Factors that make an infectious disease outbreak controllable.

By Christophe Fraser, S. Riley, R. Anderson, Neil M. Ferguson. Proceedings of the National Academy of Sciences of the United States of America, 2004

1,167 citations recorded by the source corpus.

Subfields: Infectious Diseases, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/a6a3eb86faa8ecf704c818755b91a37d86f48c33

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Citations in the corpus

Statistical physics of vaccination (2016, background): “Consequently, an important metric in the controllability of handling a disease outbreak requires the consideration of the proportion of transmission that takes place before the onset of symptoms occurs [29].”
Strategies for containing an emerging influenza pandemic in Southeast Asia (2005, background): “34 ), and rather than assuming infectiousness is constant from the end of the latent period to recovery, we model it as a function, ( ) T κ (assumed normalized), depending on the time elapsed from the end of the latent period.”
Quarantine alone or in combination with other public health measures to control COVID‐19: a rapid review (2020, positive): “Fraser 2004 Model of infectious disease outbreak dynamics of several pathogens, journal publication SARS and smallpox are easier to control than pandemic influenza and HIV using simple public health measures (i.e. isolation and quarantine).”
State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3. (2012, background): “Thus, transmission by minimally symptomatic individuals may represent a significant problem for control [52] .”
Mathematical models of infectious disease transmission (2008, methodology): “46 used this model to explore the impact of isolation or treatment and contact-tracing measures for saRs, smallpox, influenza and HIv-1 epidemics, which are This explains, in part, why saRs, which has a small θ, was comparatively easy to control and why influenza pandemics can be harder to control t”
Factors involved in the aerosol transmission of infection and control of ventilation in healthcare premises (2006, background): “This is probably the most realistic assessment, and this uncertainty is reflected in the large range of values for the basic reproductive number (R0, the number of secondary cases arising from a single index case in an otherwise totally susceptible population), ranging from 1e2(99) to 2e7(100) to <2”
Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions (2007, background): “While estimates of the reproductive rate for pandemic influenza vary considerably in the literature [3,8,19,28,29], recent studies tend to revise downward large values of R0 previously estimated [10,27,30].”
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics (2013, background): “The monitoring of R over time provides feedback on the effectiveness of interventions and on the need to intensify control efforts (1) (2) (3) (4) , given that the goal of control efforts is to reduce R below the threshold value of 1 and as close to 0 as possible, thus bringing an epidemic under con”
Networks and epidemic models (2005, background): “This reveals a new set of individuals who might be infected and who can be the subject of further tracing effort (Klovdahl 1985; Kretzschmar et al. 1996; Ghani et al. 1997; Ghani & Garnett 1998; Müller et al. 2000; Wylie & Jolly 2001; Potterat et al. 2002; Eames & Keeling 2003; Fraser et al. 2004).”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “Reduction in transmission due to adaptive behavior is either modeled as a quarantine of cases [64,65,63], or prophylactic behavior of susceptible individuals due to increased awareness of the disease [75,66,69–74,77].”