Publications

Development of microneedle array patches (MAPs) for potential use in immunization is ongoing, but the cost of manufacturing is expected to be higher than that of existing needle-and-syringe vial systems. The potential benefits of MAPs in reaching previously unvaccinated populations have been touted, but affordability, especially in low- and middle-income countries, remains an open question. In this study, we quantify the expected impact on operational costs of switching to MAPs for immunization for measles-rubella, human papilloma virus, and typhoid in both routine and campaign-based delivery modes. We endeavor to make a comprehensive estimate, including the costs of labor, syringes, waste management (i.e., sharps and trash), wastage (unused vaccine), freight and in-country cold chain transportation. We examined five potential use cases and our results show that in total, operational cost savings from a switch to MAPs are expected to range from a low of $0.24 per dose delivered (HPV, 1-dose vial, campaign) up to $0.61 per dose delivered (MR, 10-dose vial, routine). Excluding the allocated cost of labor, the estimated range of cost savings are $0.18 and $0.43, respectively. Confidence intervals are wide, due to the uncertainty in the assumptions, but in all five use cases tested, there was at least an 87 % probability of savings. These results show that operational savings from a switch to MAPs may offset at least part of the expected incremental manufacturing costs, which will make the transition more viable in settings with limited budget space. With this in mind, development agencies should continue to invest in MAPs technology and, if the product does come to market, use this evidence as part of total value of vaccines assessments and to inform investment strategies for implementation of vaccine MAPs, including alignment with policy makers.

Background Primary care networks (PCNs) are increasingly being adopted in low- and middle-income countries (LMICs) to improve the delivery of primary health care (PHC). Kenya has identified PCNs as a key reform to strengthen PHC delivery and has passed a law to guide its implementation. PCNs were piloted in two counties in Kenya in 2020 and implemented nationally in October 2023. This protocol outlines methods for a study that examines the impact, implementation experience and political economy of the PCN reform in Kenya. Methods We will adopt the parallel databases variant of convergent mixed methods study design to concurrently but separately collect quantitative and qualitative data. The two strands will be mixed during data collection to refine questions, with findings triangulated during analysis and interpretation to provide a comprehensive understanding of PCN implementation. The quantitative study will use a controlled before and after study design and collect data using health facility and client exit surveys. The primary outcome measure will be the service delivery readiness of PHC facilities. We will use a random sample of 228 health facilities and 2560 clients in four currently implementing PCNs, four planning to implement and four control counties at baseline and post-implementation. We shall undertake a preliminary cross-sectional analysis of the data at baseline from October to December 2023, followed by a difference-in-difference analysis at the endline from October to December 2024 to compare the outcome differences between the intervention and control counties over a 12-month period. The qualitative study will include a cross-sectional process evaluation and political economy analysis (PEA) using document reviews and approximately 80 in-depth interviews with national and sub-national stakeholders. The process evaluation will assess the emergence of PCN reforms, the implementation experience, the mechanism of impact and how the context affects implementation and outcomes. The PEA will examine the interaction of structural factors, institutions and actors/stakeholders’ interests and power relations in implementing PCNs. We will also examine the gendered effects of the PCNs, including power relations and norms, and their implications on PHC from the supply and demand sides. We shall undertake a thematic analysis of the qualitative data. Discussion This evaluation will contribute robust evidence on the impact, implementation experience, political economy and gendered implications of PCNs in a LMIC setting, as well as guide the refining of PCN implementation in Kenya and other LMICs implementing or planning to implement PCNs to enhance their effectiveness.

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.

Cassava is a key source of calories for smallholder farmers in sub-Saharan Africa but its role as a food security crop is threatened by the cross-continental spread of cassava brown streak disease (CBSD) that causes high yield losses. In order to mitigate the impact of CBSD, it is important to minimise the delay in first detection of CBSD after introduction to a new country or state so that interventions can be deployed more effectively. Using a computational model that combines simulations of CBSD spread at both the landscape and field scales, we model the effectiveness of different country level survey strategies in Nigeria when CBSD is directly introduced. We find that the main limitation to the rapid CBSD detection in Nigeria, using the current survey strategy, is that an insufficient number of fields are surveyed in newly infected Nigerian states, not the total number of fields surveyed across the country, nor the limitation of only surveying fields near a road. We explored different strategies for geographically selecting fields to survey and found that early and consistent CBSD detection will involve confining candidate survey fields to states where CBSD has not yet been detected and where survey locations are allocated in proportion to the density of cassava crops, detects CBSD sooner, more consistently, and when the epidemic is smaller compared with distributing surveys uniformly across Nigeria.

Background Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. Methods This study examined parasites from 3147 clinical infections sampled between the years 2012–2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, a series of Poisson generalized linear mixed-effects models were constructed to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. Results Model-predicted incidence was compared with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence ( 10‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was < 10‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

Central to the ongoing debate surrounding rights-based family planning measures is the need for a woman-centered paradigm. Despite this debate, in the 30 years since the 1994 International Conference on Population and Development, widely used family planning measures have evolved relatively slowly. In this paper, we describe the utilization of an understudied family planning indicator—women’s expressed intention to use (ITU) contraceptives—and explore its implications for developing metrics, tracking program impact, and mechanistic understanding. We leverage Performance Monitoring for Action program data in ten geographies and assess, (1) cross-sectionally, the extent to which ITU captures demand and concords with ‘unmet need’ and (2) longitudinally, the extent to which women actualize their ITU over time. We demonstrate that the utilization of ITU more accurately identifies women who have demand and who are (un) able to actualize their ITU. We also discuss the limitations of the current ITU metric, making recommendations for data efforts to improve and include ITU as routinely reported family planning indicators.

Contraceptive intention is an important woman-centered indicator for family planning. Yet, few studies have examined the determinants of women or couples actualizing their contraceptive intentions. We leverage panel data from the Performance Monitoring for Action (PMA) survey in Ethiopia to examine these dynamics among a pregnancy cohort, over the first year postpartum. Using cluster analysis on intent-to-use trajectories, we find distinct patterns across wealth categories, education levels, and regions. Additionally, we find that receiving family planning counseling in both antenatal and postnatal care visits led to a higher likelihood of intending to use. However, counseling did not increase the odds of actualization. We argue that examining actualization through model-based approaches like cluster analysis generates better insight into woman-centered contraceptive demand and provides stronger evidence for strengthening postpartum family planning interventions, than quantifying contraceptive use alone. Modeling postpartum actualization trajectories can shed light on the barriers to women’s and couple’s reproductive autonomy and inform future investments in both upstream development of better contraceptive methods and downstream implementation.

The World Health Organization’s Immunization and Vaccines-related Implementation Research Advisory Committee (IVIR-AC) serves to independently review and evaluate vaccine-related research to maximize the potential impact of vaccination programs. From 28 June – 1 July 2024, IVIR-AC was convened for an ad hoc meeting to discuss new evidence on criteria for rubella vaccine introduction and the risk of congenital rubella syndrome. This report summarizes background information on rubella virus transmission and the burden of congenital rubella syndrome, meeting structure and presentations, proceedings, and recommendations.

There is limited data on HIV evolutionary trends in African populations. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a twenty-four-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from persons living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994 to 2018) and four hyperendemic Lake Victoria fishing communities (2011 to 2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was evaluated using the Shannon diversity index and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Demographic history of HIV was inferred using a coalescent-based Bayesian Skygrid model. Evolutionary dynamics were assessed among demographic and behavioral population sub-groups, including by migration status. 9,931 HIV sequences were available from 4,999 PLHIV, including 3,060 and 1,939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (p<0.001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (p<0.001). The proportion of viruses classified as recombinants significantly increased by nearly four-fold from 12.2% in 1995 to 44.8% in 2017. Inter-subtype HIV diversity has generally increased. While intra-subtype p24 genetic diversity and divergence leveled off after 2014, intra-subtype gp41 diversity, effective population size, and divergence increased through 2017. Intra- and inter-subtype viral diversity increased across all demographic and behavioral population sub-groups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.

Since the coining of the term phylodynamics, the use of phylogenies to understand infectious disease dynamics has steadily increased. As methods for phylodynamics and genomic epidemiology have proliferated and grown more computationally expensive, the epidemiological information they extract has also evolved to better complement what can be learned through traditional epidemiological data. However, for genomic epidemiology to continue to grow, and for the accumulating number of pathogen genetic sequences to fulfill their potential widespread utility, the extraction of epidemiological information from phylogenies needs to be simpler and more efficient. Summary statistics provide a straightforward way of extracting information from a phylogenetic tree, but the relationship between these statistics and epidemiological quantities needs to be better understood. In this work we address this need via simulation. Using two different benchmark scenarios, we evaluate 74 tree summary statistics and their relationship to epidemiological quantities. In addition to evaluating the epidemiological information that can be inferred from each summary statistic, we also assess the computational cost of each statistic. This helps us optimize the selection of summary statistics for specific applications. Our study offers guidelines on essential considerations for designing or choosing summary statistics. The evaluated set of summary statistics, along with additional helpful functions for phylogenetic analysis, is accessible through an open-source Python library. Our research not only illuminates the main characteristics of many tree summary statistics but also provides valuable computational tools for real-world epidemiological analyses. These contributions aim to enhance our understanding of disease spread dynamics and advance the broader utilization of genomic epidemiology in public health efforts.