Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes
The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral bi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Dynamics |
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| Online Access: | https://www.mdpi.com/2673-8716/3/4/41 |
| _version_ | 1797381390189199360 |
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| author | Marija Mitrović Dankulov Bosiljka Tadić Roderick Melnik |
| author_facet | Marija Mitrović Dankulov Bosiljka Tadić Roderick Melnik |
| author_sort | Marija Mitrović Dankulov |
| collection | DOAJ |
| description | The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic’s main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations. |
| first_indexed | 2024-03-08T20:50:47Z |
| format | Article |
| id | doaj.art-7852f16f29d04f9db78565e5fd30b3d3 |
| institution | Directory Open Access Journal |
| issn | 2673-8716 |
| language | English |
| last_indexed | 2024-03-08T20:50:47Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Dynamics |
| spelling | doaj.art-7852f16f29d04f9db78565e5fd30b3d32023-12-22T14:04:03ZengMDPI AGDynamics2673-87162023-11-013476477610.3390/dynamics3040041Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social ProcessesMarija Mitrović Dankulov0Bosiljka Tadić1Roderick Melnik2Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, SerbiaDepartment of Theoretical Physics, Jožef Stefan Institute, 1000 Ljubljana, SloveniaMS2Discovery Interdisciplinary Research Institute, M3AI Laboratory and Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, CanadaThe essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic’s main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations.https://www.mdpi.com/2673-8716/3/4/41complex systemscomplex networksK-means clusteringtime seriesmultifractalitycyclical trends |
| spellingShingle | Marija Mitrović Dankulov Bosiljka Tadić Roderick Melnik Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes Dynamics complex systems complex networks K-means clustering time series multifractality cyclical trends |
| title | Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes |
| title_full | Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes |
| title_fullStr | Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes |
| title_full_unstemmed | Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes |
| title_short | Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes |
| title_sort | robust global trends during pandemics analysing the interplay of biological and social processes |
| topic | complex systems complex networks K-means clustering time series multifractality cyclical trends |
| url | https://www.mdpi.com/2673-8716/3/4/41 |
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