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

1. Temporal-spatial characterization

Scan statistics-based clustering

[11]

Scan software tools

[12–15]

Other applications (active foci or hotspots)

[16]

Related factors

Biology, environment, and socio-economy affecting interactions among hosts, vectors, and parasites at various scales

[17–19]

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

[22–27]

Critical value analysis of typical epidemics on complex network

[28–33]

Diffusion of rumours or innovation on social networks

[34–36]

Viral marketing and recommendation strategies

[37–39]

Cascading in virtual blog spaces, and their propagation trends

[10, 40–43]

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

[48–50]

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

[54–58]

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

[62–64]

Different levels of spatial categories

[62, 63]

Multiple environmental or biological factors incorporated

[19, 64]

Alternative AOC methods

[65–67]