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Fig A. Final epidemic size (in percent) for single genotype infections on dispersed (red) and clustered (blue) networks. Fig B. Genotype diversity index, multiplicity of infection (MOI), and epidemic prevalence for random regular graphs for five cross-immunity patterns. Fig c. Spatial correlations for clustered networks for five cross-immunity patterns. Fig D. Spatial correlations for dispersed networks for five cross-immunity patterns. Fig E. Correlation tests between a matrix based on infection barcodes distance and the network’s adjacency (top) or shortest path (bottom) matrices. Fig F. Mining for motifs of length two. Fig G. Spatial auto-correlations for dispersed (left) and clustered (right) neworks. (PDF)</p

Co-occurrence score from infection barcode outputs of simulations with various assumptions on immunological interference.

The underlying cross-immunity matrices are shown in the Materials and methods. The combinations with 100% cross-immunity correspond to the red cells with 0 co-occurrence score.</p

Precision-recall curve for multinomial models under five distinct immunity assumptions.

Precision (resp. recall) is defined as the percentage of true positives among all positives (resp. true positives plus false negatives). The area under the curve (AUC) indicates increased (compared to random AUC of 0.5) power to classify a host’s node degree using infection barcode connectivity information.</p

Summary statistics of the two types of networks simulated.

Quantities are averaged across 250 nodes and 50 stochastic replicates.</p

Link between the host connectivity estimated via the barcodes and that estimated via the shortest path.

Hosts that are found to have a high connectivity according to the multiple infection history (the barcode metric) tend to be closer in the contact network. Significance levels corresponds to p-values of a linear regression ***: p p < 0.01. The variance explained (in percent) by barcode connectivity refers to sum of squares from ANOVA analysis.</p

Effect of cross-immunity patterns on the average frequency of all possible sequence motifs with length one.

The motifs represent cross-sectional multiple infection snapshots. Here, samplings are performed at five random time points during the simulation. Note that coinfection by all 4 genotypes is only found in the homologous decreasing case. The corresponding cross-immunity matrices are shown in the Materials and methods. See also Fig 6 for additional information regarding co-occurrence between the genotypes.</p

Diagram illustrating the workflow for reconstruction of network properties and immunological interference.

In the first step, we obtain multiple infections histories of individual hosts from our simulator. Second, we calculate similarity metrics between hosts based on their multiple infection histories. Third, by comparing with simulated networks, we determine statistical associations between infection history similarity and contact network adjacency. Fourth, by calculating occurrences of infection overlaps across multiple infection histories, we obtain a matrix of immunological interference between pathogens.</p

Correlations between a matrix based on infection barcodes similarity and the network’s adjacency (top) or shortest path (bottom) matrices.

For simulated epidemics on clustered networks with 2 or 4 circulating genotypes and a variety of cross-immunity settings, we tested for correlations using p-values of two-sided Mantel tests with 104 permutations. For each setting, we re-sampled 20 times randomly 100, 50, 25, or 10% of the infected nodes and report the average p-value.</p

Genotype diversity index, multiplicity of infection (MOI), and epidemic prevalence as a function of network type and cross-immunity patterns.

The figure shows the output of 50 stochastic epidemics with four genotypes on dispersed and clustered networks (see Table 1), and five immunological interference settings. Black lines show the time-averaged fraction of nodes infected with at least one genotype (95% confidence intervals shaded grey), blue lines show the average genotype diversity index at the population level, and green lines show the average MOI of individual nodes.</p

Supplementary figures and models details from The source of individual heterogeneity shapes infectious disease outbreaks

There is known heterogeneity between individuals in infectious disease transmission patterns. The source of this heterogeneity is thought to affect epidemiological dynamics but studies tend not to control for the overall heterogeneity in the number of secondary cases caused by an infection. To explore the role of individual variation in infection duration and transmission rate on parasite emergence and spread, while controlling for this potential bias, we simulate stochastic outbreaks with and without parasite evolution. As expected, heterogeneity in the number of secondary cases decreases the probability of outbreak emergence. Furthermore, for epidemics that do emerge, assuming more realistic infection duration distributions leads to faster outbreaks and higher epidemic peaks. When parasites require adaptive mutations to cause large epidemics, the impact of heterogeneity depends on the underlying evolutionary model. If emergence relies on within-host evolution, decreasing the infection duration variance decreases the probability of emergence. These results underline the importance of accounting for realistic distributions of transmission rates to anticipate the effect of individual heterogeneity on epidemiological dynamics