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1. Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps

Author

Tadesse, Girmaw Abebe, Robinson, Caleb, Mwangi, Charles, Maina, Esther, Nyakundi, Joshua, Marotti, Luana, Hacheme, Gilles Quentin, Alemohammad, Hamed, Dodhia, Rahul, and Ferres, Juan M. Lavista

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m/pixel and a low-resolution student model on publicly available images with a resolution of 10 m/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge transfer. We evaluated our framework using Murang'a county in Kenya, renowned for its agricultural productivity, as a use case. Our local models achieved higher quality maps, with improvements of 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global model. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Our work provides valuable guidance to decision-makers for driving informed decisions to enhance food security.

Published
2024

2. Integration of Mathematical Internet Memes a Multimedia Resource into Kenya Secondary Mathematics Education

Author

Dominic Mutuku Mutua and Waweru Mwangi

Abstract

Mathematics education is compulsory in most secondary curriculums around the globe and is a critical discipline among the STEM subjects. Therefore, as a subject that is highly practical in multiple fields and curriculum subjects, developing mathematical instructional approaches that enhance mathematics education is necessary. This study investigated the influence of mathematical Internet memes in secondary mathematics education. The mixed method research approach was applied, and data collection techniques used were observation, achievement tests, questionnaires, and focus groups. The results revealed that integrating mathematical Internet memes in mathematics classrooms enhances students' motivation and engagement. Moreover, the study revealed that mathematical Internet memes provide similar feedback as traditional formative assessment and, therefore, a viable alternative tool for formative assessment. The study further showed that integrating mathematical Internet memes has no significant impact on learners' performance in mathematics. However, results from the focus group indicate that students believe that integrating mathematical Internet memes in mathematics classrooms can potentially improve their performance in mathematics.

Published
2024
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3. Adapting the re-ID challenge for static sensors

Author

Sundaresan, Avirath, Parham, Jason R., Crall, Jonathan, Warungu, Rosemary, Muthami, Timothy, Mwangi, Margaret, Miliko, Jackson, Holmberg, Jason, Berger-Wolf, Tanya Y., Rubenstein, Daniel, Stewart, Charles V., and Beery, Sara

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population estimates via expert and algorithmic curation of volunteer-captured images. A complementary, scalable, and long-term Grevy's population monitoring approach involves deploying camera trap networks. However, in both scenarios, a substantial majority of zebra images are not usable for individual identification due to poor in-the-wild imaging conditions; camera trap images in particular present high rates of occlusion and high spatio-temporal similarity within image bursts. Our proposed filtering pipeline incorporates animal detection, species identification, viewpoint estimation, quality evaluation, and temporal subsampling to obtain individual crops suitable for re-ID, which are subsequently curated by the LCA decision management algorithm. Our method processed images taken during GGR-16 and GGR-18 in Meru County, Kenya, into 4,142 highly-comparable annotations, requiring only 120 contrastive human decisions to produce a population estimate within 4.6% of the ground-truth count. Our method also efficiently processed 8.9M unlabeled camera trap images from 70 cameras at the Mpala Research Centre in Laikipia County, Kenya over two years into 685 encounters of 173 individuals, requiring only 331 contrastive human decisions., Comment: 8 pages, 11 figures. Submitted to the IET Computer Vision Special Issue on Camera Traps, AI, and Ecology. Extended version of a workshop paper presented at Camera Traps, AI, and Ecology 2023

Published
2024

4. Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information

Author

Oganisian, Arman, Hogan, Joseph, Sang, Edwin, DeLong, Allison, Mosong, Ben, Fraser, Hamish, and Mwangi, Ann

Subjects
Statistics - Methodology, Computer Science - Machine Learning
Abstract

Like many chronic diseases, human immunodeficiency virus (HIV) is managed over time at regular clinic visits. At each visit, patient features are assessed, treatments are prescribed, and a subsequent visit is scheduled. There is a need for data-driven methods for both predicting retention and recommending scheduling decisions that optimize retention. Prediction models can be useful for estimating retention rates across a range of scheduling options. However, training such models with electronic health records (EHR) involves several complexities. First, formal causal inference methods are needed to adjust for observed confounding when estimating retention rates under counterfactual scheduling decisions. Second, competing events such as death preclude retention, while censoring events render retention missing. Third, inconsistent monitoring of features such as viral load and CD4 count lead to covariate missingness. This paper presents an all-in-one approach for both predicting HIV retention and optimizing scheduling while accounting for these complexities. We formulate and identify causal retention estimands in terms of potential return-time under a hypothetical scheduling decision. Flexible Bayesian approaches are used to model the observed return-time distribution while accounting for competing and censoring events and form posterior point and uncertainty estimates for these estimands. We address the urgent need for data-driven decision support in HIV care by applying our method to EHR from the Academic Model Providing Access to Healthcare (AMPATH) - a consortium of clinics that treat HIV in Western Kenya.

Published
2024

5. Error Analysis of English Narrative Essays of Secondary School Learners in Kirinyaga Central Sub-County, Kenya

Author

Beth Muthoni Kangangi, Catherine Waithera Ndung'U, and Peter Kinyanjui Mwangi

Abstract

The study examined the types of language errors made by learners in the English narrative essays. It also assessed the feedback techniques employed by teachers of English in the handling of language errors in the English narrative essays. A descriptive research design was employed to examine errors of English narrative essays of 181 form two learners obtained through stratified random sampling and simple random sampling techniques. The eight teachers of English selected through purposive and simple random sampling completed interview schedules. Qualitative data generated from the essays of the learners was analyzed using Error Analysis and Content Analysis. The findings reveal that a total of seven categories of language errors which include: word order, verb tense and form, subject -- verb agreement, spelling, plurality, word choice, and capital letter errors were made by the learners in their English narrative essays. The most preferred corrective technique was to involve the learners actively by encouraging them to read extensively and write severally on various narratives in the target language. The findings will provide reliable feedback to teachers of English because it will guide on the development of teaching methods and evaluative measures to narrative essays. The study will also be useful to curriculum developers in designing materials for teaching English language in secondary schools. It will also be of invaluable benefit to learners of English as it will guide on the way narrative essays need to be written.

Published
2024

6. Educational Policies in Early Childhood Education Programs in Kenya and Nepal: Challenging Unjust Binary Mindset around Curricula Practices

Author

Lydiah Nganga, Samara Madrid Akpovo, John Kambutu, Sapna Thapa, and Agnes Muthoni Mwangi

Abstract

Educational policies and practices in the current age of heightened globalization are increasingly grounded on unjust binary curriculum approaches that favor educational designs from Minority-World countries at the expense of epistemologies of indigenous people in Majority-World nations that are typically deemed culturally inferior (Gupta, 2015). Essentially, those opposing binaries promote neocolonial and neoliberal ideologies while disregarding lived cultural contexts of people in Majority-World countries (Lee, 2012). To grapple with this conundrum, this ethnographic study examined how educators in Kenya and Nepal (N = 26) contested, constructed, and transformed educational practices in contexts of globalization, neocolonialism, and neoliberalism. Qualitative data from four schools in Kathmandu, Nepal (16 participants) and two schools in Kenya (10 participants) revealed that educators struggled with issues of intellectual imperialism. Therefore, they strived to deconstruct the existing unjust binary curricula policies by embracing pluralist educational discourses essential to sustaining indigenous languages and dialects. In doing so, those educators focused on advancing local cultural values, rights, and ideologies without antagonizing influential stakeholders from Minority-World nations.

Published
2024
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7. Perinatal intervention strategies providing food with micronutrients to pregnant and breastfeeding women in low- and middle-income countries: A scoping review.

Author

McDonald, Christine, Wessells, K, Stewart, Christine, Dewey, Kathryn, de Pee, Saskia, Rana, Ritu, Hafeez-Ur-Rehman, Hajra, Mwangi, Martin, and Hess, Sonja

Subjects
LNS, antenatal nutrition, balanced energy protein, lactation, maternal nutrition, perinatal nutrition, pregnancy, undernutrition, Humans, Female, Micronutrients, Pregnancy, Food, Fortified, Breast Feeding, Developing Countries, Maternal Nutritional Physiological Phenomena, Dietary Supplements, Infant, Nutritional Status, Infant, Newborn
Abstract

In resource-constrained settings, pregnant and breastfeeding women and girls (PBW/G) are particularly vulnerable to undernutrition. Micronutrient-fortified balanced energy protein (BEP) supplementation may be provided to boost maternal nutritional status and improve birth and infant outcomes. We conducted a scoping review of the published literature to determine the impact of BEP and other related nutrition interventions that provided fortified food or cash along with a minimum of 3 micronutrients on maternal, birth, and infant/child outcomes in low- and middle-income countries. We conducted a PubMed search using pre-defined keywords and controlled vocabulary search terms. All titles and abstracts were reviewed for eligibility by two independent reviewers, and data were extracted according to outcome type. We identified 149 eligible research articles that reported on a total of 21 trials and/or programme evaluations which assessed the health impact of one or more products (fortified lipid-based nutrient supplement [LNS, n = 12], fortified blended flours [n = 5], milk-based beverages [n = 2], and local food/snacks [n = 3]) that provided 118-750 kcal/day and varying levels of protein and micronutrients. Only one of these programme evaluations assessed the impact of the provision of cash and fortified food. Effects on maternal outcomes such as gestational weight gain and duration of gestation were promising but inconsistent. Birth outcomes were reported in 15 studies, and the effects on birthweight and birth length were generally positive. Seven studies demonstrated sustained benefits on infant and child growth out of the 15 studies that reported at least one of these outcomes, although data were sparse. Additional research is needed to investigate issues of dose, cost-effectiveness, and incorporation into multi-component interventions.

Published
2024

8. Chemometrics-aided Surface-enhanced Raman spectrometric detection and quantification of GH and TE hormones in blood

Author

Ondieki, Annah M., Birech, Zephania, Kaduki, Kenneth A., Mwangi, Peter W., Juma, Moses, and Chege, Boniface M.

Subjects
Physics - Medical Physics, Physics - Optics
Abstract

This work explores the use of Surface-Enhanced Raman Spectroscopy (SERS) combined with artificial neural network (ANN) models to detect and quantify growth hormone (GH) and testosterone (TE) in the blood of Sprague Dawley (SD) rats. SERS spectra were recorded from blood samples of SD rats injected with GH, TE, both hormones, and non-injected controls using 785 nm laser excitation. The samples were mixed with silver nanoparticles (AgNPs) synthesized in distilled water, applied onto a microscope slide, and air-dried. The resulting SERS spectra displayed similar profiles with intensity variations depending on the hormone, revealing specific bands at 658, 798, 878, 914, 932, 1064, 1190, 1354, 1410, and 1658 cm-1. PCA analysis indicated time-dependent intensity changes in bands centered around 1378 (all groups), 658 and 1614 cm-1 (GH-injected rats), and others for different hormone combinations. These variations reflect subtle biochemical changes induced by hormone injections. The ANN models, trained with six PCA scores of blood spiked with various hormone concentrations, showed high accuracy, with coefficients of determination greater than 87.71% and low root mean square error (RMSE) values below 0.6436. The hormone levels in injected rats increased initially and later declined, a trend confirmed by ELISA kits. Although ELISA and SERS produced similar results, SERS offered advantages such as rapid analysis (about two minutes), simple sample preparation, small sample volumes, and non-specificity to hormones. This suggests that SERS, combined with ANN models, could be used to detect exogenous sports dopants. These findings expand the potential applications of SERS in sports science, clinical diagnostics, and biomedical research.

Published
2024

11. Perceived climate change impacts and adaptation responses in ten African mountain regions

Author

Cuni-Sanchez, Aida, Aneseyee, Abreham B., Baderha, Ghislain K. R., Batumike, Rodrigue, Bitariho, Robert, Imani, Gerard, Jha, Nisha, Kaganzi, Kaiza R., Kaplin, Beth A., Klein, Julia A., Leite, Ana, Marchant, Robert A., Martin, Emanuel H., Mcharazo, Fatuma, Mwangi, Ben, Ngute, Alain S. K., Nkengurutse, Jacques, Nkurunziza, Aline, Olaka, Lydia, Soromessa, Teshome, Tchoffo, Romeo O. K., Thorn, Jessica P. R., Twinomuhangi, Isaac, Sullivan, Martin J. P., and Zafra-Calvo, Noelia

Published
2025
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12. Ferric carboxymaltose for anemia in late pregnancy: a randomized controlled trial

Author

Pasricha, Sant-Rayn, Moya, Ernest, Ataíde, Ricardo, Mzembe, Glory, Harding, Rebecca, Mwangi, Martin N., Zinenani, Truwah, Prang, Khic-Houy, Kaunda, Justina, Mtambo, Owen P. L., Vokhiwa, Maclean, Mhango, Gomezgani, Mamani-Mategula, Elisabeth, Fielding, Katherine, Demir, Ayşe, Von Dinklage, Naomi, Verhoef, Hans, McLean, Alistair RD, Manda-Taylor, Lucinda, Braat, Sabine, and Phiri, Kamija S.

Published
2025
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15. Travel time to cataract surgical services in Kenya, Malawi and Rwanda: demonstrating a standardised indicator of physical access to cataract surgery.

Author

McCormick, Ian, Nesemann, John, Zhao, Jinfeng, Mdala, Shaffi, Kitema, Gatera, Mwangi, Nyawira, Gichangi, Michael, Tang, Kevin, Burton, Matthew, and Ramke, Jacqueline

Subjects
Humans, Cataract Extraction, Rwanda, Kenya, Health Services Accessibility, Malawi, Middle Aged, Travel, Time Factors, Aged, Female, Cataract, Male
Abstract

BACKGROUND: Travel time can be used to assess health services accessibility by reflecting the proximity of services to the people they serve. We aimed to demonstrate an indicator of physical access to cataract surgery and identify subnational locations where people were more at risk of not accessing cataract surgery. METHODS: We used an open-access inventory of public health facilities plus key informants in Kenya, Malawi and Rwanda to compile a geocoded inventory of cataract facilities. For each country, gridded estimates of the population aged ≥ 50 years and a travel-time friction surface were combined and a least-cost-path algorithm applied to estimate the shortest travel time between each grid and the nearest cataract facility. We categorised continuous travel time by 1-, 2- and 3 h thresholds and calculated the proportion of the population in each category. RESULTS: At the national level, the proportion of the population aged ≥ 50 years within 2 h travel time to permanent cataract surgical services was 97.2% in Rwanda (n = 10 facilities), 93.5% in Kenya (n = 74 facilities) and 92.0% in Malawi (n = 6 facilities); this reduced to 77.5%, 84.1% and 52.4% within 1 h, respectively. The least densely populated subnational regions had the poorest access to cataract facilities in Malawi (0.0%) and Kenya (1.9%). CONCLUSION: We demonstrated an indicator of access that reflects the distribution of the population at risk of age-related cataract and identifies regions that could benefit from more accessible services. This indicator provides additional demand-side context for eye health planning and supports WHOs goal of advancing integrated people-centred eye care.

Published
2024

16. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

17. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Author

Kennedy, Eamonn, Liebel, Spencer, Lindsey, Hannah, Vadlamani, Shashank, Lei, Pui-Wa, Adamson, Maheen, Alda, Martin, Alonso-Lana, Silvia, Anderson, Tim, Arango, Celso, Asarnow, Robert, Avram, Mihai, Ayesa-Arriola, Rosa, Babikian, Talin, Banaj, Nerisa, Bird, Laura, Borgwardt, Stefan, Brodtmann, Amy, Brosch, Katharina, Caeyenberghs, Karen, Calhoun, Vince, Chiaravalloti, Nancy, Cifu, David, Crespo-Facorro, Benedicto, Dalrymple-Alford, John, Dams-OConnor, Kristen, Dannlowski, Udo, Darby, David, Davenport, Nicholas, DeLuca, John, Diaz-Caneja, Covadonga, Disner, Seth, Dobryakova, Ekaterina, Ehrlich, Stefan, Esopenko, Carrie, Ferrarelli, Fabio, Frank, Lea, Franz, Carol, Fuentes-Claramonte, Paola, Genova, Helen, Giza, Christopher, Goltermann, Janik, Grotegerd, Dominik, Gruber, Marius, Gutierrez-Zotes, Alfonso, Ha, Minji, Haavik, Jan, Hinkin, Charles, Hoskinson, Kristen, Hubl, Daniela, Irimia, Andrei, Jansen, Andreas, Kaess, Michael, Kang, Xiaojian, Kenney, Kimbra, Keřková, Barbora, Khlif, Mohamed, Kim, Minah, Kindler, Jochen, Kircher, Tilo, Knížková, Karolina, Kolskår, Knut, Krch, Denise, Kremen, William, Kuhn, Taylor, Kumari, Veena, Kwon, Junsoo, Langella, Roberto, Laskowitz, Sarah, Lee, Jungha, Lengenfelder, Jean, Liou-Johnson, Victoria, Lippa, Sara, Løvstad, Marianne, Lundervold, Astri, Marotta, Cassandra, Marquardt, Craig, Mattos, Paulo, Mayeli, Ahmad, McDonald, Carrie, Meinert, Susanne, Melzer, Tracy, Merchán-Naranjo, Jessica, Michel, Chantal, Morey, Rajendra, Mwangi, Benson, Myall, Daniel, Nenadić, Igor, Newsome, Mary, Nunes, Abraham, OBrien, Terence, Oertel, Viola, Ollinger, John, Olsen, Alexander, Ortiz García de la Foz, Victor, Ozmen, Mustafa, Pardoe, Heath, Parent, Marise, Piras, Fabrizio, and Piras, Federica

Subjects
Parkinson’s disease, attention-deficit/hyperactivity disorder, bipolar disorder, dementia, depression, memory, schizophrenia, stroke, traumatic brain injury, verbal learning
Abstract

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinsons disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Published
2024

20. White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals

Author

Dietze, Lorielle M. F., McWhinney, Sean R., Favre, Pauline, Abé, Christoph, Alexander, Nina, Barkhau, Carlotta, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Carruthers, Sean P., Corkum, Emily L. V., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Dohm, Katharina, Elvsåshagen, Torbjørn, Flinkenflügel, Kira, Fortea, Lydia, Furlong, Lisa S., Goldstein, Benjamin I., Grotegerd, Dominik, Gruber, Marius, Haarman, Bartholomeus C. M., Howells, Fleur M., Jahanshad, Neda, Jamalabadi, Hamidreza, Jansen, Andreas, Karantonis, James A., Kennedy, Kody G., Kircher, Tilo T. J., Klahn, Anna Luisa, Kochunov, Peter, Kraus, Anna, Landén, Mikael, López-Jaramillo, Carlos, MacIntosh, Bradley J., Mazza, Elena, McDonald, Colm, McIntosh, Andrew M., Meinert, Hannah, Meinert, Susanne, Melloni, Elisa M. T., Mitchell, Philip B., Nenadić, Igor, Opel, Nils, Phillips, Mary, Piguet, Camille, Polosan, Mircea, Pomarol-Clotet, Edith, Pouchon, Arnaud, Radua, Joaquim, Roberts, Gloria, Ross, Alex J., Rossell, Susan L., Salvador, Raymond, Sim, Kang, Soares, Jair C., Zunta-Soares, Giovana B., Stein, Frederike, Straube, Benjamin, Suo, Chao, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Usemann, Paula, Van Rheenen, Tamsyn E., Versace, Amelia, Vieta, Eduard, Vilajosana, Enric, Mwangi, Benson, Wen, Wei, Whalley, Heather C., Wu, Mon-Ju, Andreassen, Ole A., Ching, Christopher R. K., Thompson, Paul M., Houenou, Josselin, and Hajek, Tomas

Published
2024
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22. Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Author

Borlase, Anna, Brady, Oliver, Browning, Raiha, Chitnis, Nakul, Coffeng, Luc, Crowley, Emily, Cucunubá, Zulma, Cummings, Derek, Davis, Christopher, Davis, Emma, Dixon, Matthew, Dobson, Andrew, Dyson, Louise, French, Michael, Fronterre, Claudio, Giorgi, Emanuele, Huang, Ching-I, Jain, Saurabh, James, Ananthu, Kim, Sung, Kura, Klodeta, Lucianez, Ana, Marks, Michael, Mbabazi, Pamela, Medley, Graham, Michael, Edwin, Montresor, Antonio, Mutono, Nyamai, Mwangi, Thumbi, Rock, Kat, Saboyá-Díaz, Martha-Idalí, Sasanami, Misaki, Schwehm, Markus, Spencer, Simon, Srivathsan, Ariktha, Stawski, Robert, Stolk, Wilma, Sutherland, Samuel, Tchuenté, Louis-Albert, de Vlas, Sake, Walker, Martin, Brooker, Simon, Hollingsworth, T, Solomon, Anthony, Fall, Ibrahima, Vasconcelos, Andreia, King, Jonathan, Nunes-Alves, Cláudio, Anderson, Roy, Argaw, Daniel, Basáñez, Maria-Gloria, Bilal, Shakir, Blok, David, and Blumberg, Seth

Subjects
control, elimination, mathematical models, neglected tropical diseases, policy-making, Neglected Diseases, Humans, Tropical Medicine, COVID-19, Models, Theoretical, World Health Organization, SARS-CoV-2, Decision Making, Global Health
Abstract

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

Published
2024

24. Genomic analysis of DS-1-like human rotavirus A strains uncovers genetic relatedness of NSP4 gene with animal strains in Manhiça District, Southern Mozambique

Author

Manjate, Filomena, João, Eva D., Mwangi, Peter, Chirinda, Percina, Mogotsi, Milton, Garrine, Marcelino, Messa, Jr., Augusto, Vubil, Delfino, Nobela, Nélio, Kotloff, Karen, Nataro, James P., Nhampossa, Tacilta, Acácio, Sozinho, Weldegebriel, Goitom, Tate, Jacqueline E., Parashar, Umesh, Mwenda, Jason M., Alonso, Pedro L., Cunha, Celso, Nyaga, Martin, and Mandomando, Inácio

Published
2024
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34. Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL

Author

Wenzl, Kerstin, Stokes, Matthew E., Novak, Joseph P., Bock, Allison M., Khan, Sana, Hopper, Melissa A., Krull, Jordan E., Dropik, Abigail R., Walker, Janek S., Sarangi, Vivekananda, Mwangi, Raphael, Ortiz, Maria, Stong, Nicholas, Huang, C. Chris, Maurer, Matthew J., Rimsza, Lisa, Link, Brian K., Slager, Susan L., Asmann, Yan, Mondello, Patrizia, Morin, Ryan, Ansell, Stephen M., Habermann, Thomas M., Witzig, Thomas E., Feldman, Andrew L., King, Rebecca L., Nowakowski, Grzegorz, Cerhan, James R., Gandhi, Anita K., and Novak, Anne J.

Published
2024
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39. Host metabolomic responses in recurrent P. vivax malaria

Author

Yakubu, Michael N., Mwangi, Victor I., Netto, Rebeca L. A., Alecrim, Maria G. C., Alves, Jessica R. S., Almeida, Anne C. G., Santos, Gabriel F., Lima, Gesiane S., Machado, Lucas S., Koolen, Hector H. F., Guimarães, Tiago P., Chaves, Andrea R., Vaz, Boniek G., Monteiro, Wuelton M., Costa, Fabio T. M., Lacerda, Marcus V. G., Gardinassi, Luiz G., and de Melo, Gisely C.

Published
2024
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49. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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