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.

Epidemic risk assessment from geographic population density

Celestini A;Colaiori F;Guarino S;Mastrostefano E;Zastrow LR
2022

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.
2022
Istituto Applicazioni del Calcolo ''Mauro Picone''
Istituto dei Sistemi Complessi - ISC
SIR
Epidemic
Risk Assessment
Data Driven
Urban System
Geographic Spreading
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/417428
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