In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2022-6-27), p. e1010281-
Abstract:
In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k . Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010281
DOI:
10.1371/journal.pcbi.1010281.g001
DOI:
10.1371/journal.pcbi.1010281.g002
DOI:
10.1371/journal.pcbi.1010281.g003
DOI:
10.1371/journal.pcbi.1010281.g004
DOI:
10.1371/journal.pcbi.1010281.g005
DOI:
10.1371/journal.pcbi.1010281.g006
DOI:
10.1371/journal.pcbi.1010281.g007
DOI:
10.1371/journal.pcbi.1010281.g008
DOI:
10.1371/journal.pcbi.1010281.g009
DOI:
10.1371/journal.pcbi.1010281.g010
DOI:
10.1371/journal.pcbi.1010281.t001
DOI:
10.1371/journal.pcbi.1010281.s001
DOI:
10.1371/journal.pcbi.1010281.s002
DOI:
10.1371/journal.pcbi.1010281.r001
DOI:
10.1371/journal.pcbi.1010281.r002
DOI:
10.1371/journal.pcbi.1010281.r003
DOI:
10.1371/journal.pcbi.1010281.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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