Performance of Existing and Novel Surveillance Case Definitions for COVID-19 in a Community Cohort

Overview

This code is from the paper "Performance of existing and novel surveillance case definitions for COVID-19 in household contacts of PCR-confirmed COVID-19", which was published in BMC Public Health. With access to the analytic dataset, it will reproduce the tables and figures from our manuscript. It can also be used to test out our methods, like the combinatorial symptom search, on your own data.

Data

In the data, we have information about symptoms for each of the study participants, and we also have their SARS-CoV-2 PCR and ELISA test results. In our primary analysis, we frame the problem as one of binary classification, i.e., by predicting PCR status from different combinations of the symptoms. Here's a quick rundown of the variables:

1. study_id: participant identifier
2. hh_id: household identifier
3. age_adult: whether age is over (1) or under (0) 18 years
4. wheeze to tastesmell_combo: the symptoms
5. ili: influenza-like illness
6. cdc: CDC's COVID symptom list
7. ari: the WHO RSV ARI case definition
8. cste: the CSTE COVID case definition
9. cli: COVID-like illness
10. sero_pos: whether ELISA detected SARS-CoV-2 antibodies
11. sero_conv: whether the participant seroconverted during the observation period
12. pcr_pos: whether RT-PCR detected SARS-CoV-2 infection
13. any_pos: whether ELISA or RT-PCR was positive

Code

We used both R and Python to do the analysis for this study. There's a good deal of overlap between the two sets of code, but in general, the R code is geared toward generating our graphics, while the Python code is geared toward generating the statistics we report in the tables. Here's a quick manifest of the scripts:

Python

1. tools.py: support functions used for the analysis
2. multi.py: multiprocessing-enabled versions of functions from tools.py
3. combo_search.py: runs the combinatorial symptom search
4. rf.py: trains a random forest on the data
5. primary_analysis.py: produces the statistics and tables in the manuscript

R

1. 0-hh--data.R: Read in source CSV data file; set up names, analytic data
2. 1-hh-combos.R: Calculate performance of preconstructed rules and of symptom combinations
3. 2-hh-resampling.R: Construct pseudosamples by resampling households
4. 3-hh-graphs-combos.R: Graphics: how symptom combinations perform
5. 4-hh-graphs-adult-child.R: Graphics: how selected rules perform in adults and children

For more info, check out the respective READMEs.

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