Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Backtesting the predictability of COVID-19   The advent of the COVID-19 pandemic has instigated unprecedented changes in
many countries around the globe, putting a significant burden on the health
sectors, affecting the macro economic conditions, and altering social
interactions amongst the population. In response, the academic community has
produced multiple forecasting models, approaches and algorithms to best predict
the different indicators of COVID-19, such as the number of confirmed infected
cases. Yet, researchers had little to no historical information about the
pandemic at their disposal in order to inform their forecasting methods. Our
work studies the predictive performance of models at various stages of the
pandemic to better understand their fundamental uncertainty and the impact of
data availability on such forecasts. We use historical data of COVID-19
infections from 253 regions from the period of 22nd January 2020 until 22nd
June 2020 to predict, through a rolling window backtesting framework, the
cumulative number of infected cases for the next 7 and 28 days. We implement
three simple models to track the root mean squared logarithmic error in this
6-month span, a baseline model that always predicts the last known value of the
cumulative confirmed cases, a power growth model and an epidemiological model
called SEIRD. Prediction errors are substantially higher in early stages of the
pandemic, resulting from limited data. Throughout the course of the pandemic,
errors regress slowly, but steadily. The more confirmed cases a country
exhibits at any point in time, the lower the error in forecasting future
confirmed cases. We emphasize the significance of having a rigorous backtesting
framework to accurately assess the predictive power of such models at any point
in time during the outbreak which in turn can be used to assign the right level
of certainty to these forecasts and facilitate better planning.

15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg