Unknown uncertainties in the COVID-19 pandemic: Multi-dimensional identification and mathematical modelling for the analysis and estimation of the casualties

Abstract

Insights about the dominant dynamics, coupled structures and the unknown uncertainties of the pandemic diseases play an important role in determining the future characteristics of the pandemic diseases. To enhance the prediction capabilities of the models, properties of the unknown uncertainties in the pandemic disease, which can be utterly random, or function of the system dynamics, or it can be correlated with an unknown function, should be determined. The known structures and amount of the uncertainties can also help the state authorities to improve the policies based on the recognized source of the uncertainties. For instance, the uncertainties correlated with an unknown function imply existence of an undetected factor in the casualties. In this paper, we extend the SpID-N (Suspicious-Infected-Death with non-pharmacological policies) model as in the form of MIMO (Multi-Input-Multi-Output) structure by adding the multi-dimensional unknown uncertainties. The results confirm that the infected and death sub-models mostly have random uncertainties (due undetected casualties) whereas the suspicious sub-model has uncertainties correlated with the internal dynamics (governmental policy of increasing the number of the daily tests) for Turkey. However, since the developed MIMO model parameters are learned from the data (daily reported casualties), it can be easily adapted for other countries. Obtained model with the corresponding uncertainties predicts a distinctive second peak where the number of deaths, infected and suspicious casualties disappear in 240, 290, and more than 300 days, respectively, for Turkey.

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

“…In this regard, recent literature offers insightful perspectives on how to anticipate possible occurrence of future casualties, on which future energy demands and consequently environmental impacts may be related. For example, Tutsoy and co-authors have shown how the use of parametric models for predicting and analysing COVID-19 casualties ( Tutsoy et al, 2021 ; Tutsoy et al, 2020 ), even more when coupled with artificial intelligence techniques ( Tutsoy, 2021 ), can provide insights on how to minimize the impact of restrictive measures during pandemics by considering the multi-dimensional contribution of non-pharmacological policies such as lockdowns, schools’ closure, curfews, etc. Incorporating such knowledge on the complexity of the COVID-19 socio-economic impact mechanisms into the business, industry and public authority policy framework will be key for guiding sustainable energy productions and consumptions in the post COVID-19 era ( Aktar et al, 2021 ; Mofijur et al, 2021 ).…”

An Iterative Determination Method of an Axial Deployment Force of a Lanyard-Deployed Coilable Mast in Local Coil Mode

“…It should be noted that in real engineering problems, there are systems whose structures, uncertain properties, and all the parameters are unknown. In this case, advanced system identification and signal processing approaches can be developed and optimized with sophisticated approaches [14]. This should be the focus of further research in this paper.…”

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

“…UAV-based applications can be vulnerable to various threats if the uncertainties are not sufficiently taken into account. Designing a safety radius can be a solution to avoid the risks of collisions [ 98 , 99 , 100 , 101 , 102 ].…”