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Temporal Clustering of Disorder Events During the COVID-19 Pandemic

Authors: Campedelli G. M.; D'Orsogna M. R.;

Temporal Clustering of Disorder Events During the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has unleashed multiple public health, socio-economic, and institutional crises. Measures taken to slow the spread of the virus have fostered significant strain between authorities and citizens, leading to waves of social unrest and anti-government demonstrations. We study the temporal nature of pandemic-related disorder events as tallied by the "COVID-19 Disorder Tracker" initiative by focusing on the three countries with the largest number of incidents, India, Israel, and Mexico. By fitting Poisson and Hawkes processes to the stream of data, we find that disorder events are inter-dependent and self-excite in all three countries. Geographic clustering confirms these features at the subnational level, indicating that nationwide disorders emerge as the convergence of meso-scale patterns of self-excitation. Considerable diversity is observed among countries when computing correlations of events between subnational clusters; these are discussed in the context of specific political, societal and geographic characteristics. Israel, the most territorially compact and where large scale protests were coordinated in response to government lockdowns, displays the largest reactivity and the shortest period of influence following an event, as well as the strongest nationwide synchrony. In Mexico, where complete lockdown orders were never mandated, reactivity and nationwide synchrony are lowest. Our work highlights the need for authorities to promote local information campaigns to ensure that livelihoods and virus containment policies are not perceived as mutually exclusive.

37 pages, 16 figures

Country
Italy
Subjects by Vocabulary

Microsoft Academic Graph classification: Pandemic media_common Convergence (economics) Livelihood Geography Scale (social sciences) media_common.quotation_subject Context (language use) Civil disorder Development economics Government Diversity (politics)

Keywords

FOS: Computer and information sciences, Viral Diseases, Computer Science - Machine Learning, General Economics (econ.GN), Epidemiology, Economics, Social Sciences, Machine Learning (cs.LG), Geographical Locations, Medical Conditions, Medicine and Health Sciences, Cluster Analysis, Israel, Economics - General Economics, Multidisciplinary, Geography, Q, R, Infectious Diseases, Medicine, Public Health, Research Article, Physics - Physics and Society, Asia, Science, FOS: Physical sciences, India, Civil Disorders, Physics and Society (physics.soc-ph), Human Geography, Statistics - Applications, FOS: Economics and business, Urban Geography, Health Economics, Humans, Applications (stat.AP), Cities, Mexico, Pandemics, SARS-CoV-2, COVID-19; Civil Disorders; Cluster Analysis; Communicable Disease Control; Humans; India; Israel; Mexico; Pandemics; Public Health; SARS-CoV-2, COVID-19, Covid 19, Health Care, Medical Risk Factors, People and Places, North America, Communicable Disease Control, Earth Sciences

55 references, page 1 of 6

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[2] M.A. Andrews et al. “First confirmed case of COVID-19 infection in India: A case report”. In: The Indian Journal of Medical Research 151 (2020), pp. 490-492.

[3] M. S. Bartlett. “The spectral analysis of point processes”. In: Journal of the Royal Statistical Society: Series B 25 (1963), pp. 264-281.

[4] Peter Baudains, Alex Braithwaite, and Shane D. Johnson. “Spatial patterns in the 2011 London riots”. In: Policing: A Journal of Policy and Practice 7 (2013), pp. 21-31.

[5] Peter Baudains, Alex Braithwaite, and Shane D. Johnson. “Target choice during extreme events: A discrete spatial choice model of the 2011 London riots”. In: Criminology 51 (2013), pp. 251-285.

[6] Peter Baudains, Shane D. Johnson, and Alex Maves Braithwaite. “Geographic patterns of diffusion in the 2011 London riots”. In: Applied Geography 45 (2013), pp. 211-219.

[7] Henri Berestycki, Jean-Pierre Nadal, and Nancy Rodriguez. “A model of riots dynamics: Shocks, diffusion and thresholds”. In: Networks and Heterogeneous Media 10 (2015), pp. 443- 475.

[8] Mayank Bhardwaj. “India sets global record with single-day rise in coronavirus cases”. en. In: Reuters (Aug. 2020). url: https://www.reuters.com/article/us-health-coronavirusindia-cases-idUSKBN25Q06A (visited on 12/01/2020).

[9] Laurent Bonnasse-Gahot et al. “Epidemiological modelling of the 2005 French riots: A spreading wave and the role of contagion”. In: Scientific Reports 8 (2018), p. 107.

[10] Halvard Buhaug and Kristian Skrede Gleditsch. “Contagion or confusion? Why conflicts cluster in space”. In: International Studies Quarterly 52 (2008), pp. 215-233. [OpenAIRE]

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Average
Top 10%
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