Modeling plague transmission in Medieval European cities
By Katharine R. Dean
MA Thesis, University of Oslo, 2015
Abstract: The Black Death pandemic swept through Europe during the Middle Ages leading to high mortality from plague, caused by the bacterium Yersinia pestis. How it spread, the transmission of the disease within and between cities, remains a subject of controversy among scientists and historians.
Prior to the identiﬁcation of the bacterium in medieval tooth samples, the nature of the pandemic led to speculation that the Black Death was not the same disease as current-day plague. In the classical mode of transmission to humans, black rats act as an intermediate host and the disease spreads by infected rat ﬂeas. But in the case of Black Death, alternate modes have been proposed in which the disease spreads either through pneumonic transmission of plague or through an intermediate human ectoparasite vector, such as the human body louse.
To understand the transmission dynamics within cities, we used a spatial metapopulation model with SIR-dynamics for three transmission scenarios and compared how the epidemic curve, epidemic duration, and total mortality diﬀer between each mode and historical data. Here we show that 1) a model of louse-borne transmission of bubonic plague ﬁts the pattern of plague transmission within cities during the Black Death with regards to epidemic duration and the distribution of deaths during an epidemic, and that 2) primary pneumonic plague can produce large scale epidemics, but only under conditions highly favorable for this mode of transmission.
These results demonstrate that the louse-borne transmission of bubonic plague is a viable alternative to resolve the inconsistencies between plague during the Black Death and plague with rats. We anticipate that the models and parameters we have presented can be used in future work for more complex models that combine multiple transmission routes. For example, a model with both primary pneumonic and bubonic plague transmission during the same epidemic. Furthermore, the models can be adapted to explore the impact of immunity, public health measures, and seasonality on the disease dynamics.