Epidemic risk assessment from geographic population density
Abstract The geographic distribution of the population on a region is a significant ingredient in shaping the spatial and temporal evolution of an epidemic outbreak. Heterogeneity in the population density directly impacts the local relative risk: the chances that a specific area is reached by the c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-06-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-022-00480-0 |
| _version_ | 1828812925060513792 |
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| author | Alessandro Celestini Francesca Colaiori Stefano Guarino Enrico Mastrostefano Lena Rebecca Zastrow |
| author_facet | Alessandro Celestini Francesca Colaiori Stefano Guarino Enrico Mastrostefano Lena Rebecca Zastrow |
| author_sort | Alessandro Celestini |
| collection | DOAJ |
| description | Abstract The geographic distribution of the population on a region is a significant ingredient in shaping the spatial and temporal evolution of an epidemic outbreak. Heterogeneity in the population density directly impacts the local relative risk: the chances that a specific area is reached by the contagion depend on its local density and connectedness to the rest of the region. We consider an SIR epidemic spreading in an urban territory subdivided into tiles (i.e., census blocks) of given population and demographic profile. We use the relative attack rate and the first infection time of a tile to quantify local severity and timing: how much and how fast the outbreak will impact any given area. Assuming that the contact rate of any two individuals depends on their household distance, we identify a suitably defined geographical centrality that measures the average connectedness of an area as an efficient indicator for local riskiness. We simulate the epidemic under different assumptions regarding the socio-demographic factors that influence interaction patterns, providing empirical evidence of the effectiveness and soundness of the proposed centrality measure. |
| first_indexed | 2024-12-12T09:52:49Z |
| format | Article |
| id | doaj.art-370bdb5a8fec4b72acc99f4bb8cfb8e1 |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-12T09:52:49Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-370bdb5a8fec4b72acc99f4bb8cfb8e12022-12-22T00:28:14ZengSpringerOpenApplied Network Science2364-82282022-06-017111510.1007/s41109-022-00480-0Epidemic risk assessment from geographic population densityAlessandro Celestini0Francesca Colaiori1Stefano Guarino2Enrico Mastrostefano3Lena Rebecca Zastrow4CNR, Institute for Applied Mathematics “Mauro Picone”CNR, Institute for Complex SystemsCNR, Institute for Applied Mathematics “Mauro Picone”CNR, Institute for Applied Mathematics “Mauro Picone”CNR, Institute for Applied Mathematics “Mauro Picone”Abstract The geographic distribution of the population on a region is a significant ingredient in shaping the spatial and temporal evolution of an epidemic outbreak. Heterogeneity in the population density directly impacts the local relative risk: the chances that a specific area is reached by the contagion depend on its local density and connectedness to the rest of the region. We consider an SIR epidemic spreading in an urban territory subdivided into tiles (i.e., census blocks) of given population and demographic profile. We use the relative attack rate and the first infection time of a tile to quantify local severity and timing: how much and how fast the outbreak will impact any given area. Assuming that the contact rate of any two individuals depends on their household distance, we identify a suitably defined geographical centrality that measures the average connectedness of an area as an efficient indicator for local riskiness. We simulate the epidemic under different assumptions regarding the socio-demographic factors that influence interaction patterns, providing empirical evidence of the effectiveness and soundness of the proposed centrality measure.https://doi.org/10.1007/s41109-022-00480-0SIREpidemicRisk assessmentData drivenUrban systemGeographic spreading |
| spellingShingle | Alessandro Celestini Francesca Colaiori Stefano Guarino Enrico Mastrostefano Lena Rebecca Zastrow Epidemic risk assessment from geographic population density Applied Network Science SIR Epidemic Risk assessment Data driven Urban system Geographic spreading |
| title | Epidemic risk assessment from geographic population density |
| title_full | Epidemic risk assessment from geographic population density |
| title_fullStr | Epidemic risk assessment from geographic population density |
| title_full_unstemmed | Epidemic risk assessment from geographic population density |
| title_short | Epidemic risk assessment from geographic population density |
| title_sort | epidemic risk assessment from geographic population density |
| topic | SIR Epidemic Risk assessment Data driven Urban system Geographic spreading |
| url | https://doi.org/10.1007/s41109-022-00480-0 |
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