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Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance

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  • Meyer, Sebastian
  • Held, Leonhard
  • Höhle, Michael

Abstract

The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

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  • 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).
    • Handle: RePEc:jss:jstsof:v:077:i11
    DOI: http://hdl.handle.net/10.18637/jss.v077.i11
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    Cited by:

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    2. Hunecke, C. & Meyer, S. & Brummer, B., 2018. "Technology Diffusion through Networks - Adoption of automatic milking systems in Germany," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277543, International Association of Agricultural Economists.
    3. Maffioli, Elisa M., 2021. "The political economy of health epidemics: Evidence from the Ebola outbreak," Journal of Development Economics, Elsevier, vol. 151(C).
    4. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    5. 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.
    6. Fernando Santa & Roberto Henriques & Joaquín Torres-Sospedra & Edzer Pebesma, 2019. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments," Sustainability, MDPI, vol. 11(3), pages 1-29, January.
    7. 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.
    8. Mason Youngblood, 2020. "Extremist ideology as a complex contagion: the spread of far-right radicalization in the United States between 2005 and 2017," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    9. 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.
    10. Giada Adelfio & Marcello Chiodi, 2021. "Including covariates in a space-time point process with application to seismicity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 947-971, September.
    11. Christa Brelsford & Caterina Bacco, 2018. "Are ‘Water Smart Landscapes’ Contagious? An Epidemic Approach on Networks to Study Peer Effects," Networks and Spatial Economics, Springer, vol. 18(3), pages 577-613, September.
    12. Michael Berlemann & Erik Haustein, 2020. "Right and Yet Wrong: A Spatio-Temporal Evaluation of Germany's Covid-19 Containment Policy," CESifo Working Paper Series 8446, CESifo.
    13. Fadinger, Harald & Schymik, Jan & Alipour, Jean-Victor, 2020. "My Home Is My Castle -- The Benefits of Working from Home During a Pandemic Crisis: Evidence from Germany," CEPR Discussion Papers 14871, C.E.P.R. Discussion Papers.
    14. 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).
    15. 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.

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