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There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19.
Vaccination and herd immunity to infectious diseases
Anderson, R. M. & May, R. M · 1985
Earlier work this paper cites.
Factorial sampling plans for preliminary computational experiments
Morris, M. D · 1991
Earlier work this paper cites.
Statistical mechanics of phase transitions (Clarendon Press, 1992)
Yeomans, J. M · 1992
Earlier work this paper cites.
An introduction to the bootstrap , vol. 57 of Monographs on statistics and applied probability (Chapman and Hall New York, 1994)
Efron, B. & Tibshirani, R. J · 1994
Earlier work this paper cites.
Scaling and percolation in the small-world network model
Newman, M. E. & Watts, D. J · 1999
Earlier work this paper cites.
Containing bioterrorist smallpox
Halloran, M. E., Longini, I. M., Nizam, A. & Yang, Y · 2002
Earlier work this paper cites.
Spread of epidemic disease on networks
Newman, M. E · 2002
Earlier work this paper cites.
Applying network theory to epidemics: control measures for Mycoplasma pneumoniae outbreaks
Meyers, L. A., Newman, M., Martin, M. & Schrag, S · 2003
Earlier work this paper cites.
The role of evolution in the emergence of infectious diseases
Antia, R., Regoes, R. R., Koella, J. C. & Bergstrom, C. T · 2003
Earlier work this paper cites.
Sensitivity and Uncertainty Analysis: Theory Volume 1 (Chapman and Hall/CRC, 2003)
Cacuci, D. G · 2003
Earlier work this paper cites.
Modelling disease outbreaks in realistic urban social networks
Eubank, S. et al · 2004
Earlier work this paper cites.
Containing pandemic influenza with antiviral agents
Longini, I. M., Halloran, M. E., Nizam, A. & Yang, Y · 2004
Earlier work this paper cites.
Containing pandemic influenza at the source
Longini, I. M. et al · 2005
Earlier work this paper cites.
Strategies for containing an emerging influenza pandemic in Southeast Asia
Ferguson, N. M. et al · 2005
Earlier work this paper cites.
Mitigation strategies for pandemic influenza in the United States
Germann, T. C., Kadau, K., Longini, I. M. & Macken, C. A · 2006
Earlier work this paper cites.
Social contacts and mixing patterns relevant to the spread of infectious diseases
Mossong, J. et al · 2008
Earlier work this paper cites.
Modeling targeted layered containment of an influenza pandemic in the United States
Halloran, M. E. et al · 2008
Earlier work this paper cites.
Assortativeness and information in scale-free networks
Piraveenan, M., Prokopenko, M. & Zomaya, A. Y · 2009
Earlier work this paper cites.
Spread of infectious disease through clustered populations
Miller, J. C · 2009
Earlier work this paper cites.
An integrated modeling environment to study the co-evolution of networks, individual behavior and epidemics
Barrett, C., Bisset, K., Leidig, J., Marathe, A. & Marathe, M. V · 2010
Earlier work this paper cites.
Modeling the spatial spread of infectious diseases: The global epidemic and mobility computational model
Balcan, D. et al · 2010
Earlier work this paper cites.
FluTE, a publicly available stochastic influenza epidemic simulation model
Chao, D. L., Halloran, M. E., Obenchain, V. J. & Longini Jr, I. M · 2010
Earlier work this paper cites.
Phase diagram and critical behavior of a forest-fire model in a gradient of immunity
Guisoni, N., Loscar, E. & Albano, E · 2011
Earlier work this paper cites.
Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza
Cauchemez, S. et al · 2011
Earlier work this paper cites.
Sensitivity analysis of an individual-based model for simulation of influenza epidemics
Nsoesie, E. O., Beckman, R. J. & Marathe, M. V · 2012
Earlier work this paper cites.
Sensitivity analysis of infectious disease models: methods, advances and their application
Wu, J., Dhingra, R., Gambhir, M. & Remais, J. V · 2013
Cited alongside, same era.
A systematic review of studies on forecasting the dynamics of influenza outbreaks
Nsoesie, E. O., Brownstein, J. S., Ramakrishnan, N. & Marathe, M. V · 2014
Cited alongside, same era.
Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: an agent-based modelling approach
Cliff, O. M. et al · 2018
Cited alongside, same era.
Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: results of a census-calibrated model
Zachreson, C. et al · 2018
Cited alongside, same era.
Thermodynamic efficiency of contagions: a statistical mechanical analysis of the SIS epidemic model
Harding, N., Nigmatullin, R. & Prokopenko, M · 2018
Cited alongside, same era.
Game theoretic modelling of infectious disease dynamics and intervention methods: a review
Chang, S. L., Piraveenan, M., Pattison, P. & Prokopenko, M · 2020
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Containment efficiency and control strategies for the corona pandemic costs
Gros, C., Valenti, R., Schneider, L., Valenti, K. & Gros, D · 2020
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The global impact of COVID-19 and strategies for mitigation and suppression
Walker, P. G. T. et al · 2020
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Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation
Dignum, F. et al · 2020
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Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-j. et al · 2020
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Network properties of Salmonella epidemics
Cliff, O. et al · 2019
Cited alongside, same era.
Creating a surrogate commuter network from Australian Bureau of Statistics census data
Fair, K. M., Zachreson, C. & Prokopenko, M · 2019
Cited alongside, same era.
http://www.nhc.gov.cn/yjb/pzhgli/new_list.shtml
National Health Commission (NHC) of the People’s Republic of China. NHC daily reports (2020) · 2020
Cited alongside, same era.
A novel coronavirus outbreak of global health concern
Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F · 2020
Cited alongside, same era.
https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf (2020)
Report of the WHO–China joint mission on coronavirus disease 2019 (COVID-19) · 2020
Cited alongside, same era.
China CDC Weekly 2
Vital surveillances: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19)–China, 2020. the novel coronavirus pneumonia emergency response epidemiology team · 2020
Cited alongside, same era.
https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
WHO Director-General’s opening remarks at the media briefing on COVID-19 – 11 March 2020 (2020) · 2020
Cited alongside, same era.
Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)
Li, R. et al · 2020
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Early dynamics of transmission and control of COVID-19: a mathematical modelling study
Kucharski, A. J. et al · 2020
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The reproductive number of COVID-19 is higher compared to SARS coronavirus
Liu, Y., Gayle, A. A., Wilder-Smith, A. & Rocklöv, J · 2020
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Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia
Li, Q. et al · 2020
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Epidemic features and control of 2019 novel coronavirus pneumonia in Wenzhou, China
Huang, H. et al · 2020
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Transmission potential and severity of COVID-19 in South Korea
Shim, E., Tariq, A., Choi, W., Lee, Y. & Chowell, G · 2020
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Age specificity of cases and attack rate of novel coronavirus disease (COVID-19)
Mizumoto, K., Omori, R. & Nishiura, H · 2020
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Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case data
Linton, N. M. et al · 2020
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Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)
Nishiura, H. et al · 2020
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Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020
Mizumoto, K., Kagaya, K., Zarebski, A. & Chowell, G · 2020
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Epidemiology and transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts
Bi, Q. et al · 2020
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Epidemiological characteristics of 2143 pediatric patients with 2019 coronavirus disease in China
Dong, Y. et al · 2020
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Exit strategies: optimising feasible surveillance for detection, elimination and ongoing prevention of COVID-19 community transmission
Lokuge, K. et al · 2020
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https://www.abs.gov.au/AUSSTATS/abs@.nsf/allprimarymainfeatures/4DF23BAE08F75714CA25855B0003B1D9?opendocument (2020)
4940.0 – Household Impacts of COVID-19 Survey, 1-6 Apr 2020. The Australian Bureau of Statistics · 2020
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An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H. & Gardner, L · 2020
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Australia keeps a lid on COVID-19 – for now
Klein, A · 2020
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CityMapper Mobility Index
CityMapper · 2020
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Mobile phone location data used to track Australians’ movements during coronavirus crisis, The Sydney Morning Herald
Grubb, B · 2020
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COVID-19 is rapidly changing: Examining public perceptions and behaviors in response to this evolving pandemic
Seale, H. et al · 2020
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Snapshot of employment by industry, 2019
Vandenbroek, P · 2020
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