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 »
- Authors:
-
- Univ. of Notre Dame, IN (United States)
- Univ. of California, San Francisco, CA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of California, Davis, CA (United States)
- Centers for Disease Control and Prevention, Atlanta, GA (United States); Harvard T.H. Chan School of Public Health, Boston, MA (United States)
- Univ. of Washington, Seattle, WA (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (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. doi: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}
}
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