Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Abstract

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.

Figure 1. Representation of the network connecting the medieval European and Asian cities through pilgrimage and commercial routes during XIV century.

Bubble size is proportional to the centrality value of the cities. Blue links indicate trading routes and white links indicate pilgrimage routes (note that many pilgrimage routes travelled across Europe are hidden by the more numerous trade routes). The spatial network and the map was built using R (see Supplementary Methods for the scripts).

Figure 2. Relationship between mortality and network attributes of the cities.

Data were grouped into categories for illustration purposes, but statistical analyses testing the effect of each centrality metric on mortality rates were done following spatially-explicit GLMs using original values for individual cities (Table 1).

Figure 3. Relationship between the network attributes of the cities and the probability of multiple infections.

It is shown the mean spatially-explicit coefficient relating each centrality and transitivity metric and the number of infection undergone by each city in the medieval network under different transmission rates of the plague (N = 1000 simulations per transmission rate and network attribute). In black, significant relationships.