Lessons from Pandemics: Computational agent-based model approach for estimation of downstream and upstream measures to achieve requisite societal behavioural changes
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Pandemics such as COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind. Scientists and policymakers all around are striving to achieve R ≤ 1 alongside having less number of CoVID-19 patients. Lockdowns all across the globe have been implemented for the sake of social physical distancing. However, even if the desired R value status is achieved it becomes nowhere near safe. As normal social activity and inter-regional travel resumes, danger of contraction of the virus from undetected asymptomatic carriers and reactivation of the virus in previously affected patients looms over. The virus poses further threat due to its chances of resurgence, its mutative and adaptive nature thereby giving limited medical respite. The problems intensify with increasing population density whilst varying with several socio-economic-geo-cultural and human activity parameters. Such zoonotic pandemics unravel the primary challenges of all countries in securing the general wellbeing of the society. Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease/health indicators can be the only solution. An approach perspective is discussed for addressing the tightly-coupled UN Sustainable goals (2, 3, 6, 12 and 13) for developing a general-scale computational agent-based model to estimate the downstream and upstream measures for achieving requisite societal behavioural changes with the prognostic knowledge concerning the conditions and options for future scenarios of stable sustainability.
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