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Seminar on mathematical modeling in biology and medicine
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Structure, heterogeneity, and predictability in disease spreading on complex networks Jose Luis Herrera Diestra , |
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Abstract: The spread of infectious diseases is strongly shaped by the structure and heterogeneity of the networks over which transmission occurs. In this talk, I will present a series of network-based epidemic models that examine how degree heterogeneity, correlations, and population structure alter epidemic thresholds, outbreak dynamics, and the efficiency of surveillance strategies, often leading to departures from classical mean-field behavior. I will show how these theoretical results motivate targeted surveillance approaches that exploit network structure—such as acquaintance-based sampling—to detect outbreaks more efficiently than random monitoring. Building on this foundation, I will discuss ongoing work that extends these ideas to realistic settings where full contact networks are unavailable, focusing on hybrid surveillance strategies that combine network-informed intuition with network-free data sources such as electronic health records. Finally, I will outline current work using large-scale human mobility networks to study the predictability of disease burden across cities. This work leverages network features together with socioeconomic and demographic information to assess how structural constraints shape epidemic outcomes. Overall, the talk highlights how network theory provides a unifying framework for understanding, monitoring, and predicting disease spread in heterogeneous populations. Language: English |
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