arXiv subjects

Xingzhi Chen

Publications and source records attributed to Xingzhi Chen.

2 recordsLinked to original sources

arXiv2026

Dynamical Insights into the Current Control Strategies for Chikungunya

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.

q-bio.PE↗

arXiv2023

Addressing Class Variable Imbalance in Federated Semi-supervised Learning

Federated Semi-supervised Learning (FSSL) combines techniques from both fields of federated and semi-supervised learning to improve the accuracy and performance of models in a distributed environment by using a small fraction of labeled data and a large amount of unlabeled data. Without the need to centralize all data in one place for training, it collect updates of model training after devices train models at local, and thus can protect the privacy of user data. However, during the federal training process, some of the devices fail to collect enough data for local training, while new devices will be included to the group training. This leads to an unbalanced global data distribution and thus affect the performance of the global model training. Most of the current research is focusing on class imbalance with a fixed number of classes, while little attention is paid to data imbalance with a variable number of classes. Therefore, in this paper, we propose Federated Semi-supervised Learning for Class Variable Imbalance (FCVI) to solve class variable imbalance. The class-variable learning algorithm is used to mitigate the data imbalance due to changes of the number of classes. Our scheme is proved to be significantly better than baseline methods, while maintaining client privacy.

cs.LG↗