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Mathematical Analysis, Model and Prediction of COVID-19 Data

Tong, Y. C.

2020-08-06 epidemiology
10.1101/2020.08.04.20168195 medRxiv
Show abstract

A simple and effective mathematical procedure for the description of observed COVID-19 data and calculation of future projections is presented. An exponential function E(t) with a time-varying Growth Constant k(t) is used. E(t) closely approximates observed COVID-19 Daily Confirmed Cases with NRMSDs of 1 to 2%. An example of prediction of future cases is presented. The Effective Growth Rates of a discrete SIR model were estimated on the basis of k(t) for COVID-19 data for Germany, and were found to be consistent with those reported in a previous study (1). The proposed procedure, which involves less than ten basic algebraic, logarithm and exponentiation operations for each data point, is suitable for use in promoting interdisciplinary research, exchange and sharing of information.

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The top 3 journals account for 50% of the predicted probability mass.

1
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Top 0.4%
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10
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11
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1.7%
12
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13
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○ 46 papers in training set
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1.4%
14
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Top 0.9%
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Top 0.8%
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Top 0.3%
1.1%
17
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● 23 papers in training set
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1.0%
18
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Top 2%
0.9%
19
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Top 5%
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20
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21
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Top 58%
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22
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Top 12%
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25
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26
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.