Urgent Need for Field Surveys of Coronaviruses in Southeast Asia to Understand the SARS-CoV-2 Phylogeny and Risk Assessment for Future Outbreaks

Seyran, Murat; Hassan, Sk. Sarif; Uversky, Vladimir N.; Choudhury, Pabitra Pal; Uhal, Bruce D.; Lundström, Kenneth; Attrish, Diksha; Rezaei, Nima; Aljabali, Alaa A. A.; Ghosh, Shinjini; Pizzol, Damiano; Adadi, Parise; El-Aziz, Tarek Mohamed Abd; Kandimalla, Ramesh; Tambuwala, Murtaza M.; Lal, Amos; Azad, Gajendra Kumar; Sherchan, Samendra P.; Baetas‐da‐Cruz, Wagner; Palù, Giorgio; Brufsky, Adam

doi:10.3390/biom11030398

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…COVID-19 is neither the first nor the last pandemic that humanity will face. Risks of further pandemic outbreaks of other pathogens exist 4,52 . Until now, most research has focused on the economic impact of public health interventions 53,54 or on improving the accuracy of disease prediction 55,56 , not the feedback between pandemic growth and supply chain disruption.…”

Section: Discussionmentioning

confidence: 99%

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

Ghadami

Drake

et al. 2021

Sci Rep

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The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

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Section: Discussionmentioning

confidence: 99%

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

Ghadami

Drake

et al. 2021

Sci Rep

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“…A total of 78% (2,209/2,820) of the recorded CoV sequences in NCBI were uploaded since 2015, before the COVID-19 pandemic (https://www.ncbi.nlm.nih.gov/nuccore). However, it is challenging to assess the risk of new bat CoV isolates that cause infection or pandemics in human or other mammalian populations (Seyran et al, 2021). Traditional phylogenetic analysis can sufficiently evaluate the cross-species infection risk or any bat CoV (Lima et al, 2013;Seyran et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, it is challenging to assess the risk of new bat CoV isolates that cause infection or pandemics in human or other mammalian populations (Seyran et al, 2021). Traditional phylogenetic analysis can sufficiently evaluate the cross-species infection risk or any bat CoV (Lima et al, 2013;Seyran et al, 2021). More recently, machine learning or deep learning approaches based on big sequencing data have led to remarkable predictions of the host adaptation (Li et al, 2022;Nan et al, 2022), evolution (Hie et al, 2021), transmissibility (Fischhoff et al, 2021), virus-host interaction (Dey et al, 2020), and pathogenicity (Gussow et al, 2020) of SARS-CoV-2 and other viruses (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Tian

Zhang

et al. 2023

Front. Microbiol.

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IntroductionCoronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals.MethodsThe CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs.Results and discussionThe results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation.ConclusionGenomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

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Mental Healthcare in Pediatrics During the COVID-19 Pandemic: A Call for International Public Health Action

Aleebrahim-Dehkordi,

Soveyzi,

Deravi

et al. 2024

Advances in Experimental Medicine and Biology

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Urgent Need for Field Surveys of Coronaviruses in Southeast Asia to Understand the SARS-CoV-2 Phylogeny and Risk Assessment for Future Outbreaks

Abstract: Phylogenetic analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on a single isolate of bat coronaviruses (bat CoVs) which does not adequately represent genetically related coronaviruses (CoVs) [...]

Cited by 4 publications

References 45 publications

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Mental Healthcare in Pediatrics During the COVID-19 Pandemic: A Call for International Public Health Action

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