Back

Multicountry genomic analysis underscores regional cholera spread in Africa

Mboowa, G.; Matteson, N. L.; Tanui, C. K.; Kasonde, M.; Kamwiziku, G. K.; Akanbi, O. A.; Chitio, J. J. E.; Kagoli, M.; Essomba, R. G.; Ayitewala, A.; Ssewanyana, I.; Ngo Bitoungui, V. J.; Amuri, A. A.; Azman, A. S.; Babatunde, O. A.; Akenji, B. M.; Broban, A.; Malembaka, E. B.; Ongole, F.; Chukwu, C. E.; Ismael, N.; Kapona, O.; Laurindo, O.; Mbala, P. K.; Etoundi Mballa, G. A.; Miambo, I. C. Z.; Mwanyongo, A. A.; Najjuka, G.; Mutale, J.; Musonda, K.; Niyonzima, A. M.; Nyenje, M. E.; Popoola, M.; Shempela, D. M.; Medi Sike, C.; Sitoe, S. M.; Wanjohi, D. W.; Welo, P. O.; Yelewa, M.; Yennan, S.;

2024-11-15 infectious diseases
10.1101/2024.11.15.24317392 medRxiv
Show abstract

Cholera remains a significant public health burden in many countries in sub-Saharan Africa, though the exact mechanisms of bacterial emergence and spread remain largely undefined. We generated genomic data from 763 Vibrio cholerae O1 isolates predominantly collected between 2019-2024 to create the largest dataset of V. cholerae genomes sequenced locally in Africa. This dataset enabled us to interrogate recent patterns of spread, including the rapid circulation of the AFR15 lineage associated with unusually large outbreaks in Southern Africa. We provide evidence for the movement of the AFR15 lineage into new African Member States and confirm previously observed differences in V. cholerae transmission dynamics in West versus East Africa, though cross-border transmission is prevalent on both sides of the continent. Despite observed differences, evolutionary processes are similar across lineages and we find no evidence for significant changes in antimicrobial resistance genotypes. Overall, our findings emphasize the importance of regionally coordinated cross-border surveillance and interventions, while also demonstrating the critical role of locally generated genomic data in understanding the spread of cholera in Africa.

Published in Nature Communications (predicted rank #3) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.