Journal articles: 'Superspreading'

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Hill, Randal M. "Superspreading." Current Opinion in Colloid & Interface Science 3, no. 3 (June 1998): 247–54. http://dx.doi.org/10.1016/s1359-0294(98)80068-x.

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Althaus, Christian L. "Ebola superspreading." Lancet Infectious Diseases 15, no. 5 (May 2015): 507–8. http://dx.doi.org/10.1016/s1473-3099(15)70135-0.

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Alizon, Samuel. "Superspreading genomes." Science 371, no. 6529 (February 4, 2021): 574–75. http://dx.doi.org/10.1126/science.abg0100.

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Galvani, Alison P., and Robert M. May. "Dimensions of superspreading." Nature 438, no. 7066 (November 2005): 293–95. http://dx.doi.org/10.1038/438293a.

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Kabalnov, A. "Thermodynamics of superspreading." European Physical Journal E 2, no. 3 (July 2000): 255–64. http://dx.doi.org/10.1007/pl00013666.

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Ash, Caroline. "Phylogenetics of superspreading." Science 371, no. 6529 (February 4, 2021): 580.15–582. http://dx.doi.org/10.1126/science.371.6529.580-o.

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Nikolov, A., and D. Wasan. "Superspreading mechanisms: An overview." European Physical Journal Special Topics 197, no. 1 (August 2011): 325–41. http://dx.doi.org/10.1140/epjst/e2011-01476-1.

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Ruckenstein, Eli. "Superspreading: A possible mechanism." Colloids and Surfaces A: Physicochemical and Engineering Aspects 412 (October 2012): 36–37. http://dx.doi.org/10.1016/j.colsurfa.2012.07.011.

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James, Alex, Jonathan W. Pitchford, and Michael J. Plank. "An event-based model of superspreading in epidemics." Proceedings of the Royal Society B: Biological Sciences 274, no. 1610 (December 5, 2006): 741–47. http://dx.doi.org/10.1098/rspb.2006.0219.

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Many recent disease outbreaks (e.g. SARS, foot-and-mouth disease) exhibit superspreading, where relatively few individuals cause a large number of secondary cases. Epidemic models have previously treated this as a demographic phenomenon where each individual has an infectivity allocated at random from some distribution. Here, it is shown that superspreading can also be regarded as being caused by environmental variability, where superspreading events (SSEs) occur as a stochastic consequence of the complex network of interactions made by individuals. This interpretation based on SSEs is compared with data and its efficacy in evaluating epidemic control strategies is discussed.

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Wong, Felix, and James J. Collins. "Evidence that coronavirus superspreading is fat-tailed." Proceedings of the National Academy of Sciences 117, no. 47 (November 2, 2020): 29416–18. http://dx.doi.org/10.1073/pnas.2018490117.

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Superspreaders, infected individuals who result in an outsized number of secondary cases, are believed to underlie a significant fraction of total SARS-CoV-2 transmission. Here, we combine empirical observations of SARS-CoV and SARS-CoV-2 transmission and extreme value statistics to show that the distribution of secondary cases is consistent with being fat-tailed, implying that large superspreading events are extremal, yet probable, occurrences. We integrate these results with interaction-based network models of disease transmission and show that superspreading, when it is fat-tailed, leads to pronounced transmission by increasing dispersion. Our findings indicate that large superspreading events should be the targets of interventions that minimize tail exposure.

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Nikolov, Alex D., Darsh T. Wasan, Anoop Chengara, Kalman Koczo, George A. Policello, and Istvan Kolossvary. "Superspreading driven by Marangoni flow." Advances in Colloid and Interface Science 96, no. 1-3 (February 2002): 325–38. http://dx.doi.org/10.1016/s0001-8686(01)00087-2.

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MALDARELLI, C. "On the microhydrodynamics of superspreading." Journal of Fluid Mechanics 670 (February 22, 2011): 1–4. http://dx.doi.org/10.1017/s0022112010006531.

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Droplets of an aqueous phase placed on a very hydrophobic, waxy surface bead-up rather than spread, forming a sessile drop with a relatively large contact angle at the edge of the drop. Surfactant molecules, when dissolved in the aqueous phase, can facilitate the wetting of an aqueous drop on a hydrophobic surface. One class of surfactants, superwetters, can cause aqueous droplets to move very rapidly over a hydrophobic surface, thereby completely wetting the surface (superspreading). A recent numerical study of the hydrodynamics of superspreading by Karapetsas, Craster & Matar (J. Fluid Mech., this issue, vol. 670, 2011, pp. 5–37) provides a clear explanation of how these surfactants cause such a dramatic change in wetting behaviour. The study shows that large spreading rates occur when the surfactant can transfer directly from the air/aqueous to the aqueous/hydrophobic solid interface at the contact line. This transfer reduces the concentration of surfactant on the fluid interface, which would otherwise be elevated due to the advection accompanying the drop spreading. The reduced concentration creates a Marangoni force along the fluid surface in the direction of spreading, and a concave rim in the vicinity of the contact line with a large dynamic contact angle. Both of these effects act to increase the spreading rate. The molecular structure of the superwetters allows them to assemble on a hydrophobic surface, enabling the direct transfer from the fluid to the solid surface at the contact line.

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Shen, Zhuang, Fang Ning, Weigong Zhou, Xiong He, Changying Lin, Daniel P. Chin, Zonghan Zhu, and Anne Schuchat. "Superspreading SARS Events, Beijing, 2003." Emerging Infectious Diseases 10, no. 2 (February 2004): 256–60. http://dx.doi.org/10.3201/eid1002.030732.

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Theodorakis, Panagiotis E., Erich A. Müller, Richard V. Craster, and Omar K. Matar. "Superspreading: Mechanisms and Molecular Design." Langmuir 31, no. 8 (February 18, 2015): 2304–9. http://dx.doi.org/10.1021/la5044798.

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Venzmer, Joachim. "Superspreading — 20years of physicochemical research." Current Opinion in Colloid & Interface Science 16, no. 4 (August 2011): 335–43. http://dx.doi.org/10.1016/j.cocis.2010.11.006.

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Theodorakis, Panagiotis E., Erich A. Müller, Richard V. Craster, and Omar K. Matar. "Insights into surfactant-assisted superspreading." Current Opinion in Colloid & Interface Science 19, no. 4 (August 2014): 283–89. http://dx.doi.org/10.1016/j.cocis.2014.04.007.

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Nikolov, Alex, and Darsh Wasan. "Current opinion in superspreading mechanisms." Advances in Colloid and Interface Science 222 (August 2015): 517–29. http://dx.doi.org/10.1016/j.cis.2014.03.006.

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Theodorakis, Panagiotis E., Edward R. Smith, Richard V. Craster, Erich A. Müller, and Omar K. Matar. "Molecular Dynamics Simulation of the Superspreading of Surfactant-Laden Droplets. A Review." Fluids 4, no. 4 (October 1, 2019): 176. http://dx.doi.org/10.3390/fluids4040176.

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Superspreading is the rapid and complete spreading of surfactant-laden droplets on hydrophobic substrates. This phenomenon has been studied for many decades by experiment, theory, and simulation, but it has been only recently that molecular-level simulation has provided significant insights into the underlying mechanisms of superspreading thanks to the development of accurate force-fields and the increase of computational capabilities. Here, we review the main advances in this area that have surfaced from Molecular Dynamics simulation of all-atom and coarse-grained models highlighting and contrasting the main results and discussing various elements of the proposed mechanisms for superspreading. We anticipate that this review will stimulate further research on the interpretation of experimental results and the design of surfactants for applications requiring efficient spreading, such as coating technology.

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Pozderac, Calvin, and Brian Skinner. "Superspreading of SARS-CoV-2 in the USA." PLOS ONE 16, no. 3 (March 25, 2021): e0248808. http://dx.doi.org/10.1371/journal.pone.0248808.

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A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, β, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that σβ/μβ ≳ 3.2, where μβ is the mean infectiousness and σβ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.

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Ypma, Rolf J. F., Hester Korthals Altes, Dick van Soolingen, Jacco Wallinga, and W. Marijn van Ballegooijen. "A Sign of Superspreading in Tuberculosis." Epidemiology 24, no. 3 (May 2013): 395–400. http://dx.doi.org/10.1097/ede.0b013e3182878e19.

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Radulovica, Jovana, Khellil Sefianea, and Martin E. R. Shanahanb. "The Role of Diffusion in Superspreading." Journal of Adhesion Science and Technology 25, no. 12 (January 2011): 1361–70. http://dx.doi.org/10.1163/016942411x555944.

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Venzmer, Joachim. "Superspreading – Has the mystery been unraveled?" Advances in Colloid and Interface Science 288 (February 2021): 102343. http://dx.doi.org/10.1016/j.cis.2020.102343.

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Lau, Max S. Y., Benjamin Douglas Dalziel, Sebastian Funk, Amanda McClelland, Amanda Tiffany, Steven Riley, C. Jessica E. Metcalf, and Bryan T. Grenfell. "Spatial and temporal dynamics of superspreading events in the 2014–2015 West Africa Ebola epidemic." Proceedings of the National Academy of Sciences 114, no. 9 (February 13, 2017): 2337–42. http://dx.doi.org/10.1073/pnas.1614595114.

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The unprecedented scale of the Ebola outbreak in Western Africa (2014–2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (∼61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.

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Lau, Max S. Y., Bryan Grenfell, Michael Thomas, Michael Bryan, Kristin Nelson, and Ben Lopman. "Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA." Proceedings of the National Academy of Sciences 117, no. 36 (August 20, 2020): 22430–35. http://dx.doi.org/10.1073/pnas.2011802117.

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It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and superspreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to integrate individual surveillance data with geolocation data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the state of Georgia. First, our results show that the reproductive number reduced to below one in about 2 wk after the shelter-in-place order. Superspreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance toward later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected nonelderly cases (<60 y) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of superspreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of superspreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

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Rosen, Milton J., and Li D. Song. "Superspreading, Skein Wetting, and Dynamic Surface Tension." Langmuir 12, no. 20 (January 1996): 4945–49. http://dx.doi.org/10.1021/la9602731.

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Churaev, N. V., N. E. Esipova, R. M. Hill, V. D. Sobolev, V. M. Starov, and Z. M. Zorin. "The Superspreading Effect of Trisiloxane Surfactant Solutions." Langmuir 17, no. 5 (March 2001): 1338–48. http://dx.doi.org/10.1021/la000789r.

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Oz, Yaron, Ittai Rubinstein, and Muli Safra. "Heterogeneity and superspreading effect on herd immunity." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 3 (March 1, 2021): 033405. http://dx.doi.org/10.1088/1742-5468/abdfd1.

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Kovalchuk, Nina M., Jacques Dunn, Jack Davies, and Mark J. H. Simmons. "Superspreading on Hydrophobic Substrates: Effect of Glycerol Additive." Colloids and Interfaces 3, no. 2 (May 31, 2019): 51. http://dx.doi.org/10.3390/colloids3020051.

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The spreading of solutions of three trisiloxane surfactants on two hydrophobic substrates, polyethylene and polyvinylidenefluoride, was studied with the addition of 0–40 mass % of glycerol. It was found that all the surfactant solutions spread faster than silicone oil of the same viscosity, confirming the existence of a mechanism which accelerates the spreading of the surfactant solutions. For the non-superspreading surfactant, BT-233, addition of glycerol improved the spreading performance on polyvinylidenefluoride and resulted in a transition from partial to complete wetting on polyethylene. The fastest spreading was observed for BT-233 at a concentration of 2.5 g/L, independent of glycerol content. For the superspreading surfactants, BT-240 and BT-278, the concentration at which the fastest spreading occurs systematically increased with concentration of glycerol on both substrates from 1.25 g/L for solutions in water to 10 g/L for solutions in 40% glycerol/water mixture. Thus, the surfactant equilibration rate (and therefore formation of surface tension gradients) and Marangoni flow are important components of a superspreading mechanism. De-wetting of the solutions containing glycerol, once spread on the substrates, resulted in the formation of circular drop patterns. This is in contrast to the solely aqueous solutions where the spread film shrank due to evaporation, without any visible traces being left behind.

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Popa, Alexandra, Jakob-Wendelin Genger, Michael D. Nicholson, Thomas Penz, Daniela Schmid, Stephan W. Aberle, Benedikt Agerer, et al. "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2." Science Translational Medicine 12, no. 573 (November 23, 2020): eabe2555. http://dx.doi.org/10.1126/scitranslmed.abe2555.

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Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria’s well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 103 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.

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Hall, Richard J. "Modeling the Effects of Resource-Driven Immune Defense on Parasite Transmission in Heterogeneous Host Populations." Integrative and Comparative Biology 59, no. 5 (May 24, 2019): 1253–63. http://dx.doi.org/10.1093/icb/icz074.

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Abstract Individuals experience heterogeneous environmental conditions that can affect within-host processes such as immune defense against parasite infection. Variation among individuals in parasite shedding can cause some hosts to contribute disproportionately to population-level transmission, but we currently lack mechanistic theory that predicts when environmental conditions can result in large disease outbreaks through the formation of immunocompromised superspreading individuals. Here, I present a within-host model of a microparasite’s interaction with the immune system that links an individual host’s resource intake to its infectious period. For environmental scenarios driving population-level heterogeneity in resource intake (resource scarcity and resource subsidy relative to baseline availability), I generate a distribution of infectious periods and simulate epidemics on these heterogeneous populations. I find that resource scarcity can result in large epidemics through creation of superspreading individuals, while resource subsidies can reduce or prevent transmission of parasites close to their invasion threshold by homogenizing resource allocation to immune defense. Importantly, failure to account for heterogeneity in competence can result in under-prediction of outbreak size, especially when parasites are close to their invasion threshold. More generally, this framework suggests that differences in conditions experienced by individual hosts can lead to superspreading via differences in resource allocation to immune defense alone, even in the absence of other heterogeneites such as host contacts.

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Sneppen, Kim, Bjarke Frost Nielsen, Robert J. Taylor, and Lone Simonsen. "Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control." Proceedings of the National Academy of Sciences 118, no. 14 (March 19, 2021): e2016623118. http://dx.doi.org/10.1073/pnas.2016623118.

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Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: “close” (a small, unchanging group of mutual contacts as might be found in a household), “regular” (a larger, unchanging group as might be found in a workplace or school), and “random” (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k. We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles’ heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.

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Wei, Hsien-Hung. "Marangoni-enhanced capillary wetting in surfactant-driven superspreading." Journal of Fluid Mechanics 855 (September 14, 2018): 181–209. http://dx.doi.org/10.1017/jfm.2018.626.

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Superspreading is a phenomenon such that a drop of a certain class of surfactant on a substrate can spread with a radius that grows linearly with time much faster than the usual capillary wetting. Its origin, in spite of many efforts, is still not fully understood. Previous modelling and simulation studies (Karapetsas et al. J. Fluid Mech., vol. 670, 2011, pp. 5–37; Theodorakis et al. Langmuir, vol. 31, 2015, pp. 2304–2309) suggest that the transfer of the interfacial surfactant molecules onto the substrate in the vicinity of the contact line plays a crucial role in superspreading. Here, we construct a detailed theory to elaborate on this idea, showing that a rational account for superspreading can be made using a purely hydrodynamic approach without involving a specific surfactant structure or sorption kinetics. Using this theory it can be shown analytically, for both insoluble and soluble surfactants, that the curious linear spreading law can be derived from a new dynamic contact line structure due to a tiny surfactant leakage from the air–liquid interface to the substrate. Such a leak not only establishes a concentrated Marangoni shearing toward the contact line at a rate much faster than the usual viscous stress singularity, but also results in a microscopic surfactant-devoid zone in the vicinity of the contact line. The strong Marangoni shearing then turns into a local capillary force in the zone, making the contact line in effect advance in a surfactant-free manner. This local Marangoni-driven capillary wetting in turn renders a constant wetting speed governed by the de Gennes–Cox–Voinov law and hence the linear spreading law. We also determine the range of surfactant concentration within which superspreading can be sustained by local surfactant leakage without being mitigated by the contact line sweeping, explaining why only limited classes of surfactants can serve as superspreaders. We further show that spreading of surfactant spreaders can exhibit either the $1/6$ or $1/2$ power law, depending on the ability of interfacial surfactant to transfer/leak to the bulk/substrate. All these findings can account for a variety of results seen in experiments (Rafai et al. Langmuir, vol. 18, 2002, pp. 10486–10488; Nikolov & Wasan, Adv. Colloid Interface Sci., vol. 222, 2015, pp. 517–529) and simulations (Karapetsas et al. 2011). Analogy to thermocapillary spreading is also made, reverberating the ubiquitous role of the Marangoni effect in enhancing dynamic wetting driven by non-uniform surface tension.

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Rafaï, Salima, Dipak Sarker, Vance Bergeron, Jacques Meunier, and Daniel Bonn. "Superspreading: Aqueous Surfactant Drops Spreading on Hydrophobic Surfaces." Langmuir 18, no. 26 (December 2002): 10486–88. http://dx.doi.org/10.1021/la020271i.

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Nikolov, Alex D., Darsh T. Wasan, and Pingkeng Wu. "Marangoni flow alters wetting: Coffee ring and superspreading." Current Opinion in Colloid & Interface Science 51 (February 2021): 101387. http://dx.doi.org/10.1016/j.cocis.2020.08.012.

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Zhu, S., W. G. Miller, L. E. Scriven, and H. T. Davis. "Superspreading of water—silicone surfactant on hydrophobic surfaces." Colloids and Surfaces A: Physicochemical and Engineering Aspects 90, no. 1 (September 1994): 63–78. http://dx.doi.org/10.1016/0927-7757(94)02904-0.

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Theodorakis, Panagiotis E., Erich A. Müller, Richard V. Craster, and Omar K. Matar. "Modelling the superspreading of surfactant-laden droplets with computer simulation." Soft Matter 11, no. 48 (2015): 9254–61. http://dx.doi.org/10.1039/c5sm02090e.

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Spatio-temporal evolution of a droplet undergoing surfactant-driven superspreading facilitated by surfactant adsorption from the liquid–vapour (LV) interface onto the substrate through the contact-line, and replenishment of the LV interface with surfactant from the bulk.

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Zenk, Lukas, Gerald Steiner, Miguel Pina e Cunha, Manfred D. Laubichler, Martin Bertau, Martin J. Kainz, Carlo Jäger, and Eva S. Schernhammer. "Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19." International Journal of Environmental Research and Public Health 17, no. 21 (October 27, 2020): 7884. http://dx.doi.org/10.3390/ijerph17217884.

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Although the first coronavirus disease 2019 (COVID-19) wave has peaked with the second wave underway, the world is still struggling to manage potential systemic risks and unpredictability of the pandemic. A particular challenge is the “superspreading” of the virus, which starts abruptly, is difficult to predict, and can quickly escalate into medical and socio-economic emergencies that contribute to long-lasting crises challenging our current ways of life. In these uncertain times, organizations and societies worldwide are faced with the need to develop appropriate strategies and intervention portfolios that require fast understanding of the complex interdependencies in our world and rapid, flexible action to contain the spread of the virus as quickly as possible, thus preventing further disastrous consequences of the pandemic. We integrate perspectives from systems sciences, epidemiology, biology, social networks, and organizational research in the context of the superspreading phenomenon to understand the complex system of COVID-19 pandemic and develop suggestions for interventions aimed at rapid responses.

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Garske, T., and C. J. Rhodes. "The effect of superspreading on epidemic outbreak size distributions." Journal of Theoretical Biology 253, no. 2 (July 2008): 228–37. http://dx.doi.org/10.1016/j.jtbi.2008.02.038.

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Bouffanais, Roland, and Sun Sun Lim. "Cities — try to predict superspreading hotspots for COVID-19." Nature 583, no. 7816 (July 2020): 352–55. http://dx.doi.org/10.1038/d41586-020-02072-3.

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Badra, Ali Talha, Hanane Zahaf, Hocine Alla, and Thibault Roques-Carmes. "A numerical model of superspreading surfactants on hydrophobic surface." Physics of Fluids 30, no. 9 (September 2018): 092102. http://dx.doi.org/10.1063/1.5041804.

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Guan, Qing-Fang, and Shu-Hong Yu. "A superspreading layering process enabled high performance layered nanocomposites." Science China Chemistry 63, no. 7 (April 21, 2020): 873–74. http://dx.doi.org/10.1007/s11426-020-9751-6.

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Lakdawala, Seema S., and Vineet D. Menachery. "Catch Me if You Can: Superspreading of COVID-19." Trends in Microbiology 29, no. 10 (October 2021): 919–29. http://dx.doi.org/10.1016/j.tim.2021.05.002.

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Eilersen, Andreas, and Kim Sneppen. "SARS‐CoV‐2 superspreading in cities vs the countryside." APMIS 129, no. 7 (February 23, 2021): 401–7. http://dx.doi.org/10.1111/apm.13120.

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Endo, Akira, Sam Abbott, Adam J. Kucharski, and Sebastian Funk. "Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China." Wellcome Open Research 5 (April 9, 2020): 67. http://dx.doi.org/10.12688/wellcomeopenres.15842.1.

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Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

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Endo, Akira, Sam Abbott, Adam J. Kucharski, and Sebastian Funk. "Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China." Wellcome Open Research 5 (July 3, 2020): 67. http://dx.doi.org/10.12688/wellcomeopenres.15842.2.

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Abstract:

Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

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Endo, Akira, Sam Abbott, Adam J. Kucharski, and Sebastian Funk. "Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China." Wellcome Open Research 5 (July 10, 2020): 67. http://dx.doi.org/10.12688/wellcomeopenres.15842.3.

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Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

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Cheng, Vincent Chi-Chung, Kitty Sau-Chun Fung, Gilman Kit-Hang Siu, Shuk-Ching Wong, Lily Shui-Kuen Cheng, Man-Sing Wong, Lam-Kwong Lee, et al. "Nosocomial Outbreak of Coronavirus Disease 2019 by Possible Airborne Transmission Leading to a Superspreading Event." Clinical Infectious Diseases 73, no. 6 (April 14, 2021): e1356-e1364. http://dx.doi.org/10.1093/cid/ciab313.

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Abstract Background Nosocomial outbreaks with superspreading of coronavirus disease 2019 due to a possible airborne transmission have not been reported. Methods Epidemiological analysis, environmental samplings, and whole-genome sequencing (WGS) were performed for a hospital outbreak. Results A superspreading event that involved 12 patients and 9 healthcare workers (HCWs) occurred within 9 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was significantly higher in air grilles (>2 m from patients’ heads and not within reach) than on high-touch clinical surfaces (36.4%, 8 of 22 vs 3.4%, 1 of 29, P = .003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicles. The clinical attack rate of patients was significantly higher than of HCWs (15.4%, 12 of 78 exposed patients vs 4.6%, 9 of 195 exposed HCWs, P = .005). Moreover, the clinical attack rate of ward-based HCWs was significantly higher than of nonward-based HCWs (8.1%, 7 of 68 vs 1.8%, 2 of 109, P = .045). The episodes (mean ± standard deviation) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than among noninfected ward-based HCWs (6.0 ± 2.4 vs 3.0 ± 2.9, P = .012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage B.1.36.27 (GISAID clade GH) with the unique S-T470N mutation on WGS. Conclusions This nosocomial point source superspreading event due to possible airborne transmission demonstrates the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of ventilation systems to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service.

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Mathur, Mansi, Sudarshan Ramaswamy, Mitilesh Sharma, Meera Dhuria, Sujata Arya, Himanshu Chauhan, S. K. Jain, and Sujeet Kumar Singh. "Inter-state cross border superspreading event of SARS-CoV2 in Central India, May 2020." International Journal of Research in Medical Sciences 9, no. 8 (July 28, 2021): 2369. http://dx.doi.org/10.18203/2320-6012.ijrms20213083.

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Background: During the mid-weeks of May 2020, a superspreading event occurred in a town of Central India, where breaking bread together led to an outburst of COVID-19 cases. This led to a sudden increase of the daily average number of cases later on in the month.Methods: An epidemiological investigation was done to investigate the cause. Process of the epidemic investigation done has been described under three parts namely - Case finding, Contact tracing, Public health response.Results: Our epidemiological investigation and contact tracing of the index case confirmed a superspreading event of COVID-19 which occurred due to multiple social gatherings during mid weeks of May 2020. It was estimated that 118 cases belonged to G0 and 94 cases belonged to G1 generation of the index case.Conclusions: Most likely source of infection to the index case was from the guests who came for a social gathering on May 11, 2020 (lockdown 3) from a village across the border in Rajasthan, a high COVID-19 prevalent zone (Orange) to a low COVID-19 prevalent zone (Green).

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Choe, Seoyun, Hee-Sung Kim, and Sunmi Lee. "Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models." International Journal of Environmental Research and Public Health 17, no. 17 (August 24, 2020): 6137. http://dx.doi.org/10.3390/ijerph17176137.

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South Korea has learned a valuable lesson from the Middle East respiratory syndrome (MERS) coronavirus outbreak in 2015. The 2015 MERS-CoV outbreak in Korea was the largest outbreak outside the Middle Eastern countries and was characterized as a nosocomial infection and a superspreading event. To assess the characteristics of a super spreading event, we specifically analyze the behaviors and epidemiological features of superspreaders. Furthermore, we employ a branching process model to understand a significantly high level of heterogeneity in generating secondary cases. The existing model of the branching process (Lloyd-Smith model) is used to incorporate individual heterogeneity into the model, and the key epidemiological components (the reproduction number and the dispersive parameter) are estimated through the empirical transmission tree of the MERS-CoV data. We also investigate the impact of control intervention strategies on the MERS-CoV dynamics of the Lloyd-Smith model. Our results highlight the roles of superspreaders in a high level of heterogeneity. This indicates that the conditions within hospitals as well as multiple hospital visits were the crucial factors for superspreading events of the 2015 MERS-CoV outbreak.

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Liu, Yang, Rosalind M. Eggo, and Adam J. Kucharski. "Secondary attack rate and superspreading events for SARS-CoV-2." Lancet 395, no. 10227 (March 2020): e47. http://dx.doi.org/10.1016/s0140-6736(20)30462-1.

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Journal articles on the topic 'Superspreading'

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