The Hard Lessons and Shifting Modeling Trends of COVID-19 Dynamics: Multiresolution Modeling Approach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-11-19

AUTHORS

Olcay Akman, Sudipa Chauhan, Aditi Ghosh, Sara Liesman, Edwin Michael, Anuj Mubayi, Rebecca Perlin, Padmanabhan Seshaiyer, Jai Prakash Tripathi

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. More... »

PAGES

3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11538-021-00959-4

DOI

http://dx.doi.org/10.1007/s11538-021-00959-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142692631

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/34797415


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