Contact heterogeneity in network reconstruction: evaluating synthetic graph generators for rabies transmission modeling
- 1Swiss TPH: Schweizerisches Tropen- und Public Health-Institut
Abstract
Canine rabies remains endemic across much of Africa and Asia, causing approximately 59,000 global deaths yearly. Network-based models that reconstruct contact patterns are central to predicting canine rabies transmission and to designing targeted interventions, but the choice of network generator algorithm may alter network structure and outbreak predictions. We therefore compared five network generators using five empirical free-roaming dog contact networks from Chad, Indonesia, and Guatemala in network topology and outbreak dynamics under susceptible-exposed-infectious-removed (SEIR) and susceptible-infectious-susceptible (SIS) transmission across a range of infectious-period lengths. We found that the degree-corrected stochastic block model (DCSBM) most consistently reproduces both empirical topology and outbreak dynamics, achieving a Mahalanobis distance of 17.6 to the empirical Chad network compared to a range of 21.0 to 220.7 across the other generators and yielding similar results for the other four locations. Moreover, the SIS and SEIR outbreak dynamics reveal that the DCSBM overall best reproduces the simulated empirical outbreak dynamics, but that the consequences of generator choice are not uniform: network reconstruction matters most precisely for short-infectious-window pathogens like canine rabies and progressively less for long-infectious-window pathogens. By an infectious window of nine days, the outbreak dynamics across four of five generators converge to within two percentage points of the empirical reference, even when their underlying topologies differ substantially. This pathogen-dependent sensitivity offers a principled criterion for when generator selection is consequential and when it is not. This work reiterates the importance of both degree heterogeneity and community structure for faithful contact-network reconstruction in canine rabies modeling, particularly for short-window infectious diseases where contact heterogeneity drives transmission. For longer-window pathogens, simpler generators are likely adequate.
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BibTeX
@misc{derkx2026contact,
title = {{Contact heterogeneity in network reconstruction: evaluating synthetic graph generators for rabies transmission modeling}},
author = {I. Derkx and J. M. Hyman and F. Abdelrazak Zakaria and M. F. Abakar and S. Dürr and N. Chitnis},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.759544},
url = {https://www.biorxiv.org/content/10.64898/2026.10.16.759544v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Contact heterogeneity in network reconstruction: evaluating synthetic graph generators for rabies transmission modeling
AU - I. Derkx
AU - J. M. Hyman
AU - F. Abdelrazak Zakaria
AU - M. F. Abakar
AU - S. Dürr
AU - N. Chitnis
PY - 2026
DA - 2026/10/16
PB - bioRxiv
DO - 10.64898/2026.10.16.759544
UR - https://www.biorxiv.org/content/10.64898/2026.10.16.759544v1
AB - Canine rabies remains endemic across much of Africa and Asia, causing approximately 59,000 global deaths yearly. Network-based models that reconstruct contact patterns are central to predicting canine rabies transmission and to designing targeted interventions, but the choice of network generator algorithm may alter network structure and outbreak predictions. We therefore compared five network generators using five empirical free-roaming dog contact networks from Chad, Indonesia, and Guatemala in network topology and outbreak dynamics under susceptible-exposed-infectious-removed (SEIR) and susceptible-infectious-susceptible (SIS) transmission across a range of infectious-period lengths. We found that the degree-corrected stochastic block model (DCSBM) most consistently reproduces both empirical topology and outbreak dynamics, achieving a Mahalanobis distance of 17.6 to the empirical Chad network compared to a range of 21.0 to 220.7 across the other generators and yielding similar results for the other four locations. Moreover, the SIS and SEIR outbreak dynamics reveal that the DCSBM overall best reproduces the simulated empirical outbreak dynamics, but that the consequences of generator choice are not uniform: network reconstruction matters most precisely for short-infectious-window pathogens like canine rabies and progressively less for long-infectious-window pathogens. By an infectious window of nine days, the outbreak dynamics across four of five generators converge to within two percentage points of the empirical reference, even when their underlying topologies differ substantially. This pathogen-dependent sensitivity offers a principled criterion for when generator selection is consequential and when it is not. This work reiterates the importance of both degree heterogeneity and community structure for faithful contact-network reconstruction in canine rabies modeling, particularly for short-window infectious diseases where contact heterogeneity drives transmission. For longer-window pathogens, simpler generators are likely adequate.
ER -