Solar flare throws light on ancient trade between the Islamic Middle East and the Viking Age

Mobility shaped the human world profoundly long before the modern age. But archaeologists often struggle to create a timeline for the speed and impact of this mobility. An interdisciplinary team of researchers at the Danish National Research Foundation's Centre for Urban Network Evolutions at Aarhus University (UrbNet) has now made a breakthrough by applying new astronomical knowledge about the past activity of the sun to establish an exact time anchor for global links in the year 775 CE.

In collaboration with the Museum of Southwest Jutland in the Northern Emporium Project, the team has conducted a major excavation at Ribe, one of Viking-age Scandinavia's principal trading towns. Funded by the Carlsberg Foundation, the dig and the subsequent research project were able to establish the exact sequence of the arrival of objects from various corners of the world at the market in Ribe. In this way, they were able to trace the emergence of the vast network of Viking-age trade connections with regions such as North Atlantic Norway, Frankish Western Europe and the Middle East. To obtain a chronology for these events, the team has pioneered a new use of radiocarbon dating.

New use of radiocarbon dating

"The applicability of radiocarbon dating has hitherto been limited due to the broad age ranges of this method. Recently, however, it has been discovered that solar particle events, also known as Miyake events, cause sharp spikes in atmospheric radiocarbon for a single year. They are named after the female Japanese researcher Fusa Miyake, who first identified these events in 2012. When these spikes are identified in detailed records such as tree rings or in an archaeological sequence, it reduces the uncertainty margins considerably," says lead author Bente Philippsen.

The team applied a new, improved calibration curve, based on annual samples, to identify a 775 CE Miyake event in one floor layer in Ribe. This enabled the team to anchor the entire sequence of layers and 140 radiocarbon dates around this single year.

"This result shows that the expansion of Afro-Eurasian trade networks, characterised by the arrival of large numbers of Middle Eastern beads, can be dated in Ribe with precision to 790±10 CE -- coinciding with the beginning of the Viking Age. However, imports brought by ship from Norway were arriving as early as 750 CE," says Professor Søren Sindbæk, who is also a member of the team.

This groundbreaking result challenges one of the most widely accepted explanations for maritime expansions in the Viking Age -- that Scandinavian seafaring took off in response to growing trade with the Middle East through Russia. Maritime networks and long-distance trade were already established decades before impulses from the Middle East caused a further expansion of these networks.

The construction of the new, annual calibration curve is a global effort to which the researchers from UrbNet and the Aarhus AMS Centre at the Department of Physics and Astronomy at Aarhus University have contributed.

"The construction of a calibration curve is a huge international effort with contributions from many laboratories around the world. Fusa Miyake's discovery in 2012 has revolutionized our work, so that we now work with annual time resolution. New calibration curves are recurrently released, most recently in 2020, and Aarhus AMS centre has contributed significantly. The new high-resolution data from the present study will enter into a future update of the calibration curve and thus contribute to improve the precision of archaeological dates worldwide. This will provide better opportunities to understand rapid developments such as trade flows or environmental change in the past," says Jesper Olsen, Associate Professor at Aarhus AMS Centre.

The global trends revealed by the study are essential for the archaeology of trading towns like Ribe. "The new results enable us to date the influx of new artefacts and far-reaching contacts on a much better background. This will help us to visualise and describe Viking Age Ribe in a way that will have great value for scientists, as well as helping us to present the new insight to the general public," says Claus Feveile, curator of the Museum of Southwest Jutland.

Background facts

One of the most spectacular episodes of pre-modern global connectivity happened in the period c. 750-1000 CE, when trade with the burgeoning Islamic empire in the Middle East connected virtually all corners of Afro-Eurasia.

The spread of coins, trade beads and other exotic artefacts provides archaeological evidence of the trade links stretching from Southeast Asia and Africa to Siberia and the northernmost corners of Scandinavia. In the north, these long-distance connections mark the beginning of the maritime adventures that define the Viking Age. Researchers have even suggested that it was the arrival of silver and other valuable objects via Eastern Europe which sparked the first Scandinavian Viking expeditions.

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The impact of human mobility on disease spread

 Due to continual improvements in transportation technology, people travel more extensively than ever before. Although this strengthened connection between faraway countries comes with many benefits, it also poses a serious threat to disease control and prevention. When infected humans travel to regions that are free of their particular contagions, they might inadvertently transmit their infections to local residents and cause disease outbreaks. This process has occurred repeatedly throughout history; some recent examples include the SARS outbreak in 2003, the H1N1 influenza pandemic in 2009, and -- most notably -- the ongoing COVID-19 pandemic.

Imported cases challenge the ability of nonendemic countries -- countries where the disease in question does not occur regularly -- to entirely eliminate the contagion. When combined with additional factors such as genetic mutation in pathogens, this issue makes the global eradication of many diseases exceedingly difficult, if not impossible. Therefore, reducing the number of infections is generally a more feasible goal. But to achieve control of a disease, health agencies must understand how travel between separate regions impacts its spread.

In a paper publishing on Tuesday in the SIAM Journal of Applied Mathematics, Daozhou Gao of Shanghai Normal University investigated the way in which human dispersal affects disease control and total extent of an infection's spread. Few previous studies have explored the impact of human movement on infection size or disease prevalence -- defined as the proportion of individuals in a population that are infected with a specific pathogen -- in different regions. This area of research is especially pertinent during severe disease outbreaks, when governing leaders may dramatically reduce human mobility by closing borders and restricting travel. During these times, it is essential to understand how limiting people's movements affects the spread of disease.

To examine the spread of disease throughout a population, researchers often use mathematical models that sort individuals into multiple distinct groups, or "compartments." In his study, Gao utilized a particular type of compartmental model called the susceptible-infected-susceptible (SIS) patch model. He divided the population in each patch -- a group of people such as a community, city, or country -- into two compartments: infected people who currently have the designated illness, and people who are susceptible to catching it. Human migration then connects the patches. Gao assumed that the susceptible and infected subpopulations spread out at the same rate, which is generally true for diseases like the common cold that often only mildly affect mobility.

Each patch in Gao's SIS model has a certain infection risk that is represented by its basic reproduction number (R0) -- the quantity that predicts how many cases will be caused by the presence of a single contagious person within a susceptible population. "The larger the reproduction number, the higher the infection risk," Gao said. "So the patch reproduction number of a higher-risk patch is assumed to be higher than that of a lower-risk patch." However, this number only measures the initial transmission potential; it can rarely predict the true extent of infection.

Gao first used his model to investigate the effect of human movement on disease control by comparing the total infection sizes that resulted when individuals dispersed quickly versus slowly. He found that if all patches recover at the same rate, large dispersal results in more infections than small dispersal. Surprisingly, an increase in the amount by which people spread can actually reduce R0 while still increasing the total amount of infections.

The SIS patch model can also help elucidate how dispersal impacts the distribution of infections and prevalence of the disease within each patch. Without diffusion between patches, a higher-risk patch will always have a higher prevalence of disease, but Gao wondered if the same was true when people can travel to and from that high-risk patch. The model revealed that diffusion can decrease infection size in the highest-risk patch since it exports more infections than it imports, but this consequently increases infections in the patch with the lowest risk. However, it is never possible for the highest-risk patch to have the lowest disease prevalence.

Using a numerical simulation based on the common cold -- the attributes of which are well-studied -- Gao delved deeper into human migration's impact on the total size of an infection. When Gao incorporated just two patches, his model exhibited a wide variety of behaviors under different environmental conditions. For example, the dispersal of humans often led to a larger total infection size than no dispersal, but rapid human scattering in one scenario actually reduced the infection size. Under different conditions, small dispersal was detrimental but large dispersal ultimately proved beneficial to disease management. Gao completely classifies the combinations of mathematical parameters for which dispersal causes more infections when compared to a lack of dispersal in a two-patch environment. However, the situation becomes more complex if the model incorporates more than two patches.

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