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An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases

Pokutnaya, D.; Childers, B.; Arcury-Quandt, A.; Hochheiser, H.; Van Panhuis, W. G.

2022-11-01 infectious diseases
10.1101/2022.10.28.22281671 medRxiv
Show abstract

Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises twenty-two elements that should be described, grouped into six categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
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Top 12%
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6
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2.8%
9
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Top 0.6%
2.8%
10
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Top 5%
2.7%
11
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Top 3%
2.4%
12
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1.7%
13
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14
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● 214 papers in training set
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1.5%
15
eLife
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Top 58%
1.1%
16
American Journal of Infection Control
○ 12 papers in training set
Top 0.2%
1.0%
17
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Top 34%
0.9%
18
Nature Computational Science
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0.9%
19
Journal of the American Medical Informatics Association
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0.9%
20
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21
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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.