Study suggests R rate for tracking pandemic should be dropped in favour
of 'nowcasts'

Date:
September 29, 2021
Source:
University of Cambridge
Summary:
When the COVID-19 pandemic emerged in 2020, the R rate became
well-known shorthand for the reproduction of the disease. Yet a
new study suggests it's time for 'A Farewell to R' in favour of
a different approach based on the growth rate of infection rather
than contagiousness.



FULL STORY ==========================================================================
When the COVID-19 pandemic emerged in 2020, the R rate became well-known shorthand for the reproduction of the disease. Yet a new study suggests
it's time for 'A Farewell to R' in favour of a different approach based
on the growth rate of infection rather than contagiousness.


==========================================================================
The study, published in the Journal of the Royal Society Interface and
led by researchers from the University of Cambridge, is based on time
series models developed using classical statistical methods. The models
produce nowcasts and forecasts of the daily number of new cases and
deaths that have already proved successful in predicting new COVID-19
waves and spikes in Germany, Florida and several states in India.

The study is co-authored by Andrew Harvey and Paul Kattuman, whose
time series model reflecting epidemic trajectories, known as the Harvey-Kattuman model, was introduced last year in a paper published in
Harvard Data Science Review.

"The basic R rate quickly wanes in usefulness as soon as a pandemic
begins," said Kattuman, from Cambridge Judge Business School. "The basic
R rate looks at the number of infections expected to result from a single infectious person in a completely susceptible population, and this changes
as immunity builds up and measures such as social distancing are imposed."
In later stages of a pandemic, the researchers conclude that use of the effective R rate which takes these factors into account is also not the
best route: the focus should be not on contagiousness, but rather on the
growth rate of new cases and deaths, examined alongside their predicted
time path so a trajectory can be forecasted.

"These are the numbers that really help guide policymakers in making the crucial decisions that will hopefully save lives and prevent overcrowded hospitals as a pandemic plays out -- which, as we have seen with COVID-19,
can occur over months and even years," said Kattuman. "The data generated through this time-series model has already proved accurate and effective
in countries around the world." The study examines waves and spikes in tracking an epidemic, noting that after an epidemic has peaked, daily
cases begin to fall as policymakers seek to prevent new spikes morphing
into waves. The monitoring of waves and spikes raises different issues, primarily because a wave applies to a whole nation or a relatively large geographical area, whereas a spike is localised.

Therefore, a localised outbreak in a country with low national infection numbers can result in a jump in the national R rate, as occurred in
the Westphalia area of Germany in June 2020 after an outbreak at a meat processing factory. However, this sort of jump does not indicate that
there has been a sudden change in the way the infection spreads and so
has few implications for overall policy.

The Harvey-Kattuman model has been adapted into two trackers. The two
Cambridge academics worked with the National Institute of Economic and
Social Research to produce a UK tracker which is published biweekly by
the National Institute of Economic and Social Research. In addition,
they produce an India tracker which is published by the Centre
for Health Leadership and Excellence at Cambridge Judge Business
School. District-level pandemic trajectory forecasts using the model are
used by public health policymakers in three states in India - - Punjab,
Tamil Nadu and Kerala -- to identify regions at high risk and to frame containment and relaxation policies.

========================================================================== Story Source: Materials provided by University_of_Cambridge. The original
text of this story is licensed under a Creative_Commons_License. Note:
Content may be edited for style and length.


========================================================================== Journal References:
1. Andrew Harvey, Paul Kattuman. A farewell to R: time-series
models for
tracking and forecasting epidemics. Journal of The Royal Society
Interface, 2021; 18 (182) DOI: 10.1098/rsif.2021.0179
2. Andrew Harvey, Paul Kattuman. Time Series Models Based on Growth
Curves
with Applications to Forecasting Coronavirus. Harvard Data Science
Review, 2020; DOI: 10.1162/99608f92.828f40de ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210928193712.htm

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