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Article ; Online: SARS-CoV-2 Incubation Period during Omicron BA.5-Dominant Period, Japan.

Cheng, Hao-Yuan / Akhmetzhanov, Andrei R / Dushoff, Jonathan

2024 Volume 30, Issue 1, Page(s) 206–207

MeSH term(s)	Humans ; Japan/epidemiology ; SARS-CoV-2 ; COVID-19 ; Infectious Disease Incubation Period
Language	English
Publishing date	2024-01-02
Publishing country	United States
Document type	Letter
ZDB-ID	1380686-5
ISSN	1080-6059 ; 1080-6040
ISSN (online)	1080-6059
ISSN	1080-6040
DOI	10.3201/eid3001.230208
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Article ; Online: SARS-CoV-2 Incubation Period during Omicron BA.5–Dominant Period, Japan

Hao-Yuan Cheng / Andrei R. Akhmetzhanov / Jonathan Dushoff

Emerging Infectious Diseases, Vol 30, Iss 1, Pp 206-

2024 Volume 207

Keywords	COVID-19 ; respiratory infections ; severe acute respiratory syndrome coronavirus 2 ; SARS-CoV-2 ; Omicron ; incubation period ; Medicine ; R ; Infectious and parasitic diseases ; RC109-216
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Centers for Disease Control and Prevention
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

Parsons, Todd L / Bolker, Benjamin M / Dushoff, Jonathan / Earn, David J D

Proceedings of the National Academy of Sciences of the United States of America

2024 Volume 121, Issue 5, Page(s) e2313708120

Abstract: We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible- ... ...

Abstract	We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.
MeSH term(s)	Humans ; Stochastic Processes ; Epidemiological Models ; Models, Biological ; Communicable Diseases/epidemiology ; Epidemics ; Probability ; Disease Susceptibility ; Burnout, Psychological
Language	English
Publishing date	2024-01-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2313708120
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Article ; Online: Evaluating undercounts in epidemics: Response to Maruotti et al. (2022).

Li, Michael / Dushoff, Jonathan / Earn, David J D / Bolker, Benjamin M

Journal of medical virology

2023 Volume 95, Issue 2, Page(s) e28474

MeSH term(s)	Humans ; Epidemics ; Epidemiological Monitoring
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	752392-0
ISSN	1096-9071 ; 0146-6615
ISSN (online)	1096-9071
ISSN	0146-6615
DOI	10.1002/jmv.28474
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Article ; Online: Speed and strength of an epidemic intervention.

Dushoff, Jonathan / Park, Sang Woo

Proceedings. Biological sciences

2021 Volume 288, Issue 1947, Page(s) 20201556

Abstract: An epidemic can be characterized by its strength (i.e., the reproductive number [Formula: see text]) and speed (i.e., the exponential growth ... ...

Abstract	An epidemic can be characterized by its strength (i.e., the reproductive number [Formula: see text]) and speed (i.e., the exponential growth rate
MeSH term(s)	COVID-19/epidemiology ; Epidemics ; HIV Infections/epidemiology ; Humans ; SARS-CoV-2 ; Uncertainty
Language	English
Publishing date	2021-03-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	209242-6
ISSN	1471-2954 ; 0080-4649 ; 0962-8452 ; 0950-1193
ISSN (online)	1471-2954
ISSN	0080-4649 ; 0962-8452 ; 0950-1193
DOI	10.1098/rspb.2020.1556
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Article ; Online: Intermediate levels of asymptomatic transmission can lead to the highest epidemic fatalities.

Park, Sang Woo / Dushoff, Jonathan / Grenfell, Bryan T / Weitz, Joshua S

PNAS nexus

2023 Volume 2, Issue 4, Page(s) pgad106

Abstract: Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse ... ...

Abstract	Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse if asymptomatic individuals-unaware they are infected-transmit more than symptomatic individuals. Using an epidemic model, we show that intermediate levels of asymptomatic infection lead to the highest levels of epidemic fatalities when the decrease in symptomatic transmission, due either to individual behavior or mitigation efforts, is strong. We generalize this result to include presymptomatic transmission, showing that intermediate levels of nonsymptomatic transmission lead to the highest levels of fatalities. Finally, we extend our framework to illustrate how the intersection of asymptomatic spread and immunity profiles determine epidemic trajectories, including population-level severity, of future variants. In particular, when immunity provides protection against symptoms, but not against infections or deaths, epidemic trajectories can have faster growth rates and higher peaks, leading to more total deaths. Conversely, even modest levels of protection against infection can mitigate the population-level effects of asymptomatic spread.
Language	English
Publishing date	2023-03-29
Publishing country	England
Document type	Journal Article
ISSN	2752-6542
ISSN (online)	2752-6542
DOI	10.1093/pnasnexus/pgad106
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Article ; Online: Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.

Cygu, Steve / Seow, Hsien / Dushoff, Jonathan / Bolker, Benjamin M

Scientific reports

2023 Volume 13, Issue 1, Page(s) 1370

Abstract: The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. ... ...

Abstract	The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods-gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge-were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell's C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.
MeSH term(s)	Humans ; Retrospective Studies ; Neoplasms ; Machine Learning ; Proportional Hazards Models ; Ontario/epidemiology
Language	English
Publishing date	2023-01-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-28393-7
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Article ; Online: How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics.

Harris, Jeremy D / Park, Sang Woo / Dushoff, Jonathan / Weitz, Joshua S

Epidemics

2023 Volume 42, Page(s) 100664

Abstract: Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and ... ...

Abstract	Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Disease Outbreaks ; Epidemics ; Basic Reproduction Number
Language	English
Publishing date	2023-01-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2467993-8
ISSN	1878-0067 ; 1755-4365
ISSN (online)	1878-0067
ISSN	1755-4365
DOI	10.1016/j.epidem.2022.100664
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Article ; Online: Incubation-period estimates of Omicron (BA.1) variant from Taiwan, December 2021–January 2022, and its comparison to other SARS-CoV-2 variants: a statistical modeling, systematic search and meta-analysis

Akhmetzhanov, Andrei R. / Cheng, Hao-Yuan / Dushoff, Jonathan

medRxiv

Abstract: Background: The ongoing COVID-19 pandemic has seen several variants of concern, including the Omicron (BA.1) variant which emerged in October 2021. Accurately estimating the incubation period of these variants is crucial for predicting disease spread and ...

Abstract	Background: The ongoing COVID-19 pandemic has seen several variants of concern, including the Omicron (BA.1) variant which emerged in October 2021. Accurately estimating the incubation period of these variants is crucial for predicting disease spread and formulating effective public health strategies. However, existing estimates often conflict because of biases arising from the dynamic nature of epidemic growth and selective inclusion of cases. This study aims to accurately estimate of the Omicron (BA.1) variant incubation period based on data from Taiwan, where disease incidence remained low and contact tracing was comprehensive during the first months of the Omicron outbreak. Methods: We reviewed 100 contact-tracing records for cases of the Omicron BA.1 variant reported between December 2021 and January 2022, and found enough information to analyze 70 of these. The incubation period distribution was estimated by fitting data on exposure and symptom onset within a Bayesian mixture model using gamma, Weibull, and lognormal distributions as candidates. Additionally, a systematic literature search was conducted to accumulate data for estimates of the incubation period for Omicron (BA.1/2, BA.4/5) subvariants, which was then used for meta-analysis and comparison. Results: The mean incubation period was estimated at 3.5 days (95% credible interval: 3.1-4.0 days), with no clear differences when stratified by vaccination status or age. This estimate aligns closely with the pooled mean of 3.4 days (3.0-3.8 days) obtained from a meta-analysis of other published studies on Omicron subvariants. Conclusions: The relatively shorter incubation period of the Omicron variant, as compared to previous SARS-CoV2 variants, implies its potential for rapid spread but also opens the possibility for individuals to voluntarily adopt shorter, more resource-efficient quarantine periods. Continual updates to incubation period estimates, utilizing data from comprehensive contact tracing, are crucial for effectively guiding these voluntary actions and adjusting high socio-economic cost interventions.
Keywords	covid19
Language	English
Publishing date	2023-07-24
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.07.20.23292983
Database	COVID19

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Article ; Online: Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time

Steve Cygu / Hsien Seow / Jonathan Dushoff / Benjamin M. Bolker

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too ... ...

Abstract	Abstract The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods—gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge—were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell’s C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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