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Manhong (Mandy) Smith

Postdoc
A-1/CNLS

Information Systems and Modeling

Manhong (Mandy) Smith

Office: TA-3, Bldg 1690, Room 134
Mail Stop: B258
Phone: (505) 667-9568
Fax: (505) 665-2659

mzsmith@lanl.gov
home page

Research highlight
     Educational Background/Employment:
    • Ph.D. (2016) Food and Resource Economics, University of Florida
    • MSF (2015) Finance, University of Florida

    Research Interests:

    • Economic impact of infectious disease forecasting. LANL has been on the cutting edge of developing methods for monitoring and forecasting infectious disease. These efforts are essential for devising early surveillance and prevention programs to ultimately minimize the population and economic impacts. Although epidemic modelers have provided decision support to policy makers in response to several recent epidemics and pandemics (e.g., H1N1, SARS, Ebola, and the current COVID-19 pandemic), infectious disease forecasting has not received as much funding as weather forecasting because of the limited understanding of its potential impact. On one hand, the economic losses caused by infectious disease could be well underestimated. On the other hand, little effort has been made to estimate the economic benefit (i.e., cost averted) by implementing early detection and preventive measures in infectious disease management. Thus, there is a lack of available scientific data provided to policy makers on the economic impact of implementing these measures. Furthermore, predictive models of infectious disease have been developed during the past decade as such, few studies consider the cost-effectiveness of implementing early surveillance and epidemic forecasts. Therefore, a comprehensive economic assessment on infectious diseases is required to help decision-makers comprehend potential risks and recognize the benefit of investment in disease forecasting.
    • Assessing the optimal time of vaccination. The general public has been encouraged to get the flu vaccination as early as possible. However, the fact is that vaccine efficacy wanes over time and each season may have different incidence peak time. For example, influenza vaccine effectiveness is around 32% maximum against influenza A (H3N2) for people who are older than 60 years of age and decreases to 0% in about 5 months. It is very likely that vaccination provides little protection for senior people against flu virus around the peak time, which is usually in December, January or February. I am investigating the optimal time to get vaccinated for different age groups.
    • Equipment cost estimation.

    Selected Recent Publications:

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