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Professor Smith’s primary research has been developing Malaria Theory, validating theory using aggregated Malaria Data, and applying it to malaria control. To deal with the complexity of malaria, the need for information, the reality of having enormous data gaps, and all the associated uncertainty, Smith and collaborators have developed a bespoke inferential framework for malaria analytics, called RAMP (Robust Analytics for Malaria Policy). RAMP is a set of ideas to structure conventional and simulation-based analytics to transform data into policy advice. To support nimble model building and simulation-based analytics for RAMP, we developed a new modular framework for building malaria models and a software implementation of it called SimBA.

In collaboration with malaria control programs, Smith and collaborators have developed Robust Analytics for Adaptive Malaria Control. Adaptive malaria control is robust analytics with the goal of reducing uncertainty through iterative policy engagement. For national malaria control programs, a prototype was developed in collaboration with Uganda’s National Malaria Control Division and Department of Health Information, called Adaptive Malaria Control - Uganda. In parallel, we have been working on Bioko Island, Equatorial Guinea with a focus on developing methodology for adaptive vector control, including spatial targeting and micro-stratification, in collaboration with the Bioko Island Malaria Elimination Program (BIMEP).

Featured:

  • Hergott DEB, et al., Impact of six-month COVID-19 travel moratorium on Plasmodium falciparum prevalence on Bioko Island, Equatorial Guinea. (2024). Nat Commun 15, 8285. (View at Nat Comm)

  • Wu SL, et al., Spatial Dynamics of Malaria Transmission. (2023), PLoS Computational Biology 19(6):e1010684. (View at PLoS CB)

  • SimBA – open access software for Simulation-Based Analytics for malaria and other mosquito-transmitted pathogens

  • ramp.xds – a github R package: core computation for Simulation-Based Analytics

Recent Talks:

For more information, please see a complete list of my publications

Malaria Theory & Data

Malaria theory refers to the diverse set of concepts, principles, metrics, and models used measure malaria and to understand mosquito ecology and malaria epidemiology, transmission dynamics, and control.

Quantitative approaches to the study of malaria in populations trace back to Ronald Ross and his early attempts to measure and manage malaria (roughly, from 1900 to 1911). Since Ross, malaria theory has benefited from concepts and models developed in malariology and various related academic disciplines, including mathematics, epidemiology, ecology, entomology, anthropology, economics, and pharmacology. It is characterized by various mathematical, computational, and statistical approaches, including dynamical systems and individual-based models to simulate malaria.

We have developed an introduction to malaria theory for malaria managers. The vignettes are designed to introduce concepts and ideas needed to apply theory to reducing the burden of malaria and eradicating malaria parasites.

View the website Malaria Theory

View the website Malaria Data

Nimble Model Building & Software

A challenge for malaria research and policy has been to build models fit for purpose. In policy, there is a need for models that can serve the needs of a malaria program in a moment as well as models that can be used in repeated analysis over time. The need for building models in a timely way often exceeds local human resources or computational capacity. To support malaria policies, some models might need spatial dynamics, mosquito ecology with the potential for weighing integrated vector control, exogenous forcing by weather or other factors, realistic representation of the process of drug taking and chemo-protection, models with realistic human malaria infection and immunity, including disease, infectiousness, and detection by age.

How well has academic research responded to these needs? In a systematic review of mathematical models for mosquito-transmitted pathogens, published in 2013, covering more than 500 published models published before 2010, we found a distinct pattern. The need for models that were complex enough to address policy questions drove development of complex, individual-based simulation models (IBMs, such as OpenMalaria, eMod, MalariaTools). With few exceptions, most of the remaining models were very similar to the Ross-Macdonald model. While these models handle complexity with aplomb, they require large computer systems and specialized skills, so they are impractical for use in some settings.

To address the need for building dynamical systems models that can scale complexity to any level of realism – without the need for individual-based simulation – we developed a mathematical framework for the epidemiology, transmission dynamics, and control of malaria and other mosquito-transmitted pathogens that is modular, flexible, and extensible. The framework was translated into software for Simulation-Based Analytics called SimBA that makes it easy to build, solve, and apply models with virtually unbounded complexity. The core computational software is an open access R package that supports building and solving dynamical systems models describing malaria called ramp.xds, which is available from github.

Download ramp.xds from the github repository.

More about Nimble Model Building & SimBA and the software suite around ramp.xds

RAMP & Adaptive Malaria Control

Malaria analytics – analysis to support decisions or policy – needs a rigorous inferential framework developed around malaria research and surveillance metrics, malaria theory and knowledge, conventional methods, and contemporary policy issues. To address this need, we developed RAMP as an eclectic set of methods to support adaptive malaria control. Robust analytics puts a strong emphasis on characterizing, quantifying, and propagating uncertainty.

Adaptive malaria control is a structured, iterative approach to developing malaria intelligence and robust analytics for malaria policy. Malaria intelligence is developed around stable information systems that involve involve data processing and curation, the transformation of data into information through data analysis, and translation of information into malaria intelligence using simulation to support operational decisions, optimize the allocation of resources, improve technical efficiency, and develop evidence-based policies to reduce burden and eliminate malaria. Malaria is managed on monthly and annual cycles, and on multi-year funding and strategic planning cycles. Adaptive malaria control must support these policy cycles through development of protocols and procedures for repeated analysis to ensure policy advice is consistent, that it is of the highest quality, and that it is updated to be responsive to changing needs. Through careful analysis of uncertainty and through consultation with program managers, surveillance systems are reviewed to identify key data gaps, and plans are made to fill those gaps and improve future decisions.

For an introduction to RAMP and a scholarly discussion of the design and rationale for Adaptive Malaria Control, see RAMP

A prototype for national malaria control programs is Adaptive Malaria Control - Uganda

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David L Smith (he/him/his) is a Professor in the Department of Health Metrics Sciences (HMS) in the School of Medicine (SOM), University of Washington (UW). He is a member of the Instute for Health Metrics and Evaluation (IHME), and he is affiliated with the Center for Quantitative Science (CQS) through the training program in Quantitative Ecology and Resource Management (QERM).

Professor Smith has published extensively on malaria, including malaria elimination and eradication; malaria spatial dynamics and control; malaria epidemiology; and evolution of resistance to anti-malarial drugs. He has also published on spatio-temporal dynamics and control of rabies; evolution of antibiotic resistance in hospital-acquired bacterial pathogens; and the ecology, transmission dynamics and control of other mosquito-transmitted pathogens and infectious diseases.

Professor Smith was a member of the Malaria Atlas Project (MAP), RAPIDD (Research and Policy for Infectious Disease Dynamics), the Malaria Elimination Group (MEG), and a working group on mathematical modeling as part of malERA (the malaria Eradication Research Agenda). He headed the Malaria Modeling Consortium (MMC).

Up-to-date lists of my publications are maintained online at ORCID, Google Scholar, and Research Gate, and here (see My Publications.)

Professor Smith studied Ecology and Evolutionary Biology (EEB) at Princeton University (1993-1998), with Professor Simon A. Levin. Before that, was a graduate student and undergraduate student in mathematics at Brigham Young University (BYU). Professor Smith currently lives in Seattle, Washington.

David L. Smith
David L. Smith

Jobs

Before the University of Washington, Professor Smith held academic positions at universities, government, and non-governmental organizations including:

1999-2003 – Assistant Professor, Department of Epidemiology & Preventive Medicine, University of Maryland, Baltimore, Maryland, USA

2003-2007 – Research Scientist, Fogarty International Center for International Health, Bethesda, Maryland, USA

2007-2009 – Associate Professor, Department of Zoology, University of Florida, Gainesville, Florida, USA

2009-2011 – Professor, Department of Biology, University of Florida, Gainesville, Florida, USA

2007-2011 – Assistant Director for Disease Ecology, Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA

2007-2011 – Center for Disease Dynamics, Economics and Policy (CDDEP), Resources for the Future, Washington, DC

2011-2014 – Professor, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

2014-2015 – Senior Fellow, Sanaria Institute for Global Health and Tropical Medicine (SIGHTM), Rockville, Maryland, USA

2014-2016 – Visiting Professor, Department of Zoology, University of Oxford, Oxford, England, UK

Team

University of Washington

Current

Former

  • Sean L Wu

  • John M Henry

  • Daniel Citron

Uganda

  • Rek John

  • Catherine Maiteki-Sebuguzi

  • Doreen Mbabazi Ssebuliba, Lecturer, Department of Mathematics and Statistics, Kyambogo University, Kampala, Uganda

  • Juliet Nakakawa Nsumba, Lecturer, Department of Mathematics, School of Physical Sciences, College of Natural Science, Makerere University, Kampala, Uganda

  • Jaffer Okiring

  • Meddy Rutayisire

  • Tom Eganyu

BIMEP

  • Guillermo Garcia

  • Carlos Guerra

  • David Galick

Archive

The links to some of the computer code from old studies are stale. Over the next few months, I plan to create archival versions of old studies.

My Pubs | ORCID – Google Scholar | Malaria Data | Malaria Theory – github / SimBA – RAMP – Adaptive Malaria Control – @UW :: HMS – CQS