Michael A. Martin, PhD

Select articles in peer-reviewed journals


* contributed equally

Patterns of HIV-1 Viral Load Suppression and Drug Resistance During the Dolutegravir Transition: A Population-based Longitudinal Study

Published in Clinical Infectious Diseases, 2026

We used data from a surveillance cohort in southern Uganda to assess viral suppression and antiretroviral (ART) resistance over 10-years alongside DTG scale-up.

Recommended citation: Martin, M.A., Blenkinsop, A., Moffa, M., Reynolds, S.J., Nalugoda, F., Quinn, T.C., Kigozi, G., Ssekubugu, R., Gupta, R.K., Grayson, N.E., MacIntyre-Cockett, G., Kagaayi, J., Nakigozi, G., Abeler-Dörner, L., Fraser, C., Ratmann, O., Tobian, A.A.R., Laeyendecker, O., Moyo, S., Kennedy, C.E., Bonsall, D.*, Galiwango, R.M.*, Grabowski, M.K.* (2026). Patterns of HIV-1 viral load suppression and drug resistance during the dolutegravir transition: a population-based longitudinal study (Editor’s Choice). Clinical Infectious Diseases, ciag161. 10.1093/cid/ciag161.
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HIV drug resistance during antiretroviral therapy scale-up in Uganda, 2012-19: a population-based, longitudinal study

Published in Lancet Microbe, 2025

This study used data collected as part of the Rakai Community Cohort Study (RCCS) to quantify the population prevalence of viraemic people with HIV with non-nucleoside reverse transcriptase inhibitor (NNRTI), nucleoside reverse transcriptase inhibitor (NRTI), protease inhibitor, or multiclass resistance.

Recommended citation: Martin, M.A., Reynolds, S.J., Foley, B.T., Nalugoda, F., Quinn, T.C., Kemp, S.A., Nakalanzi, M., Kankaka, E.N., Kigozi, G., Ssekubugu, R., Gupta, R.K., Abeler-Dörner, L., Kagaayi, J., Ratmann, O., Fraser, C., Galiwango, R.M., Bonsall, D., Grabowski, M.K., on behalf of the PANGEA-HIV Consortium and the Rakai Health Sciences Program. (2024). HIV drug resistance during antiretroviral therapy scale-up in Uganda, 2012-19: a population-based, longitudinal study. Lancet Microbe, 6(12), 12101218. 10.1016/j.lanmic.2025.101218.
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Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data

Published in PLOS Pathogens, 2025

Here, we identified multiple infections in whole-genome or near whole-genome HIV RNA deep-sequence data gen- erated from plasma samples of 2,029 people living with viremic HIV who participated in the population-based Rakai Community Cohort Study (RCCS).

Recommended citation: Martin, M.A., Brizzi, A., Xiaoyue, Xi, Galiwango, R.M., Moyo, Sikhulile, Ssemwanga, D., Blenkinsop, A., Redd, A.D., Abeler-Drner, L., Fraser, C., Reynolds, S.J., Quinn, T.C., Kagaayi, J., Bonsall, D., Serwadda, D., Nakigozi, G., Kigozi, G., Grabowski, M.K., Ratmann, O., with the PANGEA-HIV Consortium and the Rakai Health Sciences Program. (2024). Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data. PLOS Pathogens, 21(4), e1013065. 10.1371/journal.ppat.1013065.
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Influenza A genomic diversity during human infections underscores the strength of genetic drift and the existence of tight transmission bottlenecks

Published in Virus Evolution, 2024

Here, we used M. tuberculosis deep-sequence data to quantify within-host genetic diversity and transmission of this diversity between putative transmission pairs.

Recommended citation: Martin, M.A., Berg, N., Koelle, K. (2024). Influenza A genomic diversity during human infections underscores the strength of genetic drift and the existence of tight transmission bottlenecks. Virus Evolution, 10(1), veae042. 10.1093/ve/veae042.
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Unrecognized introductions of SARS-CoV-2 into the state of Georgia shaped the early epidemic.

Published in Virus Evolution, 2022

Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020.

Recommended citation: Babiker, A.*, Martin, M.A.*, Marvil, C., Bellman, S., Pettit III, R.A., Bradler, H.L., Sittleburg, V.D., Kunkes, A., Ingersoll, J., Kraft, C.S., Read, T.D., Waggoner, J., Koelle, K., Piantadosi, A. (2022). Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic. Virus Evolution, 8(1), 1-13. 10.1093/ve/veac011.
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Comment on ‘Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2.’

Published in Science Translational Medicine, 2021

A reanalysis of SARS-CoV-2 deep sequencing data from donor-recipient pairs indicates that transmission bottle- necks are very narrow (one to three virions).

Recommended citation: Martin, M.A., Koelle, K. (2021). Comment on “Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2. Science Translational Medicine, 13, eabh1803. 10.1126/scitranslmed.abh1803.
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Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

Published in Nature Communications, 2020

Using phylodynamic analysis, we estimate that the basic reproduction number of SARS-CoV-2 was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures.

Recommended citation: Miller, D.*, Martin, M.A.*, Harel, N.*, Tirosh O.*, Kustin, T.*, Meir, M., Sorek, N., Gefen-Halevi, S., Amit, S., Vorontsov, O., Shaag, A., Wolf, D., Peretz, A., Shemer-Avni, Y., Roif-Kaminsky, D., Kopelman, N.M., Huppert, A., Koelle, K., Stern, A. (2020). Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel. Nature Communications, 11(5518). 10.1038/s41467-020-19248-0.
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Within-host Mycobacterium tuberculosis diversity and its utility for inferences of transmission

Published in Microbial Genomics, 2018

Here, we used M. tuberculosis deep-sequence data to quantify within-host genetic diversity and transmission of this diversity between putative transmission pairs.

Recommended citation: Martin, M.A., Lee, R.S., Cowley, L.A., Gardy, J.L., & Hanage, W.P. (2018). Within-host Mycobacterium tuberculosis diversity and its utility for inferences of transmission. Microbial genomics, 4(10). 10.1099/mgen.0.000217.
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