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These indicators can be measured by means of the disaster impact and the recuperation as quantification metrics for the various capacities and functions. Issues of data access make ideal metrics and regressors challenging to create. For starters, the household regressors are usually desirable. Nonetheless, socio-economic details including employment, ethnicity or age is also not accessible at the household level. Furthermore, the nature of the capacities makes it harder to identify a measure that is always indicative in the context of disparities within populations and infrastructure roles and socio-economic conditions before a harmful event occurs. For example, access to clean water in a developed country can provide a desired indicator, whereas it may be more useful to study different sources of drinking water, for example water tanks or wells in a developing country. Indicators will also be chosen on the grounds of data quality and importance to the area of concern. For the development of exact predictive models, the collection of data sources for indicators/regressors is important. In order for models to be used in the future, the data source should be reliable and frequently actualized. The US census, which is frequently revised and freely accessible, may be an indicator of a data base. If real-time data is available, updates to Bayesian models can be used.