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The Coronavirus (COVID-19) Outbreak and Data-driven Healthcare: A Biomedical Informatics Perspective

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The ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has led to more than 80,000 confirmed cases and nearly 3000 deaths worldwide since December 2019. There is a race in the biomedical research community to publish findings on a wide spectrum of topics, from pathogenicity, viral genome characterization, genetic epidemiology, disease management, treatment, to drug and vaccine development. I will review literature primarily from the epidemiology, genomics, computational biology, and translational bioinformatics perspectives to help us understand the basic biomedical research questions related to the COVID-19 outbreak. These questions include: what is a coronavirus, how the viral genome is organized, how it compares with SARS, what biochemical and genomic characteristics that it has to make it so virulent, and what genomics/informatics/drug discovery opportunities there are. The rapid data collection, analysis, publication, healthcare intervention, and drug development presents a promising new model for “data-driven healthcare” in response to future major disease outbreak events.

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The Coronavirus (COVID-19) Outbreak and Data-driven Healthcare: A Biomedical Informatics Perspective

  1. 1. The Coronavirus (COVID-19) Outbreak and Data-driven Healthcare: A Biomedical Informatics Perspective Jake Y. Chen, PhD, FACMI Professor & Chief Bioinformatics Officer, Informatics Institute The University of Alabama at Birmingham, USA jakechen@uab.edu | bio.informatics.uab.edu March 10th 2020
  2. 2. Outline • Epidemiology of Coronavirus Disease 2019 (COVID-19) • Virus classification, comparison, and evolution: sequence analysis • Virulent proteins: sequences  structures  functions • Virus infection: Host cell-specific functional genomics analysis • A data-driven healthcare model for pandemic diseases 2
  3. 3. COVID-19: A worldwide pandemic? 3 COVID-19 SARS (2003) Casecount Sources: China CDC, European Centre of Disease Prevention
  4. 4. 4
  5. 5. Should we be concerned with COVID-19? Pandemic Name Year Strain Approximate Number of Deaths R0 Case Fatality Rate (CFR) Spanish flu 1918–1920 H1N1 40–50 million 1.5-1.8 2-3% Asian flu 1957–1958 H2N2 1–2 million 1.5 <0.2% Hong Kong flu 1968–1970 H3N2 0.5–2 million 1.3-1.6 <0.2% Swine flu 2009–2010 H1N1 Up to 575,000 1.4-1.6 <0.08% Adapted from: Pathogens. 2016 Dec; 5(4): 66. The Spanish flu pandemic of 1918, the deadliest in history, infected ~500 million (30%) people worldwide and killed ~40-50 million (including ~675,000 Americans) people.
  6. 6. R0: The number of people that one sick person will infect on average 6 COVID-19 (2-2.5) R0 = p x c x D Probability of transmission per contact, e.g., 0.2 Contacts per unit time, e.g., 5 persons/day Duration of infectiousness, e.g., 10 days Cruise ship Prince Diamond R0 = 2.28; China R0 = 2-3.5
  7. 7. Fatality rate vs. transmission rate (R0) of different viruses Comparing COVID-19 with other infectious disease outbreaks “How Bad Will the Coronavirus Outbreak Get?” By Knvul Sheikh, et al. New York Times (Feb. 28, 2020) Fatalityrate R0
  8. 8. COVID-19 case fatality rate and risk factors 2.3% overall; >49% for critically ill patients; ~0.2% for adult patients in Zhejiang Source: China CDC Weekly, Vol. 2, No. 8, pp 113-122 Data: n=44,672 confirmed COVID-19 in China (as of 02/11/2020)
  9. 9. Typical Patterns of CoV Transmissions: Inter-species and Intra-species confirmed transmission Trends in Microbiology, June 2016, Vol. 24, No. 6 495 suspected transmission.
  10. 10. COVID-19 transmission paths: person-to-person P1 P2 P3 P4 P5 65*,#,F,R 66*,#,F,R 37 #,F,R 36F,R 10 ↑,R * Body temperature > 37C # Cough F Fever ↑ Alkaline phosphatase ↑ R RT-PCR test positive showing multifocal ground-glass changes in the lungs of P1 (A), P2 (B), P3 (C), and P5 (D) Nanopore WGS In Wuhan thoracic CT scans The Lancet Vol. 395, Issue 10223, 15–21 February 2020, Pages 514-523 Infected relatives Two non-synonymous mutations found 12/29/2019 1/5/2019 Mother of P4 infected in Shenzhen
  11. 11. Disease Severity Before Disinfecting After Disinfecting Moderate Yes None Moderate Yes None Mild Yes None Contamination through air, surface, and shoes possible Patient A Patient B Patient C Control: - Not in staff’s personal protective equipment except for shoes Patient C JAMA. Published online March 04, 2020. doi:10.1001/jama.2020.3227 Not detected Detected
  12. 12. Pattern of disease progression for COVID-19 in China 12 the relative size of the boxes for disease severity and outcome reflect the proportion of cases reported as of 20 February 2020. The size of the arrows indicates the proportion of cases who recovered or died. Disease definitions are described above. Moderate cases have a mild form of pneumonia. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) , Feb 20th 2020
  13. 13. Susceptible-Exposed-Infectious-Recovered (SEIR) Modeling of COVID-19 early epidemic in Wuhan 13 Susceptible (S): unexposed to the pathogen Exposed (E): infected but not yet infectious Infectious (I): infected and infectious) Recovered (R): recovered from infection and acquired lifelong immunity) Int. J. Environ. Res. Public Health 2016, 13, 253
  14. 14. SEIR model simulations shows Wuhan has to be quarantined to curb COVID-19 exponential growth 14 Inter-regiontravelrestrictions Intra-region quarantine The Lancet, Vol 395, No. 10225, 29 February–6 March 2020, Pages e41
  15. 15. Major intervention measures implemented in China and COVID-19 containment results 15
  16. 16. COVID-19 Trend and Prof. Jake Chen’s Predictions (jakechen@uab.edu) 16 China Other countries 10 100 1k 10k 100k ~1 month delay Jake’s prediction (if contained): • 3/20: 87k • 4/1: 130-170k • 5/1: ~300k • June: season ends Total affected: • <500K worldwide excluding China Total deaths: • <15k worldwide excluding China Warm weather appears to curb transmission Analysis Done on March 10th, 2020; Data source: JHU
  17. 17. Outline • Epidemiology of Coronavirus Disease 2019 (COVID-19) • Virus classification, comparison, and evolution: sequence analysis • Virulent proteins: sequences  structures  functions • Virus infection: Host cell-specific functional genomics analysis • A data-driven healthcare model for pandemic diseases 17
  18. 18. Coronaviruses (CoV) prior to arrival of SARS-nCoV-2 • Classified into four groups: • alpha-CoV, beta-CoV (A,B,C,D), gamma- CoV, and delta-CoV • Six known CoVs to infect humans: • HCoV-229E (229E) • HCoV-OC43 (OC43) • SARS-CoV • HCoV-NL63 (NL63) • HCoV-HKU1 (HKU1) • Middle East respiratory syndrome coronavirus (MERS-CoV) In Trends in Microbiology, Vol 24, No. 6, June 2016, pp 490-502
  19. 19. CoV evolves through mutation and recombination • Mutation rates (rate measured by per year per site) • ssRNA virus substitution rate in general: ~ 10-4 • Whole-genome CoV substitution rate: ~ 0.8-2.4 x 10-3 • S gene substitution overall: ~3-6 x 10-4 • S Gene RBD binding site : ~3-6 x 10-3 • Recombination events • Homologous recombination • “Template switching” during viral replication step • Breakpoints tend to random • Major driving force for OC43 five subtypes • Intra-species variability • Inter-species “host jump” • Novel virus derivation In Trends in Microbiology, Vol 24, No. 6, June 2016, pp 490-502
  20. 20. Phylogenetic analysis of the COVID-19 virus and its closely related reference genomes 20 Note: COVID-19 virus is referred to as 2019-nCoV in the figure, the interim virus name WHO announced early in the outbreak.
  21. 21. The SARS-nCoV-2 Genome Structure SARS-nCoV-2 (IVDC-HB-01/2019|EPI_ISL-402119) 16 nonstructural proteins (nsp1 through nsp16), encoded by ORF1a/b gene Nat Rev Drug Discov 15, 327–347 (2016)
  22. 22. Genome comparisons reveal that SARS-nCoV-2 is closer to SARS-like beta-CoV than to SARS Lancet, Vol. 395, Issue 10224, 22–28 February 2020, pp. 565-574 Genome similarity: Beta-CoV (87.6%), SARS (79%)
  23. 23. However, its spike protein S Receptor Binding Domain (RBD) is closer to SARS than to SARS-like beta-CoV Lancet, Vol. 395, Issue 10224, 22–28 February 2020, pp. 565-574
  24. 24. SARS-nCov-2 is a novel beta-CoV but with a SARS- like S protein Receptor-binding domain (RBD) ACE2 Five key residues responsible for the binding of the SARS-CoV receptor-binding domain to the ACE2 receptor. Phylogeny of 2019-nCoV (SARS-nCoV-2) is shown as a novel betacoronavirus from the subgenus Sarbecovirus (Clade 2). However, phylogenetic analysis of the RBD of S protein (S1 domain) from various betacoronaviruses shows close relationship with SARS-CoV. Lancet, Vol. 395, Issue 10224, 22–28 February 2020, pp. 565-574
  25. 25. 25Nature (2020). 03 Feb 2020. https://doi.org/10.1038/s41586-020-2012-7 Genome comparisons with bat beta-CoV and SARS (including RaTG13)
  26. 26. Genomic comparison of SARS-nCoV-2 with SARS- CoVs and bat beta-CoVs (SARSr-CoVs) 26Nature (2020). https://doi.org/10.1038/s41586-020-2012-7 SARSBeta-CoV
  27. 27. Pangolin as the potential host? 27 X National Science Review, nwaa036, https://doi.org/10.1093/nsr/nwaa036
  28. 28. Patient zero and evolving paths of SARS-CoV-2 28 Yu et al.(2020) ChinaXiv:202002.00033
  29. 29. H3 (outside Hua Nan Seafood market) seems to be Patient Zero 29 Yu et al.(2020) ChinaXiv:202002.00033
  30. 30. Outline • Epidemiology of Coronavirus Disease 2019 (COVID-19) • Virus classification, comparison, and evolution: sequence analysis • Virulent proteins: sequences  structures  functions • Virus infection: Host cell-specific functional genomics analysis • A data-driven healthcare model for pandemic diseases 30
  31. 31. SARS-nCoV-2 acquired PRRA, a peculiar sequence, at the S1/S2 cleavage site in the S-protein 31 Red asterisks indicate the presence of a canonical furin-like cleavage motif at the S1/S2 site, which is implicated for the viral life cycle and pathogenicity.
  32. 32. SARS-ncov-2 Spike (S): sequence analysis 32 In Antiviral Research. Vol 176, April 2020, 104742 Insertion of furin like cleavage site (R/K)-(2X)n-(R/K)↓ motif
  33. 33. RaTG13 doesn’t contain PRRA furin site, either! 33 Match Score Expect Method Identities Positives Gaps 2565 bits(6649) 0.0 Composition al matrix adjust. 1240/1273( 97%) 1252/1273( 98%) 4/1273(0%) spike glycoprotein [Wuhan seafood market pneumonia virus] QHR63250.1 | QHR63260.1 | QHR63270.1 | QHR63280.1 | QHR63290.1 S1/S2
  34. 34. S Protein Structure explains strong affinity binding to host ACE2 receptors through “up” RBD domain 34In Science 19 Feb 2020: eabb2507 In order to engage a host- cell receptor, the receptor- binding domain (RBD) of S1 undergoes hinge-like conformational movements that transiently hide (down) or expose (up) the determinants of receptor binding.
  35. 35. S protein variants mapped to the protein structure SARS-nCoV-2/RaTG13 variants vs. 61 SARS-nCoV-2 isolate variants 35 In Science 19 Feb 2020: eabb2507 RaTG13 S variant residues SARS-nCoV-2 variant residues (n=61) SARS-nCoV-2 isolates (n=61) SARS-nCoV-2 vs. RaTG13
  36. 36. The PRRA furin motif is likely acquired through viral recombination event! 36 RmYN02 Yes RmYN01 No BioRxiv, Mar 6 2020 https://doi.org/10.1101/2020.03.02.974139
  37. 37. Understanding Protein Structure  Drug Repositioning 37 G Li & ED Clercq (2020) in Nature Review Drug Discovery Remdesivir is an adenosine analogue, which incorporates into nascent viral RNA chains and results in pre-mature termination. “Remdesivir treated successfully the pneumonia viral infection of the first US patient.” N Engl J Med. 2020;10.1056/NEJMoa2001191.
  38. 38. Outline • Epidemiology of Coronavirus Disease 2019 (COVID-19) • Virus classification, comparison, and evolution: sequence analysis • Virulent proteins: sequences  structures  functions • Virus infection: Host cell-specific functional genomics analysis • A data-driven healthcare model for pandemic diseases 38
  39. 39. COVID-19 causes primary damage in lung 39 https://healthjade.com/lungs/
  40. 40. Source: gtexportal.org Lung expression ACE2 not found. Why?
  41. 41. Lung Protein Expression of ACE2 also not found in Lung tissues. Why? 41 Sources: GTEx Portal (left); The Human Protein Atlas Portal (right)
  42. 42. Damaged lung air sacs (Alveoli) consists of two cell types 42 www.cancer.org • Cube-shaped • Secrete a liquid “surfactant” to hold air sacs from collapsing  “keep breathing” • Function as lung stem cells: damaged alveoli cells  self-renewed AT2 cells or reinvented AT1 cells Alveolar type 2 (AT2) cells Alveolar type 1 (AT1) cells • long and thin • Allow O2 to enter and CO2 to exit. • Helps us “take a breath”
  43. 43. scRNA-seq experiments show that ACE2 are uniquely expressed in AT2 lung cells 43 Single-cell analysis of normal human lung Cellular cluster map of the Asian male. Asian males also has a much higher ACE2-expressing cell ratio (2.5%) than white and African Americans (0.47%) bioRxiv 2020.01.26.919985; doi: https://doi.org/10.1101/2020.01.26.919985
  44. 44. GO Analysis of ACE2-expression in AT2 cells suggests SARS-nCoV-2 attack sites in lung • “positive regulation of viral process” (P value=0.001), • “viral life cycle” (P value=0.005), • “virion assembly” (P value=0.03) • “positive regulation of viral genome replication” (P value=0.04). 44 SLC1A5, CXADR, CAV2, NUP98, CTBP2, GSN,HSPA1B,STOM, RAB1B, HACD3, ITGB6, IST1,NUCKS1,TRIM27, APOE, SMARCB1, UBP1, CHMP1A, NUP160, HSPA8, DAG1, STAU1, ICAM1, CHMP5, DEK, VPS37B, EGFR, CCNK, PPIA, IFITM3, PPIB, TMPRSS2, UBC, LAMP1 and CHMP3 DEG genes in the AT2 Cell Cluster Gene Ontology (GO) analysis of AT2-cell DEGs: bioRxiv 2020.01.26.919985; doi: https://doi.org/10.1101/2020.01.26.919985 SARS-nCov-2 has cleverly evolved to hijack lung AT2 cells for its reproduction and transmission.
  45. 45. Outline • Epidemiology of Coronavirus Disease 2019 (COVID-19) • Virus classification, comparison, and evolution: sequence analysis • Virulent proteins: sequences  structures  functions • Virus infection: Host cell-specific functional genomics analysis • A data-driven healthcare model for pandemic diseases 45
  46. 46. Moving from diagnosis to new therapeutics and vaccines for ongoing research 46 Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) , Feb 20th 2020
  47. 47. Data-driven healthcare for rapid response to future pandemic Clinical Data from Healthcare Genome and Imaging Data from Labs PopulationHealth Informatics Clinical Informatics Translational Bioinformatics Epidemiology Data from Surveillance Network Drug development Health Policy recommendations Diagnostic methods Clinical trials Research publications Social media news International cooperation Accelerations inInformation Exchanges & Predictive Engines
  48. 48. 48
  49. 49. 50 GenBank (51 complete genomes; 03/06/2020) CNCB (200 complete genomes; 03/06/2020)
  50. 50. A growing list of publishers with an rapid publication and open access model to SARS-nCoV-2
  51. 51. 166 (~25%) Papers (bioRxiv/ChinaXiv/arXiv) vs. 534 (~75%) papers (PubMed) 52
  52. 52. Robot-nurse and AI radiologist 53
  53. 53. Biopharma products in development for COVID-19 Favipiravir Fujifilm Holdings Corp.; Fujifilm Toyama Chemical Co. Ltd.; Medivector Inc.; Zhejiang Hisun Pharmaceutical Co. Ltd. Launched Rintatolimod Aim Immunotech Inc. (formerly Hemispherx Biopharma Inc.); GP Pharm SA; Goethe University Frankfurt Launched ASC-09 + ritonavir (oral tablet) Ascletis Pharma Inc. Phase III Remdesivir Gilead Sciences Inc. Phase III BDB-1 Beijing Defengrei Biotechnology Co., a subsidiary of Staidson (Beijing) Pharmaceutical Co., Ltd. Phase II Brilacidin Innovation Pharmaceuticals Inc. Phase II INO-4800 Beijing Advaccine Biotechnology Co Ltd; GeneOne Life Science Inc; Inovio Pharmaceuticals Inc Preclinical CYNK-001 Celularity Inc.; Sorrento Therapeutics Inc. Preclinical SARS-CoV-2 vaccine Chongqing Zhifei Biological Products Co. Ltd.; Institute of Microbiology, Chines Academy of Sciences Preclinical Matrix-M adjuvant and COVID-19 vaccine Novavax Inc. Preclinical S-protein/ACE2 targeted prophylactic polypeptide (inhalant) Sichuan Kelun Pharmaceutical Co. Ltd. Preclinical ChAdOx1 nCoV-19 The Jenner Institute Preclinical Umbilical cord-derived mesenchymal stem cells (intravenous) Wuhan Hamilton Biotechnology Co. Ltd. Preclinical Dendritic cell vaccine Beijing Dingcheng Taiyuan Biotechnology; Betta Pharmaceuticals Co. Ltd. Precision-driven treatment Beroni Group; Tianjin University Recombinant subunit-trimer vaccine Clover Biopharmaceuticals Inc. Live-attenuated vaccine Codagenix Inc.; Serum Institute of India Ltd. SARS-CoV-2 mRNA vaccine Curevac AG Monoclonal antibodies and vaccine Dyadic International Inc.; The Israel Institute for Biological Research SARS-CoV-2 vaccine (injectable) Fudan University; ID Pharma Co. Ltd. Z-VacciRNA Guanhao Biotech Co. Ltd.; Zy Therapeutics Inc. Antibodies Immunoprecise Antibodies Ltd. SARS-CoV-2 vaccine Medigen Biotechnology Corp.; National Institutes of Health mRNA-1273 Moderna Therapeutics Inc. Anti-SARS-CoV-2 program Nanoviricides Inc. Antibodies Regeneron Pharmaceuticals Inc. Protein subunit vaccine Sanofi Pasteur Coronavirus vaccine (oral, enteric-coated tablet) Vaxart Inc. Protein subunit vaccine (molecular clamp) University of Queensland SARS-CoV-2 targeting human monoclonal antibodies Vir Biotechnology Inc. In Development (N=13) In Discovery (N=17) Source: Cortellis and BioWorld (3/5/2020)
  54. 54. SARS-nCoV-2 drug repositioning using AI 55 Baricitinib is an approved drug for the treatment of rheumatoid arthritis. Lancet. Vol 395, No. 10223, PE30-E31, Feb 15, 2020 Source: EIHealth by Huawei Repositioning candidate for SARS-nCoV-2 by docking RdRp
  55. 55. At UAB, U-BRITE COVID-19 portal has been planned! To encourage interdisciplinary data-driven team science 56
  56. 56. Final thoughts • COVID-19 is a serious infectious disease with R0 > 2 and case fatality rate < 3%, likely to infect ~500k population even with effective control. • Genome sequencing and bioinformatics sequence analysis are essential to helping us find SARS-nCoV-2 classification, origin, and evolution paths. • Population health informatics guides policy makers on quarantine strategies, travel restrictions, etc. • Clinical informatics helps us identify risk factors through EMR • Information exchanges and data sharing helps multi-disciplinary international cooperation • AI accelerate healthcare management and rapid drug development 57
  57. 57. Acknowledgment U54TR002731 U01CA223976 R01AI134023 R01AR073850 http://bio.informatics.uab.edu/

Editor's Notes

  • Genomic haplotypes of SARS-CoV-2 changes between the collection dates of samples. The confirmed samples from the Hua Nan market are indicated using red circles, and a confirmed sample with no link to the market is indicated using a blue circle.
  • Schematic representation of the human 2019-nCoV S-protein with a focus on the putative maturation sites. The domains were previously characterized in SARS-CoV and MERS-CoV: Signal peptide (SP), N-terminal domain (NTD), receptor-binding domain (RBD), fusion peptide (FP), internal fusion peptide (IFP), heptad repeat 1/2 (HR1/2), and the transmembrane domain (TM). The SP, S1↓S2 and S2′ cleavage sites are indicated by arrows. The sequence of different CoV S1/S2 and S2′ cleavage sites were aligned using Multalin webserver (http://multalin.toulouse.inra.fr/multalin/) with manual adjustments and the figure prepared using ESPript 3 (http://espript.ibcp.fr/ESPript/ESPript/) presenting the secondary structure of SARS-CoV S-protein at the bottom of the alignment (PDB 5X58) (Yuan et al., 2017). Insertion of furin like cleavage site is surrounded by a black frame. Red asterisks indicate the presence of a canonical furin-like cleavage motif at the S1/S2 site. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
  • On 31 January, a paper was released on the bioRxiv preprint platform suggesting similarities between the Sars-CoV-2 virus and HIV. A stream of comments on Twitter and bioRxiv soon followed, questioning the methodology and conclusions, and the paper was withdrawn on 2 February. The withdrawal note stated that the authors “intend to revise it in response to comments received from the research community on their technical approach and their interpretation of the results”.

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    The emergence of Covid-19 is testing the limits of many global systems, and not the least among them is the quality control system for academic preprints.
    On 31 January, a paper was released on the bioRxiv preprint platform suggesting similarities between the Sars-CoV-2 virus and HIV. A stream of comments on Twitter and bioRxiv soon followed, questioning the methodology and conclusions, and the paper was withdrawn on 2 February. The withdrawal note stated that the authors “intend to revise it in response to comments received from the research community on their technical approach and their interpretation of the results”.
    The combination of open digital platforms, the hive mind and a pressing health emergency resulted in an extraordinary situation: authors receiving useful feedback on their work within two days. You could argue that it was a case of community-based peer review coming of age.
    Read more
    Covid-19 coronavirus crisis: university offers itself as online fallback
    After all, in times of crisis, the time it takes to get a paper through traditional peer review can be a significant drag on addressing the issues at hand, even when journals make efforts to speed it up. By contrast, preprint servers allow information – unrestricted by text limits or demands for complete narratives – to be communicated immediately, allowing interested parties to read, analyse and give feedback in real time, unmediated by publishing houses.
    This ability for anyone to share anything and for anybody to comment is a welcome step towards the democratisation of research, and concerns about the provenance of ideas are being reduced by public date-stamping.
    Needless to say, however, there are significant risks associated with such an unregulated process. The Covid-19 case could equally be taken as illustrating the dangers of allowing material into the public domain without third-party screening. Indeed, above the withdrawal notice for the Covid-19 paper is a note stating that “bioRxiv is receiving many new papers on coronavirus 2019-nCoV” and reminding readers that “these are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information.”
    Want to write for THE? Click for more information
    It is accurate to say that community-based review is currently unable to prevent the release of erroneous material into the public domain. Errors are made in data released and in comments given, intentionally or not. But that problem is not exclusive to community-based review; although traditional peer review has checks in place to reduce the risk, it is not foolproof.
    Still, if the benefits of speed and inclusivity offered by community-based review are important to the sector then a solution to the quality-control issue is needed. The current process whereby people report erroneous material to authors or platform hosts, who then have the responsibility and prerogative to withdraw the content, is flawed. It is worth noting that even the biggest, wealthiest social media platforms still struggle with preventing unsuitable (offensive) content from being released. Human moderators are employed, but their capacity is necessarily limited. AI tools are being developed by Instagram and Twitter to detect more than just keywords, and scanning preprints will require similarly sophisticated algorithms.
    Nevertheless, the popularity of preprint platforms, the exchanges they support and the power of the hive mind are providing a window into future possibilities. The Covid-19 case is just one particularly striking example of experts’ willingness to take time out from their own work to comment on preprints despite the absence of any tangible incentives in terms of professional recognition. Imagine what could be achieved if publishers, funders and employers were willing to offer recognition – perhaps by upgrading such contributors from the acknowledgements section to a category of “contributors whose input significantly impacted the work”. Innovation in incentive systems would help to ensure that the “right” people were involved at the right time. Ultimately, it might even make traditional, publisher-administered peer review obsolete.
    As technology continues to evolve rapidly, the tardiness and exclusivity of traditional peer review are becoming ever more inappropriate, particularly for imminent or current crises. There is certainly a place for lengthy and exclusive review, publication and retraction. But this should complement rather than exclude a faster, decentralised process. 


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