Browsing by Author "Hiraoka, Takayuki"
Now showing 1 - 11 of 11
- Results Per Page
- Sort Options
- Adaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-04) Molla, Jeta; de León Chávez, Alejandro Ponce; Hiraoka, Takayuki; Ala-Nissila, Tapio; Kivelä, Mikko; Leskelä, LasseWe evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering. - Burst-tree decomposition of time series reveals the structure of temporal correlations
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020-07-22) Jo, Hang Hyun; Hiraoka, Takayuki; Kivelä, MikkoComprehensive characterization of non-Poissonian, bursty temporal patterns observed in various natural and social processes is crucial for understanding the underlying mechanisms behind such temporal patterns. Among them bursty event sequences have been studied mostly in terms of interevent times (IETs), while the higher-order correlation structure between IETs has gained very little attention due to the lack of a proper characterization method. In this paper we propose a method of representing an event sequence by a burst tree, which is then decomposed into a set of IETs and an ordinal burst tree. The ordinal burst tree exactly captures the structure of temporal correlations that is entirely missing in the analysis of IET distributions. We apply this burst-tree decomposition method to various datasets and analyze the structure of the revealed burst trees. In particular, we observe that event sequences show similar burst-tree structure, such as heavy-tailed burst-size distributions, despite of very different IET distributions. This clearly shows that the IET distributions and the burst-tree structures can be separable. The burst trees allow us to directly characterize the preferential and assortative mixing structure of bursts responsible for the higher-order temporal correlations. We also show how to use the decomposition method for the systematic investigation of such correlations captured by the burst trees in the framework of randomized reference models. Finally, we devise a simple kernel-based model for generating event sequences showing appropriate higher-order temporal correlations. Our method is a tool to make the otherwise overwhelming analysis of higher-order correlations in bursty time series tractable by turning it into the analysis of a tree structure. - Close and more distant relatives are associated with child mortality risk in historical Finland
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2025-01-20) Lahdenperä, Mirkka; Salonen, Milla; Hiraoka, Takayuki; Seltmann, Martin W.; Saramäki, Jari; Lummaa, VirpiHumans are characterised as cooperative breeders, as not only the parents but also other members of the social group take part in raising offspring. The individuals who invest most in childrearing are usually the more closely related individuals. However, most studies have concentrated on close kin and the effects of more distant kin remain unknown. Here, we investigated the associations of child mortality (<5 years, n = 32,000 children) with the presence of 36 different types of relatives, divided by lineage and sex, in a historical Finnish population. We found that the presence and greater number of several paternal relatives were associated with an increase in child mortality and many of these associations were seen among the wealthiest families, due to inheritance practices and shared resources. The presence of the maternal grandmother was associated with a decrease in child mortality and the most among poorer families, who probably needed the grandmother's contribution more than the wealthy. Our results bring new insights into the importance of kin and suggest that relatives can provide support or other resources but also compete for limited resources and care. The results give a broader perspective of human family life and increase understanding of the evolution of cooperative breeding. - Copula-based algorithm for generating bursty time series
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019-08-14) Jo, Hang Hyun; Lee, Byoung Hwa; Hiraoka, Takayuki; Jung, Woo SungDynamical processes in various natural and social phenomena have been described by a series of events or event sequences showing non-Poissonian, bursty temporal patterns. Temporal correlations in such bursty time series can be understood not only by heterogeneous interevent times (IETs) but also by correlations between IETs. Modeling and simulating various dynamical processes requires us to generate event sequences with a heavy-tailed IET distribution and memory effects between IETs. For this, we propose a Farlie-Gumbel-Morgenstern copula-based algorithm for generating event sequences with correlated IETs when the IET distribution and the memory coefficient between two consecutive IETs are given. We successfully apply our algorithm to the cases with heavy-tailed IET distributions. We also compare our algorithm to the existing shuffling method to find that our algorithm outperforms the shuffling method for some cases. Our copula-based algorithm is expected to be used for more realistic modeling of various dynamical processes. - Correlated bursts in temporal networks slow down spreading
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2018-12-01) Hiraoka, Takayuki; Jo, Hang HyunSpreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results. - Estimating inter-regional mobility during disruption: Comparing and combining different data sources
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-04) Heydari, Sara; Huang, Zhiren; Hiraoka, Takayuki; Ponce de Leon Chavez, Alejandro; Ala-Nissila, Tapio; Leskelä, Lasse; Kivelä, Mikko; Saramäki, JariA quantitative understanding of people’s mobility patterns is crucial for many applications. However, it is difficult to accurately estimate mobility, in particular during disruption such as the onset of the COVID-19 pandemic. Here, we investigate the use of multiple sources of data from mobile phones, road traffic sensors, and companies such as Google and Facebook in modelling mobility patterns, with the aim of estimating mobility flows in Finland in early 2020, before and during the disruption induced by the pandemic. We find that the highest accuracy is provided by a model that combines a past baseline from mobile phone data with up-to-date road traffic data, followed by the radiation and gravity models similarly augmented with traffic data. Our results highlight the usefulness of publicly available road traffic data in mobility modelling and, in general, pave the way for a data fusion approach to estimating mobility flows. - Herd immunity and epidemic size in networks with vaccination homophily
Letter(2022-05-12) Hiraoka, Takayuki; K. Rizi, Abbas; Kivelä, Mikko; Saramäki, JariWe study how the herd immunity threshold and the expected epidemic size depend on homophily with respect to vaccine adoption. We find that the presence of homophily considerably increases the critical vaccine coverage needed for herd immunity and that strong homophily can push the threshold entirely out of reach. The epidemic size monotonically increases as a function of homophily strength for a perfect vaccine, while it is maximized at a nontrivial level of homophily when the vaccine efficacy is limited. Our results highlight the importance of vaccination homophily in epidemic modeling. - Individual-driven versus interaction-driven burstiness in human dynamics: The case of Wikipedia edit history
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-07-26) Choi, Jeehye; Hiraoka, Takayuki; Jo, Hang HyunThe origin of non-Poissonian or bursty temporal patterns observed in various data sets for human social dynamics has been extensively studied, yet its understanding still remains incomplete. Considering the fact that humans are social beings, a fundamental question arises: Is the bursty human dynamics dominated by individual characteristics or by interaction between individuals? In this paper we address this question by analyzing the Wikipedia edit history to see how spontaneous individual editors are in initiating bursty periods of editing, i.e., individual-driven burstiness, and to what extent such editors' behaviors are driven by interaction with other editors in those periods, i.e., interaction-driven burstiness. We quantify the degree of initiative (DoI) of an editor of interest in each Wikipedia article by using the statistics of bursty periods containing the editor's edits. The integrated value of the DoI over all relevant timescales reveals which is dominant between individual-driven and interaction-driven burstiness. We empirically find that this value tends to be larger for weaker temporal correlations in the editor's editing behavior and/or stronger editorial correlations. These empirical findings are successfully confirmed by deriving an analytic form of the DoI from a model capturing the essential features of the edit sequence. Thus our approach provides a deeper insight into the origin and underlying mechanisms of bursts in human social dynamics. - Limits of the memory coefficient in measuring correlated bursts
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2018-03-16) Jo, Hang Hyun; Hiraoka, TakayukiTemporal inhomogeneities in event sequences of natural and social phenomena have been characterized in terms of interevent times and correlations between interevent times. The inhomogeneities of interevent times have been extensively studied, while the correlations between interevent times, often called correlated bursts, are far from being fully understood. For measuring the correlated bursts, two relevant approaches were suggested, i.e., memory coefficient and burst size distribution. Here a burst size denotes the number of events in a bursty train detected for a given time window. Empirical analyses have revealed that the larger memory coefficient tends to be associated with the heavier tail of the burst size distribution. In particular, empirical findings in human activities appear inconsistent, such that the memory coefficient is close to 0, while burst size distributions follow a power law. In order to comprehend these observations, by assuming the conditional independence between consecutive interevent times, we derive the analytical form of the memory coefficient as a function of parameters describing interevent time and burst size distributions. Our analytical result can explain the general tendency of the larger memory coefficient being associated with the heavier tail of burst size distribution. We also find that the apparently inconsistent observations in human activities are compatible with each other, indicating that the memory coefficient has limits to measure the correlated bursts. - Modeling temporal networks with bursty activity patterns of nodes and links
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020-04-24) Hiraoka, Takayuki; Masuda, Naoki; Li, Aming; Jo, Hang-HyunThe concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node, and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation, and we show that it leads to heavy-tailed interevent time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems. - Waiting-Time Paradox in 1922
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020-05) Masuda, Naoki; Hiraoka, TakayukiWe present an English translation and discussion of an essay that a Japanese physicist, Torahiko Terada, wrote in 1922. In the essay, he described the waiting-time paradox, also called the bus paradox, which is a known mathematical phenomenon in queuing theory, stochastic processes, and modern temporal network analysis. He also observed and analyzed data on Tokyo City trams to verify the relevance of the waiting-time paradox to busy passengers in Tokyo at the time. This essay seems to be one of the earliest documentations of the waiting-time paradox in a sufficiently scientific manner.