English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Optimizing testing strategies for early detection of disease outbreaks in animal trade networks via MCMC

Ansari, S., Heitzig, J., Moosavi, M. R. (2023): Optimizing testing strategies for early detection of disease outbreaks in animal trade networks via MCMC. - Chaos, 33, 4, 043144.
https://doi.org/10.1063/5.0125434

Item is

Files

show Files
hide Files
:
Ansari_2023_043144_1_5.0125434.pdf (Publisher version), 3MB
 
File Permalink:
-
Name:
Ansari_2023_043144_1_5.0125434.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Ansari, Sara1, Author              
Heitzig, Jobst1, Author              
Moosavi, Mohammad R. 2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: The animal trades between farms and other livestock holdings form a complex livestock trade network. The movement of animals between trade actors plays an important role in the spread of infectious diseases among premises. Particularly, the outbreak of silent diseases that have no clinically obvious symptoms in the animal trade system should be diagnosed by taking special tests. In practice, the authorities regularly conduct examinations on a random number of farms to make sure that there was no outbreak in the system. However, these actions, which aim to discover and block a disease cascade, are yet far from the effective and optimum solution and often fail to prevent epidemics. A testing strategy is defined as making decisions about distributing the fixed testing budget between farms/nodes in the network. In this paper, first, we apply different heuristics for selecting sentinel farms on real and synthetic pig-trade networks and evaluate them by simulating disease spreading via the SI epidemic model. Later, we propose a Markov chain Monte Carlo (MCMC) based testing strategy with the aim of early detection of outbreaks. The experimental results show that the proposed method can reasonably well decrease the size of the outbreak on both the realistic synthetic and real trade data. A targeted selection of an fraction of nodes in the real pig-trade network based on the MCMC or simulated annealing can improve the performance of a baseline strategy by 89%. The best heuristic-based testing strategy results in a 75% reduction in the average size of the outbreak compared to that of the baseline testing strategy.

Details

show
hide
Language(s): eng - English
 Dates: 2023-04-282023-04-28
 Publication Status: Finally published
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0125434
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
MDB-ID: No data to archive
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Research topic keyword: Complex Networks
Regional keyword: Germany
Model / method: Machine Learning
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 33 (4) Sequence Number: 043144 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)