Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.
PLoS Comput Biol
; 18(6): e1010281, 2022 06.
Article
Dans Anglais
| MEDLINE | ID: covidwho-1910467
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.
Texte intégral:
Disponible
Collection:
Bases de données internationales
Base de données:
MEDLINE
Sujet Principal:
Maladies transmissibles
/
COVID-19
Type d'étude:
Étude observationnelle
/
Étude pronostique
Limites du sujet:
Humains
langue:
Anglais
Revue:
PLoS Comput Biol
Thème du journal:
Biologie
/
Informatique médicale
Année:
2022
Type de document:
Article
Pays d'affiliation:
Journal.pcbi.1010281
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