Moogdho is a postdoctoral scholar at the Graduate School of Business and at the Woods Institute for the Environment. He is on the academic 2023-2024 job market. He is a development economist, concentrating on political economy, environment and public health. He received his Ph.D. in Economics from the University of Virginia (UVa) in 2021. His research at UVa focused on how dishonest politicians (tax-evaders) affect public goods provision and public health in constituencies the politicians represent. At Stanford, he is working with Erica Plambeck, Stephen Luby, and Grant Miller on environmental and health related issues in Bangladesh.

Professional Education

  • Doctor of Philosophy, University of Virginia (2021)
  • Master of Science, University Of Dhaka (2012)
  • Master of Arts, University of Virginia (2016)
  • Bachelor of Social Science, University Of Dhaka (2009)
  • Master of Arts, Williams College (2014)
  • M.A., Williams College, Policy Economics (2014)
  • M.A., University of Virginia, Economics (2016)
  • Ph.D., University of Virginia, Economics (2021)

Stanford Advisors

Lab Affiliations

All Publications

  • Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia. Scientific reports Shonchoy, A. S., Mahzab, M. M., Mahmood, T. I., Ali, M. 2023; 13 (1): 3732


    In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

    View details for DOI 10.1038/s41598-023-30348-x

    View details for PubMedID 36878910

    View details for PubMedCentralID PMC9987367