Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.
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...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-06-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010281 |
| _version_ | 1818471726673035264 |
|---|---|
| author | Shi Zhao Marc K C Chong Sukhyun Ryu Zihao Guo Mu He Boqiang Chen Salihu S Musa Jingxuan Wang Yushan Wu Daihai He Maggie H Wang |
| author_facet | Shi Zhao Marc K C Chong Sukhyun Ryu Zihao Guo Mu He Boqiang Chen Salihu S Musa Jingxuan Wang Yushan Wu Daihai He Maggie H Wang |
| author_sort | Shi Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:55:31Z |
| format | Article |
| id | doaj.art-814c004c3d39447a98e11c2b24a414ba |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T03:55:31Z |
| publishDate | 2022-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-814c004c3d39447a98e11c2b24a414ba2022-12-22T02:13:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-06-01186e101028110.1371/journal.pcbi.1010281Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.Shi ZhaoMarc K C ChongSukhyun RyuZihao GuoMu HeBoqiang ChenSalihu S MusaJingxuan WangYushan WuDaihai HeMaggie H WangIn 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.https://doi.org/10.1371/journal.pcbi.1010281 |
| spellingShingle | Shi Zhao Marc K C Chong Sukhyun Ryu Zihao Guo Mu He Boqiang Chen Salihu S Musa Jingxuan Wang Yushan Wu Daihai He Maggie H Wang Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. PLoS Computational Biology |
| title | Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. |
| title_full | Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. |
| title_fullStr | Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. |
| title_full_unstemmed | Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. |
| title_short | Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. |
| title_sort | characterizing superspreading potential of infectious disease decomposition of individual transmissibility |
| url | https://doi.org/10.1371/journal.pcbi.1010281 |
| work_keys_str_mv | AT shizhao characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT marckcchong characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT sukhyunryu characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT zihaoguo characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT muhe characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT boqiangchen characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT salihusmusa characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT jingxuanwang characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT yushanwu characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT daihaihe characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility AT maggiehwang characterizingsuperspreadingpotentialofinfectiousdiseasedecompositionofindividualtransmissibility |