Baobab Index

Human mobility, climate change and demographic trends increase the risk of pathogen spillover and expansion. Data that can inform our responses to outbreaks have increased in availability and volume, but access to highly confidential outbreak data and commercially sensitive contextual information remains difficult. Despite ongoing efforts to adopt global health data infrastructures and sharing protocols, there remain regulatory, logistical, human and computational barriers to data sharing. Federated approaches-in which data remain stored locally but analyses are performed across datasets from different sources-offer a potential way to address these challenges. While federated approaches have been used in some clinical and biomedical contexts, their adoption in infectious disease surveillance and modeling has been limited. Here, we discuss global approaches to infectious disease modeling and analysis, with a focus on federated methods. We outline how these can be used to address key epidemiological questions during outbreaks by enabling the secure use of multimodal data and integration with existing surveillance and modeling efforts. We summarize current methods for combining distributed and locally stored data and identify limitations, opportunities and organizational structures needed to achieve equitable global public health impacts.

Sustaining and transitioning mpox vaccination requires establishing and resourcing a global mpox vaccine stockpile under the Inter-Agency Coordinating Group (ICG), supported by sustainable financing, transparent allocation criteria, diversified manufacturing (including local production in Africa), and robust logistics, surveillance, and operational systems. Moreover, it requires strengthened collaboration among global, regional, and community actors; continuous data generation and research to inform policy, including genomic and vaccine effectiveness studies; and accelerated R&D with fast-tracked local manufacturing to enhance supply resilience and long-term epidemic preparedness.

Despite intensive international efforts and broad implementation of control and prevention efforts, malaria continues to take a devastating toll on the most vulnerable populations, especially infants and young children. Emerging data support an important role for gut microbiome disruption in exacerbating, and potentially contributing to, adverse outcomes in malaria in young children. Less well understood are the role of the gut microbiome in early infancy in determining malaria susceptibility and how malaria exposure may impact gut microbial communities during this highly dynamic and sensitive period of microbiome development. To address these gaps, we recruited mother-infant dyads at birth in malaria-endemic eastern Democratic Republic of Congo. Infant fecal samples collected at six weeks, and at three, six and 12 months of age, as well as at passive malaria sick and post-treatment visits, were subjected to full length 16S rRNA sequencing. Significant differences in relative abundance of a number of bacterial species distinguished those infants who never had a malaria visit from those who did, and those malaria episodes resulted in gut dysbiosis. Classifier analysis with Boruta selection revealed preliminary predictive capacity of the six-week fecal microbiome for malaria susceptibility through the first year of life, with a modest signal partially intertwined with bednet use. Healthy gut-associated These results provide the first evidence that gut microbial composition in early infancy is associated with subsequent malaria susceptibility. These associations, if confirmed in larger cohorts, may inform future investigation of microbiome-targeted strategies to support resistance to malaria in early life.

Copy number variants (CNV) contribute significantly to the pathogenic variation associated with developmental disorders. CNV detection is often not included in standard exome sequencing (ES) analysis. Complementary methods such as chromosomal microarray are typically offered in diagnostic laboratories to diagnose pathogenic CNV. In this study, we aimed to develop an effective approach for incorporating CNV detection within our ES analysis process for the Deciphering Developmental Disorders in Africa (DDD-Africa) cohort. We analyzed ES data from 505 probands with a developmental disorder, applying a CNV detection approach that assessed data generated using the tools CANOES and XHMM. When available, parental ES data was used to assess inheritance patterns. We confirmed a diagnosis in 41/505 (8,1%) patients with 43 pathogenic CNV identified in the probands. There were 31 deletions and 12 duplications. Among the 26 probands with parental data, all identified CNV were de novo. The addition of CNV analysis to our ES analysis pipeline resulted in an 8.1% increase in diagnostic yield in the DDD-Africa cohort without additional laboratory cost. This offers a feasible approach which is likely to reduce analytical cost and is suitable for low- and middle-income countries where funding and resources for genomic medicine initiatives are limited.

The greatest current threat to cassava in sub-Saharan Africa, is the continued expansion of plant virus pandemics being driven by super-abundant populations of the whitefly vector, Bemisia tabaci. To track the association of putatively genetically distinct populations of B. tabaci with pandemics of cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), a comprehensive region-wide analysis examined the phylogenetic relationships and population genetics of 642 B. tabaci adults sampled from cassava in six countries of East and Central Africa, between 1997 and 2010, using a mitochondrial DNA cytochrome oxidase I marker (780 bases). Eight phylogenetically distinct groups were identified, including one, designated herein as 'East Africa 1' (EA1), not previously described. The three most frequently occurring groups comprised >95% of all samples. Among these, the Sub-Saharan Africa 2 (SSA2) group diverged by c. 8% from two SSA1 sub-groups (SSA1-SG1 and SSA1-SG2), which themselves were 1.9% divergent. During the 14-year study period, the group associated with the CMD pandemic expansion shifted from SSA2 to SSA1-SG1. Population genetics analyses of SSA1, using Tajima's D, Fu's Fs and Rojas' R2 statistics confirmed a temporal transition in SSA1 populations from neutrally evolving at the outset, to rapidly expanding from 2000 to 2003, then back to populations more at equilibrium after 2004. Based on available evidence, hybrid introgression appears to be the most parsimonious explanation for the switch from SSA2 to SSA1-SG1 in whitefly populations driving cassava virus pandemics in East and Central Africa.

The evaluation of facial dysmorphism is a critical step toward reaching a diagnostic. The aim of the present study was to evaluate the ability to interpret facial morphology in African children with intellectual disability (ID). First, 10 experienced clinicians (five from Africa and five from Europe) rated gestalt in 127 African non-Down Syndrome (non-DS) patients using either the score 2 for 'clearly dysmorphic', 0 for 'clearly non dysmorphic' or 1 for 'uncertain'. The inter-rater agreement was determined using kappa coefficient. There was only fair agreement between African and European raters (kappa-coefficient = 0.29). Second, we applied the FDNA Face2Gene solution to assess Down Syndrome (DS) faces. Initially, Face2Gene showed a better recognition rate for DS in Caucasian (80%) compared to African (36.8%). We trained the Face2Gene with a set of African DS and non-DS photographs. Interestingly, the recognition in African increased to 94.7%. Thus, training improved the sensitivity of Face2Gene. Our data suggest that human based evaluation is influenced by ethnic background of the evaluator. In addition, computer based evaluation indicates that the ethnic of the patient also influences the evaluation and that training may increase the detection specificity for a particular ethnic.

Knowledge about the biogeographic affinities of the world's tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world's tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (

Bocaparvoviruses are members of the family Parvovirinae and human bocaviruses have been found to be associated with respiratory and gastrointestinal disease. There are four known human bocaviruses, as well as several distinct ones in great apes. The goal of the presented study was to detect other non-human primate (NHP) bocaviruses in NHP species in the Democratic Republic of the Congo using conventional broad-range PCR. We found bocavirus DNA in blood and tissues samples in 6 out of 620 NHPs, and all isolates showed very high identity (>97 %) with human bocaviruses 2 or 3. These findings suggest cross-species transmission of bocaviruses between humans and NHPs.

Tracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. With such a large variety of methods available, informed selection of an appropriate approach for determining transmissions within a given setting/time period is difficult. This study combines whole genome sequence (WGS) data derived from 324 isolates collected 2005-2010 in Kinshasa, Democratic Republic of Congo (DRC), a high endemic setting, with phylodynamics to unveil the timing of transmission events posited by a variety of standard genotyping methods. Clustering data based on Spoligotyping, 24-loci MIRU-VNTR typing, WGS based SNP (Single Nucleotide Polymorphism) and core genome multi locus sequence typing (cgMLST) typing were evaluated. Our results suggest that clusters based on Spoligotyping could encompass transmission events that occurred almost 200 years prior to sampling while 24-loci-MIRU-VNTR often represented three decades of transmission. Instead, WGS based genotyping applying low SNP or cgMLST allele thresholds allows for determination of recent transmission events, e.g. in timespans of up to 10 years for a 5 SNP/allele cut-off. With the rapid uptake of WGS methods in surveillance and outbreak tracking, the findings obtained in this study can guide the selection of appropriate clustering methods for uncovering relevant transmission chains within a given time-period. For high resolution cluster analyses, WGS-SNP and cgMLST based analyses have similar clustering/timing characteristics even for data obtained from a high incidence setting.

Among the local processes that determine species diversity in ecological communities, fluctuation-dependent mechanisms that are mediated by temporal variability in the abundances of species populations have received significant attention. Higher temporal variability in the abundances of species populations can increase the strength of temporal niche partitioning but can also increase the risk of species extinctions, such that the net effect on species coexistence is not clear. We quantified this temporal population variability for tree species in 21 large forest plots and found much greater variability for higher latitude plots with fewer tree species. A fitted mechanistic model showed that among the forest plots, the net effect of temporal population variability on tree species coexistence was usually negative, but sometimes positive or negligible. Therefore, our results suggest that temporal variability in the abundances of species populations has no clear negative or positive contribution to the latitudinal gradient in tree species richness.