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• W3214441026 abstract "The COVID-19 pandemic has placed epidemiologists, modelers, and policy makers at the forefront of the global discussion of how to control the spread of coronavirus. The main challenges confronting modelling approaches include real-time projections of changes in the numbers of cases, hospitalizations, and fatalities, the consequences of public health policy, the understanding of how best to implement varied non-pharmaceutical interventions and potential vaccination strategies, now that vaccines are available for distribution. Here, we: (i) review carefully selected literature on COVID-19 modeling to identify challenges associated with developing appropriate models along with collecting the fine-tuned data, (ii) use the identified challenges to suggest prospective modeling frameworks through which adaptive interventions such as vaccine strategies and the uses of diagnostic tests can be evaluated, and (iii) provide a novel Multiresolution Modeling Framework which constructs a multi-objective optimization problem by considering relevant stakeholders' participatory perspective to carry out epidemic nowcasting and future prediction. Consolidating our understanding of model approaches to COVID-19 will assist policy makers in designing interventions that are not only maximally effective but also economically beneficial." @default.
• W3214441026 created "2021-11-22" @default.
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• W3214441026 date "2021-11-19" @default.
• W3214441026 modified "2023-10-06" @default.
• W3214441026 title "The Hard Lessons and Shifting Modeling Trends of COVID-19 Dynamics: Multiresolution Modeling Approach" @default.
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• W3214441026 doi "https://doi.org/10.1007/s11538-021-00959-4" @default.
• W3214441026 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8602007" @default.
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• W3214441026 hasPublicationYear "2021" @default.
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