SUPPLY CHAIN SHORTAGES

We define supply chains (SCs) as sequences of processes that link the demand and supply of goods or services within a network. SCs are prone to shortages in delivering their output goals due to several factors such as personnel undersupply, inefficient processes, policy failure, equipment malfunction, natural hazards, pandemic outbreaks, power outages, or economic crises. Recent notable supply-chain failures include the 2021 Texas power crisis, personal protection equipment shortages during the COVID-19 pandemic, and regional or global food chain shortages. The consequences of such shortages can range from negligible to devastating. The Texas power crisis resulted in the death of 70 people and left approximately 4.5 billion homes and businesses without power for multiple days. In this paper, we presented a methodology to quantify the failure probability of the throughput of a supply chain. We divided the methodology into two major categories of steps. In the first step, we converted the given or assumed supply chain data into fault trees and quantify them. In the second step, we iterated the quantification of the fault tree to build a supply chain shortage risk profile. We introduced the notion of success criteria for the output from a facility, based on which we included or excluded the facility for quantification. With the inclusion of relevant field data, we believe that our methodology can enable the stakeholders in the supply-chain decision-making process to detect vulnerable facilities and risk-inform prevention and mitigation actions. Applications for this methodology can include construction, inventory stocking, assessing manufacturing quantities, policy changes, personnel allocation, and financial investment for critical industries such as nuclear, pharmaceutical, aviation, etc. Copyright © 2021 by ASME

Does PCR Cycle Threshold for SARS-CoV-2 have Association with ABO Blood Group Types?

Introduction: Corona Virus Disease-19 (COVID-19) caused by the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) has led to a global pandemic. Currently, the standard method for the diagnosis of COVID-19 is the detection of SARS-CoV-2 by Reverse Transcriptase Quantitative Polymerase Chain Reaction (RT-qPCR). Lower Cycle threshold (Ct) value of PCR is found to be associated with increased probability of progression to severe disease and infectivity. Blood group A individuals have been found to be more susceptible to infection by SARS-CoV-2 while that of O blood group have lower risk of infection. There has been growing scientific interest to obtain data which may characterise the susceptibility to COVID-19 infection and determine the risk factors which may be associated with progression and severity of the disease. Aim: To study the association of RT-qPCR Ct values for SARS-CoV-2 with ABO blood group types. Materials and Methods: A prospective observational study was conducted at tertiary care centre in western Maharashtra, India where in a total of 116 symptomatic admitted patients between mid-July to mid August 2020 and was found to be positive for SARS-CoV-2 by RT-qPCR were enrolled in the study. Ct values for pan-Sarbecovirus E-gene and SARS-CoV-2 specific RdRP gene were recorded for each patient. In addition, all the patients were typed for ABO blood grouping. Distribution of different ABO blood group types in SARS-CoV-2 positive patients were analysed and was compared with blood groups in 882 non-COVID-19 blood donors comprising control group. Ct values for E and RdRP gene were also analysed in respect to different age group, gender and blood group types. Blood group records of voluntary healthy blood donors reporting at our centre between September to November 2019 were obtained. Results: Of the 116 patients, 33 (28.45%) comprised of type A, 34 (29.31%) of type B, 41 (35.34%) were of type O and remaining 8 (6.90%) had AB blood group type. Compared to the control group, no significant difference was noted in the distribution of ABO blood types in SARS-CoV-2 positive patients. Also, no statistical significance was found in the Ct values with respect to age, gender and different ABO blood group types. Conclusion: RT-qPCR Ct values for SARS-CoV-2 infection have no association with ABO blood group types.