nosoi: a stochastic agent-based transmission chain simulation framework in R

Abstract

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavor, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed. We here introduce nosoi, an opensource R package that offers a complete, tunable, and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations. Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can -for example -be used to validate a wide range of reconstruction methods, including epidemic modeling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a handson exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.Infectious disease events, especially those resulting from novel emerging pathogens, have significantly increased over the past few decades, possibly as a result of alterations in various environmental, biological, socioeconomic, and political factors [1]. By definition, infectious agents need to spread through transmission between hosts. If successful, the resulting transmission process creates a connected chain of hosts linked by transmission events, usually called a transmission chain. Transmission is highly stochastic and can be influenced by a wide array of intrinsic and extrinsic factors, such as within-host dynamics and environmental or host behavioral factors. Reconstruction of transmission chains, however, remains difficult to achieve, except in certain rare cases characterized by intense surveillance and epidemiological inquiry [2,3].Molecular data may represent a critical asset in reconstructing the transmission history of a pathogen [3][4][5][6][7]. Often, however, the relationship between individual cases is too distant to allow for the perfect reconstruction of a tr...

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics

“…1a illustrates the entire computational workflow for dynamic transmission cluster prediction. Briefly, simulation of an early-midway epidemic outbreak was performed using the nosoi (19) agent-based stochastic simulation platform, which is designed to take into account the influence of multiple variables on the transmission process (e.g., population structure and dynamics) to create complex epidemiological simulations (see Methods and Table 2). The resulting transmission network is translated in nosoi to a strictly bifurcating tree, representative of the underlying pathogen evolution, and used as ground truth in the training and evaluation of the prediction algorithms.…”

“…Simulation of an early-midway epidemic outbreak was performed using the nosoi (19) agent-based stochastic simulation platform, which is designed take into account the influence of multiple variables on the transmission process (e.g. population structure and dynamics) to create complex epidemiological simulations.…”

Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for phylogenetics

“…We simulated a growing epidemic using a stochastic, agent-based model [11] with limited migration between ten subpopulations, or regions (a, . .…”

Using genetic data to identify transmission risk factors: statistical assessment and application to tuberculosis transmission

“…All simulated data sets were generated in R version 4.0.2 or 4.0.4 using the nosoi package version 1.0.3 [16,17]. nosoi simulates an infectious disease outbreak by simulating individual cases which generate new cases in discrete units of time until either a specified time limit is reached or a specified total number of cases is reached.…”