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Spreading the disease: Protest in times of pandemics.

ABSTRACT: This study analyzes the impact of large anti-lockdown protests on the spread of SARS-CoV-2 in Germany. Since protesters at such large gatherings are very mobile and largely neglect SARS-CoV-2 containment strategies, they may contribute to the regional transmission of the coronavirus. Employing novel data on bus connections of travel companies specialized in driving protesters to these gatherings, and exploiting the timing of two large-scale demonstrations in November 2020, we estimate the causal impact of these protests on the spread of SARS-CoV-2 using an event study framework. Our findings imply sizable increases in infection rates in protesters' origin regions after these demonstrations. A month after the protests, treated areas face a relative increase in infection rates up to 35% compared to non-treated areas. Our results shed light on public health consequences of behavior that ignores potential externalities for the society during a pandemic.

SUBMITTER: Lange M

PROVIDER: S-EPMC9538087 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Spreading the disease: Protest in times of pandemics.

Lange Martin M Monscheuer Ole O

Health economics 20220920 12

This study analyzes the impact of large anti-lockdown protests on the spread of SARS-CoV-2 in Germany. Since protesters at such large gatherings are very mobile and largely neglect SARS-CoV-2 containment strategies, they may contribute to the regional transmission of the coronavirus. Employing novel data on bus connections of travel companies specialized in driving protesters to these gatherings, and exploiting the timing of two large-scale demonstrations in November 2020, we estimate the causal ...[more]

PMID: 36127807

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Project description:Highlights • Epidemic curves created by using an agent-based model are presented.• The network of interactions, and thus the possibility of infection, is modeled with the scale-free Barabasi-Albert model. Modifications based on whether these interactions belong to a particular group are proposed.• A simple model of passing an infection via a direct interaction is proposed.• The influence of the network creation method, division into different numbers of groups and different times of intervention is presented.• The results are shown for two cases: the well-known influenza virus and the recently reported COVID-19. Background and Objective The currently active COVID-19 pandemic has increased, among others, public interest in the computational techniques enabling the study of disease-spreading processes. Thus far, numerous approaches have been used to study the development of epidemics, with special attention paid to the identification of crucial elements that can strengthen or weaken the dynamics of the process. The main thread of this research is associated with the use of the ordinary differential equations method. There also exist several approaches based on the analysis of flows in the Cellular Automata (CA) approach. Methods In this paper, we propose a new approach to disease-spread modeling. We start by creating a network that reproduces contacts between individuals in a community. This assumption makes the presented model significantly different from the ones currently dominant in the field. It also changes the approach to the act of infection. Usually, some parameters that describe the rate of new infections by taking into account those infected in the previous time slot are considered. With our model, we can individualize this process, considering each contact individually. Results The typical output from calculations of a similar type are epidemic curves. In our model, except of presenting the average curves, we show the deviations or ranges for particular results obtained in different simulation runs, which usually lead to significantly different results. This observation is the effect of the probabilistic character of the infection process, which can impact, in different runs, individuals with different significance to the community. We can also easily present the effects of different types of intervention. The effects are studied for different methods used to create the graph representing a community, which can correspond to different social bonds. Conclusions We see the potential usefulness of the proposition in the detailed study of epidemic development for specific environments and communities. The ease of entering new parameters enables the analysis of several specific scenarios for different contagious diseases.

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Spreading the disease: Protest in times of pandemics.

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Spreading the disease: Protest in times of pandemics.

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