Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.

Background: Inappropriate antimicrobial usage is a key driver of antimicrobial resistance (AMR). Low- and middle-income countries (LMICs) are disproportionately burdened by AMR and young children are especially vulnerable to infections with AMR-bearing pathogens. The impact of antibiotics on the microbiome, selection, persistence, and horizontal spread of AMR genes is insufficiently characterized and understood in children in LMICs. This systematic review aims to collate and evaluate the available literature describing the impact of antibiotics on the infant gut microbiome and resistome in LMICs. Methods and findings: In this systematic review, we searched the online databases MEDLINE (1946 to 28 January 2023), EMBASE (1947 to 28 January 2023), SCOPUS (1945 to 29 January 2023), WHO Global Index Medicus (searched up to 29 January 2023), and SciELO (searched up to 29 January 2023). A total of 4,369 articles were retrieved across the databases. Duplicates were removed resulting in 2,748 unique articles. Screening by title and abstract excluded 2,666 articles, 92 articles were assessed based on the full text, and 10 studies met the eligibility criteria that included human studies conducted in LMICs among children below the age of 2 that reported gut microbiome composition and/or resistome composition (AMR genes) following antibiotic usage. The included studies were all randomized control trials (RCTs) and were assessed for risk of bias using the Cochrane risk-of-bias for randomized studies tool. Overall, antibiotics reduced gut microbiome diversity and increased antibiotic-specific resistance gene abundance in antibiotic treatment groups as compared to the placebo. The most widely tested antibiotic was azithromycin that decreased the diversity of the gut microbiome and significantly increased macrolide resistance as early as 5 days posttreatment. A major limitation of this study was paucity of available studies that cover this subject area. Specifically, the range of antibiotics assessed did not include the most commonly used antibiotics in LMIC populations. Conclusion: In this study, we observed that antibiotics significantly reduce the diversity and alter the composition of the infant gut microbiome in LMICs, while concomitantly selecting for resistance genes whose persistence can last for months following treatment. Considerable heterogeneity in study methodology, timing and duration of sampling, and sequencing methodology in currently available research limit insights into antibiotic impacts on the microbiome and resistome in children in LMICs. More research is urgently needed to fill this gap in order to better understand whether antibiotic-driven reductions in microbiome diversity and selection of AMR genes place LMIC children at risk for adverse health outcomes, including infections with AMR-bearing pathogens.

Introduction: Shigellosis, is a leading cause of moderate-to-severe diarrhoea and related mortality in young children in low and middle income countries (LMICs). Knowledge on naturally acquired immunity can support the development of Shigella candidate vaccines mostly needed in LMICs. We aimed to quantify Shigella-specific antibodies of maternal origin and those naturally acquired in Zambian infants. Methods: Plasma samples collected from infants at age 6, 14 and 52-weeks were tested for Shigella (S. sonnei and S. flexneri 2a) lipopolysaccharide (LPS) antigen specific immunoglobulin G (IgG) and A (IgA) by enzyme-linked immunosorbent assay. Results: At 6 weeks infant age, the IgG geometric mean titres (GMT) against S. sonnei (N = 159) and S. flexneri 2a (N = 135) LPS were 311 (95% CI 259–372) and 446 (95% CI 343–580) respectively. By 14 weeks, a decline in IgG GMT was observed for both S. sonnei to 104 (95% CI 88–124), and S. flexneri 2a to 183 (95% CI 147–230). Both S. sonnei and S. flexneri 2a specific IgG GMT continued to decrease by 52 weeks infant age when compared to 6 weeks. In 27% and 8% of infants a significant rise in titre (4 fold and greater) against S. flexneri 2a and S. sonnei LPS, respectively, was detected between the ages of 14 and 52 weeks. IgA levels against both species LPS were very low at 6 and 14 weeks and raised significantly against S. flexneri 2a and S. sonnei LPS in 29% and 10% of the infants, respectively. Conclusion: In our setting, transplacental IgG anti-Shigella LPS is present at high levels in early infancy, and begins to decrease by age 14 weeks. Our results are consistent with early exposure to Shigella and indicate naturally acquired IgG and IgA antibodies to S. flexneri 2a and S. sonnei LPS in part of infants between 14 and 52 weeks of age. These results suggest that a potential timing of vaccination would be after 14 and before 52 weeks of age to ensure early infant protection against shigellosis.

The re-emergence of vector borne diseases affecting millions of people in recent years has drawn attention to arboviruses globally. Here, we report on the sero-prevalence of chikungunya virus (CHIKV), dengue virus (DENV), mayaro virus (MAYV) and zika virus (ZIKV) in a swamp community in Zambia. We collected blood and saliva samples from residents of Lukanga swamps in 2016 during a mass-cholera vaccination campaign. Over 10,000 residents were vaccinated with two doses of Shanchol^™ during this period. The biological samples were collected prior to vaccination (baseline) and at specified time points after vaccination. We tested a total of 214 baseline stored serum samples for IgG antibodies against NS1 of DENV and ZIKV and E2 of CHIKV and MAYV on ELISA. We defined sero-prevalence as the proportion of participants with optical density (OD) values above a defined cut-off value, determined using a finite mixture model. Of the 214 participants, 79 (36.9%; 95% CI 30.5–43.8) were sero-positive for Chikungunya; 23 (10.8%; 95% CI 6.9–15.7) for Zika, 36 (16.8%; 95% CI 12.1–22.5) for Dengue and 42 (19.6%; 95% CI 14.5–25.6) for Mayaro. Older participants were more likely to have Zika virus whilst those involved with fishing activities were at greater risk of contracting Chikungunya virus. Among all the antigens tested, we also found that Chikungunya saliva antibody titres correlated with baseline serum titres (Spearman’s correlation coefficient = 0.222; p = 0.03). Arbovirus transmission is occurring in Zambia. This requires proper screening tools as well as surveillance data to accurately report on disease burden in Zambia.

The Sanitation and Hygiene Applied Research for Equity (SHARE) Research Programme consortium is a programme funded by the United Kingdom Department for International Development (DFID) that aims to contribute to achieving universal access to effective, sustainable, and equitable sanitation and hygiene worldwide. The capacity development component is an important pillar for this programme and different strategies were designed and implemented during the various phases of SHARE. This paper describes and reflects on the capacity-building strategies of this large multi-country research consortium, identifying lessons learnt and proposing recommendations for future global health research programmes. In the first phase, the strategy focused on increasing the capacity of individuals and institutions from low- and middle-income countries in conducting their own research. SHARE supported six PhD students and 25 MSc students, and organised a wide range of training events for different stakeholders. SHARE peer-reviewed all proposals that researchers submitted through several rounds of funding and offered external peer-review for all the reports produced under the partner’s research platforms. In the second phase, the aim was to support capacity development of a smaller number of African research institutions to move towards their independent sustainability, with a stronger focus on early and mid-career scientists within these institutions. In each institution, a Research Fellow was supported and a specific capacity development plan was jointly developed. Strategies that yielded success were learning by doing (supporting institutions and postgraduate students on sanitation and hygiene research), providing fellowships to appoint mid-career scientists to support personal and institutional development, and supporting tailored capacity-building plans. The key lessons learnt were that research capacity-building programmes need to be driven by local initiatives tailored with support from partners. We recommend that future programmes seeking to strengthen research capacity should consider targeted strategies for individuals at early, middle and later career stages and should be sensitive to other institutional operations to support both the research and management capacities.

The circulation of both West Nile Virus (WNV) and Chikungunya Virus (CHIKV) in humans and animals, coupled with a favorable tropical climate for mosquito proliferation in Zambia, call for the need for a better understanding of the ecological and epidemiological factors that govern their transmission dynamics in this region. This study aimed to examine the contribution of climatic variables to the distribution of Culex and Aedes mosquito species, which are potential vectors of CHIKV, WNV, and other arboviruses of public-health concern. Mosquitoes collected from Lusaka as well as from the Central and Southern provinces of Zambia were sorted by species within the Culex and Aedes genera, both of which have the potential to transmit viruses. The MaxEnt software was utilized to predict areas at risk of WNV and CHIKV based on the occurrence data on mosquitoes and environmental covariates. The model predictions show three distinct spatial hotspots, ranging from the high-probability regions to the medium- and low-probability regions. Regions along Lake Kariba, the Kafue River, and the Luangwa Rivers, as well as along the Mumbwa, Chibombo, Kapiri Mposhi, and Mpika districts were predicted to be suitable habitats for both species. The rainfall and temperature extremes were the most contributing variables in the predictive models.