Nov 23, 2023
EpiPopSynth
- km.zhu 1,
- Kang Liu1,
- Junli Liu2,
- Yepeng Shi1,
- Xuan Li1,
- Hongyang Zou3,
- Huibin Du3,
- Ling Yin1
- 1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;
- 2Hangzhou Institute of Technology, Xidian University, Hangzhou, China;
- 3College of Management and Economics, Tianjin University, Tianjin 300072, China
- Ling Yin: Corresponding author;
External link: https://github.com/ZhuKemin/EpiPopSynth
Protocol Citation: km.zhu , Kang Liu, Junli Liu, Yepeng Shi, Xuan Li, Hongyang Zou, Huibin Du, Ling Yin 2023. EpiPopSynth. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn3jn6l5d/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 22, 2023
Last Modified: November 23, 2023
Protocol Integer ID: 91327
Keywords: population synthesis, agent-based model, epidemic model, infectious disease transmission simulation, generating synthetic population, synthetic population, subsequent disease transmission simulation, yielding synthetic population, biases in subsequent disease transmission simulation, world epidemic scenario, explicit synthetic population, epidemic projection, agent structural relationship, structural relationships among agent, variations in population synthesizer, spread of infectious disease, population synthesizer, agent, world demographic structure, demographic structure, comprehensive population data, capturing nonlinear interaction dynamic, demographic benchmark, population, stochastic agent, infectious disease, accurate portrayal of both population, current population synthesizer, agents for shenzhen, based model, nonlinear interaction dynamic, interaction
Abstract
Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.
Guidelines
plese just follow the steps.
Materials
High-performance computing environments with more than 500 CPU cores, the MPI (Message Passing Interface) framework is recommended.
Safety warnings
There is no particularly dangerous operation in this experiment.
Population Generation
Process micro household survey data, re-group individuals into age groups, and remove irrelevant fields and non-family households.
Recode members with the same household ID into family structure strings to generate a pool of family structures.
Conduct a logistic regression test on the distribution of family structures in the pool, determine the number of family structures needed to cover a given proportion α of the population, and select the top k types of families as family motifs.
Combinatorial Optimization
Initialize the combinatorial optimization iterator with the distribution of motifs in the pool as the initial guess, with the decision variable being a k-dimensional vector.
Use the number of households/people of different household sizes, age groups, and genders in regional demographic data as the least squares optimization target function, optimize based on the trf algorithm, and output the optimal decision variable upon reaching the termination condition.
Generate the synthetic population within the region based on the optimal decision variable values as the proportion of each family motif.
Population Generation
Execute steps 4-6 in parallel to generate synthetic populations for multiple subzones within the city and merge them into the final synthetic population.
Epidemic Simulation
Run the infectious disease agent-based model using the above synthetic population as the carrier and test the results.