• Researchers outline bias in epidemic res

    From ScienceDaily@1:317/3 to All on Thu Mar 31 22:30:46 2022
    Researchers outline bias in epidemic research -- and offer new
    simulation tool to guide future work
    Analysis yields way to improve data collection, clinical trials, and
    public policy

    Date:
    March 31, 2022
    Source:
    New York University
    Summary:
    A team of researchers unpacks a series of biases in epidemic
    research, ranging from clinical trials to data collection,
    and offers a game-theory approach to address them, in a new
    analysis. The work sheds new light on the pitfalls associated with
    technology development and deployment in combating global crises
    like COVID-19, with a look toward future pandemic scenarios.



    FULL STORY ==========================================================================
    A team of researchers unpacks a series of biases in epidemic research,
    ranging from clinical trials to data collection, and offers a game-theory approach to address them, in a new analysis. The work sheds new light
    on the pitfalls associated with technology development and deployment
    in combating global crises like COVID-19, with a look toward future
    pandemic scenarios.


    ========================================================================== "Even today, empirical methods used by epidemic researchers suffer from
    defects in design and execution," explains Bud Mishra, a professor at
    New York University's Courant Institute of Mathematical Sciences and the
    senior author of the paper, which appears in the journal Technology
    & Innovation. "In our work, we illuminate common, but remarkably oft-overlooked, pitfalls that plague research methodologies -- and
    introduce a simulation tool that we think can improve methodological decision-making." Even in an era when vaccines can be successfully
    developed in a matter of months, combatting afflictions in ways not
    imaginable in previous centuries, scientists may still be unwittingly
    hindered by flaws in their methods.

    In the paper, Mishra and his co-authors, Inavamsi Enaganti and Nivedita
    Ganesh, NYU graduate students in computer science, explore some standard paradoxes, fallacies, and biases in the context of hypothesizing and show
    how they are relevant to work aimed at addressing epidemics. These include
    the Grue Paradox, Simpson's Paradox, and confirmation bias, among others:
    The Grue Paradox The authors note that research has often been hampered
    by errors linked to inductive reasoning, falling under what is known as
    the Grue Paradox. For example, if all emeralds observed during a given
    period are green, then all emeralds must be green. However, if we define
    "grue" as the property of being green up to a certain period in time and
    then blue thereafter, inductive evidence supports the conclusion that
    all emeralds are "grue" and supports the conclusion that all emeralds
    are green, preventing one from reaching a definitive conclusion on the
    color of emeralds.



    ========================================================================== "While constructing and comparing hypotheses in the context of epidemics,
    it is vital to identify the temporal dependence of the predicate,"
    the authors write.

    These include hypotheses on the mutation of a virus, inducement of herd immunity, or recurring waves of infection.

    Simpson's Paradox "Simpson's Paradox is a phenomenon where trends that
    are observed in data when stratified into different groups are reversed
    when combined," the authors write. "This effect has widespread presence in academic literature and notoriously perverts the truth." For instance,
    if in a clinical trial 100 subjects undergo Treatment 1 and 100 subjects undergo Treatment 2 with success rates of 40 percent and 37 percent, respectively, one would assume Treatment 1 is more effective. However,
    if you split these data by genetic markers -- say, Genetic Marker A and
    Genetic Marker B -- the efficacy of the treatments may yield different
    results. For example, Treatment 1 may look superior when you look at an aggregated population, but its worth may diminish for certain subgroups.

    Confirmation Bias The widely known Confirmation Bias, or the tendency
    to look for and recall data with greater emphasis when it supports a researcher's hypothesis, also plagues epidemic research, the authors note.



    ========================================================================== "This phenomenon can already be seen in the COVID-19 context in the
    selective marshaling of data to paint a picture that supports popular
    belief," they write. "For instance, evidence that supports countries
    practicing strict lockdown and social distancing improves public health
    has been given more weight than evidence suggesting countries relaxing
    their measures have a similar reduction in their caseloads. Additionally,
    other variables that could be as influential as lockdown, but are
    contextual and varied for different geographies, might have been ignored,
    such as population density or history of vaccinations." In addressing
    these methodological challenges, the team created an open-source Epidemic Simulation platform (Episimmer) that seeks to provide decision support
    to help answer users' questions related to policies and restrictions
    during an epidemic.

    Episimmer, which the researchers tested under several simulated
    public-health emergencies, performs "counterfactual" analyses,
    measuring what would have happened to an ecosystem in the absence of interventions and policies, thereby helping users discover and hone
    the opportunities and optimizations they could make to their COVID-19 strategies (Note: The platform's python package is available on this page: https://pypi.org/project/episimmer/ ). These could include decisions
    such as "Which days to be remote or in-person" for schools and workplaces
    as well as "Which vaccination routine is more efficient given the local interaction patterns?" "Faced with a rapidly evolving virus, inventors
    must experiment, iterate, and deploy both creative and effective solutions while avoiding pitfalls that plague clinical trials and related work,"
    says Enaganti.

    The team carried out its research as part of a self-assembled larger
    multi- disciplinary international research group, dubbed RxCovea, and
    enabled its tools' deployment in India as part of Campus-Rakshak program.


    ========================================================================== Story Source: Materials provided by New_York_University. Note: Content
    may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Inavamsi Enaganti, Nivedita Ganesh, Bud (Bhubaneswar)
    Mishra. Inventions
    of Interventions: Data Driven Strategies in Pandemic Research and
    Control. Technology & Innovation, 2022; 22 (2): 233 DOI: 10.21300/
    22.2.2021.12 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220331101504.htm

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