Network inference from multimodal data: A review of approaches from infectious disease transmission

Abstract

Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.

Keywords: Bayesian inference; Infectious disease; Multimodal data; Network inference; Transmission.

Graphical abstract

Fig. 1

Examples of multimodal network inference methods in different applications. Different modalities of data have been integrated in several applications for inferring specific networks. Most network inference methods focus on recovering network topology.

Fig. 2

Modeling transmission of infectious diseases, an area in which use of multiple modalities of data has been developed. (a) Several key questions can be answered such as who infected whom or how did the infection transmit through the population or region. (b) Possible inputs to the model include pathogen genomic sequences, spatial and temporal information, point-of-care diagnostic information, and mobile health information. The data are brought together in multimodal network inference frameworks. (c) Some possible outputs are the transmission tree, latency period, epidemic reproduction number, phylogenetic tree, and proportion of infected hosts sampled.

Fig. 3

Study design and inclusion-exclusion criteria. This is a decision tree showing our searches and selection criteria for both PubMed and Google Scholar. We focused only on genomic epidemiology methods utilizing Bayesian inference for infectious disease transmission.

Fig. 4

Different spatial and genomic resolutions utilized to study disease spread. (a) Regions of interest considered for different studies. Influenza studies considered world-wide spread, SARS was studied in Singapore, Tuberculosis (TB) dataset was from British Columbia, Norovirus in a university hospital in the Netherlands, and Foot and Mouth Disease (FMD) in 12 farms in Durham. (b) Different genomic sequencing platforms utilized in studies. For the TB study, Whole genome sequencing was performed on Illumina HiSeq platform with M. tuberculosis CDC1551 reference sequence and aligned using Burrows-Wheeler Aligner algorithm. SARS DNA sequences were obtained from GenBank and aligned using MUSCLE. For avian influenza, RNA consensus sequences of the haemagglutinin, neuriminidase and polymerase PB2 genes were sequenced. For H1N1 influenza, isolates were typed for hemagglutinin (HA) and neuraminidase (NA) genes.