# Joshua L. Proctor's research while affiliated with Bill & Melinda Gates Foundation and other places

## Publications (51)

Preprint
Background As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulatio...
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Genomic data constitute a valuable adjunct to routine surveillance that can guide programmatic decisions to reduce the burden of infectious diseases. However, genomic capacities remain low in Africa. This study aims to operationalise a functional malaria molecular surveillance system in Mozambique for guiding malaria control and elimination. This p...
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The bidirectional interaction between undernutrition and infection can be devastating to child health. Nutritional deficiencies impair immunity and increase susceptibility to infection. Simultaneously, infections compound undernutrition by increasing metabolic demand, and impairing nutrient absorption. Treatment of acute malnutrition (wasting) can...
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Introduction: Adolescent pregnancy is a known health risk to mother and child. Statements and reports of health outcomes typically group mothers under 20 years old together. Few studies examined this risk at a finer age resolution, none of them comprehensively, and with differing results. Methods: We analysed Demographic and Health Surveys data...
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Background: During the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples' attitudes and actions during the pandemic and public health efforts to influence behavior. Methods: W...
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Molecular data and analysis outputs are being integrated into malaria surveillance efforts to provide valuable programmatic insights for national malaria control programs (NMCPs). A plethora of studies from diverse geographies have demonstrated that malaria parasite genetic data can be an important tool for drug resistance monitoring, species ident...
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Understanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecede...
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Background: Guinea worm (Dracunculus medinensis) was detected in Chad in 2010 after a supposed ten-year absence, posing a challenge to the global eradication effort. Initiation of a village-based surveillance system in 2012 revealed a substantial number of dogs infected with Guinea worm, raising questions about paratenic hosts and cross-species tr...
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BackgroundCOVID-19 case counts are the predominant measure used to track epidemiological dynamics and inform policy decision-making. Case counts, however, are influenced by testing rates and strategies, which have varied over time and space. A method to consistently interpret COVID-19 case counts in the context of other surveillance data is needed,...
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Background The World Health Organization (WHO) has highlighted the health risks to adolescent mothers and their children. Statements and reports typically group all mothers aged under 20 together. Some studies have examined the risk variations within this age group, and several child health outcomes have yet to be examined. Methods We analyzed data...
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Background: Scaling up access to safe, effective, and voluntary family planning (FP) services to achieve universal access for women and families will require increased commitment by countries and international organizations. On the way toward universal access, quantitative family planning goals have also been established by the United Nations throu...
Preprint
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Background COVID-19 case counts are the predominant measure used to track epidemiological dynamics and inform policy decision-making. Case counts, however, are influenced by testing rates and strategies, which have varied over time and space. A method to consistently interpret COVID-19 case counts in the context of other surveillance data is needed...
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Full-text available
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 principl...
Preprint
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As COVID-19 cases resurge in the United States, understanding the complex interplay between human behavior, disease transmission, and non-pharmaceutical interventions during the pandemic could provide valuable insights to focus future public health efforts. Cell-phone mobility data offers a modern measurement instrument to investigate human mobilit...
Preprint
Full-text available
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 principl...
Preprint
Full-text available
Background Guinea worm ( Dracunculus medinensis ) was detected in Chad in 2010 after a supposed ten year absence, posing a challenge to the global eradication effort. Initiation of a village-based surveillance system in 2012 revealed a substantial number of dogs infected with Guinea worm, raising questions about paratenic hosts and cross-species tr...
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Full-text available
In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having re...
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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 preval...
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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...
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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 f...
Preprint
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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 f...
Preprint
Full-text available
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...
Preprint
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 pop...
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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-Sparse Identification of Non...
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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, we integrate and unify two recent innovations that extend DMD to systems with actuation [Proctor et al., 2...
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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 characte...
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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-...
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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 information crit...
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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 dy...
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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 ℓ1-minimization or greedy algorithms. If full-state...
Book
Data-driven dynamical systems is a burgeoning field—it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various sc...
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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 Galerkin p...
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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 terms of the governing equations that most accurately r...
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Understanding the interplay of order and disorder in chaotic systems is a central challenge in modern quantitative science. We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. This work combines Takens' delay embedding with modern Koopman operator theory and sparse regression to obtain linear repres...
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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 algorithm can be enacted to c...
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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 goodness-of-fit metrics...
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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 from d...
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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 [Koopman 1931...
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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 engines, satellite...
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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 identi...
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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. Dynamic mode decomposition (DMD) is a recently developed method focused o...
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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 co...
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A model of malaria transmission dynamics is presented that tracks the genetic barcodes of individual Plasmodium falciparum infections using seasonally-driven effective reproduction rates for clonal propagation, external importation, and the outcrossing of strains both within and between infections. We explore quantitatively the relationship between...
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The haemozoin crystal continues to be investigated extensively for its potential as a biomarker for malaria diagnostics. In order for haemozoin to be a valuable biomarker, it must be present in detectable quantities in the peripheral blood and distinguishable from false positives. Here, dark-field microscopy coupled with sophisticated image process...
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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 crea...
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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 $\ell_1$-minimization or greedy algorithms....

## Citations

... The copy number variations (CNVs) such as gene deletions and duplications can be determined based on the mean depth of coverage of individual amplicons between the reference sample (3D7, without CNVs) and test sample. This approach can also be combined with other aspects of malaria elimination activities including capturing lowfrequency resistant mutants [60,61]. Moreover, combining NGS with pooling of individual isolates can potentially provide a faster and cheaper surveillance tool at the population level [61]. ...
... increase in the risk of stillbirth, and 2.62-fold (95% CI: 2.22-3.08) increase in the risk of neonatal mortality (4). A combination of biological and social factors ranging from immature reproductive systems and low socioeconomic status (5), to unhealthy lifestyle behaviors (i.e., alcohol/tobacco misuse during pregnancy) (4), and unplanned pregnancy may affect the health of teenage mothers. ...
... During the final stages of processing, the hb_shrink function was used to perform hierarchical Bayesian shrinkage on the county-to-CBG level to improve the reliability of the estimates. SafeGraph's stay-at-home index has been used in many studies in order to analyze the impact of mobility patterns and physical distancing after the implementation of shelter-in-place policies in the USA [35][36][37]40]. ...
... Genomic data played an important role in identifying dogs as a reservoir of Dracunculus medinensis, the nematode that causes Guinea worm disease (Eberhard et al., 2014;Durrant et al., 2020). An investigation of the spatial scale of D. medinensis transmission using geocoded samples and genomic data shed light on the area that should be investigated when cases are detected (~20 km) -key information for efforts to eliminate the disease (Ribado et al., 2021). Genomic data are also being integrated into malaria surveillance and control programs to monitor drug resistance in Plasmodium parasites and insecticide resistance in Anopheles vectors (Neafsey et al., 2021) For example, surveillance for genomic signatures of resistance to artemisinin, a key malaria drug, informs regionally tailored treatment guidelines (Hupalo et al., 2016). ...
... 10,11 The variability in the consistency of the official data presented by the different nations can represent another considerable limit and means that this type of analysis has a heuristic rather than a hypothesis value. 12 The aim of this work is to table whether parameters relating to the lethality and diffusivity of the pandemic may negatively/inversely correlate with the percentage of people vaccinated in different countries. We will also try to establish whether the results can be considered comparable in the countries that have achieved higher vaccination rates with the different types of vaccines, the results can be considered comparable. ...
... Children born to adolescent mothers had higher hazard of mortality compared to those 25-34 years old. This is in accord with recent studies indicating up to 4 times increased of risk of death of children born to adolescent mothers compared to those born to young adult mothers [28,29]. Given that more than six in ten women included in this study gave their first birth before 20 years of age, still an unexpectedly high proportion of rural Ethiopian girls are suffering from teenage pregnancy. ...
... Despite opportunities to improve clinical care in a cost-aware mindset, there remains a paucity of data on the use of CDSS for infectious disease etiology determination and to support appropriate antibiotic use in LMICs Tuon et al., 2017. Our team previously derived and internally validated a series of clinical prediction models using data from GEMS, integrating characteristics of the present patient's diarrhea episode (patient-specific factors including age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) with external data sources (such as characteristics of recent patients, historical prevalence, climate, and seasonal patterns of viral diarrhea 'viral seasonality') in a modular approach Brintz et al., 2021. The best-performing model, which used 'present patient'+ location-specific viral seasonality data, had an internally validated area under the receiver-operating characteristic curve (AUC) of 0.83 Brintz et al., 2021. ...
... A correlation between changes in the amount of time spent at home, the workplace, and shopping, and rates of infection has been shown [8][9][10]. It has been shown that compliance rates for staying at home are not uniform across different regions [11]. Stagnation of economic activity with an increase in the number of infections has been observed based on night-time illumination and power consumption [12][13][14]. ...
... 23 Therefore, our assessment of the R T for P. falciparum malaria using the individual-level spatio-temporal model developed by Routledge et al. is the first to apply these methods in an African population, and in the context of an outbreak. 12,29 Understanding the transmission dynamics of malaria in a setting without an initial high coverage of vector control interventions and without protective immunity provides important insights into the transmissibility of P. falciparum. Furthermore, in settings highly vulnerable to imported malaria infections, identifying the areas receptive to malaria transmission can help programs identify where to target vector control interventions to prevent re-establishment of malaria transmission and maintain a state of malaria freedom. ...
... In contrast, model-based estimates use some kind of model in order to take the sampling design into account [5]. Nevertheless, spatial approaches to survey-based small area estimation usually follow hybrid approaches where (non-spatial) design-based estimates are derived, which take the sampling design followed into account, and spatial dependence is later induced on these estimates by means of a (spatial) smoothing model [6,7,8]. Although the uncertainty in the first stage of these models is usually considered for the second stage, the different processes modelled in both phases could interact; however, that interaction can hardly be corrected in these two-phase proposals. ...