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

Overview of attention for article published in BMC Medicine, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

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42 tweeters

Citations

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12 Dimensions

Readers on

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84 Mendeley
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Title
Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity
Published in
BMC Medicine, August 2018
DOI 10.1186/s12916-018-1127-2
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 42 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 27%
Student > Master 19 23%
Student > Ph. D. Student 5 6%
Student > Bachelor 4 5%
Student > Doctoral Student 4 5%
Other 13 15%
Unknown 16 19%
Readers by discipline Count As %
Medicine and Dentistry 12 14%
Agricultural and Biological Sciences 10 12%
Biochemistry, Genetics and Molecular Biology 7 8%
Immunology and Microbiology 5 6%
Mathematics 5 6%
Other 21 25%
Unknown 24 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 August 2019.
All research outputs
#1,282,222
of 22,564,527 outputs
Outputs from BMC Medicine
#913
of 3,384 outputs
Outputs of similar age
#27,486
of 298,593 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 22,564,527 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,384 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 42.6. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,593 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them