IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v288y2021ics0277953620305141.html
   My bibliography  Save this article

Long-term changes in endemic threshold populations for pertussis in England and Wales: A spatiotemporal analysis of Lancashire and South Wales, 1940-69

Author

Listed:
  • Munro, Alastair D.
  • Smallman-Raynor, Matthew
  • Algar, Adam C.

Abstract

Metapopulation dynamics play a critical role in driving endemic persistence and transmission of childhood infections. The endemic threshold concept, also referred to as critical community size (CCS), is a key example and is defined as the minimum population size required to sustain a continuous chain of infection transmission. The concept is fundamental to the implementation of effective vaccine-based disease control programmes. Vaccination serves to increase endemic threshold population size, promoting disease fadeout and eventual elimination of infection. To date, empirical investigations of the relationship between vaccination and endemic threshold population size have tended to focus on isolated populations in island communities. Very few studies have examined endemic threshold dynamics in ‘mainland’ regional populations with complex hierarchical spatial structures and varying levels of connectivity between subpopulations. The present paper provides the first spatially explicit analysis of the temporal changes in endemic threshold populations for one vaccine-preventable childhood infection (pertussis) in two dynamic regions of England and Wales: Lancashire and South Wales. Drawing upon weekly disease records of the Registrar-General of England and Wales over a 30-year period (January 1940–December 1969) regression techniques were used to estimate the endemic threshold size for pertussis in the two study regions. Survival analyses were performed to compare disease fadeout duration and probability for both regions in the pre-vaccine and vaccine eras, respectively. Our findings reveal the introduction of mass vaccination led to a considerable increase in threshold size for both Lancashire (~387,333) and South Wales (~1,460,667). Significant growth in fadeout duration was observed in the vaccine era for pertussis non-hotspots in both regions, consistent with geographical synchronisation of epidemic activity. Regional differences in endemic threshold populations reflect significant regional variations in spatial connectivity, population dispersion and level of geographical isolation.

Suggested Citation

  • Munro, Alastair D. & Smallman-Raynor, Matthew & Algar, Adam C., 2021. "Long-term changes in endemic threshold populations for pertussis in England and Wales: A spatiotemporal analysis of Lancashire and South Wales, 1940-69," Social Science & Medicine, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:socmed:v:288:y:2021:i:c:s0277953620305141
    DOI: 10.1016/j.socscimed.2020.113295
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953620305141
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2020.113295?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    2. B. T. Grenfell & O. N. Bjørnstad & J. Kappey, 2001. "Travelling waves and spatial hierarchies in measles epidemics," Nature, Nature, vol. 414(6865), pages 716-723, December.
    3. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    4. Roman Jandarov & Murali Haran & Ottar Bjørnstad & Bryan Grenfell, 2014. "Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 423-444, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emily S Nightingale & Lloyd A C Chapman & Sridhar Srikantiah & Swaminathan Subramanian & Purushothaman Jambulingam & Johannes Bracher & Mary M Cameron & Graham F Medley, 2020. "A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(7), pages 1-21, July.
    2. Maffioli, Elisa M., 2021. "The political economy of health epidemics: Evidence from the Ebola outbreak," Journal of Development Economics, Elsevier, vol. 151(C).
    3. Bracher, Johannes & Held, Leonhard, 2022. "Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1221-1233.
    4. Xia Li, 2024. "Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10200-10226, September.
    5. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    6. James Mitchell & Martin Weale, 2023. "Censored density forecasts: Production and evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 714-734, August.
    7. Alipour, Jean-Victor & Fadinger, Harald & Schymik, Jan, 2021. "My home is my castle – The benefits of working from home during a pandemic crisis," Journal of Public Economics, Elsevier, vol. 196(C).
    8. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Lu, Ye & Suthaharan, Neyavan, 2023. "Electricity price spike clustering: A zero-inflated GARX approach," Energy Economics, Elsevier, vol. 124(C).
    10. Christa Brelsford & Caterina De Bacco, 2018. "Are `Water Smart Landscapes' Contagious? An epidemic approach on networks to study peer effects," Papers 1801.10516, arXiv.org.
    11. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    12. Chryssi Giannitsarou & Stephen Kissler & Flavio Toxvaerd, 2021. "Waning Immunity and the Second Wave: Some Projections for SARS-CoV-2," American Economic Review: Insights, American Economic Association, vol. 3(3), pages 321-338, September.
    13. Suresh, R. & Senthilkumar, D.V. & Lakshmanan, M. & Kurths, J., 2016. "Emergence of a common generalized synchronization manifold in network motifs of structurally different time-delay systems," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 235-245.
    14. Denis Valle & James Clark, 2013. "Improving the Modeling of Disease Data from the Government Surveillance System: A Case Study on Malaria in the Brazilian Amazon," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    15. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    16. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    17. Nicholas G. Reich & Justin Lessler & Krzysztof Sakrejda & Stephen A. Lauer & Sopon Iamsirithaworn & Derek A. T. Cummings, 2016. "Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 285-292, July.
    18. Dag Tjøstheim, 2012. "Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 413-438, September.
    19. Shuangshuang Xu & Marco A. R. Ferreira & Erica M. Porter & Christopher T. Franck, 2023. "Bayesian model selection for generalized linear mixed models," Biometrics, The International Biometric Society, vol. 79(4), pages 3266-3278, December.
    20. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:288:y:2021:i:c:s0277953620305141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.