Multidisciplinary research methods and models for spatial spreading in light of diffusion processes related to the COVID-19 pandemic

Abstract

Spatial diffusion research is a multi- and interdisciplinary research field with a history of roughly a century – spatial researchers started to be involved from around the 1950s. A wide range of disciplines have studied the topic, most notably: anthropology, sociology, marketing, communication, geography, regional science, epidemiology, epidemic mathematics, history, computer science, engineering, economics, and network science. The aim of this paper is to summarize the extremely broad literature on spatial diffusion research, to examine the basic concepts, history, theoretical questions, and methods used in spatial diffusion processes, and to summarize the conclusions relevant to regional social sciences formulated in these works. Finally, the paper compares these findings with the results of empirical research related to the COVID-19 pandemic.

Different disciplines have dealt with characteristics of geographical space in different ways, but their methodologies and theoretical approaches have enriched geographical diffusion research in many ways. Regional science initially focused on the study of innovations, drawing heavily on related findings in rural sociology and social geography. Analysis of innovation diffusion led to the development of spatial diffusion models, while the theory of economic development facilitated by diffusion impulses was part of a wider scientific discourse regarding spatial development theories. In the context of pandemics, epidemiology and epidemic mathematics and their methods are decisive. In this research area, regional science has played a complementary role: it has added its methods and models of spatial analysis and possible spatial frameworks for the control of epidemics. Finally, research on the information diffusion, which until the 1990s was essentially a research topic of marketing and communication, has developed significantly, due to the emergence of network science. In addition, network models are increasingly being used in spatial diffusion research of innovations and epidemics as well.

The development of computer technology has been blurring the boundaries between disciplines in recent decades. Models and methods have been used to study a wide range of phenomena, and new forms of modelling have emerged. This has been facilitated by similar conclusions of different methods: diffusion wave models and epidemiological models provide similar interpretations of the temporality of spreading processes, while the network characteristics explain the spatial diffusion patterns rather well. Similarities can also be highlighted in relation to the role of certain key actors and territorial heterogeneity. However, the study of relationships between different spreading processes can be still considered an under-researched area in the field of spatial diffusion, with a few exceptions.

Empirical analyses related to the COVID-19 pandemic highlighted that while the temporality of the studied diffusion processes could be described by the diffusion wave model, the hierarchical and contagious diffusion patterns were only partially observable. This was due to the specific structure of the networks used for the diffusion of the phenomena and the role of propagators relevant for these processes. Spatial inequalities that emerged as a result of the diffusion processes can be interpreted along the lines of the European centre-periphery inequalities. Finally, the analysis revealed that the spread of information, the virus, and vaccines interacted in multiple ways: while during the first pandemic wave the relative speed of information diffusion was crucial, during the subsequent waves the interpretation of information and trust in government measures and in vaccination became more important.