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COVID-19 pandemic super-spreaders

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An illustration on how super-spreaders impact in CoVid-19

2020 coronavirus pandemic super-spreaders are event or individuals infected with Coronavirus disease 2019 who can maximize their impact on other individuals.[1] The term "super-spreaders" was coined by Lloyd Smith in 2005, where a small percentage of individuals controls the transmission of the infection in a large susceptible population resulting from interaction with others.[2] Super-spreaders are of particular concern to public health authorities while dealing with the pandemic. The criteria for identifying super-spreaders are complicated with some related to biology. A person may get infected with a strain of a virus that can stay in the hosts body for longer duration and spreads easily or one having a weak immune system which allows the infection to spread for longer periods.[3] An observation made during the 2003 SARS outbreak in Hong Kong noted that a person suffering from another disease could also become a super-spreader. The main objectives of public health authorities in such cases during a pandemic is to identify a few percentage of individuals, who could become probable super-spreaders and control the infections in the rest of the population.[4]

Identifying super-spreaders[edit]

There are evidence-based studies conducted by scientists that identify and define the event or an individual as a super-spreader. The study performed by Woolhouse et al. suggests 20% of infected individuals are capable of spreading the infections to at least 80% of the population.[5] To identify the severity of the outbreak, a mathematical function known as the basic reproduction number represented by R0 is used. It is a variable which indicates the probable number of infections a person could spread while interacting with the rest of the population. There are mainly three scenarios in the spread or decline of infectious diseases:

  • If R0 is less than 1, each host is capable of infecting less than one new infection among the contact and the disease eventually dies out.
  • If R0 is equal to 1, each host is capable of infecting one more from the population. In such cases, the disease persists with stable numbers but there is no outbreak.
  • If R0 is more than 1, each host is capable of infecting more than one individual. In such cases, the prevalence of the disease increases and an epidemic may occur.[6]

For example, some studies conducted during the SARS outbreak, though the Reproduction ratio i.e. (R0) for the same was at 2.75; however, it was estimated that super-spreaders could infect more than 10 individuals from a susceptible population with various means of contact, even though the super-spreader may be asymptomatic.[7][8] As most of the studies conducted have considered all individuals having same transmission rate and have assumed of homogeneous mixing among populations, thus arriving at a standardized form of R0;[9] However, (Stien, 2011) argues most studies do not take in to account the diverse characterstics of transmission rate among different hosts of the virus, which would enable public health officials to identify the super-spreading events and control the infection.[10]

Many scientific organizations have estimated R0 for Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. The Imperial College group announced an R0 ranging from 1.5 to 3.5, whereas The Lancet estimated it to be around 1.6 to 2.6 before travel restrictions were imposed in China in January 2020.[11]

Effects of COVID-19 super-spreaders[edit]

As the main cause of transmission of this virus is contact between person to persons. Super-spreaders are of major concern since the virus from the exhaled droplets of a person infected can remain on the surface for several days.[12][13] Studies conducted during the SARS pandemic showed that when there were no super-spreaders, most individuals infected very few secondary contacts.[2] However, when a microbe infects a patient its remains contained within the host, but when it finds a super-spreader, it infect many individuals.[14] Arriving at an accurate R0 is difficult for various reasons, such as the properties of the pathogen-like basic life of infection and asymptomatic cases."Eisenberg 2020" /> Even though R0 for COVID-19 ranges from 1.0 to 3.0, a super-spreader could potentially infect 10 or more people they come in contact with. Amajor issue is dealing with asymptomatic individuals infected with COVID-19, as they may unintentionally transmit the disease to many people. It is estimated that some asymptomatic individuals have infected a sizable population. It is a well-known fact that, most of the current infections of COVID-19 are travel-related and large outbreaks result primarily within health-care settings and closed community gatherings.[citation needed]

Information on the CoVid-19 and its impact are reviewed and updated regularly as we write. Unrecognized cases and misdiagnoses are some common causes for super-spreaders.[15] Voluntary disclosure of the travel and contact history from the infected patients and active surveillance of the contacts is essential to negate the possibility of few becoming super-spreaders.[16] However, super-spreading is a normal feature of disease spread.[15]

See also[edit]

References[edit]

  1. Madotto, Andrea; Liu, Jiming (2016). "Super-Spreader Identification Using Meta-Centrality". Scientific Reports. 6 (1): 38994. Bibcode:2016NatSR...638994M. doi:10.1038/srep38994. ISSN 2045-2322. PMC 5180094. PMID 28008949.
  2. 2.0 2.1 Stein, Richard (August 2011). "Super-spreaders in infectious diseases". International Journal for Infectious Diseases. 15 (8): e510–e513. doi:10.1016/j.ijid.2010.06.020. PMC 7110524 Check |pmc= value (help). PMID 21737332.
  3. "A coronavirus 'super spreader' was identified—what that means and what you need to know". XNBC. 11 February 2020. Retrieved 4 April 2020.
  4. Stein, Eichard (August 2011). "Super-spreaders in infectious diseases". International Journal for Infectious Diseases. 15 (8): e510–3. doi:10.1016/j.ijid.2010.06.020. PMC 7110524 Check |pmc= value (help). PMID 21737332.
  5. M. E. J. Woolhouse; C. Dye; J.-F. Etard; T. Smith; J. D. Charlwood; G. P. Garnett; P. Hagan; J. L. K. Hii; P. D. Ndhlovu; R. J. Quinnell; C. H. Watts; S. K. Chandiwana; R. M. Anderson (7 January 1997). "Heterogeneities in the transmission of infectious agents: Implications for the design of control programs". PNAS. 94 (1): 338–342. Bibcode:1997PNAS...94..338W. doi:10.1073/pnas.94.1.338. PMC 19338. PMID 8990210.
  6. "What is R0?: Gauging Contagious Infections". Healthline. June 2016. Retrieved 4 April 2020. Unknown parameter |url-status= ignored (help)
  7. Eisenberg, Joseph (17 March 2020). "How Scientists Quantify the Intensity of an Outbreak Like COVID-19". labblog.uofmhealth.org. Unknown parameter |url-status= ignored (help)
  8. Stein, Richard (August 2011). "Super-spreaders in infectious diseases". International Journal of Infectious Diseases. 15 (8): e510–3. doi:10.1016/j.ijid.2010.06.020. PMC 7110524 Check |pmc= value (help). PMID 21737332.
  9. RodrÍguez, D (2001). "Models of Infectious Diseases in Spatially Heterogeneous Environments". Bulletin of Mathematical Biology. 63 (3): 547–571. doi:10.1006/bulm.2001.0231. ISSN 0092-8240.
  10. Stein, Richard A. (2011). "Super-spreaders in infectious diseases". International Journal of Infectious Diseases. 15 (8): e510–e513. doi:10.1016/j.ijid.2010.06.020. ISSN 1201-9712.
  11. Adam j.Kucharski; Timothy W.Russel; Charlie Diamond; Yang liu; John Edmunds; Sebastian Funk (March 2020). "Early dynamics of transmission and control of COVID-19: a mathematical modelling study". The Lancet.
  12. Alberto Matteeli; Nuccia Saleri (2008). Travel Medicine. Search this book on
  13. Alberto Matteeli; Nuccia Saleri. "Respiratory Diseases". ScienceDirect. Unknown parameter |url-status= ignored (help)
  14. Ryo Fujie; Takashi Odagaki (February 2007). "Effects of Superspreaders in spread of epidemic". Physica A: Statistical Mechanics and Its Applications. 374 (2): 843–852. Bibcode:2007PhyA..374..843F. doi:10.1016/j.physa.2006.08.050.
  15. 15.0 15.1 J. O. Lloyd Smith; S. J. Schreiber; P. .E Kopp; W. M. Getz (November 2005). "Superspreading and the effect of individual variation on disease emergence". Nature. 438 (7066): 355–359. Bibcode:2005Natur.438..355L. doi:10.1038/nature04153. PMC 7094981 Check |pmc= value (help). PMID 16292310.
  16. Allen C Cheng; Deborah A Williamson (March 2020). "An outbreak of COVID-19 caused by a new Coronavirus: What we know so far" (PDF). Insightplus. Unknown parameter |url-status= ignored (help)

Further reading[edit]


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