Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coupling Power Laws Offers a Powerful Method for Problems such as
  Biodiversity and COVID-19 Fatality Predictions   Power laws have been found to describe a wide variety of natural (physical,
biological, astronomic, meteorological, geological) and man-made (social,
financial, computational) phenomena over a wide range of magnitudes, although
their underlying mechanisms are not always clear. In statistics, power law
distribution is often found to fit data exceptionally well when the normal
(Gaussian) distribution fails. Nevertheless, predicting power law phenomena is
notoriously difficult because some of its idiosyncratic properties such as lack
of well-defined average value, and potentially unbounded variance. TPL
(Taylor's power law), a power law first discovered to characterize the spatial
and/or temporal distribution of biological populations and recently extended to
describe the spatiotemporal heterogeneities (distributions) of human
microbiomes and other natural and artificial systems such as fitness
distribution in computational (artificial) intelligence. The power law with
exponential cutoff (PLEC) is a variant of power-law function that tapers off
the exponential growth of power-law function ultimately and can be particularly
useful for certain predictive problems such as biodiversity estimation and
turning-point prediction for COVID-19 infection/fatality. Here, we propose
coupling (integration) of TPL and PLEC to offer improved prediction quality of
certain power-law phenomena. The coupling takes advantages of variance
prediction using TPL and the asymptote estimation using PLEC and delivers
confidence interval for the asymptote. We demonstrate the integrated approach
to the estimation of potential (dark) biodiversity and turning point of
COVID-19 fatality. We expect this integrative approach should have wide
applications given the duel relationship between power law and normal
statistical distributions.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph