Inference of epidemic networks: the effect of different data types
By: Oscar Fajardo-Fontiveros, Carl J. E. Suster, Eduardo G. Altmann
Potential Business Impact:
Tracks disease spread better with more data.
We investigate how the properties of epidemic networks change depending on the availability of different types of data on a disease outbreak. This is achieved by introducing mathematical and computational methods that estimate the probability of transmission trees by combining generative models that jointly determine the number of infected hosts, the probability of infection between them depending on location and genetic information, and their time of infection and sampling. We introduce a suitable Markov Chain Monte Carlo method that we show to sample trees according to their probability. Statistics performed over the sampled trees lead to probabilistic estimations of network properties and other quantities of interest, such as the number of unobserved hosts and the depth of the infection tree. We confirm the validity of our approach by comparing the numerical results with analytically solvable examples. Finally, we apply our methodology to data from COVID-19 in Australia. We find that network properties that are important for the management of the outbreak depend sensitively on the type of data used in the inference.
Similar Papers
Network Topology Matters, But Not Always: Mobility Networks in Epidemic Forecasting
Physics and Society
Helps predict disease spread using movement.
Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks
Methodology
Helps protect computer networks from attacks.
A statistical framework for comparing epidemic forests
Quantitative Methods
Compares how diseases spread to find best cures.