arXiv subjects

Ryosuke Omori

Publications and source records attributed to Ryosuke Omori.

3 recordsLinked to original sources

arXiv2026

Risk-mediated transmission dynamics govern epidemic trajectories beyond physical mobility

Standard epidemiological models rely on physical mobility and policy indicators, which fail when physical movement decouples from actual transmission risk. To address this, we establish a unified theoretical framework governed by risk-mediated transmission dynamics. Rather than treating societal responses as independent phenomenological proxies, we embed the underlying risk-avoidance tendency directly into the transmission mechanism. This is parsimoniously formulated via the Weber-Fechner law as a logarithmically scaled response to disease incidence, alongside behavioral fatigue. Analyzing multi-regional COVID-19 data, our risk-mediated model significantly outperforms traditional frameworks. While mobility metrics merely track movement volume, our approach directly captures unobserved qualitative contact changes, such as mask-wearing. By integrating this intrinsic behavioral principle, our framework provides a robust, mobility-independent baseline for predicting future epidemic trajectories.

physics.soc-ph↗

arXiv2023

Human movement decisions during Coronavirus Disease 2019

Modelling host behavioral change in response to epidemics is important to describe disease dynamics and many previous studies proposed mathematical models describing it. Indeed, the epidemic of COVID-19 clearly demonstrated that people changed their activity in response to the epidemic, which subsequently modified the disease dynamics. To predict the behavioral change relevant to the disease dynamics, we need to know the epidemic situation (e.g., the number of reported cases) at the moment of decision to change behavior. However, it is difficult to identify the timing of decision-making. In this study, we analyzed travel accommodation reservation data in four prefectures of Japan to observe decision-making timings and how it responded to the changing epidemic situation during Japan's Coronavirus Disease 2019 (eight waves until February 2023). To this end, we defined 'mobility avoidance index' to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. Our analysis revealed semi-quantitative rules for day-to-day decision-making of human mobility under a given epidemic situation. We observed matches of the peak dates of the index and the number of reported cases. Additionally, we found that mobility avoidance index increased/decreased linearly with the logarithmic number of reported cases during the first epidemic wave. This pattern agrees with Weber-Fechner law in psychophysics. We also found that the slope of the mobility avoidance index against the change of the logarithmic number of reported cases were similar among the waves, while the intercept of that was much reduced as the first epidemic wave passed by. It suggests that the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.

physics.soc-ph↗

arXiv2018

$\mathcal{R}_{0}$ fails to predict the outbreak potential in the presence of natural-boosting immunity

Time varying susceptibility of host at individual level due to waning and boosting immunity is known to induce rich long-term behavior of disease transmission dynamics. Meanwhile, the impact of the time varying heterogeneity of host susceptibility on the shot-term behavior of epidemics is not well-studied, even though the large amount of the available epidemiological data are the short-term epidemics. Here we constructed a parsimonious mathematical model describing the short-term transmission dynamics taking into account natural-boosting immunity by reinfection, and obtained the explicit solution for our model. We found that our system show "the delayed epidemic", the epidemic takes off after negative slope of the epidemic curve at the initial phase of epidemic, in addition to the common classification in the standard SIR model, i.e., "no epidemic" as $\mathcal{R}_{0}\leq1$ or normal epidemic as $\mathcal{R}_{0}>1$. Employing the explicit solution we derived the condition for each classification.

q-bio.PE↗