How generation intervals shape the relationship between growth rates and reproductive numbers

By Jacco Wallinga, Marc Lipsitch. Proceedings of the Royal Society of London. Biological Sciences, 2007

1,338 citations recorded by the source corpus.

Subfields: Genetics, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/7e19baac5a52be2da6ac1bbbcd958e00136c0f4f

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Citations in the corpus

The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks (2012, methodology): “It will complement package developed for epidemiological surveillance [4] and cost-effectiveness analyses [5].”
Serial Interval of COVID-19 among Publicly Reported Confirmed Cases (2020, background): “The distribution of COVID-19 serial intervals is a critical input for determining the basic reproduction number (R0) and the extent of interventions required to control an epidemic (3).”
Mathematical models of infectious disease transmission (2008, background): “substituting an exponential function for growth in the number of infections -I(t) = I(t 0 )exp(r(t-t 0 )) -into the renewal equation (Equation 3 ) yields the so-called lotka-Euler estimating equation 32, 40 (Equation 5).”
Inferring the effectiveness of government interventions against COVID-19 (2020, background): “We assume that the effect of each NPI on Rt,c is stable across time but can vary across countries to some degree.”
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England (2021, background): “116) 50 (47-52) 85 (81-90) UK sequence separate-slopes multinomial spline model REGION * ns(DATE, df=2) a UK B.”
Estimating the burden of SARS-CoV-2 in France (2020, background): “(31) to relate the basic reproduction number to the epidemic growth rate r.”
Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period (2020, background): “To quantify variation in transmission strength over time, we estimated the weekly effective reproduction number, defined as the average number of secondary infections caused by a single infected individual ( 20 , 21 ).”
Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study (2020, methodology): “Here we report the results for a city (Shenzhen-a major city with more than 12 million inhabitants in Guangdong province) and two provinces (Hunan and Shandong) for which we have validated our individual records against the full official line lists (appendix pp [3] [4] [5] [6] [7] [8] .”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “When the network is known and details of individual infections are available, contact tracing data may be used to infer the network; this data could also be used for inference on the epidemic parameters [116, 149].”
Improved inference of time-varying reproduction numbers during infectious disease outbreaks (2019, methodology): “As with most previous methods, we propose estimation of the reproduction number based on the incidence of symptomatic cases and the serial interval distribution, rather than the incidence of infections and the distribution of the generation time (time between infection of a case and infection of the”
Statistical physics of vaccination (2016, background): “Assuming a stationary distance between any two consecutive generations of infection, what today we call the infection generation time [87], and that this equals D, the duration of the infective phase, leads to the fundamental relation between generational growth and real time growth: R0 = e, which e”
Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak (2020, neutral): “, the moment generating function, of the probability distribution for the serial interval (SI) of the disease [14, 16], denoted by h(k) and k is the mean SI.”