ArticleScience advances2025
Predictability of infectious disease outbreak severity: Chikungunya as a case study.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Estimating the epidemic size of chikungunya virus infection in Guangzhou, China, from July to September 2025: a single-center cross-sectional study.Emerging microbes & infections · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
A single pathogen can cause outbreaks of varying size and duration in different populations. Anticipating severe outbreaks would facilitate public health preparedness, but the extent to which this is possible is unclear. We conducted a data-driven investigation into the predictability of outbreak severity, using chikungunya virus (CHIKV) as a case study. For mosquito-transmitted viruses like CHIKV, the potential for severe outbreaks is often assessed using climate-based estimates of the basic reproduction number, [Formula: see text] . We derived a large set of [Formula: see text] estimates for CHIKV by fitting a mechanistic model to data from 86 chikungunya outbreaks. These [Formula: see text] estimates were weakly predicted by climatic and other factors. Among deterministic drivers of outbreak severity, the contribution of [Formula: see text] was comparable to that of generation interval length, transmission distance, and population network structure. While aspects of chikungunya outbreak severity are predictable, innovative approaches are needed that look beyond the impacts of climate on [Formula: see text].
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