Ivan Specht

All Publications

• Genomic epidemiology of the 2025 mpox epidemic in Sierra Leone. Nature medicine Campbell, A. K., Sandi, J. D., Omah, I. F., Faye, M., Parker, E., Brock-Fisher, T., Gigante, C. M., Folorunso, V., Kamara, M. S., Williams, A. J., Kane, M., Kallon, T. M., Campbell, J., Sesay, K. S., Mani, S., Miller, C., Sesay, N. D., Baimba, F., Ndiaye, M., Lansana, R., Fofanah, I. U., Ruhweza, S., Souma, Z., Kargbo, A., Koninga, K., Tia, A., Ngobeh, J., Thoronka, F., Fofanah, A., Ozonoff, A., Wilkason, C., Park, D., Tomkins-Tinch, C., Paye, M. F., Shin, C., Baudi, I., Blumenstiel, B., Varilly, P., Specht, I., Fry, B., Zhao, K., Cronan, P., Laning, E., Ope-Ewe, O. O., Sijuwola, A. E., Saibu, F., Soumare, H., Ogundana, E. K., Obaado, R. O., Adole, J. A., Kio, I. K., Njeandoh, F. A., Tuttle, A., Oguta, W. O., Greene, J., Koroma, A., Kanu, J., Barry, M. A., Gaye, A., Diouf, A. M., Hughes, C., Levy, J. I., N'jai, A., Diagne, M. M., Nosamiefan, D., Ameh, G., Klena, J., Foster, M. A., Kebede, A., Tanui, C., Diallo, B., Happi, A., Tessema, S., Sow, A., Kebede, Y., Wurie, I., Squire, J., Harding, D., Koroma, Z., Jalloh, M. B., Sall, A. A., Andersen, K. G., Rambaut, A., Vandi, M. A., Fall, I. S., Sabeti, P., Happi, C., Sahr, F., Grant, D. S. 2026

  Abstract

  Mpox is a re-emerging zoonotic disease caused by MPXV, which has led to outbreaks across multiple countries in recent years. Sierra Leone reported its first mpox case in 8 years in January 2025, rapidly becoming the epicenter of a continental outbreak with more than 5,000 confirmed cases by August, a surge with unknown origins, timings and drivers. Phylodynamic analyses using 338 genomes generated from 14 districts suggests that the outbreak was caused by lineage G.1 (A.2.2.1) which descended from lineages circulating in Nigeria. Here we observed a strong APOBEC3 mutational enrichment, consistent with sustained human transmission that circulated undetected for ~3 months before the first confirmed case in January 2025. The Western Area Urban district served as the primary hub for nationwide spread and persistence, as well as multiple international export events. We further estimated that the true epidemic size was nearly double official case counts, highlighting substantial surveillance gaps. These findings underscore the urgent need for strengthened genomic and diagnostic surveillance systems across West Africa to pre-empt epidemics.

  View details for DOI 10.1038/s41591-026-04385-8

  View details for PubMedID 42120729

  View details for PubMedCentralID 9534090

• Efficient Bayesian Phylogenetics under the Infinite Sites Model. Genetics Specht, I., Palacios, J. A. 2026

  Abstract

  Bayesian inference of gene genealogies and evolutionary parameters from molecular sequences can provide key insights into the evolutionary history of populations. Existing tools, however, often scale poorly with sample size. We present inPhynite, a highly-efficient Bayesian inference algorithm for genomic datasets compatible with the infinite sites mutation model. A key advantage of this model is that likelihood calculation, which typically incurs a substantial computational cost, becomes trivial. We show that under the infinite sites assumption, it is possible to sample a coarse space of mutations and coalescences from which we may recover complete genealogies. We design an efficient Markov chain for this space together with effective population size trajectories, modeled as piecewise constant functions. Based on real and synthetic data, our method significantly outperforms competing methods, offering a speedup of over 225 times in statistical efficiency on large datasets without incurring any loss in accuracy. Finally, we demonstrate how inPhynite can help us understand the evolutionary history and past effective population sizes of human populations based on mitochondrial DNA.

  View details for DOI 10.1093/genetics/iyag103

  View details for PubMedID 41996569

• Efficient Bayesian Phylogenetics under the Infinite Sites Model. bioRxiv : the preprint server for biology Specht, I., Palacios, J. A. 2025

  Abstract

  Bayesian phylogenetic inference from molecular sequences can provide key insights into the evolutionary history of populations. Existing tools, however, often scale poorly with sample size. We present inPhynite, a highly-efficient Bayesian phylogenetics algorithm for genomic datasets compatible with the infinite sites mutation model. A key advantage of this model is that likelihood calculation, which typically incurs a substantial computational cost, becomes trivial. We show that under the infinite sites assumption, it is possible to sample a coarse space of mutations and coalescences from which we may recover complete phylogenetic trees. We design an efficient Markov chain for this space together with effective population size trajectories, modeled as piecewise constant functions. Based on real and synthetic data, our method significantly outperforms competing methods, offering a speedup of over 225 times in statistical efficiency on large datasets without incurring any loss in accuracy. Finally, we demonstrate how inPhynite can help us understand the evolutionary history and past effective population sizes of human populations based on mitochondrial DNA.

  View details for DOI 10.1101/2025.11.14.688551

  View details for PubMedID 41292938

  View details for PubMedCentralID PMC12642419