Tail risk of contagious diseases

Cirillo, Pasquale; Taleb, Nassim Nicholas

doi:10.1038/s41567-020-0921-x

Cited by 172 publications

(212 citation statements)

References 16 publications

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“…A valuable example is the probabilistic study of contagion risk developed in Ref. 41 corresponding to different environmental conditions and typology of interactions. Simulations have been developed with explorative aims, namely, by a sensitivity analysis of the dynamical response depending on the parameters of the model.…”

Section: Looking Forward To Research Perspectivesmentioning

confidence: 99%

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

Bellomo

Bingham

Chaplain

et al. 2020

Math. Models Methods Appl. Sci.

151

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This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called SARS–CoV–2 Severe Acute Respiratory Syndrome, corona virus n.2. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focussed also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.

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Section: Looking Forward To Research Perspectivesmentioning

confidence: 99%

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

Bellomo

Bingham

Chaplain

et al. 2020

Math. Models Methods Appl. Sci.

151

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“…But it is an open question how representative the macroeconomic responses in the case of the Black Death are of large pandemics in general. (1918)(1919)(1920) Asian flu (1957)(1958) Encephalitis lethargica pandemic (1915)(1916)(1917)(1918)(1919)(1920)(1921)(1922)(1923)(1924)(1925)(1926) Plague in Kingdom of Naples (1656-1658 Third cholera pandemic (1852-1860) Russian flu (1889-1890) Hong Kong flu (1968)(1969) Sixth cholera pandemic Plague in Spain (1596-1602) Fourth cholera pandemic (1863)(1864)(1865)(1866)(1867)(1868)(1869)(1870)(1871)(1872)(1873)(1874)(1875) Swine Flu 2009 Notes: The main data are taken from the recently published study of Cirillo and Taleb (2020). See the references therein.…”

Section: Introductionmentioning

confidence: 99%

Longer-Run Economic Consequences of Pandemics

Jordà

Singh

Taylor

2020

ERWP

146

135

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What are the medium-to long-term effects of pandemics? How do they differ from other economic disasters? We study major pandemics using the rates of return on assets stretching back to the 14th century. Significant macroeconomic after-effects of pandemics persist for decades, with real rates of return substantially depressed, in stark contrast to what happens after wars. Our findings are consistent with the neoclassical growth model: capital is destroyed in wars, but not in pandemics; pandemics instead may induce relative labor scarcity and/or a shift to greater precautionary savings.

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“…The spread of Covid-19 has induced the introduction of a large variety of epidemic models, aimed to identify specific mechanisms relevant for policy design 1 . Although mathematical models contribute to generate relevant information both on the diffusion of the virus and on the socio-economic consequences of the epidemics 2,3 , scientific uncertainties are still high, and available data do not sustain neither univocal evaluations of the proposed policies nor exact predictions of the potential future outcomes 4 .…”

mentioning

confidence: 99%

“…2 Big Data in Health Society, Rome, Italy. 3 Gubkin Russian State University of Oil and Gas, Leninsky Prospekt, Moscow, Russia. 4 Impact, Department of Management, Economics and Industrial Engineering, Politecnico Di Milano, Milan, Italy.…”

mentioning

confidence: 99%

Time, space and social interactions: exit mechanisms for the Covid-19 epidemics

Scala

Flori

Spelta

et al. 2020

Sci Rep

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www.nature.com/scientificreports/ process: geographical boundaries and age classes 9. Finally, we show how these dimensions can shape policy interventions aiming at containing the epidemic outbreak. This paper contributes to the extant literature on trade-offs between mitigation, i.e. slowing down the epidemic contagion, and suppression, i.e. temporarily compressing the risk of contagion 9,16-18. Notwithstanding micro data on individual profiles are not taken into account in our compartmental model, we show how the inclusion of geographical and age classes uncovers relevant features impacting on model results on virus diffusion, providing some guidance to policy makers. We show that an early lockdown shifts the epidemic in time and that beyond a critical threshold of the intensity of the lockdown, the epidemic would tend to fully recover its strength as soon as the lockdown is lifted. As a consequence, specific mitigation strategies for a second wave must be prepared during the lockdown phase. To provide some guidance on the relative importance of different general strategies, we first study how the heterogeneity of the intensity of mobility flows across Italian administrative Regions influences the observed delays of the contagion. The relative strength of intra-Regional mobility with respect to interRegional mobility flows implies that, once the epidemic has started, it then tends to develop independently within each Region, as also empirically observed in simulations of Covid-19 spread in China 19. Then, we study the impact of patterns of interaction within and between age classes, finding that the structure of the interactions is of primarily importance in estimating post-lockdown effects. According to our results, age-based mitigation strategies, which can affect local behaviours can represent a key ingredient to contain a second wave and to protect age-classes with higher incidence of severe cases.

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Tail risk of contagious diseases

Cited by 172 publications

References 16 publications

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

Longer-Run Economic Consequences of Pandemics

Time, space and social interactions: exit mechanisms for the Covid-19 epidemics

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