Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2204.10188

To Simulate the Spread of Infectious Diseases by the Random Matrix

Ting Wang·Gui-Yun Li·Xin-Hui Li·Chi-Chun Zhou·Yuan-Yuan Wang·Li-Juan Li·Yan-Ting Yang
View original source ↗Read source document ↗Export metadata ↓

Abstract

The main aim to build models capable of simulating the spreading of infectious diseases is to control them. And along this way, the key to find the optimal strategy for disease control is to obtain a large number of simulations of disease transitions under different scenarios. Therefore, the models that can simulate the spreading of diseases under scenarios closer to the reality and are with high efficiency are preferred. In the realistic social networks, the random contact, including contacts between people in the public places and the public transits, becomes the important access for the spreading of infectious diseases. In this paper, a model can efficiently simulate the spreading of infectious diseases under random contacts is proposed. In this approach, the random contact between people is characterized by the random matrix with elements randomly generated and the spread of the diseases is simulated by the Markov process. We report an interesting property of the proposed model: the main indicators of the spreading of the diseases such as the death rate are invariant of the size of the population. Therefore, representative simulations can be conducted on models consist of small number of populations. The main advantage of this model is that it can easily simulate the spreading of diseases under more realistic scenarios and thus is able to give a large number of simulations needed for the searching of the optimal control strategy. Based on this work, the reinforcement learning will be introduced to give the optimal control strategy in the following work.

Explore related subjects

cs.SI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ting Wang, Gui-Yun Li, Xin-Hui Li, Chi-Chun Zhou, Yuan-Yuan Wang, Li-Juan Li, Yan-Ting Yang. 2022-04-11. To Simulate the Spread of Infectious Diseases by the Random Matrix. https://arxiv.org/abs/2204.10188

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

KEEP EXPLORING

Related papers

arXiv2026

Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of data missingness. Our approach leverages a highly complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of the contact network before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

cs.SI↗
arXiv2026

Threat Amplified, Blame Restrained: LLM-Assisted Media Framing Analysis of the 2026 Bangladesh Measles Outbreak

How news media frame and emotionally code a public health emergency shapes public risk perception and trust, yet outbreak-coverage dynamics remain understudied for low- and middle-income countries (LMICs). We examine sentiment and stance in English-language Bangladeshi coverage of the 2026 measles outbreak -- the country's most severe in two decades, with over 97,000 suspected cases and 600 deaths across 61 of 64 districts, unfolding after the 2024 change of government and a 2024-2025 vaccine stockout. Using the Internet Archive, we build a reproducible corpus of 403 headlines from seven national outlets (396 in-window in 2026), label them for binary sentiment and four-way stance via a large language model under a locked codebook, and validate against a two-coder human-adjudicated gold standard (n=153; Cohen's kappa=0.89 stance, 0.75 sentiment). Aligned to the DGHS epidemic curve, coverage grew significantly more negative (56% to 88% negative; Cochran-Armitage z=4.12, p<.001) and risk-amplification framing intensified (44% to 84%; z=4.15, p<.001). Media negativity lagged incidence, tracking cumulative mortality. Contrary to the political backdrop, blame remained a minority frame (~9% overall) and was overwhelmingly systemic (32 of 37, 86%) rather than directed at named actors. The pipeline offers a scalable, transparent method for LMIC outbreak-media analysis; Bangladeshi coverage amplified threat far more than it assigned political blame.

cs.SI↗
arXiv2026

OranSim: Simulating Social Media Marketing

Social simulation studies how individual behavior and social interaction produce collective outcomes. In social media marketing, campaign actions shape which consumers encounter the content and how they respond; these responses then spread through the population. We propose OranSim, a social simulation framework that connects creative, creator, targeting, and budget choices to this process. Heterogeneous consumers receive exposure according to content matching and platform allocation and generate initial responses, which propagate among 60 population segments. Candidate campaigns share the initial population and aligned random numbers, making their response trajectories comparable under action changes. In a controlled synthetic campaign, doubling the budget approximately doubles reach while lowering mean content match and engagement probability among the reached consumers; mean 14-day cumulative simulated response mass rises to 1.96 times the baseline. LightGBM predictors fitted to 39,000 historical RedNote notes estimate platform engagement with log-scale $R^2$ of 0.56--0.62 in five-fold cross-validation; a separate 12,154-note corpus supplies temporal, unseen-creator, and held-out-niche test splits. Public-data experiments evaluate policy value and audience ranking, and paired synthetic outcomes test counterfactual scoring. Together, scenario trajectories and engagement estimates support campaign selection according to a prespecified marketing objective. Code is available at https://github.com/OranAi-Ltd/oransim.

cs.SI↗