Temporal Clustering of Disorder Events During the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2101.06458v1
• Date: Sat, 16 Jan 2021 15:34:42 GMT
• Title: Temporal Clustering of Disorder Events During the COVID-19 Pandemic
• Authors: Gian Maria Campedelli and Maria Rita D'Orsogna
• Abstract summary: We study the temporal nature of pandemic-related disorder events as tallied by the "COVID-19 Disorder Tracker" initiative. We find that disorder events are inter-dependent and self-excite in all three countries. Considerable diversity is observed among countries when computing correlations of events between subnational clusters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
  We develop a framework to extract and analyze epidemic-related events from social media posts. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics. We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
  arXiv  Detail & Related papers  (2024-04-02T06:31:17Z)
• Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of na\"ive populations faced with epidemics [43.50312332512221]
  The COVID-19 pandemic has brought to light profound variations among different countries in terms of their adaptive dynamics. This emphasizes the crucial role of cultural characteristics in natural disaster analysis.
  arXiv  Detail & Related papers  (2023-11-23T11:05:54Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• Efficacy the of Confinement Policies on the COVID-19 Spread Dynamics in the Early Period of the Pandemic [0.11049608786515838]
  We analyze the spread dynamics based on the early and post stages of COVID-19 for different countries based on different geographical locations. We found that implementations of the same confinement policies exhibit different results in different countries. Distrust in government policies and fake news instigate the spread in both developed and under-developed countries.
  arXiv  Detail & Related papers  (2021-11-04T17:25:32Z)
• Impact of COVID-19 Policies and Misinformation on Social Unrest [0.0]
  We focus on the interplay between social unrest (protests), health outcomes, public health orders, and misinformation in eight countries of Western Europe and four regions of the United States. We created 1-3 week forecasts of both a binary protest metric for identifying times of high protest activity and the overall protest counts over time. We found that for all regions, except Belgium, at least one feature from our various data streams was predictive of protests.
  arXiv  Detail & Related papers  (2021-10-07T16:05:10Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• A country comparison of place-based activity response to COVID-19 policies [0.913755431537592]
  We show that people's activity response to government action varies widely both across nations as well as regionally within them. We discuss these findings and the relationship between government action and residents' response.
  arXiv  Detail & Related papers  (2020-05-18T14:06:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.