Key concepts and considerations | Representative examples | References |
---|---|---|
1. Temporal-spatial characterization | Scan statistics-based clustering | [11] |
 | Scan software tools | |
 | Other applications (active foci or hotspots) | [16] |
Related factors | Biology, environment, and socio-economy affecting interactions among hosts, vectors, and parasites at various scales | |
 | 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 | |
 | Critical value analysis of typical epidemics on complex network | |
 | Diffusion of rumours or innovation on social networks | |
 | Viral marketing and recommendation strategies | |
 | Cascading in virtual blog spaces, and their propagation trends | |
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 | |
 | Social contact activities | |
 | 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 | |
 | Interaction networks between proteins in a cell | |
 | 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 | |
 | Different levels of spatial categories | |
 | Multiple environmental or biological factors incorporated | |
 | Alternative AOC methods |