Creating a surrogate commuter network from Australian Bureau of Statistics census data

Abstract

Between the 2011 and 2016 national censuses, the Australian Bureau of Statistics changed its anonymity policy compliance system for the distribution of census data. The new method has resulted in dramatic inconsistencies when comparing low-resolution data to aggregated high-resolution data. Hence, aggregated totals do not match true totals, and the mismatch gets worse as the data resolution gets finer. Here, we address several aspects of this inconsistency with respect to the 2016 usual-residence to place-of-work travel data. We introduce a re-sampling system that rectifies many of the artifacts introduced by the new ABS protocol, ensuring a higher level of consistency across partition sizes. We offer a surrogate high-resolution 2016 commuter dataset that reduces the difference between the aggregated and true commuter totals from ~34% to only ~7%, which is on the order of the discrepancy across partition resolutions in data from earlier years.

Modelling transmission and control of the COVID-19 pandemic in Australia

“…These social mixing layers represent the demographics of Australia as close as possible to the ABS and other datasets, as described in Appendix F in Supplementary information. Potential interactions between spatially distributed agents are represented using data on mobility in terms of commuting patterns (work, study and other activities), adjusted to increase precision and fidelity of commute networks 47 . Each simulation scenario runs in 12-h cycles ("day" and "night") over the 196 days (28 weeks) of an epidemic, and agents interact across distinct social mixing groups depending on the cycle, for example, in working groups and/or classrooms during a "day" cycle, and their HHs, HCs and local communities during the "night" cycle.…”

Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

“…In order to formulate the GEP model for India, it is really very important to investigate existing models and analyse if the proposed GEP models will be significant enough or not. Various models such as Ace-Mod (Australian Census-based Epidemic Model) [27] , neural network based models [28] and others have been employed to access the situation and provide exact predictions. Though these models are a bit significant but the first AceMod model has been used for influenza prediction [27] and has little relevance to COVID-19.…”

“…Various models such as Ace-Mod (Australian Census-based Epidemic Model) [27] , neural network based models [28] and others have been employed to access the situation and provide exact predictions. Though these models are a bit significant but the first AceMod model has been used for influenza prediction [27] and has little relevance to COVID-19. The other neural network based model uses shallow long term memory (LSTM) method along with the fuzzy rule based model to predict the present scenario.…”

Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries

“…In literature, there are numerous models which have been proposed to analyse the effect of coronavirus and provide reliable predictions. These models include neural network based models [28] for prediction analysis, Australian Census-based Epidemic Model (AceMod) [29] for influenza virus prediction, and others. The neural network based model used fuzzy logic along with long shallow term memory (LSTM) modelling [28] to predict the possible impact of COVID-19.…”