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Estimating and forecasting COVID-19 attack rates and mortality

Ketcheson, D. I.; Ombao, H. C.; Moraga, P.; Ballal, T.; Duarte, C. M.

2020-05-14 epidemiology
10.1101/2020.05.11.20097972 medRxiv
Show abstract

We describe a model for estimating past and current infections as well as future deaths due to the ongoing COVID-19 pandemic. The model does not use confirmed case numbers and is based instead on recorded numbers of deaths and on the age-specific population distribution. A regularized deconvolution technique is used to infer past infections from recorded deaths. Forecasting is based on a compartmental SIR-type model, combined with a probability distribution for the time from infection to death. The effect of non-pharmaceutical interventions (NPIs) is modelled empirically, based on recent trends in the death rate. The model can also be used to study counterfactual scenarios based on hypothetical NPI policies.

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

1
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Top 2%
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50% of probability mass above
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10
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14
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15
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16
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17
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18
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19
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20
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21
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22
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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.