scholarly journals Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo

2017 ◽  
Vol 34 (8) ◽  
pp. 2065-2084 ◽  
Author(s):  
R.A. Smith ◽  
E.L. Ionides ◽  
A.A. King
Keyword(s):  
Infectious Disease ◽  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Genetic Data ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

scholarly journals Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo

2016 ◽  
Author(s):  
R. A. Smith ◽  
E. L. Ionides ◽  
A. A. King
Keyword(s):  
Infectious Disease ◽  
Monte Carlo ◽  
Phylogenetic Trees ◽  
Sequential Monte Carlo ◽  
Genetic Data ◽  
Monte Carlo Algorithm ◽  
Transmission Model ◽  
Disease Dynamics ◽  
Genetic Sequences ◽  
Infectious Disease Dynamics

AbstractGenetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.

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

scholarly journals Network Perspectives on Infectious Disease Dynamics

2011 ◽  
Vol 2011 ◽  
pp. 1-2 ◽  
Author(s):  
Lauren Ancel Meyers ◽  
Ben Kerr ◽  
Katia Koelle
Keyword(s):  
Infectious Disease ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics
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