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Adaptive short term COVID-19 prediction for India

Jana, S.; Ghose, D.

2020-07-21 infectious diseases
10.1101/2020.07.18.20156745 medRxiv
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In this paper, a data-driven adaptive model for infection of COVID-19 is formulated to predict the confirmed total cases and active cases of an area over 4 weeks. The parameter of the model is always updated based on daily observations. It is found that the short term prediction of up to 3-4 weeks can be possible with good accuracy. Detailed analysis of predicted value and the actual value of confirmed total cases and active cases for India from 1st June to 3rd July is provided. Prediction over 7, 14, 21, 28 days has the accuracy about 0.73% {+/-} 1.97%, 1.92% {+/-} 2.95%, 4.34% {+/-} 3.91%, 6.40% {+/-} 9.26% of the actual value of confirmed total cases. Similarly, the 7, 14, 21, 28 days prediction has the accuracy about 1.24% {+/-} 6.57%, 3.04% {+/-} 10.00%, 6.33% {+/-} 16.12%, 10.20% {+/-} 24.14% of the actual value of confirmed active cases.

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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.