Who infects whom? Social networks and tuberculosis transmission in wild meerkats

Abstract

Transmission of infectious diseases is strongly influenced by who contacts whom. Despite the global distribution of tuberculosis (TB) in free-living wild mammal populations, little is known of the mechanisms of social transmission of Mycobacterium bovis between individuals. Here, I use a network approach to examine for correlations between five distinct types of intra- and intergroup social interaction and changes in TB status of 110 wild meerkats (Suricata suricatta) in five social groups over two years. Contrary to predictions, the most socially interactive animals were not at highest risk of acquiring infection, indicating that in addition to contact frequency, the type and direction of interactions must be considered when quantifying disease risk. Within social groups, meerkats that groomed others most were more likely to become infected than individuals who received high levels of grooming. Conversely, receiving, but not initiating, aggression was associated with M. bovis infection. Incidence of intergroup roving by male meerkats was correlated with the rovers themselves subsequently testing TB-positive, suggesting a possible route for transmission of infection between social groups. Exposure time was less important than these social interactions in influencing TB risk. This study represents a novel application of social network analysis using empirical data to elucidate the role of specific interactions in the transmission of an infectious disease in a free-living wild animal population.

Figure 1.

Tuberculosis (TB) dynamics over the study period. Meerkats were sampled at eight time points (t1–t8) throughout 2006 and 2007. The number of meerkats sampled at each time point varied owing to births and deaths. Prevalence refers to the total number of meerkats testing positive at each time point; incidence refers to new cases testing positive since the previous time point. Only deaths attributable to TB (confirmed by mycobacterial culture) are shown. Dotted line, sampled; black line, prevalence; grey line, incidence; dashed line, deaths.

Figure 2.

Social networks and TB transmission in a meerkat group. Comparative networks of (a) grooming and (b) aggressive interactions over a three-month period are shown. Each node (circle) represents a meerkat; node size is proportional to outdegree centrality (an indication of how much interaction each individual initiated). Arrowhead size is proportional to frequency of interactions, thus the sum of arrowheads around each node gives an indication of indegree centrality (the relative amount of interaction received by that meerkat). Asterisks indicate the three individuals that became TB test-positive during the time period for which the interaction data are shown. Meerkats are arranged in descending order of age from top to bottom of each diagram. White nodes, females; grey nodes, males; D, dominant individuals.

Figure 3.

Fitted logistic regressions of probability of individual meerkats testing positive for TB as a function of (a) grooming outdegree (n = 94, r = 0.37, p = 0.001); (b) aggression indegree (n = 94, r = 0.50, p < 0.001); (c) roving male outdegree (n = 64, r = 0.58, p < 0.001); (d) intergroup encounters degree (n = 96 meerkats in five groups, r = 0.06, p = 0.57). Regression coefficients and their statistical significance were assessed using network permutation tests. Data shown are from time point 4 (October–December 2006).

Figure 4.

Degree distributions for (a) grooming interactions initiated and (b) aggressive interactions received over a three-month period (t4, October–December 2006) by meerkats testing negative (white bars) or positive (black bars) for TB at the end of this period. Both interactions were positively correlated with risk of TB infection (grooming outdegree, r = 0.37, p = 0.001; aggression indegree, r = 0.50, p < 0.001; network permutation tests with n = 94 meerkats in both cases).