American Institute of Mathematical Sciences

Advanced
2007, 4(4): 739-754. doi: 10.3934/mbe.2007.4.739

The utility of preemptive mass influenza vaccination in controlling a SARS outbreak during flu season

Qingling Zeng 1, , Kamran Khan 2, , Jianhong Wu 3, and  Huaiping Zhu 1,

1. 

Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada, Canada
2. 

The Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute, of St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON M5B 1W8, Canada
3. 

Centre for Disease Modeling, Department of Mathematics and Statistics, York University, 4700 Keele Street Toronto, ON, M3J 1P3, Canada

Received  November 2006 Revised  April 2007 Published  August 2007

During flu season, respiratory infections can cause non-specific influenza-like-illnesses (ILIs) in up to one-half of the general population. If a future SARS outbreak were to coincide with flu season, it would become exceptionally difficult to distinguish SARS rapidly and accurately from other ILIs, given the non-specific clinical presentation of SARS and the current lack of a widely available, rapid, diagnostic test. We construct a deterministic compartmental model to examine the potential impact of preemptive mass influenza vaccination on SARS containment during a hypothetical SARS outbreak coinciding with a peak flu season. Our model was developed based upon the events of the 2003 SARS outbreak in Toronto, Canada. The relationship of different vaccination rates for influenza and the corresponding required quarantine rates for individuals who are exposed to SARS was analyzed and simulated under different assumptions. The study revealed that a campaign of mass influenza vaccination prior to the onset of flu season could aid the containment of a future SARS outbreak by decreasing the total number of persons with ILIs presenting to the health-care system, and consequently decreasing nosocomial transmission of SARS in persons under investigation for the disease.
Keywords: disease outbreaks, theoretical models., mass immunization, respiratory tract infections, influenza, Severe acute respiratory syndrome (SARS).
Mathematics Subject Classification: 92D3.
Citation: Qingling Zeng, Kamran Khan, Jianhong Wu, Huaiping Zhu. The utility of preemptive mass influenza vaccination in controlling a SARS outbreak during flu season. Mathematical Biosciences & Engineering, 2007, 4 (4) : 739-754. doi: 10.3934/mbe.2007.4.739
[1]

Sunmoo Yoon, Da Kuang, Peter Broadwell, Haeyoung Lee, Michelle Odlum. What can we learn about the Middle East Respiratory Syndrome (MERS) outbreak from tweets?. Big Data & Information Analytics, 2017, 2 (5) : 1-5. doi: 10.3934/bdia.2017013
[2]

Frédéric Bernicot, Bertrand Maury, Delphine Salort. A 2-adic approach of the human respiratory tree. Networks & Heterogeneous Media, 2010, 5 (3) : 405-422. doi: 10.3934/nhm.2010.5.405
[3]

Mary P. Hebert, Linda J. S. Allen. Disease outbreaks in plant-vector-virus models with vector aggregation and dispersal. Discrete & Continuous Dynamical Systems - B, 2016, 21 (7) : 2169-2191. doi: 10.3934/dcdsb.2016042
[4]

Saroj P. Pradhan, Janos Turi. Parameter dependent stability/instability in a human respiratory control system model. Conference Publications, 2013, 2013 (special) : 643-652. doi: 10.3934/proc.2013.2013.643
[5]

Folashade B. Agusto, Abba B. Gumel. Theoretical assessment of avian influenza vaccine. Discrete & Continuous Dynamical Systems - B, 2010, 13 (1) : 1-25. doi: 10.3934/dcdsb.2010.13.1
[6]

Yanyu Xiao, Xingfu Zou. On latencies in malaria infections and their impact on the disease dynamics. Mathematical Biosciences & Engineering, 2013, 10 (2) : 463-481. doi: 10.3934/mbe.2013.10.463
[7]

Diána H. Knipl, Gergely Röst. Modelling the strategies for age specific vaccination scheduling during influenza pandemic outbreaks. Mathematical Biosciences & Engineering, 2011, 8 (1) : 123-139. doi: 10.3934/mbe.2011.8.123
[8]

Heikki Haario, Leonid Kalachev, Marko Laine. Reduction and identification of dynamic models. Simple example: Generic receptor model. Discrete & Continuous Dynamical Systems - B, 2013, 18 (2) : 417-435. doi: 10.3934/dcdsb.2013.18.417
[9]

Karen R. Ríos-Soto, Baojun Song, Carlos Castillo-Chavez. Epidemic spread of influenza viruses: The impact of transient populations on disease dynamics. Mathematical Biosciences & Engineering, 2011, 8 (1) : 199-222. doi: 10.3934/mbe.2011.8.199
[10]

Andrea Pugliese, Abba B. Gumel, Fabio A. Milner, Jorge X. Velasco-Hernandez. Sex-biased prevalence in infections with heterosexual, direct, and vector-mediated transmission: a theoretical analysis. Mathematical Biosciences & Engineering, 2018, 15 (1) : 125-140. doi: 10.3934/mbe.2018005
[11]

Roberto A. Saenz, Herbert W. Hethcote. Competing species models with an infectious disease. Mathematical Biosciences & Engineering, 2006, 3 (1) : 219-235. doi: 10.3934/mbe.2006.3.219
[12]

Burcu Adivar, Ebru Selin Selen. Compartmental disease transmission models for smallpox. Conference Publications, 2011, 2011 (Special) : 13-21. doi: 10.3934/proc.2011.2011.13
[13]

Juvencio Alberto Betancourt-Mar, Víctor Alfonso Méndez-Guerrero, Carlos Hernández-Rodríguez, José Manuel Nieto-Villar. Theoretical models for chronotherapy: Periodic perturbations in hyperchaos. Mathematical Biosciences & Engineering, 2010, 7 (3) : 553-560. doi: 10.3934/mbe.2010.7.553
[14]

Muntaser Safan, Klaus Dietz. On the eradicability of infections with partially protective vaccination in models with backward bifurcation. Mathematical Biosciences & Engineering, 2009, 6 (2) : 395-407. doi: 10.3934/mbe.2009.6.395
[15]

Jing-Jing Xiang, Juan Wang, Li-Ming Cai. Global stability of the dengue disease transmission models. Discrete & Continuous Dynamical Systems - B, 2015, 20 (7) : 2217-2232. doi: 10.3934/dcdsb.2015.20.2217
[16]

Bruno Buonomo, Eleonora Messina. Impact of vaccine arrival on the optimal control of a newly emerging infectious disease: A theoretical study. Mathematical Biosciences & Engineering, 2012, 9 (3) : 539-552. doi: 10.3934/mbe.2012.9.539
[17]

Vasiliy N. Leonenko, Sergey V. Ivanov. Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models. Mathematical Biosciences & Engineering, 2018, 15 (1) : 209-232. doi: 10.3934/mbe.2018009
[18]

Juvencio Alberto Betancourt-Mar, José Manuel Nieto-Villar. Theoretical models for chronotherapy: Periodic perturbations in funnel chaos type. Mathematical Biosciences & Engineering, 2007, 4 (2) : 177-186. doi: 10.3934/mbe.2007.4.177
[19]

Jifa Jiang, Qiang Liu, Lei Niu. Theoretical investigation on models of circadian rhythms based on dimerization and proteolysis of PER and TIM. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1247-1259. doi: 10.3934/mbe.2017064
[20]

E. Almaraz, A. Gómez-Corral. On SIR-models with Markov-modulated events: Length of an outbreak, total size of the epidemic and number of secondary infections. Discrete & Continuous Dynamical Systems - B, 2018, 23 (6) : 2153-2176. doi: 10.3934/dcdsb.2018229

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (8)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences