Impact of temporal scales and recurrent mobility patterns on the unfolding of epidemics

Abstract

Human mobility plays a key role on the transformation of local disease outbreaks into global pandemics. Thus, the inclusion of human movements into epidemic models has become mandatory for understanding current epidemic episodes and to design efficient prevention policies. Following this challenge, here we develop a Markovian framework which enables to address the impact of recurrent mobility patterns on the epidemic onset at different temporal scales. This formalism is validated by comparing their predictions with results from mechanistic simulations. The fair agreement between both theory and simulations enables to get an analytical expression for the epidemic threshold which captures the critical conditions triggering epidemic outbreaks. Finally, by performing an exhaustive analysis of this epidemic threshold, we reveal that the impact of tuning human mobility on the emergence of diseases is strongly affected by the temporal scales associated to both epidemiological and mobility processes.

A mathematical model for the spatiotemporal epidemic spreading of COVID19

“…We propose a tailored model for the epidemic spread of COVID-19. We use a previous framework for the study of epidemics in structured metapopulations, with heterogeneous agents, subjected to recurrent mobility patterns [18][19][20]31].To understand the geographical diffusion of the disease, as a result of human-human interactions in small geographical patches, one has to combine the contagion process with the long-range disease propagation due to human mobility across different spatial scales. For the case of epidemic modeling, the metapopulation scenario is as follows.…”

“…Here, we propose mathematical model particularly designed to capture the main ingredients characterising the propagation of SARS-CoV-2 and the clinical characteristics reported for the cases of COVID-19. To this aim, we rely on previous metapopulation models by the authors [18][19][20][21] including the spatial demographical distribution and recurrent mobility patterns, and develop a more refined epidemic model that incorporates the stratification of population by age in order to consider the different epidemiological and clinical features associated to each group age that have been reported so far. The mathematical formulation of these models rely on the Microscopic Markov Chain Approach formulation for epidemic spreading in complex networks [22][23][24][25][26][27].…”

Derivation of the effective reproduction number ℛ for COVID-19 in relation to mobility restrictions and confinement

“…We propose a tailored model for the epidemic spread of COVID-19. We use a previous framework for the study of epidemics in structured metapopulations, with heterogeneous agents, subjected to recurrent mobility patterns [7,17,10,18].To understand the geographical diffusion of the disease, as a result of human-human interactions in small geographical patches, one has to combine the contagion process with the long-range disease propagation due to human mobility across different spatial scales. For the case of epidemic modeling, the metapopulation scenario is as follows.…”

“…Data from Spain Regarding the population structure in Spain, we have obtained the population distribution, population pyramid, daily population flows and average household size at the municipality level from Instituto Nacional de Estadística 18 whereas the age-specific contact matrices have been extracted from 19…”

Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

“…Therefore real-world data on contact networks are rare [30,45,23,32,43] and not available for large-scale populations. A reasonable approach is to generate the data synthetically, for instance by using mobility and population data based on geographical diffusion [46,17,36,3]. For instance, this has been applied to the influenza virus [33].…”