Estimating the time interval between transmission generations and the presymptomatic period by contact tracing surveillance data from 31 provinces in the mainland of China

Ding, Zhongxing; Wang, Kai; Shen, Mingwang; Zhao, Shi; Song, Wenyu; Li, Rui; Li, Zhongjie; Wang, Liping; Feng, Ganzhu; Hu, Zhiliang; Wei, Hongxia; Xiao, Yanni; Bao, Changjun; Hu, Jianli; Zhu, Liguo; Li, Yong; Chen, Xufeng; Yin, Yi; Wang, Weiming; Cai, Yongli; Peng, Zhihang; Shen, Hongbing

doi:10.1016/j.fmre.2021.02.002

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…In other words, it is particularly the variations in c(t) that might help us to decouple the effects of these two phenotypic traits. Simply adding an exposed state E makes the selection gradient surprisingly much more difficult to express but the importance of the variations in c(t) for this purpose (although less clear-cut) remains nevertheless relevant as suggested by (11).…”

Section: Step 2: Evolutionary Analysismentioning

confidence: 99%

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

et al. 2023

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Since its emergence in late 2019, the SARS-CoV-2 virus has spread globally, causing the ongoing COVID-19 pandemic. In the fall of 2020, the Alpha variant (lineage B.1.1.7) was detected in England and spread rapidly, outcompeting the previous lineage. Yet, very little is known about the underlying modifications of the infection process that can explain this selective advantage. Here, we try to quantify how the Alpha variant differed from its predecessor on two phenotypic traits: the transmission rate and the duration of infectiousness. To this end, we analysed the joint epidemiological and evolutionary dynamics as a function of the Stringency Index, a measure of the amount of Non-Pharmaceutical Interventions. Assuming that these control measures reduce contact rates and transmission, we developed a two-step approach based on SEIR models and the analysis of a combination of epidemiological and evolutionary information. First, we quantify the link between the Stringency Index and the reduction in viral transmission. Secondly, based on a novel theoretical derivation of the selection gradient in an SEIR model, we infer the phenotype of the Alpha variant from its frequency changes. We show that its selective advantage is more likely to result from a higher transmission than from a longer infectious period. Our work illustrates how the analysis of the joint epidemiological and evolutionary dynamics of infectious diseases can help understand the phenotypic evolution driving pathogen adaptation.

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Section: Step 2: Evolutionary Analysismentioning

confidence: 99%

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

et al. 2023

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“…The infectious period of the pre-symptomatic stage is d 2 = 2 . 4 [49]. The infection probabilities are Ph j,i and Po j,i in household and non-household.…”

Section: Methodsmentioning

confidence: 99%

“…Alternatively, the j th individual can be in the pre-symptomatic stage. The infectious period of the pre-symptomatic stage is d2 = 2.4 [49]. The infection probabilities are Phj,i and Poj,i in household and non-household.…”

Section: Agent-based Modelmentioning

confidence: 99%

Using a real-world network to model the tradeoff between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics

Nashebi

Sarı

Kotil

2022

Preprint

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Background. Human behavior, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination and social distance on the containment of pandemics such as COVID-19. Methods. To achieve this, we developed an agent-based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The graph's edges have been categorized into three categories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type (WT) COVID-19 and the delta variant have been analyzed. Our purposeful agent-based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (Re), transmission rate, work hours, vaccination, and stay at home restrictions. Results. For the WT, it has been found that a 13% increase in vaccination impacts the reproduction number, like the magnitude of decreasing nine hours of work to four and a single day of stay-at-home order. For the delta, 16% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the Re. Household infections are not limited by transmission rate due to the high frequency of connections. For COVID-19's specifications, the Re depends on the non-household transmissions rate. Conclusions. Vaccination and transmission reduction are almost interchangeable. Without vaccination or teaching people how to lower their transmission probability significantly, changing work hours or weekend restrictions will only make people more frustrated

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“…We used additional information from the litterature to set the value of some parameters of the model. For instance, we set the mean latency period to 5 days [9] and the mean duration of infectiousness to 10 days [2] – that is, an average infection period of 15 days. Besides, we set the basic reproduction number R 0 to 2.5 [10, 21, 23] and we approximated the initial states within compartment I .…”

Section: Methodsmentioning

confidence: 99%

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

Benhamou

Lion

Choquet

et al. 2022

Preprint

View full text Add to dashboard Cite

Since its emergence in late 2019, the SARS-CoV-2 virus has spread globally, causing the ongoing COVID-19 pandemic. In the fall of 2020, the Alpha variant (lineage B.1.1.7) was detected in England and rapidly spread and outcompeted the previous lineage. Yet, very little is known about the underlying modifications of the infection process that can explain this selective advantage. Here, we try to quantify how the Alpha variant differs from its predecessor on two phenotypic traits: the transmission rate and the duration of infectiousness. To this end, we analysed the joint epidemiological and evolutionary dynamics of SARS-CoV-2 as a function of the Stringency Index, a measure of the amount of Non-Pharmaceutical Interventions. We developed a two-step approach based on a SEIR model and the analysis of a combination of epidemiological and evolutionary information. First, we infer how the Stringency Index reduces the amount of viral transmission. Secondly, based on a novel theoretical derivation of the selection gradient in a SEIR model, we infer the phenotype of the Alpha variant from the analysis of the change in its frequency and we show that its selective advantage is more likely to result from a higher transmission than from a longer infectious period.

show abstract

Estimating the time interval between transmission generations and the presymptomatic period by contact tracing surveillance data from 31 provinces in the mainland of China

Cited by 7 publications

References 23 publications

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

Using a real-world network to model the tradeoff between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics

Phenotypic evolution of SARS-CoV-2: a statistical inference approach

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