Contact heterogeneity in network reconstruction: evaluating synthetic graph generators for rabies transmission modeling
Year: 2026
Type: preprint
Abstract: Canine rabies remains endemic across much of Africa and Asia, causing approxi 59,000 global deaths yearly. Network-based models that reconstruct contact pat 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 generat using five empirical free-roaming dog contact networks from Chad, Indonesia, an Guatemala in network topology and outbreak dynamics under susceptible-exposed-infectious-removed (SEIR) and susceptible-infectious-suscep (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, achievi Mahalanobis distance of 17.6 to the empirical Chad network compared to a rang 21.0 to 220.7 across the other generators and yielding similar results for the othe locations. Moreover, the SIS and SEIR outbreak dynamics reveal that the DCSB overall best reproduces the simulated empirical outbreak dynamics, but that the consequences of generator choice are not uniform: network reconstruction matter precisely for short-infectious-window pathogens like canine rabies and progressive for long-infectious-window pathogens. By an infectious window of nine days, the outbreak dynamics across four of five generators converge to within two percent points of the empirical reference, even when their underlying topologies differ substantially. This pathogen-dependent sensitivity offers a principled criterion for generator selection is consequential and when it is not. This work reiterates the importance of both degree heterogeneity and commu structure for faithful contact-network reconstruction in canine rabies modeling, particularly for short-window infectious diseases where contact heterogeneity dri transmission. For longer-window pathogens, simpler generators are likely adequate. Author summary Canine rabies causes around 59,000 deaths worldwide each year, and its spread l depends on the contact networks domestic dogs form with each other. In these networks, some dogs have far more contacts than others, and this heterogeneous structure shapes how diseases like rabies are spread through a population. Thes structures can be reconstructed using different models, though the choice of mod impacts how well it can reconstruct an observed contact network. Here, we ther compare five synthetic network models against real contact data collected from Chad, Indonesia, and Guatemala. We evaluate both the structure of the network generate and how well they reproduce outbreak dynamics, comparing the outbre resulting from the networks generated by each model to outbreaks simulated on observed networks themselves. One model, the degree-corrected stochastic block best reproduces the observed contact structures across locations and comes close matching outbreak simulations based on the observed network. We also find that choice matters more when pathogens have short infectious periods: the models d more in their outbreak outcomes under short infectious windows and all but one b more similar when infectious periods are longer.
Language: English
Subfield: Virology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: green
Funder: Universität Basel
OpenAlex ID: W7223208145