Elaine Nsoesie

Elaine O. Nsoesie, PhD, MS

Assistant Professor, Global Health - Boston University School of Public Health


Dr. Nsoesie applies data science methodologies to global health problems, using digital data and technology to improve health, particularly in the realm of surveillance of chronic and infectious diseases. She has also been appointed as a BU Data Science Faculty Fellow, as part of the BU Data Science Initiative at the Hariri Institute for Computing. The Data Science Faculty Fellows program assembles a cluster of uniquely talented faculty whose expertise transcends traditional disciplinary boundaries to enable fundamental advances in data science.

Dr. Nsoesie completed her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech, and her PhD dissertation, Sensitivity Analysis and Forecasting in Network Epidemiology Models, at the Network Dynamics and Simulations Science Lab at Virginia Tech BioComplexity Institute. After postdoctoral associate positions at Harvard Medical School and Boston Children’s Hospital, Dr. Nsoesie joined the faculty of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.

Other Positions

  • Founding Assistant Professor, Computing & Data Sciences Administration - Boston University
  • Member, Evans Center for Interdisciplinary Biomedical Research - Boston University


  • Virginia Tech, PhD Field of Study: Genetics
  • Virginia Tech, MS Field of Study: Statistics
  • University of Maryland, BS Field of Study: Mathematics


  • Published on 3/1/2022

    Holtzman GS, A Khoshkhoo N, Nsoesie EO. The Racial Data Gap: Lack of Racial Data as a Barrier to Overcoming Structural Racism. Am J Bioeth. 2022 03; 22(3):39-42. PMID: 35258425.

    Read At: PubMed
  • Published on 1/14/2022

    Ghassemi M, Nsoesie EO. In medicine, how do we machine learn anything real? Patterns (N Y). 2022 Jan 14; 3(1):100392. PMID: 35079713.

    Read At: PubMed
  • Published on 11/3/2021

    Maharana A, Amutorine M, Sengeh MD, Nsoesie EO. COVID-19 and beyond: Use of digital technology for pandemic response in Africa. Sci Afr. 2021 Nov; 14:e01041. PMID: 34746524.

    Read At: PubMed
  • Published on 10/2/2021

    Fatal police violence by race and state in the USA, 1980-2019: a network meta-regression. Lancet. 2021 10 02; 398(10307):1239-1255. PMID: 34600625.

    Read At: PubMed
  • Published on 9/7/2021

    Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Serghiou S, Rader B, Ingerman A, Mellem S, Kairouz P, Nsoesie EO, MacFarlane J, Vullikanti A, Marathe M, Eastham P, Brownstein JS, Arcas BAY, Howell MD, Hernandez J. Privacy-first health research with federated learning. NPJ Digit Med. 2021 Sep 07; 4(1):132. PMID: 34493770.

    Read At: PubMed
  • Published on 4/29/2021

    Oladeji O, Zhang C, Moradi T, Tarapore D, Stokes AC, Marivate V, Sengeh MD, Nsoesie EO. Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study. JMIR Public Health Surveill. 2021 04 29; 7(4):e24348. PMID: 33913815.

    Read At: PubMed
  • Published on 3/24/2021

    Nsoesie EO, Oladeji O, Abah ASA, Ndeffo-Mbah ML. Forecasting influenza-like illness trends in Cameroon using Google Search Data. Sci Rep. 2021 03 24; 11(1):6713. PMID: 33762599.

    Read At: PubMed
  • Published on 12/15/2020

    Nsoesie EO, Cesare N, Müller M, Ozonoff A. COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study. J Med Internet Res. 2020 12 15; 22(12):e24425. PMID: 33264102.

    Read At: PubMed
  • Published on 11/3/2020

    Jay J, Bor J, Nsoesie EO, Lipson SK, Jones DK, Galea S, Raifman J. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat Hum Behav. 2020 12; 4(12):1294-1302. PMID: 33144713.

    Read At: PubMed
  • Published on 9/24/2020

    Zhao N, Charland K, Carabali M, Nsoesie EO, Maheu-Giroux M, Rees E, Yuan M, Garcia Balaguera C, Jaramillo Ramirez G, Zinszer K. Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis. 2020 09; 14(9):e0008056. PMID: 32970674.

    Read At: PubMed

News & In the Media