Cellphone data helps scientists track spread of pathogens and ‘superbugs’

South Africans' travel patterns taken from anonymised mobile phone data has been analysed to help scientists map the spread and evolution of pathogens and the resistance of bacterium behind pneumonia and meningitis in particular to vaccines and antibiotics.

The intent is to help predict and prevent future outbreaks, the University of the Witwatersrand said.

Researchers from Wits, the National Institute for Communicable Diseases, the Wellcome Sanger Institute, the University of Cambridge and partners across the Global Pneumococcal Sequencing project integrated genomic data from nearly 7,000 Streptococcus pneumoniae strains collected in South Africa with travel patterns taken from mobile phone data.

Researchers inferred the ability of the “fitness” of different pneumococcal strains to survive and reproduce from the data, saying the insights could inform vaccine development to target the most harmful strains. It may also be applicable to other pathogens.

Cebile Lekhuleni, a PhD student in the division of clinical microbiology and infectious diseases at Wits who was involved in the study, said: “We have insights on the transmission dynamics of the pneumococcus in South Africa and supporting evidence of the fitness of important pneumococcal strains after vaccine introduction.”

Pneumococcus, a bacterium that is a leading cause of pneumonia, meningitis and sepsis worldwide, has more than 100 types and 900 genetic strains globally, which hamper control efforts as vaccines targeting major strains leave room for others to fill the vacant niches.

Co-author of the study Dr Anne von Gottberg, associate professor in the division of clinical microbiology and infectious diseases, School of Pathology at Wits University, said: “Despite vaccination efforts, pneumonia remains one of the leading causes of death for children under five in South Africa. With continuous genomic surveillance and adaptable vaccination strategies to counter the remarkable adaptability of these pathogens we may be able to better target interventions to limit the burden of disease.”

In the study titled “Geographical migration and fitness dynamics of Streptococcus pneumoniae” researchers analysed genome sequences from 6,910 pneumococcus samples collected in South Africa over 14 years to track the distribution of different strains over time.

They combined the data with anonymised records of human travel patterns between January 2020 to July 2021, sourced from Meta's Data for Good initiative, released during the coronavirus pandemic. The team said the data relied on personal consent for location sharing and Data for Good ensured individual privacy by preventing reidentification in aggregated data sets.

The team found while introduction of a pneumococcal vaccine against certain types of the bacteria in 2009 reduced the number of cases caused by those types, it also made other non-targeted strains of the bacteria gain a 68% competitive advantage, with an increasing proportion becoming resistant to antibiotics such as penicillin. This suggests the vaccine-linked protection against antibiotic resistance may be short-lived.

Prof Stephen Bentley, senior author of the study at the Wellcome Sanger Institute, said: “The pneumococcus' diversity has obscured our view on how any given strain spreads from one region to the next. This integrated approach using bacterial genome and human travel data finally allows us to cut through the complexity, uncovering hidden migratory paths in high definition for the first time. This could allow researchers to anticipate where emerging high-risk strains may take hold next, putting us a step ahead of potential outbreaks.”

Lead author Dr Sophie Belman, a former PhD student at the Wellcome Sanger Institute and now a Schmidt Science Fellow at the Barcelona Supercomputing Centre, Spain, said: “While we found pneumococcal bacteria generally spread slowly, the use of vaccines and antimicrobials can quickly and significantly change the 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.”

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