Predicting community attitudes towards alternative virus-management plans - Ray Wyatt (Journal of Urban Management, 2021)
In mid 2020 people vigorously debated which Corona virus-management strategy should be implemented - "total Lockdown", "Partial Lockdown" or "do Nothing". For success, the chosen strategy would need considerable public support. So here we demonstrate how support, or otherwise, could have been predicted using our freely available Planticipate app. It self improves by sending its users' judgements to the cloud, where learning routines formulate regression- and neural network-based relationships between thirteen, key, plan-evaluation criteria and overall plan desirability. Hence whenever any set of plans are scored on the criteria, these relationships generate forecasts of plan desirability according to a number of demographic groups of past users. Our app predicted that many community groups will regard the "do-Nothing" option as statistically significantly inferior to the other two plans, and it also made several less-than-statistically-significant forecasts which were extremely thought provoking. Using innovative face charts to better interpret complicated, thirteen-dimensional data, Planticipate also suggested probable reasons for such forecasts. These included an apparent fixation upon only permissiveness and productivity by people living in North America and relative pragmatism amongst females. Such revelations immediately suggested possible modifications for making different plans more acceptable to certain community groups. Given that in reality several of these modifications were only implemented later on during the pandemic, an early application of our app would almost certainly have prompted faster, more creative and more empathetic urban management.
Keywords: 710; Planning; Prediction; Community; Statistical significance; Regression; Neural network; Virus management; Urban management