Intrinsic and realized generation intervals in infectious-disease transmission

Champredon, David; Dushoff, Jonathan

doi:10.1098/rspb.2015.2026

Cited by 88 publications

(147 citation statements)

References 24 publications

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“…The findings of the present study clearly show the non-constant behaviour of both 338 backward and forward generation interval, in line with the literature [5,6,8,20]. 339 Moreover, this has been the first attempt to thoroughly examine the cause of the contact and the infection time of the next case is t (2) = t * (1)1 .…”

supporting

confidence: 86%

“…32 Statistical development led to approaches for the estimation of the generation time 33 distribution [7,12,13] or jointly of the basic reproduction number and the generation 34 time distribution [14][15][16]. However, several authors described a non-constant evolution over time for 39 both backward and forward generation interval [5,6,8,20]. SARS outbreaks and the 36 pandemic influenza A(H1N1)V2009 outbreak [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers typically assign to the 53 phenomenon of contraction only one among two explanations: competition among 54 infectors [8] and depletion of susceptible individuals [5,20]. The principal aim of this paper is to clarify the 52 epidemiological mechanisms that cause contraction.…”

mentioning

confidence: 99%

“…The mean backward generation 311 time is known to be increasing [6,20] and differently from the forward scheme a single 312 generation is considered for every time point. The mean backward generation 311 time is known to be increasing [6,20] and differently from the forward scheme a single 312 generation is considered for every time point.…”

mentioning

confidence: 99%

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Realized generation times: contraction and impact of infectious period, reproduction number and population size

Torneri

Azmon

Faes³

et al. 2019

Preprint

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One of the key characteristics of the transmission dynamics of infectious diseases is the generation time which refers to the time interval between the infection of a secondary case and the infection of its infector. The generation time distribution together with the reproduction number determines the rate at which an infection spreads in a population. When defining the generation time distribution at a calendar time t two definitions are plausible according whether we regard t as the infection time of the infector or the infection time of the infectee. The resulting measurements are respectively called forward generation time and backward generation time. It has been observed that the mean forward generation time contracts around the peak of an epidemic. This contraction effect has previously been attributed to either competition among potential infectors or depletion of susceptibles in the population. The first explanation requires many infectives for contraction to occur whereas the latter explanation suggests that contraction occurs even when there are few infectives. With a simulation study we show that both competition and depletion cause the mean forward generation time to contract. Our results also reveal that the distribution of the infectious period and the reproduction number have a strong effect on the size and timing of the contraction, as well as on the mean value of the generation time in both forward and backward scheme. These observations relate to the 'serial interval', the time interval between symptom 26 onset of a secondary case and symptom onset of its infector [10]. Many authors have 27 used the serial interval as a synonym for the generation time. However, unlike the 28 generation time, the serial interval can have a negative duration when the clinical 29 March 5, 2019 2/23 . CC-BY-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/568485 doi: bioRxiv preprint 75 impact of competition and depletion on the mean forward generation interval. In 76 addition to a baseline scenario we present three artificial scenarios accounting for 77 different levels of competition and depletion level since in the SIR model these 78 phenomena cannot be disentangle. Finally, we conclude by explaining under which 79 conditions the two hypotheses give rise to the generation interval contraction.Average values of duration (T max ), final size (F S), mean forward (ω f ) and mean backward (ω b ) generation times, maximum, minimum and range of the loess regression (Max loess, Min loess and Range loess) and depletion intensity (ϕ).

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supporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Realized generation times: contraction and impact of infectious period, reproduction number and population size

Torneri

Azmon

Faes³

et al. 2019

Preprint

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“…When new infections are generated at approximately exponential rate, observed mean generations interval are smaller than the equilibrium value as most infectious people will only just have been infected [31]. This issue has recently been generalised to the whole distribution of generation intervals, and beyond assumptions of exponential growth [32]. …”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus

et al. 2016

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The pacific islands of Micronesia have experienced several outbreaks of mosquito-borne diseases over the past decade. In outbreaks on small islands, the susceptible population is usually well defined, and there is no co-circulation of pathogens. Because of this, analysing such outbreaks can be useful for understanding the transmission dynamics of the pathogens involved, and particularly so for yet understudied pathogens such as Zika virus. Here, we compared three outbreaks of dengue and Zika virus in two different island settings in Micronesia, the Yap Main Islands and Fais, using a mathematical model of transmission dynamics and making full use of commonalities in disease and setting between the outbreaks. We found that the estimated reproduction numbers for Zika and dengue were similar when considered in the same setting, but that, conversely, reproduction number for the same disease can vary considerably by setting. On the Yap Main Islands, we estimated a reproduction number of 8.0–16 (95% Credible Interval (CI)) for the dengue outbreak and 4.8–14 (95% CI) for the Zika outbreak, whereas for the dengue outbreak on Fais our estimate was 28–102 (95% CI). We further found that the proportion of cases of Zika reported was smaller (95% CI 1.4%–1.9%) than that of dengue (95% CI: 47%–61%). We confirmed these results in extensive sensitivity analysis. They suggest that models for dengue transmission can be useful for estimating the predicted dynamics of Zika transmission, but care must be taken when extrapolating findings from one setting to another.

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Estimation of the serial interval of monkeypox during the early outbreak in 2022

Guo

Zhao

Sun

et al. 2022

Journal of Medical Virology

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With increased transmissibility and novel transmission mode, monkeypox poses new threats to public health globally in the background of the ongoing COVID‐19 pandemic. Estimates of the serial interval, a key epidemiological parameter of infectious disease transmission, could provide insights into the virus transmission risks. As of October 2022, little was known about the serial interval of monkeypox due to the lack of contact tracing data. In this study, public‐available contact tracing data of global monkeypox cases were collected and 21 infector‐infectee transmission pairs were identified. We proposed a statistical method applied to real‐world observations to estimate the serial interval of the monkeypox. We estimated a mean serial interval of 5.6 days with the right truncation and sampling bias adjusted and calculated the reproduction number of 1.33 for the early monkeypox outbreaks at a global scale. Our findings provided a preliminary understanding of the transmission potentials of the current situation of monkeypox outbreaks. We highlighted the need for continuous surveillance of monkeypox for transmission risk assessment.

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Intrinsic and realized generation intervals in infectious-disease transmission

Cited by 88 publications

References 24 publications

Realized generation times: contraction and impact of infectious period, reproduction number and population size

Realized generation times: contraction and impact of infectious period, reproduction number and population size

Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus

Estimation of the serial interval of monkeypox during the early outbreak in 2022

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