Epidemic models often pool air travel, commuting, and other mobility layers into a single weighted network. Pooling keeps the total imported infections into a region but discards the transport mode and route that delivered them, the information a mode-specific intervention needs. We make this precise for directed multilayer flows with heavy-tailed variation. Pooling acts as an asymmetric filter. The magnitude of an extreme importation, its radial tail index, is an exact invariant of sampling and nonnegative aggregation, whereas its composition across layers and routes, the angular part, is reweighted by layer-specific sampling and can be irrecoverably merged. We give a necessary and sufficient condition for recovering route composition, attach a surveillance decision cost to the loss, and show that the first established route need not be the busiest. A controlled metapopulation study calibrates the cost, and two contrasting reconstructions show where it bites. In US pandemic influenza, air adds fitted information beyond commuting, and a layer-resolved ranking targets more air-import burden than a pooled one. In the early Italian COVID-19 wave, commuting improves onset reconstruction while air attribution stays unresolved. Pooling can therefore support total-importation surveillance but cannot in general identify the mode or route behind an importation.
Aggregated epidemics, characterized by rapid transmission over short periods, pose severe threats to public health security, necessitating the development of precise source tracing and simulation methods to support efficient prevention and control. However, existing studies suffer from three major gaps: (1) predominant...
Wen Cao, Gang Chen, Si-Qi Zhao et al.· ISPRS International Journal...· 0 citations
Animal trade links livestock holdings through contacts that change from day to day, making movement networks vital for epidemic analysis and control. Official movement records reveal these transmission routes, but privacy concerns often restrict access to the original data. Furthermore, epidemiological studies frequent...
Tian-Jian Qin, Büsra Atamer Balkan, Boris V. Schmid et al.· bioRxiv· 0 citations
The recent pandemics remind us that the speed and extent of infectious disease spread heavily depend on human mobility patterns. Despite their long history, mobility restrictions have not always been successful in controlling disease spread. Understanding the implications of mobility restrictions on the geographic dist...
Abstract Cities are held together by a small subset of recurrent origin–destination ties, but extracting this backbone from noisy mobility data remains difficult, and most models still prioritize distance over connectivity. We build directed mobility networks from 48 months of mobile-phone data across eight US cities (...
Wei-Yu Zhang, Fu-Rong Jia, Jianying Wang et al.· Royal Society Open Science· 0 citations
In the era of Information 2.0, the intricate coupling between information diffusion and human behavioral patterns is reshaping the spatiotemporal dynamics of disease transmission, thereby posing substantial challenges to containing the cross-regional spread of epidemics. To address this issue, we modify the classical m...
Cong-Ying Liu, Xiao-Qun Wu, Xiao-Yang Liu et al.· Chaos· 0 citations
Classical epidemic models assume that infected individuals are homogeneous, reducing disease progression to a single aggregate variable. This hides the role of within‑host variability, in particular differences in viral load that directly influence both infectivity and recovery. We propose a geometric perspective in wh...
Jamal Adetola, Charbel Z. J. Mamlankou, K. W. Houédanou· Biomath· 0 citations
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