Vector-borne epidemics driven by human mobility

Abstract

Vector-borne epidemics are the result of the combination of different factors such as the crossed contagions between humans and vectors, their demographic distribution and human mobility among others. The current availability of information about the former ingredients demands their incorporation to current mathematical models for vector-borne disease transmission. Here, relying on metapopulation dynamics, we propose a framework whose results are in fair agreement with those obtained from mechanistic simulations. This framework allows us to derive an expression of the epidemic threshold capturing with high accuracy the conditions leading to the onset of epidemics. Driven by these insights, we obtain a prevalence indicator to rank the patches according to the risk of being affected by a vector-borne disease. We illustrate the utility of this epidemic risk indicator by reproducing the spatial distribution Dengue cases reported in the city of Santiago de Cali (Colombia) from 2015 to 2016.

A mathematical model for the spatiotemporal epidemic spreading of COVID19

“…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].…”

Early evidence of a higher incidence of COVID-19 in the air-polluted regions of eight severely affected countries

“…Another evident predictor variable is transportation. The surrounding areas of transport hubs such as airports and large train stations should witness the appearance of the virus earlier than other zones increasing its transmission [43][44][45]37,46 .…”

COVID-19 higher induced mortality in Chinese regions with lower air quality

“…Another evident predictor variable is transportation. The surrounding areas of transport hubs such as airports and large train stations should witness the appearance of the virus earlier than other geographical areas and they act as transmission hubs [52][53][54][55][56] .…”