skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Abstract

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015–2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers ofmore » transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.« less

Authors:
 [1];  [2]; ORCiD logo [3];  [1];  [1];  [4];  [5];  [6]
  1. Univ. of Notre Dame, IN (United States)
  2. Univ. of California, San Francisco, CA (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Univ. of California, Davis, CA (United States)
  5. Centers for Disease Control and Prevention, Atlanta, GA (United States); Harvard T.H. Chan School of Public Health, Boston, MA (United States)
  6. Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF)
OSTI Identifier:
1604042
Report Number(s):
LA-UR-19-27650
Journal ID: ISSN 1755-4365
Grant/Contract Number:  
89233218CNA000001; DEB 1641130
Resource Type:
Accepted Manuscript
Journal Name:
Epidemics
Additional Journal Information:
Journal Volume: 29; Journal Issue: C; Journal ID: ISSN 1755-4365
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Biological Science; Mathematics; Emerging disease; Spatial dynamics; Spatial heterogeneity; Time series data; Vector-borne disease

Citation Formats

Perkins, T. Alex, Rodriguez-Barraquer, Isabel, Manore, Carrie Anna, Siraj, Amir S., España, Guido, Barker, Christopher M., Johansson, Michael A., and Reiner, Jr., Robert C.. Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data. United States: N. p., 2019. Web. https://doi.org/10.1016/j.epidem.2019.100357.
Perkins, T. Alex, Rodriguez-Barraquer, Isabel, Manore, Carrie Anna, Siraj, Amir S., España, Guido, Barker, Christopher M., Johansson, Michael A., & Reiner, Jr., Robert C.. Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data. United States. https://doi.org/10.1016/j.epidem.2019.100357
Perkins, T. Alex, Rodriguez-Barraquer, Isabel, Manore, Carrie Anna, Siraj, Amir S., España, Guido, Barker, Christopher M., Johansson, Michael A., and Reiner, Jr., Robert C.. Mon . "Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data". United States. https://doi.org/10.1016/j.epidem.2019.100357. https://www.osti.gov/servlets/purl/1604042.
@article{osti_1604042,
title = {Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data},
author = {Perkins, T. Alex and Rodriguez-Barraquer, Isabel and Manore, Carrie Anna and Siraj, Amir S. and España, Guido and Barker, Christopher M. and Johansson, Michael A. and Reiner, Jr., Robert C.},
abstractNote = {Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015–2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.},
doi = {10.1016/j.epidem.2019.100357},
journal = {Epidemics},
number = C,
volume = 29,
place = {United States},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

The Spatial Resolution of Epidemic Peaks
journal, April 2014


Zika infection GIS-based mapping suggest high transmission activity in the border area of La Guajira, Colombia, a northeastern coast Caribbean department, 2015–2016: Implications for public health, migration and travel
journal, May 2016

  • Rodriguez-Morales, Alfonso J.; García-Loaiza, Carlos Julian; Galindo-Marquez, Maria Leonor
  • Travel Medicine and Infectious Disease, Vol. 14, Issue 3
  • DOI: 10.1016/j.tmaid.2016.03.018

The epidemiology and transmissibility of Zika virus in Girardot and San Andres island, Colombia, September 2015 to January 2016
journal, July 2016


Spatial and Temporal Clustering of Dengue Virus Transmission in Thai Villages
journal, November 2008


A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010
journal, April 2013

  • Reiner, Robert C.; Perkins, T. Alex; Barker, Christopher M.
  • Journal of The Royal Society Interface, Vol. 10, Issue 81
  • DOI: 10.1098/rsif.2012.0921

The Importance of Being Discrete (and Spatial)
journal, December 1994


Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries
journal, July 2016


Forecasting Chikungunya spread in the Americas via data-driven empirical approaches
journal, February 2016


Timescales, dynamics, and ecological understanding
journal, December 2010


Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola
journal, May 2015

  • King, Aaron A.; Domenech de Cellès, Matthieu; Magpantay, Felicia M. G.
  • Proceedings of the Royal Society B: Biological Sciences, Vol. 282, Issue 1806
  • DOI: 10.1098/rspb.2015.0347

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
journal, November 1987


Retracing Zika’s footsteps across the Americas with computational modeling
journal, May 2017

  • Perkins, T. Alex
  • Proceedings of the National Academy of Sciences, Vol. 114, Issue 22
  • DOI: 10.1073/pnas.1705969114

Complex Dynamics in Ecological Time Series
journal, February 1992

  • Turchin, Peter; Taylor, Andrew D.
  • Ecology, Vol. 73, Issue 1
  • DOI: 10.2307/1938740

Countering the Zika epidemic in Latin America
journal, July 2016


High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020
journal, September 2015

  • Sorichetta, Alessandro; Hornby, Graeme M.; Stevens, Forrest R.
  • Scientific Data, Vol. 2, Issue 1
  • DOI: 10.1038/sdata.2015.45

How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study
journal, November 2016

  • Salje, Henrik; Lessler, Justin; Paul, Kishor Kumar
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 47
  • DOI: 10.1073/pnas.1611391113

Characteristic length scales of spatial models in ecology via fluctuation analysis
journal, November 1997

  • Keeling, M. J.; Mezić, I.; Hendry, R. J.
  • Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, Vol. 352, Issue 1361
  • DOI: 10.1098/rstb.1997.0143

Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission
journal, December 2013


Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms
journal, March 2006

  • Reynolds, A. P.; Richards, G.; de la Iglesia, B.
  • Journal of Mathematical Modelling and Algorithms, Vol. 5, Issue 4
  • DOI: 10.1007/s10852-005-9022-1

Human mobility patterns predict divergent epidemic dynamics among cities
journal, September 2013

  • Dalziel, Benjamin D.; Pourbohloul, Babak; Ellner, Stephen P.
  • Proceedings of the Royal Society B: Biological Sciences, Vol. 280, Issue 1766
  • DOI: 10.1098/rspb.2013.0763

Model-based projections of Zika virus infections in childbearing women in the Americas
journal, July 2016


Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand
journal, January 2004

  • Cummings, Derek A. T.; Irizarry, Rafael A.; Huang, Norden E.
  • Nature, Vol. 427, Issue 6972
  • DOI: 10.1038/nature02225

Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead
journal, August 2017

  • Metcalf, C. Jessica E.; Walter, Katharine S.; Wesolowski, Amy
  • Proceedings of the Royal Society B: Biological Sciences, Vol. 284, Issue 1860
  • DOI: 10.1098/rspb.2017.0901

Assessing the global threat from Zika virus
journal, July 2016


Recasting the theory of mosquito-borne pathogen transmission dynamics and control
journal, March 2014

  • Smith, D. L.; Perkins, T. A.; Reiner, R. C.
  • Transactions of the Royal Society of Tropical Medicine and Hygiene, Vol. 108, Issue 4
  • DOI: 10.1093/trstmh/tru026

Make Data Sharing Routine to Prepare for Public Health Emergencies
journal, August 2016


A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
journal, September 2013

  • Cori, Anne; Ferguson, Neil M.; Fraser, Christophe
  • American Journal of Epidemiology, Vol. 178, Issue 9
  • DOI: 10.1093/aje/kwt133

Transmission Dynamics of Zika Virus in Island Populations: A Modelling Analysis of the 2013–14 French Polynesia Outbreak
journal, May 2016

  • Kucharski, Adam J.; Funk, Sebastian; Eggo, Rosalind M.
  • PLOS Neglected Tropical Diseases, Vol. 10, Issue 5
  • DOI: 10.1371/journal.pntd.0004726

Disentangling Extrinsic from Intrinsic Factors in Disease Dynamics: A Nonlinear Time Series Approach with an Application to Cholera
journal, June 2004

  • Koelle, Katia; Pascual, Mercedes
  • The American Naturalist, Vol. 163, Issue 6
  • DOI: 10.1086/420798

The Interplay between Determinism and Stochasticity in Childhood Diseases
journal, May 2002

  • Rohani, Pejman; Keeling, Matthew J.; Grenfell, Bryan T.
  • The American Naturalist, Vol. 159, Issue 5
  • DOI: 10.1086/339467

The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture
journal, December 1992


Dengue diversity across spatial and temporal scales: Local structure and the effect of host population size
journal, March 2017

  • Salje, Henrik; Lessler, Justin; Maljkovic Berry, Irina
  • Science, Vol. 355, Issue 6331
  • DOI: 10.1126/science.aaj9384

Travelling waves and spatial hierarchies in measles epidemics
journal, December 2001

  • Grenfell, B. T.; Bjørnstad, O. N.; Kappey, J.
  • Nature, Vol. 414, Issue 6865
  • DOI: 10.1038/414716a

Noisy Clockwork: Time Series Analysis of Population Fluctuations in Animals
journal, July 2001


Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America
journal, December 2017


Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity
journal, August 2018


Summary results of the 2014-2015 DARPA Chikungunya challenge
journal, May 2018

  • Del Valle, Sara Y.; McMahon, Benjamin H.; Asher, Jason
  • BMC Infectious Diseases, Vol. 18, Issue 1
  • DOI: 10.1186/s12879-018-3124-7

Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia
journal, April 2018

  • Siraj, Amir S.; Rodriguez-Barraquer, Isabel; Barker, Christopher M.
  • Scientific Data, Vol. 5, Issue 1
  • DOI: 10.1038/sdata.2018.73

Towards clinically actionable digital phenotyping targets in schizophrenia
journal, May 2020


Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission
journal, May 2018

  • Huber, John H.; Childs, Marissa L.; Caldwell, Jamie M.
  • PLOS Neglected Tropical Diseases, Vol. 12, Issue 5
  • DOI: 10.1371/journal.pntd.0006451

Assessing the global threat from Zika virus.
text, January 2016

  • Lessler, Justin; Chaisson, Lelia H.; Kucirka, Lauren M.
  • Apollo - University of Cambridge Repository
  • DOI: 10.17863/cam.62165

    Works referencing / citing this record:

    Identifying high risk areas of Zika virus infection by meteorological factors in Colombia
    journal, October 2019