id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-229393-t3cpzmwj	Srivastava, Ajitesh	Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic	2020-04-23		.txt	text/plain	3671	228	62	By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends and provide extremely accurate predictions of confirmed cases at the level of countries and states of the United States. We do so by proposing two measures: (i) Contact Reduction Score that measure how much a region has reduced transmission; (ii) and Epidemic Reduction Score that measures how much reduction in confirmed cases a region has achieved compared to a hypothetical scenario where the trends had remained the same as a reference day in the past. Applying such machine learning-based models to a finer level (from countries to states/cities) and larger scale (more 'regions' of the world) brings unique challenges in terms of unreported/noisy data and large number of model parameters, which will be explored in a future work. To incorporate the fast evolving trend of COVID-19 due to changing policies, we use weighted least squared to learn parameters β p i and δ p i from available reported data.	./cache/cord-229393-t3cpzmwj.txt	./txt/cord-229393-t3cpzmwj.txt