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Markovian Structures in Modeling COVID-19

Cui, E. H.

2022-09-27 epidemiology
10.1101/2022.09.23.22280294 medRxiv
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AO_SCPLOWBSTRACTC_SCPLOWThe aim of this paper is to model COVID-19 based on Markov chains. First, we introduce basic concepts of Markov chains with examples from different disciplines. Second, we use different types of Markov chains to model COVID-19, including confirmed cases, death and recovered cases and forecasting future confirmed cases. Third, we give conclusions based on these models and ideas for future work.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
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50% of probability mass above
8
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9
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○ 11 papers in training set
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10
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11
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12
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13
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14
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19
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20
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21
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25
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26
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27
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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.