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Over the past 27 years, my primary research has been on Measuring Malaria and developing and applying Malaria Theory.

Recently, my research focus has been to develop Adaptive Malaria Control. Inspired by adaptive management, developed for natural resources managment, Adaptive Malaria Control is a pragmatic methodology for malaria decision support. A primary goal is to provide robust policy advice despite uncertainty, and – through various cycles of iterative policy engagement and adaptive surveillance – to reduce uncertainty.

Working with Uganda’s National Malaria Elimination Division and Department of Health Information, we’ve developed a prototype adaptive malaria control program. We’ve also been working with the Bioko Island Malaria Elimination Program (BIMEP) on issues related to adaptive vector control.

To support adaptive malaria control, we developed a new mathematical framework for nimble model building and implemented it in software for malaria research and Simulation-Based Analytics called SimBA.

Models & Data

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

Quantitative approaches to the study of malaria in populations trace back to Ronald Ross and his early attempts to measure and manage malaria. 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 websites to introduce malaria theory for malaria analysts and managers. The vignettes are designed to introduce concepts and ideas needed to apply theory to reducing the burden of malaria and eradicating malaria parasites.

See more: Measuring Malaria | Malaria Theory

Adaptive Malaria Control

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 data processing and curation, the transformation of data into information through data analysis, and translation of information into malaria intelligence using simulation models 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.

Adaptive Malaria Control | Uganda Prototype

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 the moment as well as models that can be used in repeated analysis and updated over time. The need to and solve models can exceed local capacity. To support malaria policies, some models might need to be complex enough to weigh competing options. The models might need spatial dynamics; mosquito ecology; multiple vector control modalities; 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 diagnostics and detection by age; and estimates of the burden and averted burden of disease.

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. These IBMs are 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.

Go to the website for SimBA or download github R packages: ramp.xds | ramp.library | ramp.control | ramp.work | ramp.falciparum | ramp.micro

RECENT PUBLICATIONS:

PREPRINTS:

SOFTWARE:

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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 | github | SimBA | ORCID | Google Scholar | Measuring Malaria | Malaria Theory | Adaptive Malaria Control