Mapping the movement of people (and their pathogen travel companions)

Many infectious diseases such as tuberculosis, HIV, and Covid-19 exist in multiple strains or variants circulating simultaneously, making them difficult to study. Pneumococcus, a bacterium that is a leading cause of pneumonia, meningitis, and sepsis worldwide⁴, is a prime example with over 100 types and 900 genetic strains globally. Pneumonia alone kills around 740,000 children under the age of five each year⁵, making it the single largest infectious cause of death in children. Pneumococcal diversity hampers control efforts, as vaccines targeting major strains leave room for others to fill the vacant niches. How these bacteria spread, how vaccines affect their survival, and their resistance to antibiotics remains poorly understood. 

In this new study, researchers analysed genome sequences from 6,910 pneumococcus samples collected in South Africa between 2000 and 2014 to track the distribution of different strains over time. They combined these data with anonymised records of human travel patterns collected by Meta². The team developed computational models which revealed pneumococcal strains take around 50 years to fully mix throughout South Africa’s population, largely due to localised human movement patterns. They found that while introduction of a pneumococcal vaccine against certain types of these bacteria in 2009 reduced the number of cases caused by those types⁶, it also made other non-targeted strains of these bacteria gain a 68% competitive advantage, with an increasing proportion of them becoming resistant to antibiotics such as penicillin. This suggests that the vaccine-linked protection against antibiotic resistance is short-lived. 

Dr Sophie Belman, first author of the study, former PhD student at the Wellcome Sanger Institute and now a Schmidt Science Fellow at the Barcelona Supercomputing Centre, Spain, said: “While we found that pneumococcal bacteria generally spread slowly, the use of vaccines and antimicrobials can quickly and significantly change these dynamics. Our models could be applied to other regions and pathogens to better understand and predict pathogen spread, in the context of drug resistance and vaccine effectiveness.”