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Table 1 A summary of key concepts, representative examples, and corresponding references

From: Malaria transmission modelling: a network perspective

Key concepts and considerations Representative examples References
1. Temporal-spatial characterization Scan statistics-based clustering [11]
  Scan software tools [1215]
  Other applications (active foci or hotspots) [16]
Related factors Biology, environment, and socio-economy affecting interactions among hosts, vectors, and parasites at various scales [1719]
  Entomological inoculation rates, vector capacity, or force of infection [20]
  A combination of epidemiological, geographical, and demographic factors [21]
2. Modelling disease and/or information dynamics on networks Dynamics of infectious diseases on regular, small-world, or scale-free networks [2227]
  Critical value analysis of typical epidemics on complex network [2833]
  Diffusion of rumours or innovation on social networks [3436]
  Viral marketing and recommendation strategies [3739]
  Cascading in virtual blog spaces, and their propagation trends [10, 4043]
Related factors Alternative spatial representations [44]
  Effects of human mobility on the dynamics of disease transmission [45]
3. Understanding the structures of underlying transmission networks via indirect means Population travelling and mobility patterns [46, 47]
  Social contact activities [4850]
  Sexual relationships [51]
4. Inferring transmission parameters from data EM-based estimation algorithm to infer daily transmission rate between households [52]
  Markov Chain Monte Carlo (MCMC) method to estimate transmission parameters [53]
5. Inferring an underlying network from data Social networks based on the interpersonal interaction records [5458]
  Interaction networks between proteins in a cell [59, 60]
  Supervised classification [7]
  Expectation-maximization (EM)-like algorithm [10]
  Narrow and deep tree-like structure analysis [8]
  Likelihood-maximization [9]
  Independent cascading models [41]
6. Computational issues Conventional optimization methods [61]
  Potentially large-scale and/or dynamically-evolving surveillance data, e.g., over decades of temporal intervals [6264]
  Different levels of spatial categories [62, 63]
  Multiple environmental or biological factors incorporated [19, 64]
  Alternative AOC methods [6567]