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:

Perkins, T. Alex ^[1]; Rodriguez-Barraquer, Isabel ^[2]; Manore, Carrie Anna  ^[3];

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Siraj, Amir S. ^[1]; España, Guido ^[1]; Barker, Christopher M. ^[4]; Johansson, Michael A. ^[5]; Reiner, Jr., Robert C. ^[6]

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+ Show Author Affiliations

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:

2019-07-22

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