A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area

Abstract

Cities play an important role in fostering and amplifying the transmission of airborne diseases (e.g., influenza) because of dense human contacts. Before an outbreak of airborne diseases within a city, how to determine an appropriate containment area for effective vaccination strategies is unknown. This research treats airborne disease spreads as geo-social interaction patterns, because viruses transmit among different groups of people over geographical locations through human interactions and population movement. Previous research argued that an appropriate scale identified through human geo-social interaction patterns can provide great potential for effective vaccination. However, little work has been done to examine the effectiveness of such vaccination at large scales (e.g., city) that are characterized by spatially heterogeneous population distribution and movement. This article therefore aims to understand the impact of geo-social interaction patterns on effective vaccination in the urbanized area of Portland, Oregon. To achieve this goal, we simulate influenza transmission on a large-scale location-based social network to 1) identify human geo-social interaction patterns for designing effective vaccination strategies, and 2) and evaluate the efficacy of different vaccination strategies according to the identified geo-social patterns. The simulation results illustrate the effectiveness of vaccination strategies based on geosocial interaction patterns in containing the epidemic outbreak at the source. This research can provide evidence to inform public health approaches to determine effective scales in the design of disease control strategies.

Modelo de Intervención Epidemiológica y Psicológica para el Abordaje de los cuidados y precauciones poblacionales frente al Coronavirus (COVID-19)

“…Abordando las aportaciones que la psicología está consiguiendo hacer para beneficiar a la población mundial con estrategias, herramientas y técnicas que están en condiciones de contribuir a mejorar la salud mental de la población, es más que evidente que, los encierros prolongados generan situaciones adversas en el ser humano. Debido a esto, es que se ha optado por rescatar los avances de autores como Duan & Zhu (2020), en materia de intervenciones psicológicas a poblaciones afectadas por la COVID-19, además de los modelos propios de la Terapia de Aceptación y Compromiso -ACT (Hofmann & Hayes, 2019), añadiendo los modelos de interacción geo-social para temporadas de brotes y epidemias, expuestos por Luo, Gao & Cassels (2018).…”

Mining the Hidden Link Structure from Distribution Flows for a Spatial Social Network

“…There are several advantages to assume that the spreading process and distribution flows occurred within . First, the embedding of the node set into feature space R allows us to characterize the feature information of nodes that are external to the network structure [21,22,27], which are usually as important as the network structure itself in determining the spreading process and distribution flows. Luo et al [22] argue that including social-economic factors such as the intensity of population gathering in a set of locations can significantly increase the capacity of forecast of illness spreading among residents.…”

“…Under ABM framework, this interaction network is supposed to be known and prescribed in prior. Prior network may lose critical information of the interaction patterns of population [15,21,22]. For instance, in the Twitter network, a natural interaction network structure is the network formed by friendship or followership relation between users which is also frequently used as the prior network for rumor spreading studies [14,18].…”

“…These mechanisms are often parametrized and assumed homogeneous for all agents; i.e., the mechanism is determined by a set of parameters that are constant and invariant for different agents. In reality, both of the relative positions of an agent within the network, such as the degree, centrality, betweenness of an agent [17,18,27], and many social-economic factors external to the entire network, such as the geographic location, social status, education level, and wealth [22,27], can drastically affect the likelihood that agents get infected by the rumor. But the heterogeneity among agents is often missing from the standard ABM framework.…”

Maximum Recommendation in Geo-social Network for Business

“…With the development of the geo-social network technology and the geo-social media platform [1], more and more companies are using the geo-social network to promote their products [2]. Therefore, the relevant business procedures become increasingly necessary in applications [3].…”