Quantifying superspreading for COVID-19 using Poisson mixture distributions.
Sci Rep
; 11(1): 14107, 2021 07 08.
Article
in English
| MEDLINE | ID: covidwho-1303788
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Infectious Disease Transmission, Vertical
/
SARS-CoV-2
/
COVID-19
Type of study:
Observational study
Limits:
Humans
Country/Region as subject:
Africa
/
Asia
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-93578-x
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