Modeling the Transmission of Infectious Disease in a Dynamic Network

Abstract

The transmission of infectious disease in epidemiological models usually is based on the assumption that population within random-mixing. Although medical developments can reduce the consequences of the spread of infectious diseases, prevention of plague remains a major toehold. After a model is formulated containing the main fitur the development and transmission of infectious disease, onward to the model can be used to predict, making eradication strategies, control or prevent the spread. Modeling the spread of the disease has the potential to improve the quality of human life. The social life of humans far more complex exceed a diverse population. The transmission dynamics of infectious diseases is sensitive to patterns the interaction between the individual vulnerable (susceptible) and contracted (infectious). Human social contact very heterogeneous group. To predict the impact of this pattern against the transmission of infectious diseases, the use of epidemiological random network model, where the nodes serves individuals exposed, contracting or cured and connectedness presents contact transmission. Type the model spread (epidemic) that examined the model type is exposed, tetular exposed, and cured, or better known as a type of SIRS.

Temporal network analysis using zigzag persistence

“…5(a) shows the times associated to each edge, and the resulting The one-dimensional feature (the cycle represented in H 1 ) is present twice in the persistence diagram. This is due to it first appearing in G (3,5) and then disappearing at G (4,5) with corresponding persistence pair (4, 4.5). The cycle then reappears at G (5,7) and disappears at G (8,9) resulting in persistence pair at (6, 8.5).…”

“…In particular, we are interested in the case of temporal networks [2,3]; that is, the case of a dynamical system represented by a network evolving over time. These networks can arise in many different cases, such as social networks [4], disease spread dynamics [5], manufacturer-supplier networks [6], power grid network [7], and transportation networks [8]. Many important characteristics of a dynamical network can be extracted from the data.…”

Pencegahan Penularan Tuberkulosis pada Keluarga dan Masyarakat Melalui Strategi Promosi Kesehatan di Puskesmas Wonorejo, Puskesmas Karang Asam, dan Puskesmas Loa Bakung, Kota Samarinda

“…Kaskade penularan tuberkulosis adalah (1) kasus sumber tuberkulosis (2) menghasilkan partikel infeksius (3) yang bertahan di udara dan (4) terhirup oleh individu yang rentan (5) yang mungkin terinfeksi dan (6) kemudian berpotensi mengembangkan tuberkulosis (Churchyard et al, 2017). Kehidupan sosial manusia jauh lebih kompleks melebihi populasi yang beragam, kontak sosial manusia sangat heterogen sehingga dinamika penularan penyakit infeksi sensitif terhadap pola interaksi antara individu yang rentan dan terinfeksi bisa terjadi ketika intraksi sosial terjadi terlebih kontak dekat secara fisik (Bustamante-Rengifo et al, 2020;Husein et al, 2019).…”

Temporal Network Analysis Using Zigzag Persistence

“…While time series analysis tools can be leveraged for bifurcation detection and dynamic state analysis, many complex and high-dimensional dynamical systems and their corresponding measurements can more naturally be represented as complex networks. For example, there are dynamical systems models for social networks [18], disease spread dynamics [19], manufacturer-supplier networks [20], power grid network [5], and transportation networks [21]. These dynamical system models demonstrate how dynamical networks can be representative of highly complex real-world systems.…”