Computational protocol to perform a spatiotemporal reconstruction of an epidemic.

Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. For complete details on the use and execution of this protocol, please refer to Galli et al. (2023).1.

Milan's forgotten epidemic of summer 1629, a few months before the last great plague: An investigation into the possible cause.

An epidemic not attributable to plague caused thousands of deaths in Milan in the summer of 1629, a time of war and famine that immediately preceded the even more fatal Great Plague of 1630 that killed an estimated ten of thousands of people. The 5,993 deaths of 1629 recorded in the Liber Mortuorum of Milan (a city with an estimated population of 130,000 inhabitants at the time) were 45.7% more than the average number recorded between 1601 and 1628. Registered deaths peaked in July, and 3,363 of the deaths (56,1%) were attributed to a febrile illness which, in most cases (2,964, 88%), was not associated with a rash or organ involvement. These deaths involved 1,627 males and 1,334 females and occurred at a median age of 40 years (range 0-95). In this paper, we discuss the possible cause of the epidemic, which may have been an outbreak of typhoid fever.

A spatiotemporal reconstruction of the 1630 plague epidemic in Milan.

In 1630, a devastating plague epidemic struck Milan, one of the most important Italian cities of that time, deeply affecting its demography and economy for decades. The lack of digitized historical data strongly limits our comprehension of that important event. In this work, we digitized and analyzed the Milan death registers of 1630. The study revealed that the epidemic evolved differently among the areas of the city. Indeed, we were able to group the parishes of the city (comparable with modern neighborhoods) in two groups based on their epidemiological curves. These different epidemiological progressions could reflect socio-economical and/or demographic features specific of the neighborhoods, opening questions about the relationship between these features and the evolution of epidemics in the pre-modern period. The study of historical records, like the one presented here, can help us to better understand European history and pre-modern epidemics.

Spatial and Temporal Analyses of the Event of Death for 1480 in Milan Using the Data Contained in the Sforza's Registers of the Dead.

Historical death registration was conducted primarily to assess the presence of plague. The Liber Mortuorum of Milan was one of Europe's first registers with many socio-demographical details. In this work, we consider 1480 to make spatial and temporal analyses of the event of death to find possible explanations about the events' distribution and the events' trend over time. The spatial analyses involved Moran's I, the LISA, and the heatmaps; the temporal analysis applied the Durbin-Watson test. All the analyses were conducted separately on all subjects (1813), children (765), and adults (1046). Contrade (districts) were considered for spatial analysis. Moran's I and the Durbin Watson test were significant on all subjects and children's analyses, and the LISA showed the same results for those groups. Children may significantly impact the distribution of death and the trend over time. At least half of the children were 0 years old, and survival in the very first childhood period was closely linked to the family, so that it could be a proxy of the conditions of an area.