Capturing hidden data for asymptomatic COVID-19 cases provides a better pandemic picture
Measuring the disease spread poses a challenge when not all carriers show
it

Date:
February 10, 2022
Source:
DOE/Los Alamos National Laboratory
Summary:
Asymptomatic COVID-19 cases are the bane of computer modelers'
existences -- they throw off the modeling data to an unknown
degree. A new approach explores using historic epidemic data from
eight different countries to estimate the transmission rate and
fraction of under-reported cases.



FULL STORY ========================================================================== Asymptomatic COVID-19 cases are the bane of computer modelers' existences
- - they throw off the modeling data to an unknown degree. You can't
measure what you can't detect, right? A new approach from Los Alamos
National Laboratory's Theoretical Division, however, explores using
historic epidemic data from eight different countries to estimate the transmission rate and fraction of under- reported cases.


========================================================================== "Asymptomatic cases are the 'dark matter' of epidemics," said Nick
Hengartner, one of the authors on the report published today in the
journalPLOS ONE. "We see only the indirect evidence that more people
are sick than reported, and if we don't account for them, we may wrongly conclude that the epidemic is under control. So we changed the model to
focus on the observed counts instead of trying to model the 'perfect'
world. By looking back through the time series of historical data, we can
see from their dynamics what's missing." The importance of capturing
the undocumented cases is significant, especially in a disease such
as COVID-19, where asymptomatic individuals have accounted for 20-70
percent of all infections.

Co-author Imelda Trejo, a postdoctoral fellow at Los Alamos noted, "This
is a new extension of the standard SIR (susceptible-infected-recovered) epidemiological models to study the underreported incidence of infectious disease. The new model reveals that trying to fit an SIR model type
directly to raw incidence data will underestimate the true infectious
rate. This could actually lead decisionmakers to declare the epidemic
under control prematurely." Instead, the team presented a Bayesian method
(a statistical model using probability to represent all uncertainty
within the model) to estimate the transmission rate and fraction of underreported cases.

As tested against the data of eight countries (Argentina, Brazil, Chile, Colombia, Mexico, Panama, Peru and the U.S.), the new approach directly describes the dynamics of the observed, under-reported cases. "We use
the local dynamics of the observed cases to propose a model that gives us
a conditional expectation of new cases, based on the observed history,"
Trejo said.

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by
DOE/Los_Alamos_National_Laboratory. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Imelda Trejo, Nicolas W. Hengartner. A modified
Susceptible-Infected-
Recovered model for observed under-reported incidence data. PLOS
ONE, 2022; 17 (2): e0263047 DOI: 10.1371/journal.pone.0263047 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220210154142.htm

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