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: Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality. Method: The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software. Result: By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality. Conclusion: The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

Cellular immunity against rotavirus in children is incompletely understood. This review describes the current understanding of T-cell immunity to rotavirus in children. A systematic literature search was conducted in Embase, MEDLINE, Web of Science, and Global Health databases using a combination of “t-cell”, “rotavirus” and “child” keywords to extract data from relevant articles published from January 1973 to March 2020. Only seventeen articles were identified. Rotavirus-specific T-cell immunity in children develops and broadens reactivity with increasing age. Whilst occurring in close association with antibody responses, T-cell responses are more transient but can occur in absence of detectable antibody responses. Rotavirus-induced T-cell immunity is largely of the gut homing phenotype and predominantly involves Th1 and cytotoxic subsets that may be influenced by IL-10 Tregs. However, rotavirus-specific T-cell responses in children are generally of low frequencies in peripheral blood and are limited in comparison to other infecting pathogens and in adults. The available research reviewed here characterizes the T-cell immune response in children. There is a need for further research investigating the protective associations of rotavirus-specific T-cell responses against infection or vaccination and the standardization of rotavirus-specific T-cells assays in children.

Diarrhoea is a major contributor to childhood morbidity and mortality in developing countries, with diarrhoeagenic Escherichia coli being among the top aetiological agents. We sought to investigate the burden and describe the diarrhoeagenic E. coli pathotypes causing diarrhoea among children in peri-urban areas of Lusaka, Zambia. This was a facility-based surveillance study conducted over an 8-month period from 2020 to 2021. Stool samples were collected from children aged 0–3 years presenting with diarrhoea at five peri-urban health facilities in Lusaka. Stool samples were tested for diarrhoeagenic E. coli using the Novodiag bacterial GE+^® panel, a platform utilising real-time PCR and microarray technology to detect bacterial pathogens. Of the 590 samples tested, diarrhoeagenic E. coli were detected in 471 (76.1%). The top three pathogens were enteropathogenic E. coli 45.4% (n = 268), enteroaggregative E. coli 39.5% (n = 233), and enterotoxigenic E. coli 29.7% (n = 176). Our results revealed that 50.1% of the diarrhoeagenic E. coli positive samples comprised multiple pathotypes of varying virulence gene combinations. Our study demonstrates a high prevalence of diarrhoeagenic E. coli in childhood diarrhoea and the early exposure (<12 months) of children to enteric pathogens. This calls for the early implementation of preventive interventions for paediatric diarrhoea.

Emerging and re-emerging mosquito-borne viral diseases are a threat to global health. This systematic review aimed to investigate the available evidence of mosquito-borne viral pathogens reported in Zambia. A search of literature was conducted in PubMed and Google Scholar for articles published from 1 January 1930 to 30 June 2020 using a combination of keywords. Eight mosquito-borne viruses belonging to three families, Togaviridae, Flaviviridae and Phenuiviridae were reported. Three viruses (Chikungunya virus, Mayaro virus, Mwinilunga virus) were reported among the togaviruses whilst four (dengue virus, West Nile virus, yellow fever virus, Zika virus) were among the flavivirus and only one virus, Rift Valley fever virus, was reported in the Phenuiviridae family. The majority of these mosquito-borne viruses were reported in Western and North-Western provinces. Aedes and Culex species were the main mosquito-borne viral vectors reported. Farming, fishing, movement of people and rain patterns were among factors associated with mosquito-borne viral infection in Zambia. Better diagnostic methods, such as the use of molecular tools, to detect the viruses in potential vectors, humans, and animals, including the recognition of arboviral risk zones and how the viruses circulate, are important for improved surveillance and design of effective prevention and control measures.

Background: Impaired growth among infants remains one of the leading nutrition problems globally. In this study, we aimed to compare the growth trajectory rate and evaluate growth trajectory characteristics among children, who are HIV exposed uninfected (HEU) and HIV unexposed uninfected (HUU), under two years in Zambia. Method: Our study used data from the ROVAS II study (PACTR201804003096919), an open-label randomized control trial of two verses three doses of live, attenuated, oral Rotarix^TM administered 6 &10 weeks or at 6 &10 weeks plus an additional dose at 9 months of age, conducted at George clinic in Lusaka, Zambia. Anthropometric measurements (height and weight) were collected on all scheduled and unscheduled visits. We defined linear growth velocity as the rate of change in height and estimated linear growth velocity as the first derivative of the mixed effect model with fractional polynomial transformations and, thereafter, used the second derivative test to determine the peak height and age at peak heigh. Results: We included 212 infants in this study with median age 6 (IQR: 6–6) weeks of age. Of these 97 (45.3%) were female, 35 (16.4%) were stunted, and 59 (27.6%) were exposed to HIV at baseline. Growth velocity was consistently below the 3^rd percentile of the WHO linear growth standard for HEU and HUU children. The peak height and age at peak height among HEU children were 74.7 cm (95% CI = 73.9–75.5) and 15.5 months (95% CI = 14.7–16.3) respectively and those for HUU were 73 cm (95% CI = 72.1–74.0) and 15.6 months (95% CI = 14.5–16.6) respectively. Conclusion: We found no difference in growth trajectories between infants who are HEU and HUU. However, the data suggests that poor linear growth is universal and profound in this cohort and may have already occurred in utero.