Evidence map›Paper›PMID 40341193›Full record

ArticleNature communications2025

Estimating transmissibility of Zika virus in Colombia in the presence of surveillance bias.

Tim K Tsang, Diana P Rojas, Fei Xu, Yanfang Xu, Xiaolin Zhu, M Elizabeth Halloran, Ira M Longini, Yang Yang

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Tim K TsangWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China. timtsang@connect.hku.hk.ORCID http://orcid.org/0000-0001-5037-6776
Diana P RojasDepartment of Epidemiology, University of Florida, Gainesville, FL, USA.
Fei XuDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Yanfang XuWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Xiaolin ZhuDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
M Elizabeth HalloranVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-3127-1757
Ira M LonginiDepartment of Biostatistics, University of Florida, Gainesville, FL, USA.
Yang YangDepartment of Biostatistics, University of Florida, Gainesville, FL, USA. yang.yang4@uga.edu.

Funding

Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI VESPIGNANI, ALESSANDRO · 2014 to 2018
$12.5M
NIGMS NIH HHS U54 GM111274
6 · The paper itself

Abstract

The 2015-2016 Zika virus outbreak in the Americas presented significant challenges in understanding the transmission dynamics due to substantial reporting biases, as women of reproductive age (15-39 years) were disproportionately represented in the surveillance data when public awareness of relationship between Zika and microcephaly increased. Using national surveillance data from Colombia during July 27, 2015-November 21, 2016, we developed a Bayesian hierarchical modeling framework to reconstruct the true numbers of symptomatic cases and estimate transmission parameters while accounting for differential reporting across age-sex groups. Our model revealed that the detection rate of symptomatic cases among women of reproductive age was 99% (95% CI: 98.7-100), compared to 85.4% (95% CI: 84.7-86.1) in other demographic groups. After correcting for these biases, our results showed that females aged 15-39 years remained 82.8% (95% CI: 80.2-85.2%) more susceptible to Zika symptomatic infection than males of the same age, independent of differential reporting areas. Departments with medium-high altitude, medium-high population density, low coverage of forest, or high dengue incidence from 2011-2015 exhibited greater Zika risk. This study underscores the importance of accounting for surveillance biases in epidemiological studies to better understand factors influencing Zika transmission and to inform disease control and prevention.

Indexed as

Zika VirusZika Virus InfectionAdolescentAdultBayes TheoremBiasChildColombiaDisease OutbreaksFemaleHumansMaleMicrocephalyMiddle AgedPopulation SurveillanceYoung Adult

Identifiers

PMID40341193
PMCPMC12062338

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.