Skip to main content
main-content

01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Medicine 1/2018

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

Zeitschrift:
BMC Medicine > Ausgabe 1/2018
Autoren:
Sean M. Moore, Quirine A. ten Bosch, Amir S. Siraj, K. James Soda, Guido España, Alfonso Campo, Sara Gómez, Daniela Salas, Benoit Raybaud, Edward Wenger, Philip Welkhoff, T. Alex Perkins
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12916-018-1127-2) contains supplementary material, which is available to authorized users.

Abstract

Background

Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data.

Methods

We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data.

Results

We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting.

Conclusions

Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.
Zusatzmaterial
Additional file 1: Figure S1. (A) Cumulative incidence as a function of the maximum adaptive sampling population size. Dashed line represents the mean, and the dotted lines are the mean ± the standard deviation. (B) Epidemic time series for three different maximum adaptive sampling population sizes. Solid lines are means and shaded areas represent the range. Figures S2S9. The joint distribution of parameter estimates for amount of rainfall-associated temporary larval mosquito habitat and the decay rate of that temporary habitat. Left panels are estimates from the single-patch departmental model, and right panels are estimated from the multi-patch departmental model. Each figure contains results from four departments, with the departments ordered from lowest to highest relative MASE as displayed in Fig. 2. Figures S10S17. The joint distribution of parameter estimates for the timing of the initial importation event(s) and the magnitude of importation. Left panels are estimates from the single-patch departmental model, and right panels are estimated from the multi-patch departmental model. Each figure contains results from four departments, with the departments ordered from lowest to highest relative MASE as displayed in Fig. 2. Figures S18S19. Comparisons of department-level results for single-patch and multi-patch models for three different symptomatic rates (0.54, 0.72, and 0.90). Black dots represent the observed time series, darker colored lines are the single best-fitting simulations, and lighter colored lines are the other 40 top simulations. (PDF 40161 kb)
Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 1/2018

BMC Medicine 1/2018 Zur Ausgabe

Neu im Fachgebiet Allgemeinmedizin

Meistgelesene Bücher aus dem Fachgebiet

2018 | Buch

Repetitorium Geriatrie

Geriatrische Grundversorgung - Zusatz-Weiterbildung Geriatrie - Schwerpunktbezeichnung Geriatrie

Das vorliegende Werk orientiert sich an den Fort-bzw. Weiterbildungsinhalten der Zusatz-Weiterbildung „Geriatrie“ , der Schwerpunktbezeichnung „Geriatrie“ sowie der strukturierten curricularen Fortbildung „Geriatrische Grundversorgung“ und wendet …

Herausgeber:
Dr. Rainer Neubart

2012 | Buch

Häufige Hautkrankheiten in der Allgemeinmedizin

Klinik Diagnose Therapie

Patienten mit Hautkrankheiten machen einen großen Anteil der Patienten in der Allgemeinarztpraxis aus. Prägnante Texte und zahlreiche Abbildungen zu Klinik, Pathogenese, Diagnose und Therapie helfen, die häufigsten dermatologischen Probleme zu lösen.

Autor:
Prof. Dr. med. Dietrich Abeck

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Allgemeinmedizin und bleiben Sie gut informiert – ganz bequem per eMail.

Bildnachweise