id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-312366-8qg1fn8f	Adiga, Aniruddha	Mathematical Models for COVID-19 Pandemic: A Comparative Analysis	2020-10-30		.txt	text/plain	8797	472	49	As the pandemic takes hold, researchers begin investigating: (i) various intervention and control strategies; usually pharmaceutical interventions do not work in the event of a pandemic and thus nonpharmaceutical interventions are most appropriate, (ii) forecasting the epidemic incidence rate, hospitalization rate and mortality rate, (iii) efficiently allocating scarce medical resources to treat the patients and (iv) understanding the change in individual and collective behavior and adherence to public policies. Like projection approaches, models for epidemic forecasting can be broadly classified into two broad groups: (i) statistical and machine learning-based data-driven models, (ii) causal or mechanistic models-see 29, 30, 2, 31, 32, 6, 33 and the references therein for the current state of the art in this rapidly evolving field. In the context of COVID-19 case count modeling and forecasting, a multitude of models have been developed based on different assumptions that capture specific aspects of the disease dynamics (reproduction number evolution, contact network construction, etc.).	./cache/cord-312366-8qg1fn8f.txt	./txt/cord-312366-8qg1fn8f.txt