scholarly journals Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes

2022 ◽  
Vol 127 ◽  
pp. 334-346
Author(s):  
Shan Gao ◽  
Hanyi Wang
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Dynamics ◽  
Dynamics Model ◽  
Public Health Emergencies ◽  
Scenario Prediction ◽  
Infectious Disease Dynamics

scholarly journals Adaptive social contact rates induce complex dynamics during epidemics

2021 ◽  
Vol 17 (2) ◽  
pp. e1008639
Author(s):  
Ronan F. Arthur ◽  
James H. Jones ◽  
Matthew H. Bonds ◽  
Yoav Ram ◽  
Marcus W. Feldman
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Utility Function ◽  
Adaptive Behavior ◽  
Social Contact ◽  
Health Costs ◽  
Contact Rate ◽  
Disease Dynamics ◽  
Oscillatory Dynamics ◽  
Infectious Disease Dynamics

Epidemics may pose a significant dilemma for governments and individuals. The personal or public health consequences of inaction may be catastrophic; but the economic consequences of drastic response may likewise be catastrophic. In the face of these trade-offs, governments and individuals must therefore strike a balance between the economic and personal health costs of reducing social contacts and the public health costs of neglecting to do so. As risk of infection increases, potentially infectious contact between people is deliberately reduced either individually or by decree. This must be balanced against the social and economic costs of having fewer people in contact, and therefore active in the labor force or enrolled in school. Although the importance of adaptive social contact on epidemic outcomes has become increasingly recognized, the most important properties of coupled human-natural epidemic systems are still not well understood. We develop a theoretical model for adaptive, optimal control of the effective social contact rate using traditional epidemic modeling tools and a utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic strategic model reveals the existence of an endemic equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for any epidemic in which adaptive behavior influences infectious disease dynamics include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections.

scholarly journals Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

Epidemics ◽  
2018 ◽  
Vol 22 ◽  
pp. 56-61 ◽  
Author(s):  
Sebastian Funk ◽  
Anton Camacho ◽  
Adam J. Kucharski ◽  
Rosalind M. Eggo ◽  
W. John Edmunds
Keyword(s):  
Infectious Disease ◽  
Real Time ◽  
Mechanistic Model ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

scholarly journals Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

2019 ◽  
Vol 7 (8) ◽  
pp. 277
Author(s):  
Yong-jun Chen ◽  
Qing Liu ◽  
Cheng-peng Wan
Keyword(s):  
Infectious Disease ◽  
Risk Factors ◽  
Causal Model ◽  
Cloud Model ◽  
Rapid Development ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Disease Dynamics ◽  
Transmission Process ◽  
Infectious Disease Dynamics

Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

scholarly journals Game theoretic modelling of infectious disease dynamics and intervention methods: a review

2020 ◽  
Vol 14 (1) ◽  
pp. 57-89 ◽  
Author(s):  
Sheryl L. Chang ◽  
Mahendra Piraveenan ◽  
Philippa Pattison ◽  
Mikhail Prokopenko
Keyword(s):  
Infectious Disease ◽  
Disease Dynamics ◽  
Game Theoretic ◽  
Infectious Disease Dynamics

scholarly journals Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e58802 ◽  
Author(s):  
Gonzalo M. Vazquez-Prokopec ◽  
Donal Bisanzio ◽  
Steven T. Stoddard ◽  
Valerie Paz-Soldan ◽  
Amy C. Morrison ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Urban Environment ◽  
Human Mobility ◽  
Disease Dynamics ◽  
Gps Technology ◽  
Resource Poor ◽  
Infectious Disease Dynamics
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