The IDM team is composed of dedicated and diverse teams of research scientists and software professionals, working closely together towards disease eradication and other global health endeavors.
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Caitlin Bever has a Ph.D. in Biological Engineering from the Massachusetts Institute of Technology (MIT), along with a Bachelor’s degree (B.Sc.) with Combined Honors in Physics and Astronomy from the University of British Columbia (UBC). Caitlin received a Medtronic Fellowship for her post-graduate work at MIT and the Rudy Haering Medal for outstanding graduating physics student from UBC. Her academic research focused on understanding how to select useful predictions from uncertain mathematical models of biology. Prior to joining IDM, Caitlin worked on a team at Entelos that built a novel model of atherosclerosis in mouse, paired with an analogous model of cardiovascular disease in human, which improved the design of pre-clinical experiments and identified key indicators for translating results from mouse to human. Caitlin was on assignment in Switzerland for a year and a half as a consultant for Entelos, after which she worked with the malaria modeling group at the Swiss Tropical and Public Health Institute. In her role there, she developed new methods for spatial modeling of entomological inoculation rates and co-wrote a WHO report on how country-specific considerations contribute to the impact of malaria vaccines. As a member of IDM’s research team, Caitlin leads the projects on malaria vaccines and human African trypanosomiasis (HAT) with a focus on disease eradication.
Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness.…
Background While bed nets and insecticide spraying have had significant impact on malaria burden in many endemic regions, outdoor vector feeding and insecticide resistance may ultimately limit their contribution to elimination and control campaigns. Complementary vector control methods such as endectocides or systemic insecticides, where humans or…
Background Control of gambiense sleeping sickness relies predominantly on passive and active screening of people, followed by treatment. Methods Mathematical modeling explores the potential of 3 complementary interventions in high- and low-transmission settings. …
Background reactive case detection is best poised to push a region to elimination. This study uses mathematical modelling to assess how baseline transmission intensity and local interconnectedness affect the impact of reactive activities in the context of other possible intervention packages. Methods…
As more regions approach malaria elimination, understanding how different interventions interact to reduce transmission becomes critical. The Lake Kariba area of Southern Province, Zambia, is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission…
Mass campaigns with antimalarial drugs are potentially a powerful tool for local elimination of malaria, yet current diagnostic technologies are insufficiently sensitive to identify all individuals who harbor infections. At the same time, overtreatment of uninfected individuals increases the risk of accelerating emergence of drug resistance and losing community…
Background The phase 3 trial of the RTS,S/AS01 malaria vaccine candidate showed modest efficacy of the vaccine against Plasmodium falciparum malaria, but was not powered to assess mortality endpoints. Impact projections and cost-effectiveness estimates for longer timeframes than the trial follow-up and across a range of settings are needed to inform policy…
Background Plasmodium falciparum gametocytes are essential for malaria transmission. Malaria control measures that aim at reducing transmission require an accurate characterization of the human infectious reservoir. Methods We longitudinally determined human infectiousness to mosquitoes and P…
Modeling mosquito transmission of pathogens has a long history starting with the foundational work of Ross and Macdonald, who established the mathematical formalisms for modeling the transmission of malaria between a vector and a host population. The Ross–Macdonald model identifies five key quantities: mosquito population density, mosquito survival probabilities,…
Mathematical analyses and modelling have an important role informing malaria eradication strategies. Simple mathematical approaches can answer many questions, but it is important to investigate their assumptions and to test whether simple assumptions affect the results. In this note, four examples demonstrate both the effects of model structures and assumptions and…
Targeted interventions used to interrupt malaria transmission in an endemic population depend on an accurate description of the parasite burden: who is at risk for infection, who is infectious, and what is the appropriate response given data from both active and passive surveillance measures. Sampling of parasite densities in malaria-endemic settings gives limited information on the infection…
Modern anti-malarial campaigns often layer multiple interventions, such as indoor residual spraying (IRS), distribution of insecticide-treated bed nets (ITN), mass drug campaigns (MDA), and deployment of community health workers (CHWs). However, when these interventions are combined in an operationally realistic way with spatially varying coverages and timings, it can be difficult to…
As global progress in reducing the burden of malaria slows, strategies for appropriately allocating resources for elimination and control become paramount. The past decade has seen the development of a range of versatile malaria transmission models capable of incorporating detailed climate and vector information into predictions of burden under a wide variety of intervention scenarios. At the…
Existing rapid diagnostic tests (RDTs) detect current or very recent malaria infections, which means that surveillance programs conducted in regions where few individuals are infected may need to test large numbers of individuals to estimate the transmission intensity. RDTs that detect long-lasting antibodies (‘serology RDTs’) have been proposed as a tool that might help reduce the sampling…
Senior Research Manager Caitlin Bever, PhD, discusses the challenges of preventing and treating malaria and how modeling plays a role in determining which interventions would be the most effective for a particular country.
Sr. Research Manager
Affiliate Assistant Professor, Applied Mathematics, University of Washington Affiliate Assistant Professor, Mechanical Engineering, University of Washington Joshua Proctor is currently a research manager and senior research scientist at IDM leading the Data, Dynamics and Analytics (DDA) team. DDA is focused on applying modern data science, machine-learning, and statistical techniques to a wide-range of data sources, i.e., surveillance of infectious diseases, household surveys, genome sequencing of viruses or parasites, and demography. We aim to leverage the growing success of these modern analytic and numerical methods to advise on near-term public policy questions facing the global health community. In conjunction, we also identify the short-comings of current methodological approaches and develop novel, principled algorithms to face the challenges of surveillance data. Joshua Proctor earned a Ph.D. in Mechanical and Aerospace Engineering from Princeton. Before graduate school, Joshua earned a Bachelor of Science in Aeronautics and Astronautics Engineering, and a Bachelor of Arts in English Literature, both from the University of Washington, Seattle. His doctoral research focused on investigating the effects of neural feedback on rapidly running insects (specifically, cockroaches). The research required the development of complex mathematical models describing legged locomotion, the application of dimensionality reduction techniques, and the characterization of these nonlinear dynamical systems through bifurcation analyses [1,2]. The research led to several important discoveries about the role of neural feedback during running, while also inspiring better robotic designs for maneuverability, stability, and control. Joshua joined IDM in 2011 after his Ph.D. and contributed to the development of the compartmental modeling simulations software. This software package is soon to be released to the global health community. Joshua then joined the nascent Applied Mathematics group at IDM where he developed algorithms and novel mathematical methods for the study of infectious disease data. He became interested in equation-free modeling and data-driven analyses, inspired by the mathematical developments around fluid dynamics at Princeton University. Generally, equation-free modeling does not require a set of pre-determined, derived from first-principles equations. Instead, the time-series data is utilized to discover dynamic models and/or dynamic characteristics. This led to a number of important methodological innovations [3,4,5,6], including a book describing the mathematical method Dynamic Mode Decomposition [7]. These methodologies are poised to have a substantial impact on infectious disease modeling. During his previous position in the Applied Mathematics group, Joshua also focused on leveraging current data science and machine-learning methodologies for epidemiological and demographic applications. For example, he is interested in characterizing genomes of viruses or parasites and their metadata (GPS location, age, gender, sex, etc.) to better understand the transmission dynamics of infectious diseases [8,9]. Understanding these modern genomic data sets can help inform disease surveillance efforts for elimination and eradication efforts. Joshua is also interested in questions around demography, specifically in the family planning group and estimating child mortality.
Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test…
Background Ambitious global goals have been established to provide universal access to affordable modern contraceptive methods. To measure progress toward such goals in populous countries like Nigeria, it’s essential to characterize the current levels and trends of family planning (FP) indicators such as unmet need and modern contraceptive prevalence…
The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS. In…
Background Pediatric diarrhea can be caused by a wide variety of pathogens, from bacteria to viruses to protozoa. Pathogen prevalence is often described as seasonal, peaking annually and associated with specific weather conditions. Although many studies have described the seasonality of diarrheal disease, these studies have occurred predominantly in temperate regions. In tropical and…
China reported zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-…
The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS. In…
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, two recent innovations that extend dynamic mode decomposition to systems with actuation and systems with heavily subsampled measurements are integrated…
Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate…
Background: Pediatric diarrhea can be caused by a wide variety of pathogens, from bacteria to viruses to protozoa. Pathogen prevalence is often described as seasonal, peaking annually and associated with specific weather conditions. Although many studies have described the seasonality of diarrheal disease, these studies have occurred predominantly in temperate regions. In tropical and resource…
Ambitious global goals have been established to provide universal access to affordable modern contraceptive methods. The UN's sustainable development goal 3.7.1 proposes satisfying the demand for family planning (FP) services by increasing the proportion of women of reproductive age using modern methods. To measure progress toward such goals in populous countries like Nigeria, it's essential…
Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-…
We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of nonlinear dynamical systems. Moreover, Koopman is intimately connected to Dynamic Mode Decomposition (DMD), a method that discovers spatial-temporal coherent modes…
Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Operationally, health workers and surveillance teams treated cases, collected genetic samples, and tracked case contacts. Despite the substantial progress in analyzing and modeling EBOV epidemiological data, a complete…
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, two recent innovations that extend dynamic mode decomposition to systems with…
Understanding the interplay of order and disorder in chaos is a central challenge in modern quantitative science. Approximate linear representations of nonlinear dynamics have long been sought, driving considerable interest in Koopman theory. We present a universal, data-driven decomposition of chaos as an intermittently forced linear…
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the…
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the…
Ordinary and partial differential equations are widely used throughout the engineering, physical, and biological sciences to describe the physical laws underlying a given system of interest. We implicitly assume that the governing equations are known and justified by first principles, such as conservation of mass or…
Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. This is consistent with the lack of biophysical evidence for a central pattern generator, and recent experimental evidence that proprioception drives locomotion. The low…
We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a disadvantage of standard model selection which typically limits the number candidate models considered due to the intractability of computing…
Identifying governing equations from data is a critical step in the modeling and control of complex dynamical systems. Here, we investigate the data-driven identification of nonlinear dynamical systems with inputs and forcing using regression methods, including sparse regression. Specifically, we generalize the sparse identification of nonlinear dynamics (SINDY) algorithm to include external…
This work develops compressed sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or compressed data. The resulting DMD eigenvalues are equal to DMD eigenvalues from the full-state data. It is then possible to reconstruct full-state DMD eigenvectors using …
The increasing ubiquity of complex systems that require control is a challenge for existing methodologies in characterization and controller design when the system is high-dimensional, nonlinear, and without physics-based governing equations. We review standard model reduction techniques such as Proper Orthogonal Decomposition (POD) with…
Choosing a limited set of sensor locations to characterize or classify a high-dimensional system is an important challenge in engineering design. Traditionally, optimizing the sensor locations involves a brute-force, combinatorial search, which is NP-hard and is computationally intractable for even moderately large problems. Using recent advances in…
We consider the application of Koopman theory to nonlinear partial differential equations. We demonstrate that the observables chosen for constructing the Koopman operator are critical for enabling an accurate approximation to the nonlinear dynamics. If such observables can be found, then the dynamic mode decomposition…
Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological networks. The increasing quality and quantity of experimental data enable statistical approaches based on information theory for model selection and…
Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine…
In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system.…
We develop a new method which extends Dynamic Mode Decomposition (DMD) to incorporate the effect of control to extract low-order models from high-dimensional, complex systems. DMD finds spatial-temporal coherent modes, connects local-linear analysis to nonlinear operator theory, and provides an equation-free architecture which is compatible with compressive sensing. In…
In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to a subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves observable functions on the state-space of a dynamical system. Dominant terms in the Koopman…
The ability to discover physical laws and governing equations from data is one of humankind’s greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technological achievements, including aircraft, combustion…
To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying…
Background The development and application of quantitative methods to understand disease dynamics and plan interventions is becoming increasingly important in the push toward eradication of human infectious diseases, exemplified by the ongoing effort to stop the spread of poliomyelitis. Methods Dynamic mode…
Complex systems exhibit dynamics that typically evolve on low-dimensional attractors and may have sparse representation in some optimal basis. Recently developed compressive sensing techniques exploit this sparsity for state reconstruction and/or categorical identification from limited measurements. We argue that data-driven dimensionality reduction methods integrate…
Although disease transmission in the near eradication regime is inherently stochastic, deterministic quantities such as the probability of eradication are of interest to policy makers and researchers. Rather than running large ensembles of discrete stochastic simulations over long intervals in time to compute these deterministic quantities, we…
We develop a new method which extends Dynamic Mode Decomposition (DMD) to incorporate the effect of control to extract low-order models from high-dimensional, complex systems. DMD finds spatial-temporal coherent modes, connects local-linear analysis to nonlinear operator theory, and provides an equation-free architecture which is compatible with compressive sensing. In…
Methods Thin smears from Plasmodium falciparum-infected and uninfected patients were imaged in both dark field (DF) unstained and bright field (BF) Giemsa-stained modes. The images were co-registered such that each parasite had thumbnails in both BF and DF modes, providing an accurate map between parasites and DF objects. This map was used to find the abundance of…
This work develops compressive sampling strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or output-projected data. The resulting DMD eigenvalues are equal to DMD eigenvalues from the full-state data. It is then possible to reconstruct full-state DMD eigenvectors using ℓ1-minimization or greedy algorithms. If full-state snapshots are available, it may…
The algorithm solves an l1-minimization to find the fewest entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space. Once the sensor locations have been identified from the…
As global progress in reducing the burden of malaria slows, strategies for appropriately allocating resources for elimination and control become paramount. The past decade has seen the development of a range of versatile malaria transmission models capable of incorporating detailed climate and vector information into predictions of burden under a wide variety of intervention scenarios. At the…
Principal Research Scientist
Adam Akullian is a Sr. Research Scientist at IDM and an Affiliate Associate Professor in the Department of Global Health at the University of Washington. Adam has a Ph.D. in Epidemiology from the University of Washington and an Sc.B. in Environmental Science from Brown University. He is a recipient of a National Science Foundation (NSF) Graduate Research Fellowship. As a doctoral candidate, Adam studied the spatial epidemiology of emerging enteric diseases and the geography of HIV in sub-Saharan Africa. As a member of the IDM’s research team, Adam is dedicated to understanding what it will take to end the HIV epidemic, considering heterogeneous risk of acquisition and transmission across populations, and uses mathematical modeling and spatial epidemiology in his work.
Background The number of people on antiretroviral therapy (ART) requiring treatment monitoring in low-resource settings is rapidly increasing. Point-of-care (POC) testing for ART monitoring might alleviate burden on centralised laboratories and improve clinical outcomes, but its cost-effectiveness is unknown. Methods We used cost and effectiveness data from the…
Introduction Over one hundred implementation studies of HIV pre‐exposure prophylaxis (PrEP) are completed, underway or planned. We synthesized evidence from these studies to inform mathematical modelling of the prevention cascade for oral and long‐acting PrEP in the setting of western Kenya, one of the world’s most…
Background The rapid scale-up of antiretroviral therapy (ART) towards the UNAIDS 90-90-90 goals over the last decade has sparked considerable debate as to whether universal test and treat can end the HIV-1 epidemic in sub-Saharan Africa. We aimed to develop a network transmission model, calibrated to capture age-specific and sex-specific gaps in the scale-up of ART, to estimate the…
Over the past decade, there has been a massive scale-up of primary and secondary prevention services to reduce the population-wide incidence of HIV. However, the impact of these services on HIV incidence has not been demonstrated using a prospectively followed, population-based cohort from South Africa—the country with the world’s highest rate of new infections. To quantify HIV incidence…
Mobility in sub-Saharan Africa links geographically-separate HIV epidemics, intensifies transmission by enabling higher-risk sexual behavior, and disrupts care. This population-based observational cohort study measured complex dimensions of mobility in rural Uganda and Kenya. Survey data were collected every 6 months beginning in 2016 from a random sample of 2308 adults in 12 communities…
Background Despite policies for universal HIV testing and treatment (UTT) regardless of CD4 count, there are still 1.8 million new HIV infections and 1 million AIDS-related deaths annually. The UNAIDS 90-90-90 goals target suppression of HIV viral load in 73% of all HIV-infected people worldwide by 2030. However, achieving these targets may not lead to expected reductions in HIV…
Background Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not…
Introduction There are significant knowledge gaps concerning complex forms of mobility emergent in sub‐Saharan Africa, their relationship to sexual behaviours, HIV transmission, and how sex modifies these associations. This study, within an ongoing test‐and‐treat trial (SEARCH, NCT01864603), sought to measure effects of…
Western Kenya suffers a highly endemic and also very heterogeneous epidemic of human immunodeficiency virus (HIV). Although female sex workers (FSW) and their male clients are known to be at high risk for HIV, HIV prevalence across regions in Western Kenya is not strongly correlated with the fraction of women engaged in commercial sex. An agent-based network model of HIV transmission,…
Non-typhoidal Salmonella (NTS) is a leading cause of bloodstream infections in Africa, but the various contributions of host susceptibility versus unique pathogen virulence factors are unclear. We used data from a population-based surveillance platform (population ~25,000) between 2007–2014 and NTS genome-sequencing to compare host and pathogen-specific factors between individuals…
Though a wide body of observational and model-based evidence underscores the promise of Universal Test and Treat (UTT) to reduce population-level HIV incidence in high-burden areas of Sub-Saharan Africa (SSA), the only cluster- randomized trial of UTT completed to date, ANRS 12249, did not show a significant reduction in incidence. More UTT…
Under the premise that in a resource-constrained environment such as Sub-Saharan Africa it is not possible to do everything, to everyone, everywhere, detailed geographical knowledge about the HIV epidemic becomes essential to tailor programmatic responses to specific local needs. However, the design and evaluation of national HIV…
Objective To quantify the contribution of specific sexual partner age groups to the risk of HIV acquisition in men and women in hyperendemic region of South Africa. Design We conducted a population-based cohort study among women (15–49 years of age) and men (15–55 years of age) between…
Voluntary Medical Male Circumcision (VMMC) for human immunodeficiency virus (HIV) prevention has scaled up rapidly among young men in western Kenya since 2008. Whether the program has successfully reached uncircumcised men evenly across the region is largely unknown…
Introduction The availability of specialized HIV services is limited in rural areas of sub-Saharan Africa where the need is the greatest. Where HIV services are available, people living with HIV (PLHIV) must overcome large geographic, economic and social barriers to access healthcare. The objective of this study was to understand the…
Senior Research Manager Caitlin Bever, PhD, discusses the challenges of preventing and treating malaria and how modeling plays a role in determining which interventions would be the most effective for a particular country.
Modeling Eradication: what will it take to 'treat our way out' of the Hiv epidemic?
Sr. Research Scientist
Ashlee Mehaddi is a Project Manager at IDM. Ashlee has her Bachelor's degree in Writing Studies from the University of Washington, Tacoma as well as a Technical Writing certificate from the University of Washington, Seattle. Ashlee has over ten years of experience managing and coordinating projects for a small business owner. In her role at IDM Ashlee helps coordinate and drive web content and development, is involved in planning and organizing IDM's annual Symposium as well as many other projects related to IDM's mission.
Project Manager
Prashanth Selvaraj has a Ph.D. in Mechanical Engineering from the University of California, Berkeley, and a Master's degree in Fluid Mechanics from Ecole Polytechnique, Paris, France. During his Ph.D., he studied the dynamics of epileptic seizures that form in the human cortex using a phsyiologically relevant mathematical model of cortical dynamics. Additionally, he proposed the use of optogenetics, which involves light sensitive ion channels expressed by certain cell types in the cortex, to inhibit the spread of seizures before they are fully formed using closed loop control methods. After his Ph.D., Prashanth worked as a postdoctoral researcher at the University of California, San Francisco, as part of the DARPA SUBNETS initiative. He investigated the underlying meso-scale networks in the brain that contribute to neuropsychiatric disorders like PTSD and depression, and worked on a closed loop method of electrical stimulation to disrupt the function of these networks alleviating symptoms of these disorders. As part of the research team at IDM, Prashanth works on calibrating mathematical models of disease spread and control to the dynamics of malaria in different regions around the world.
Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness.…
Background Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria…
Background While bed nets and insecticide spraying have had significant impact on malaria burden in many endemic regions, outdoor vector feeding and insecticide resistance may ultimately limit their contribution to elimination and control campaigns. Complementary vector control methods such as endectocides or systemic insecticides, where humans or…
Background The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked…
Background Malaria transmission is both seasonal and heterogeneous, and mathematical models that seek to predict the effects of possible intervention strategies should accurately capture realistic seasonality of vector abundance, seasonal dynamics of within-host effects, and heterogeneity of exposure…
Sr. Research Scientist
Josh Suresh has a PhD in astrophysics from Harvard University, as well as dual BS degrees in mathematics and physics from MIT. His doctoral work involved using large-scale hydrodynamic simulations to study the formation and evolution of galaxies. Specifically, his thesis focused on how to leverage observations of the circumgalactic medium to inform and constrain theoretical models of key galaxy physics, e.g. feedback from supernovae and supermassive black holes. As part of the malaria team at IDM, Josh is focused on using epidemiological modeling to directly inform and support malaria elimination efforts in sub-Saharan Africa.
Background While bed nets and insecticide spraying have had significant impact on malaria burden in many endemic regions, outdoor vector feeding and insecticide resistance may ultimately limit their contribution to elimination and control campaigns. Complementary vector control methods such as endectocides or systemic insecticides, where humans or…
Research Scientist
Kate worked for the Malaria Atlas Project (MAP) at the University of Oxford from 2010 to 2020, with whom she completed her DPhil in 2015. Kate obtained her BA in Secondary Education and Biology from Boston College in 2002. She spent six years teaching in the Chicago area where she received an MA in Teaching from Roosevelt University. In 2008, Kate transitioned to public health research through an MSc in the Control of Infectious Diseases at the London School of Hygiene and Tropical Medicine. Following field entomology work in the Kenyan highlands, Kate joined MAP. For her doctoral work, Kate focused on the spatial epidemiology and burden estimation of Plasmodium vivax malaria. As a postdoc and then the Director of Malaria Mapping for Elimination at MAP, Kate worked closely with the Clinton Health Access Initiative (CHAI) and other partners to provide technical support for control and elimination planning. Kate’s main interest is increasing the accessibility and utility of modelled outputs to malaria control programs.
Background In 2017, an estimated 14 million cases of Plasmodium vivax malaria were reported from Asia, Central and South America, and the Horn of Africa. The clinical burden of vivax malaria is largely driven by its ability to form dormant liver stages…
Sr. Research Scientist
Jamie Cohen is a Research Scientist at IDM. Jamie has a Ph.D. in Health Policy from Harvard University, with a concentration in Decision Sciences, and a Master of Science in Health Policy and Management from the Harvard T.H. Chan School of Public Health. As a doctoral candidate, Jamie developed a novel microsimulation model of HIV, HPV and cervical disease among women in South Africa. This model was used to evaluate cervical cancer screening and vaccination strategies in an HIV-endemic setting. As a member of the IDM research team, Jamie will use her background in mathematical modeling and applied health policy analysis to support the development, calibration, and application of IDM’s numerous disease models.
Research Scientist
Shirley Leung has a Ph.D. in physical oceanography and an M.S. in environmental engineering from the University of Washington. Her PhD work focused on modeling how climate variability and change affect ocean ecosystems and the people dependent on them. She has also analyzed hydrologic model output at the United States Geological Survey and traffic patterns with the Washington State Transportation Center. As part of the malaria team at IDM, Shirley seeks to evaluate and refine models of mosquito gene drives for malaria elimination.
Postdoctoral Research Scientist
Greer Fowler has a Masters Degree in Health Administration from the University of Washington. She has a Bachelors Degree in Studio Art with an emphasis on painting and drawing from Whitman College. Greer has always enjoyed working to benefit the greater good. Prior to IDM she worked in healthcare with roles in program management, administration and process improvement. At IDM she supports project management and coordination for the Research team. In her spare time, Greer enjoys travel, art, and thrives on being active in the great outdoors.
Project Manager
Josh Herbeck is a Senior Research Scientist at the Institute for Disease Modeling (IDM) in Seattle, WA. He is also an Assistant Professor in the Department of Global Health at the University of Washington. He has a BS in Biology from Tufts University, and a PhD from the University of California, Berkeley, where he focused on the molecular evolution of mitochondrial genes. At IDM, he uses mathematical modeling and phylogenetics to study infectious disease transmission and surveillance. Right now he is mostly focused on HIV evolution and epidemiology and is interested in the capacity of HIV to evolve in response to vaccines or broadly neutralizing antibodies, and in the use of phylogenetic data to understand patterns of transmission.
Sr. Research Scientist
Michelle O'Brien has a Ph.D. in Sociology from the University of Washington, where she specialized in Demographic Methods. Her research asks how violent conflict (including war, genocide, and hate crimes) affect demographic decision-making. As a doctoral student, she worked on a team developing a survey-initialized agent-based model to assess the demographic consequences of hypothetical conflict scenarios in Nepal. For her dissertation, she examined the long-term consequences of the Tajikistani Civil War for abortion and miscarriage, migration, and girls' education. Prior to joining IDM, Michelle was a postdoctoral research fellow with the Mershon Center for International Security Studies and at New York University Abu Dhabi, where she extended her work to the aftermath of the 1994 genocide in Rwanda. Michelle has joined the Family Planning team as a research scientist to support data-driven efforts to increase access to safe, effective, and voluntary family planning services.
Research Scientist
Matt Schneider is a research scientist focused on quantitative approaches to improve healthcare delivery and health systems. Most recently before joining IDM and the Bill and Melinda Gates Foundation, Matt completed his dissertation and research at the University of Washington's Institute for Health Metrics and Evaluation. His PhD focused on using Bayesian statistical approaches to estimate health financing for primary health care systems in low- and middle-income countries and econometric analyses to measure the relationship between these expenditures and health outcomes. He also contributed to the Prospective Country Evaluation of the Global Fund, a mixed methods evaluation of the Global Fund business model's impact. Prior to returning to Washington for his PhD studies, Matt was a technical advisor at the United States Agency for International Development within the Office of HIV/AIDS' health system strengthening team, working with US government programs and partners in Africa, Asia, the Caribbean, and Eastern Europe.
Research Scientist