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This vignette summarises the findings from the 100 days and 100 lines of code workshop, hosted in December 2022 by Epiverse-TRACE.

This document is a draft, the final version will be published on Epiverse’s blog after it has been reviewed by other Epiverse members and workshop participants * Participants who have contributed so far: Sara Hollis, Anne Cori, Geraldine Gomez

What should the first 100 lines of code written during an epidemic look like?

To answer this question, we invited 40 experts, including academics, field epidemiologists, and software engineers, to take part in a 3-day workshop, where they discussed the current challenges, and potential solutions, in data analytic pipelines used to analyse epidemic data. In addition to highlighting existing technical solutions and their use cases, presentations on best practices in fostering collaboration across institutions and disciplines set the scene for the subsequent workshop scenario exercises.

What R packages and tools are available to use during an epidemic?

To investigate this in a similar setting to what an outbreak response team would experience, workshop participants were divided into groups, and asked to develop a plausible epidemic scenario, that included:

  • A situation report, describing the characteristics of the epidemic

  • A linelist of cases and contact tracing data, by modifying provided datasets containing simulated data

  • A set of questions to address during the analytic process

Groups then exchanged epidemic scenarios and analysed the provided data to answer the questions indicated the previous group, as if they were a response team working to solve an outbreak. Details about each of these outbreak scenarios and the analytic pipelines developed by the groups are summarised in this vignette.

Simulating epidemic data

Before the workshop, a fictitious dataset was created, which consisted of a linelist and contact tracing information.

To generate linelist data, the package bpmodels was used to generate a branching process network. Cases were then transformed from the model output to a linelist format. To add plausible hospitalisations and deaths, delay distributions for SARS-CoV were extracted from epiparameter.

To create the contact tracing database, a random number of contacts was generated for each of the cases included in the linelist. These contacts were then assigned a category of became case, under follow up or lost to follow up, at random.

  • Through this workshop, we identified the need for a tool to simulate outbreak data in a linelist format, to test analysis methods and other packages while having control over the characteristics of the test data. For this purpose, an R package is currently in progress, see simulist.

Scenario 1: Novel respiratory disease in The Gambia



Analytic pipeline for scenario 1 (analysed by group 2)

  • Data cleaning

  • Delay distributions

    • fitdisrplus to fit parameteric distributions to scenario data
    • epiparameter to extract delay distributions from respiratory pathogens
    • EpiNow2 to fit reporting delays
    • EpiEstim / coarseDataTools to estimate generation time/serial interval of disease
    • epicontacts
    • mixdiff to estimate delay distributions and correct erroneous dates at the same time (still under development)

  • Population demographics

    • Would like to have had access to an R package similar to ColOpenData

  • Risk factors of infection

    • Used R4epis as a guide on how to create two-way tables and perform Chi-squared tests

  • Severity of disease

  • Contact matching

    • diyar to match and link records
    • fuzzyjoin to join contact and case data despite misspellings or missing cell contents

  • Epi curve and maps

    • Used incidence and incidence2 for incidence calculation and visualisation
    • raster to extract spatial information from library of shapefiles

  • Reproduction number

  • Superspreading, by using these resources:

  • Epidemic projections

    • incidence R estimation using a loglinear model
    • projections using Rt estimates, SI distributions and overdispersion estimates

  • Transmission chains and strain characterisation

    • IQtree and nextclade to build a maximum likelihood tree and mannually inspect it
    • Advanced modelling through phylodynamic methods, using tools like BEAST

Data analysis step Challenges
Data cleaning Not knowing what packages are available for this purpose
Delay distributions Dealing with right truncation
Accounting for multiple infectors
Population demographics Lacking tools that provide information about population by age, gender, etc.
Risk factors of infection Distinguishing between risk factors vs detecting differences in reporting frequencies among groups
Severity of disease Knowing the prevalence of disease (denominator)
Right truncated data
Varying severity of different strains
Contact matching Missing data
Misspellings
Epicurve and maps NA dates entries not included
Reporting levels varying over time
Offspring distribution Right truncation
Time varying reporting efforts
Assumption of a single homogeneous epidemic
Importation of cases
Forecasting Underlying assumption of a given R distribution, e.g., single trend, homogeneous mixing, no saturation

Scenario 2: Outbreak of an unidentified disease in rural Colombia



Analytic pipeline for scenario 2 (analysed by group 3)

  • Data cleaning: manually, using R (no packages specified), to
    • Fix data entry issues in columns onset_date and gender
    • Check for missing data
    • Check sequence of dates: symptom onset → hospitalisation → death
  • Data anonymisation to share with partners
    • fastlink for probabilistic matching between cases ↔ contacts, based on names, dates, and ages
  • Case demographics
    • apyramid to stratify data by age, gender, and health status
  • Reproductive number calculation, by using two approaches:
    • Manually, by calculating the number of cases generated by each source case, data management through dplyr and data.table
    • Using serial interval of disease, through EpiEstim or EpiNow2
  • Severity of disease
    • Manual calculation of CFR and hospitalisation ratio
  • Projection of hospital bed requirements
    • EpiNow2 to calculate average hospitalisation duration and forecasting
  • Zoonotic transmission of disease
    • Manual inspection of cases’ occupation
    • Use of IQtree and ggtree to plot phylogenetic data
  • Superspreading
  • Calculation of attack rate
    • Unable to calculate, given the lack of seroprevalence data

Data analysis step Challenges
Data anonymisation Dealing with typos and missing data when generating random unique identifiers
Reproduction number Right truncation
Underestimation of cases due to reporting delays
Projection of hospital bed requirements Incomplete data (missing discharge date)
Undocumented functionality in R packages used
Zoonotic transmission Poor documentation
Unavailability of packages in R
Differentiation between zoonotic transmission and risk factors- need for population data
Attack rate Not enough information provided

Scenario 3: Reston Ebolavirus in the Philippines



Analytic pipeline for scenario 3 (analysed by group 4)


Data analysis step Challenges
Detection of outliers No known tools to use
Severity of disease Censoring
Spillover events Missing data

Scenario 4: Emerging avian influenza in Cambodia



Analytic pipeline for scenario 4 (analysed by group 5)

  • Data cleaning
    • readxl to import data
    • dplyr to remove names
    • Manually scanning through excel to check for errors
  • Reproduction number
  • Severity of disease
    • Manually using R to detect missing cases
    • epiR to check for data censoring

Data analysis step Challenges
Data cleaning No available R packages specific for epidemic data
Reproduction number Difficulty finding parameter estimations in the literature
Severity Missing cases
Need for an R package for systematic censoring analysis

Scenario 5: Outbreak of respiratory disease in Canada



Analytic pipeline for scenario 5 (analysed by group 1)


Data analysis step Challenges
Project structure Working simultaneously on the same script and managing parallel tasks
Anticipating future incoming data in early pipeline design
Data cleaning Large amount of code lines used on (reasonably) predictable cleaning (e.g. data sense checks)
Omitting too many data entries when simply removing NA rows
Non standardised data formats
Implementing rapid quality check reports before analysis
Delay distributions Identifying the best method to calculate, or compare functionality of tools
Need to fit multiple parametric distributions and return best, and store as usable objects
Severity of disease Censoring and truncation
Underestimation of mild cases
Need database of age/gender pyramids for comparisons
Forecasts Need option for fitting with range of plausible pathogen serial intervals and comparing results
Changing reporting delays over time
Matching inputs/outputs between packages
Zoonotic transmisison Need for specific packages with clear documentation
How to compare simple trend-based forecasts