why development of outbreak analytics tools should be valued supported and funded CORD-Papers-2022-06-02 (Version 1)

Title: Why development of outbreak analytics tools should be valued supported and funded
Published: 2021-01-11
Journal: Lancet Infect Dis
DOI: 10.1016/s1473-3099(20)30996-8
DOI_URL: http://doi.org/10.1016/s1473-3099(20)30996-8
Author Name: Jombart Thibaut
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jombart_thibaut
license: no-cc
license_url: [no creative commons license associated]
source_x: PMC
source_x_url: https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 33444558
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/33444558
pmcid: PMC7832113
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832113
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832113/
has_full_text: TRUE
Keywords Extracted from PMC Text: health-research COVID-19 Imperial College London's COVID-19 reproducible.1
Extracted PMC Text Content in Record: First 5000 Characters:The COVID-19 pandemic has brought infectious disease modelling to the forefront, with mainstream media uncovering the good, the bad, and sometimes, the ugly in a field of research that is being used more than ever to inform public health decision-making. A dramatic example is the code release of Imperial College London's COVID-19 simulations, which sparked waves of criticisms for its poor coding practices, although the results themselves were later found to be reproducible.1 Does good coding matter in science? If by good coding we mean using practices that make the code clear and easy to reuse, maintain, expand on, and test—in short, reliable—then the answer is yes. And it matters even more when the corresponding piece of software is used to inform public health operations. Unfortunately, scientific software development has struggled to gain recognition,2, 3 and there has been little incentive so far for academic researchers to make code free to access and transparent in infectious disease modelling. The issue is not limited to modelling. The emergence of outbreak analytics as a new field of research emphasises the need for high-quality, freely available, and open-source software tools for informing the response to infectious disease outbreaks, from data collection to advanced statistical analyses.4 Nor is the issue new. Development of tools for outbreak analytics has been chronically undervalued and underfunded. Despite the emergence of initiatives, such as the R Epidemics Consortium,5 to promote open-source software for outbreak response, such projects typically fall outside the scopes of health-research funders, lying somewhere between theoretical modelling work and interventions. As a result, we have faced an absurd situation where data scientists involved in outbreak responses have encountered the same issues at every new outbreak, without ever being able to focus on developing software tools to solve these problems once and for all. While it is frustrating to see this issue finally acknowledged during the biggest public health crisis in recent times, it is not too late for a cultural shift to take place. Solutions are simple. The development of high-quality scientific software must be as valued as other academic outputs. Dedicated career profiles for scientific software engineers must be created to build long-term capacity in academic institutions. Last, and perhaps most importantly, funders need to lead—not follow—this cultural shift by acknowledging the development of outbreak analytics tools as a field deserving recognition and support.
PMC JSON Files: document_parses/pmc_json/PMC7832113.xml.json
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