Searcharxiv⌕ Search

arXiv · 2610.08856

Dynamical Insights into the Current Control Strategies for Chikungunya

Abstract

This paper addresses the challenge that, under China's current mosquito-control policy for chikungunya---\textit{clearing stagnant water, killing adult mosquitoes, and preventing bites}---epidemics can still recur and continue to rise. By constructing a vector--host dynamical model that incorporates asymptomatic infections and a screening rate, we derive approximate analytical expressions for the epidemic peak value and peak time, and reveal the intrinsic link between short-term transients and long-term outcomes. Simulations across three scenarios---human-seeded, mosquito-seeded, and joint human--mosquito-seeded---show that (i) even when the vector population is effectively suppressed, asymptomatic carriers can still sustain transmission; (ii) the effect of the screening rate on the peak time is non-monotonic, and there exists a critical threshold that maximizes the delay of the epidemic; and (iii) moderate screening can both suppress the first wave and avoid leaving an excessively large susceptible pool that would amplify subsequent waves. Accordingly, we upgrade the original policy to a \textit{Twelve-character} strategy for prevention, mitigation, and control---\textit{clearing stagnant water, killing adult mosquitoes, preventing bites, and screening cases early}---providing theoretical support for chikungunya and other mosquito-borne diseases.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bo Zheng, Xingzhi Chen, Jianglong Xiao, Kai Wang, Jianshe Yu, Huaiping Zhu. 2026-10-12. Dynamical Insights into the Current Control Strategies for Chikungunya. https://arxiv.org/abs/2610.08856

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Outbreak dynamics and population vulnerability in stochastic epidemic models on networks

During infectious disease epidemics, pathogen transmission occurs in host populations made up of interacting subpopulations. Using stochastic simulation and analytical approximations, we examine how outbreak sizes in networked populations depend on network architecture, subpopulation sizes and the strength of coupling between subpopulations. We find, as expected, that mean outbreak sizes are frequently lower in networked populations than in homogeneous populations with the same basic reproduction number. However, after an outbreak ends, a networked population is often vulnerable to further outbreaks, and the ending of an outbreak may not imply herd immunity in any sense. Another key finding is that a relatively small amount of randomly distributed prior immunity can be more protective in a networked population than a homogeneous population, a phenomenon which can be reproduced analytically in certain cases. We also find that in networked populations, randomly distributed prior immunity is often more protective than infection-acquired immunity; but this conclusion can be reversed in populations with highly variable susceptibility. All of these conclusions have implications for designing outbreak control strategies that aim to reduce pathogen transmission during epidemics.

q-bio.PE↗

Consensus-level substitution rates are distinct from the virion-level rate

Estimating viral substitution rates is central to evolutionary epidemiology, and recent interest in within-host evolution has sharpened the question of what such rates measure. I distinguish two classes of evolutionary rate estimand that are rarely separated in phylogenetic analysis: the virion-level substitution rate (VLSR), a molecular quantity counting mutational events along lineages, and consensus-level substitution rates (CLSRs), population-summary quantities counting allele frequency threshold crossings from below. CLSRs are indexed by the consensus-generation rule. The VLSR and CLSRs are both biologically meaningful, but not interchangeable. Because the consensus-generation rule defines a given CLSR, it should be reported routinely. This reflection should help analysts make more informed methodological choices when working with sets of virus sequences.

q-bio.PE↗

Probability generating functions for epidemiological time series

Probability generating functions (PGFs) are a fundamental tool in mathematical epidemiology to characterize the stochastic evolution of epidemics via branching process theory. However, they are rarely used as operational tools in real-time during an outbreak, the main barrier being a lack of a systematic, computational methodology to describe epidemiological time series, such as cases or hospital admissions over time. Here, we introduce a general and computationally efficient framework for outbreak analytics based on PGFs. We first develop a method to encode joint distributions of incidences and their noisy measurements by realistic surveillance systems. Then, by transforming PGFs to cumulant generating functions (CGFs), we enable efficient evaluation of these joint distributions. We demonstrate the practicality of our approach for Bayesian inference and short-term forecasts, offering a significant computational advantage over simulation-based methods. Altogether, our framework operationalizes generating functions and paves the way for their broader adoption in computational epidemiology.

q-bio.PE↗