Table 2.
Summary of contextual factors on methods of space–time disease surveillance.
| Class | Type | Scale | Scope | Function | Characteristics | Technical |
|---|---|---|---|---|---|---|
| Test | CUSUM | Temporal statistical framework useful for long time periods of sequential surveillance | Univariate CUSUM useful for single diseases while multivariate CUSUM useful when multiple diseases or syndromes are under surveillance | Primarily for outbreak detection | Multivariate CUSUM is not sensitive to outbreak type (one extreme vs. many subtle rises) whereas the univariate is | Difficulty in specification and understanding of the threshold parameter |
| Test | Interaction | Population shift bias increases with spatial and temporal scale | Cannot analyze interactions and relationships in multiple host diseases | Can only detect presence of interaction. Limited utility for outbreak detection. Best used as screening method | Interaction tests cannot capture interactions and flows between units under surveillance (spatial autocorrelation) | Require geo-coded event data of cases of disease. Ease of understanding and interpretation. Subjectivity in specification of critical distances in space and time |
| Test | Scan | Space–time scan statistics are able to detect and locate clusters. Using the permutation-based approach can make use of temporal history of data. Appropriate mostly where there is a large volume of data in space and time | Scan statistics are designed to monitor one data stream, and therefore in and of themselves are not suitable for multiple disease. Can be combined with models as in Kleinman et al. (2005) | Monitoring p-values of primary and secondary clusters can be useful for assessing trends over time, although primary function is for discrete localized outbreak detection | Cylindrical search areas assume compact cluster form. Extensions using graph-based connectivity for search areas are computationally very demanding. Spatial relationships not defined by proximity may be more important for disease spatial processes | Can be used with point event data or count data. Ease of understanding and interpretation of results of analysis |
| Model | GLMM | Increase in utility as the size of the surveillance database grows. Temporal trends can be incorporated as model parameters. Frequent refitting of complex models can be difficult | Models can be formulated for risks, incidence and counts of diseases. Very flexible in how dependent variable is structured | Monitoring space–time trends in disease incidence, however, all modelling approaches need to be coupled with a statistical test to determine unexpected events (i.e., outbreaks) | Can incorporate hierarchical effects of covariates easily including spatial effects | The most accessible of modelling approaches but requires knowledge of statistical distributions. Limited mostly to researchers and statistical analysts. Flexible choice of statistical distributions compared to OLS modelling |
| Model | Bayesian | Same as above | Same as above | Same as above | Same as above | Priors need to be specified for model parameters. Advanced statistical knowledge required. Fitting complex space–time Bayesian models requires MCMC methods. Not suitable if need to be re-fit often |
| Model | Processes | Can be used with data of any scale as testing is against a specified process | Multiple hosts and pathogens can be accounted for though may be difficult to parameterize | Generally high sensitivity to detecting different types of change such as periodic outbreaks or gradual shifts away from the process. Needs to be coupled with a statistical test | Characteristics of disease (e.g., transmission, serial interval) can determine choice of process. Can also be used as exploratory tool | Models in this class vary greatly. Technical factors will be specific to individual process models selected |