Modelling the spatial-temporal progression of the 2009 A/H1N1 influenza pandemic in Chile

  • A spatial-temporal transmission model of 2009 A/H1N1 pandemic influenza across Chile,a country that spans a large latitudinal range, is developed to characterize the spatial variation in peak timingof that pandemic as a function of local transmission rates, spatial connectivity assumptions for Chilean regions, andthe putative location of introduction of the novel virus into the country. Specifically, ametapopulation SEIR (susceptible-exposed-infected-removed) compartmental model that tracks the transmissiondynamics of influenza in 15 Chilean regions is calibrated. The model incorporates population mobility among neighboringregions and indirect mobility to and from other regions via themetropolitan central region (``hub region''). The stability of the disease-freeequilibrium of this model is analyzed and compared with thecorresponding stability in each region, concluding that stability mayoccur even with some regions having basic reproduction numbersabove 1.The transmission model is used along with epidemiological data to explorepotential factors that could have driventhe spatial-temporal progression of the pandemic. Simulations and sensitivity analyses indicate that thisrelatively simple model is sufficient to characterize the south-north gradient in peak timing observed during the pandemic, and suggest that south Chile observed the initial spread of the pandemic virus, which is in line with a retrospective epidemiological study. The ``hub region'' in our model significantly enhanced population mixing in a short time scale.

    Citation: Raimund Bürger, Gerardo Chowell, Pep Mulet, Luis M. Villada. Modelling the spatial-temporal progression of the 2009 A/H1N1 influenza pandemic in Chile[J]. Mathematical Biosciences and Engineering, 2016, 13(1): 43-65. doi: 10.3934/mbe.2016.13.43

    Related Papers:

    [1] Rodolfo Acuňa-Soto, Luis Castaňeda-Davila, Gerardo Chowell . A perspective on the 2009 A/H1N1 influenza pandemic in Mexico. Mathematical Biosciences and Engineering, 2011, 8(1): 223-238. doi: 10.3934/mbe.2011.8.223
    [2] Gilberto González-Parra, Cristina-Luisovna Pérez, Marcos Llamazares, Rafael-J. Villanueva, Jesus Villegas-Villanueva . Challenges in the mathematical modeling of the spatial diffusion of SARS-CoV-2 in Chile. Mathematical Biosciences and Engineering, 2025, 22(7): 1680-1721. doi: 10.3934/mbe.2025062
    [3] Eunha Shim . Prioritization of delayed vaccination for pandemic influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 95-112. doi: 10.3934/mbe.2011.8.95
    [4] Fangyuan Chen, Rong Yuan . Dynamic behavior of swine influenza transmission during the breed-slaughter process. Mathematical Biosciences and Engineering, 2020, 17(5): 5849-5863. doi: 10.3934/mbe.2020312
    [5] Hamdy M. Youssef, Najat A. Alghamdi, Magdy A. Ezzat, Alaa A. El-Bary, Ahmed M. Shawky . A new dynamical modeling SEIR with global analysis applied to the real data of spreading COVID-19 in Saudi Arabia. Mathematical Biosciences and Engineering, 2020, 17(6): 7018-7044. doi: 10.3934/mbe.2020362
    [6] Hiroshi Nishiura . Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 49-64. doi: 10.3934/mbe.2011.8.49
    [7] Suman Ganguli, David Gammack, Denise E. Kirschner . A Metapopulation Model Of Granuloma Formation In The Lung During Infection With Mycobacterium Tuberculosis. Mathematical Biosciences and Engineering, 2005, 2(3): 535-560. doi: 10.3934/mbe.2005.2.535
    [8] Ahmed Alshehri, Saif Ullah . A numerical study of COVID-19 epidemic model with vaccination and diffusion. Mathematical Biosciences and Engineering, 2023, 20(3): 4643-4672. doi: 10.3934/mbe.2023215
    [9] Christian Costris-Vas, Elissa J. Schwartz, Robert Smith? . Predicting COVID-19 using past pandemics as a guide: how reliable were mathematical models then, and how reliable will they be now?. Mathematical Biosciences and Engineering, 2020, 17(6): 7502-7518. doi: 10.3934/mbe.2020383
    [10] Olivia Prosper, Omar Saucedo, Doria Thompson, Griselle Torres-Garcia, Xiaohong Wang, Carlos Castillo-Chavez . Modeling control strategies for concurrent epidemics of seasonal and pandemic H1N1 influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 141-170. doi: 10.3934/mbe.2011.8.141
  • A spatial-temporal transmission model of 2009 A/H1N1 pandemic influenza across Chile,a country that spans a large latitudinal range, is developed to characterize the spatial variation in peak timingof that pandemic as a function of local transmission rates, spatial connectivity assumptions for Chilean regions, andthe putative location of introduction of the novel virus into the country. Specifically, ametapopulation SEIR (susceptible-exposed-infected-removed) compartmental model that tracks the transmissiondynamics of influenza in 15 Chilean regions is calibrated. The model incorporates population mobility among neighboringregions and indirect mobility to and from other regions via themetropolitan central region (``hub region''). The stability of the disease-freeequilibrium of this model is analyzed and compared with thecorresponding stability in each region, concluding that stability mayoccur even with some regions having basic reproduction numbersabove 1.The transmission model is used along with epidemiological data to explorepotential factors that could have driventhe spatial-temporal progression of the pandemic. Simulations and sensitivity analyses indicate that thisrelatively simple model is sufficient to characterize the south-north gradient in peak timing observed during the pandemic, and suggest that south Chile observed the initial spread of the pandemic virus, which is in line with a retrospective epidemiological study. The ``hub region'' in our model significantly enhanced population mixing in a short time scale.


    [1] SIAM J. Appl. Math., 67 (2007), 1283-1309.
    [2] Amer. J. Epidemiol., 165 (2007), 1434-1442.
    [3] Oxford Science Publications, 1991.
    [4] Modeling and Dynamics of Infectious Diseases, Higher Education Press, Beijing, 11 (2009), 64-122.
    [5] Mathematical Medicine and Biology, 22 (2005), 129-142.
    [6] Math. Popul. Studies, 10 (2003), 175-193.
    [7] BMC Med., 7 (2009), p45 (12pp).
    [8] Second Ed., Springer, New York, 2012.
    [9] Lancet Infect. Dis., 9 (2009), 473-481.
    [10] PLoS One, 7 (2012), e41069 (10pp).
    [11] PloS Med., 8 (2011), e1000436 (13pp).
    [12] BMC Infect. Dis., 12 (2012), p298 (12pp).
    [13] PLoS One, 6 (2011), e21287 (10pp).
    [14] Proc. Biol. Sci., 277 (2010), 1857-1866.
    [15] PLoS Med., 4 (2007), e13 (16pp).
    [16] Princeton Series in Theoretical and Computational Biology, Princeton University Press, 2013.
    [17] J. Math. Biol., 28 (1990), 365-382.
    [18] In F. Brauer, P. van den Driessche and J. Wu (Eds.), Mathematical Epidemiology, Springer-Verlag, Berlin, 1945 (2008), 147-157.
    [19] Mathematical Epidemiology, Springer-Verlag, Berlin, 1945 (2008), 179-189.
    [20] Math. Biosci., 228 (2010), 71-77.
    [21] Math. Biosci., 180 (2002), 29-48.
    [22] Math. Biosci., 246 (2013), 164-175.
    [23] PLoS Comput. Biol., 10 (2014), e1003635 (11pp).
    [24] Math. Biosci. Eng., 8 (2011), 21-48.
    [25] Available from: http://www.ine.cl/canales/chile_estadistico/demografia_y_vitales/demo_y_vita.php
    [26] BMJ open, 3 (2) (2013), e002149 (10pp).
    [27] Cochrane Database Syst. Rev., April 10, 2014 (560pp).
    [28] Proc. Roy. Soc. A, 115 (1927), 700-721.
    [29] New Engl. J. Med., 361 (2009), 212-214.
    [30] Nonlin. Anal. Real World Appl., 14 (2013), 1135-1143.
    [31] Math. Biosci. Eng., 11 (2014), 1375-1393.
    [32] Canad. Appl. Math. Quart., 17 (2009), 175-187.
    [33] PLoS Pathog., 3 (2007), 1470-1476.
    [34] J. Virol.., 82 (2008), 5650-5652.
    [35] J. Infect. Dis., 199 (2009), 858-865.
    [36] PLoS Pathog. 7 (2011), e1002225 (10pp).
    [37] Eurosurveillance 15 (2010), 19456 (9pp).
    [38] Math. Biosci., 246 (2013), 47-54.
    [39] Princeton University Press, 2009.
    [40] Math. Biosci., 128 (1995), 71-91.
    [41] PLoS One, 6 (2011), e21471 (8pp).
    [42] PLoS One, 7 (2012), e41918 (10pp).
    [43] Proc. Natl. Acad. Sci. USA, 106 (2009), 3243-3248.
    [44] PLoS Biol., 8 (2010), e1000316 (13pp).
    [45] PLoS Med., 10 (2013), e1001558 (17pp).
    [46] J. Virol., 85 (2011), 1400-1402.
    [47] Comput. Math. Applic., 60 (2010), 2286-2291.
    [48] Environ. Health Perspect., 119 (2011), 439-445.
    [49] Science, 312 (2006), 447-451.
    [50] Oxford University Press, 2010.
    [51] PLoS One, 5 (2010), e10187 (11pp).
    [52] Emerg. Infect. Dis., 18 (2012), 758-766.
  • This article has been cited by:

    1. Sergey V. Ivanov, Vasiliy N. Leonenko, Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models, 2017, 15, 1551-0018, 209, 10.3934/mbe.2018009
    2. Vasiliy N. Leonenko, Sergey V. Ivanov, Yulia K. Novoselova, A Computational Approach to Investigate Patterns of Acute Respiratory Illness Dynamics in the Regions with Distinct Seasonal Climate Transitions, 2016, 80, 18770509, 2402, 10.1016/j.procs.2016.05.538
    3. Raimund Bürger, Gerardo Chowell, Elvis Gavilán, Pep Mulet, Luis M. Villada, Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents, 2017, 15, 1551-0018, 95, 10.3934/mbe.2018004
    4. Gerardo Chowell, Lisa Sattenspiel, Shweta Bansal, Cécile Viboud, Mathematical models to characterize early epidemic growth: A review, 2016, 18, 15710645, 66, 10.1016/j.plrev.2016.07.005
    5. Francesc Aràndiga, Antonio Baeza, Isabel Cordero-Carrión, Rosa Donat, M. Carmen Martí, Pep Mulet, Dionisio F. Yáñez, A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility, 2020, 8, 2227-7390, 1677, 10.3390/math8101677
    6. Christian Garcia-Calavaro, Lee H. Harrison, Darya Pokutnaya, Christina F. Mair, Maria M. Brooks, Wilbert van Panhuis, North to south gradient and local waves of influenza in Chile, 2022, 12, 2045-2322, 10.1038/s41598-022-06318-0
    7. Kangwei Tu, Andras Reith, Changes in Urban Planning in Response to Pandemics: A Comparative Review from H1N1 to COVID-19 (2009–2022), 2023, 15, 2071-1050, 9770, 10.3390/su15129770
  • Reader Comments
  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3321) PDF downloads(645) Cited by(7)

Article outline

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog