Evidence map›Paper›PMID 30111778›Full record

ArticleScientific reports2018

Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014-2016 outbreak in Colombia.

Guido España, John Grefenstette, Alex Perkins, Claudia Torres, Alfonso Campo Carey, Hernando Diaz, Fernando de la Hoz, Donald S Burke, Willem G van Panhuis

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Guido EspañaUniversity of Notre Dame, Department of Biological Sciences and Eck Institute for Global Health, Notre Dame, IN, United States. guido.espana@nd.edu.
John GrefenstetteUniversity of Pittsburgh, Department of Health Policy and Management, Pittsburgh, PA, United States.
Alex PerkinsUniversity of Notre Dame, Department of Biological Sciences and Eck Institute for Global Health, Notre Dame, IN, United States.
Claudia TorresUniversidad Nacional de Colombia, Department of Electrical Engineering, Bogotá, Colombia.
Alfonso Campo CareyColombia Instituto Nacional de Salud, Grupo de Gestión del Riesgo y Respuesta Inmediata, Bogotá, Colombia.
Hernando DiazUniversidad Nacional de Colombia, Department of Electrical Engineering, Bogotá, Colombia.
Fernando de la HozUniversidad Nacional de Colombia, Department of Public Health, Bogotá, Colombia.
Donald S BurkeUniversity of Pittsburgh, Department of Epidemiology, Pittsburgh, PA, United States.ORCID 0000-0002-5704-8094
Willem G van PanhuisUniversity of Pittsburgh, Department of Epidemiology, Pittsburgh, PA, United States.ORCID 0000-0002-7278-9982

Funding

Training, Outreach & Diversity ComponentU54GM088491 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LAKOSKI, JOAN M · 2009 to 2018
$16.7M
NIGMS NIH HHS U54 GM088491
6 · The paper itself

Abstract

New epidemics of infectious diseases can emerge any time, as illustrated by the emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in Latin America. During new epidemics, public health officials face difficult decisions regarding spatial targeting of interventions to optimally allocate limited resources. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks. Our model represents CHIKV transmission in a realistic population of Colombia with 45 million individuals in 10.6 million households, schools, and workplaces. Our model uses high-resolution probability maps for the occurrence of the Ae. aegypti mosquito vector to estimate mosquito density in Colombia. We found that vector control in all 521 municipalities with mosquito populations led to 402,940 fewer clinical cases of CHIKV compared to a baseline scenario without intervention. We also explored using data about previous dengue virus (DENV) epidemics to inform CHIKV mitigation strategies. Compared to the baseline scenario, 314,437 fewer cases occurred when we simulated vector control only in 301 municipalities that had previously reported DENV, illustrating the value of available data from previous outbreaks. When varying the implementation parameters for vector control, we found that faster implementation and scale-up of vector control led to the greatest proportionate reduction in cases. Using available data for epidemic simulations can strengthen decision making against new epidemic threats.

Indexed as

AedesAnimalsChikungunya FeverChikungunya virusColombiaDengueDengue VirusDisease OutbreaksEpidemicsHumansInsect VectorsModels, TheoreticalMosquito VectorsPublic HealthZika VirusZika Virus Infection

Identifiers

PMID30111778
PMCPMC6093909

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.