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The Effect Of An Intervention On Nutrient Intake And Dietary Exposure To Mycotoxin Among Children In Kongwa District
(NM-AIST, 2025-12) Kayanda, Rosemary
Some common food staples are frequently contaminated with mycotoxins, particularly aflatoxin (AF) and fumonisin (FUM), which pose potential health risks, including impaired growth, immune suppression, liver cancer, and mortality. Few studies have focused on infant diets, even though children are more vulnerable to toxins due to their relatively small body weights and underdeveloped immune systems. This study was conducted between 2020 and 2022 in the Kongwa District, Tanzania, and aimed to assess and compare nutrient intake, adequacy, and dietary exposure to AF and FUM in a sub-sample of children aged 11 to 13 months enrolled in the Mycotoxin Mitigation Trial (MMT). Children enrolled in the MMT were randomly allocated in clusters to the intervention and Standard of Care (SoC) arms. Intervention households received aflatoxin safe complementary foods, and both intervention and SoC households received the same education on health infant feeding. The present study involved 282 children from 20 MMT clusters, equally balanced between study arms. Data collection involved structured multiple-pass 24-hour dietary recalls, with nutrient intake estimated using mixed-level regression models. Aflatoxin and fumonisin levels were measured using Enzyme Linked Immunosorbent Assay (ELISA) and High-Performance Liquid Chromatography (HPLC). Chi-square test was used to compare categories of AF contamination
in infant foods, and two sample t-test with equal variances was used for AF dietary exposure (μg/kg/kgbw/day) between arms. There were no significant differences in energy and lipid consumption between the two arms (p = 0.32 and p = 0.16, respectively). However, Intervention infants consumed more protein (p = 0.03). No significant difference was observed in iron, zinc, calcium, or vitamin A consumption between the arms. Maize, groundnuts, and blended flours fed to infants in the SoC households were frequently contaminated with high levels of AF. Forty-five percent of groundnut flour samples and 43% of blended flour samples from the SoC households had AF levels greater than 10 μg/kg, the legal limit in Tanzania, compared to 23% and 6%, respectively; in the intervention arm (p < 0.05). Likewise, AF exposure was significantly lower in the intervention arm for both blended flour (p = 0.04) and groundnuts (p = 0.01). There were no significant differences in FUM in blended flours between the arms (p > 0.05). A modest difference for FUM in maize flour was observed between arms (p = 0.05). The FUM exposure was significantly lower (p < 0.05) for both blended and maize flour in the intervention arm. Inadequate nutrients intake and reliance on mycotoxins prone complementary food ingredients potentially sets infants in Kongwa on a poor growth and health trajectory. This sub-study underscores the need to promote adequate complementary feeding practices, especially diet diversity, to improve nutrient intake and reduce the risk of mycotoxin exposure at a critical age when infants are introduced to family foods.
Contamination Status And Genetic Diversity Of Aflatoxigenic Fungi In Maize Based Cropping Systems In The Central Corridor Of Tanzania
(NM-AIST, 2026-01) Kwigizile, Owekisha
Aflatoxin contamination produced by Aspergillus species, remains a major threat to food security, human health and trade in Tanzania, particularly in maize-based farming systems. Maize as a staple crop is highly susceptible to contamination, yet comprehensive data on the prevalence, genetic diversity and distribution of aflatoxigenic fungi in the central corridor regions remain limited. A study was conducted between 2022 and 2024 aimed to assess the genetic diversity of aflatoxigenic fungi, levels of contamination and effect in soils and maize kernels in different cropping systems. A total of 126 maize and 126 soil samples were collected from seven districts of Kondoa, Chemba, Bahi, Kiteto, Babati, Urambo and Nzega, followed by fungal isolation, morphological characterization, and molecular identification using ITS sequencing and aflatoxin biosynthesis gene markers. Quantitative aflatoxin analysis was performed using High Performance Liquid Chromatography methods to evaluate contamination intensity. Results revealed significant variation in aflatoxin contamination across locations and
cropping systems at P≤ 0.001, with 60.3% of maize samples testing positive for Aflatoxin B1 and 50.8% of Total samples exceeding the East African Maximum Permissible Limits of 5 µg/kg and 10 µg/kg, respectively. However, all maize samples collected from Bahi district detected positive. Highly significance difference P≤0.001 observed in soil physiochemical parameters, and higher level of contaminations in monocropping particularly in Dodoma region. The dominant aflatoxigenic species identified from Aspergillus flavus, showing diverse genetic profiles and varying aflatoxin biosynthetic gene expressions. Genetic analyses demonstrated considerable diversity among isolates, suggesting multiple strains co-exist within maize cropping systems, with potential implications for aflatoxin management. The study further identified substantial intraspecific diversity with three aflatoxigenic MBD 127-1, MBD 225-5 and SBD 127-6, four strain potential for bio control SKD 74-4, SKD 79-1, SKD 150-2 and SKD 50-9 and two strains SBD 116-5 and SBD 234 with non-specific binding. These findings provide important information for developing integrated aflatoxin management interventions including promoting sustainable cropping systems, biocontrol applications, frequent
surveillance and targeted awareness for farmers. Overall, this research contributes to the understanding of aflatoxin risk in maize systems and supports evidence-based interventions to enhance food safety and crop security.
Development of deep learning model using satellite images to enhance forest-fire occurrence prediction linked with seasonality at Mount Kilimanjaro, Tanzania
(NM-AIST, 2026-06) Mambile, Cesilia
Forest fires represent a growing environmental and socio-economic challenge, particularly in ecologically sensitive regions such as Mount Kilimanjaro in Tanzania. Accurate prediction of forest fire outbreaks is critical for timely response, resource allocation, and environmental protection. Traditional models and statistical techniques have been in use for decades but often struggle with capturing spatial-temporal complexity and integrating important human activity data. While deep learning approaches show potential, they encounter challenges including data imbalance, noise, and limited validation against real-world events. This study aimed to develop a deep learning-based fire prediction model incorporating seasonality and human activities specific to the Mount Kilimanjaro area. A ConvLSTM model was trained using integrated data sources, including Sentinel-2 satellite imagery, meteorological patterns, vegetation indices (NDVI, NDWI, NBR), and anthropogenic features such as beekeeping and tourism activities. The validation process employed a three-tier framework: Statistical evaluation using classification metrics, historical validation using VIIRS fire records, and expert validation from fire management professionals. The model achieved high performance, with an accuracy of 97.65%, an AUROC of 0.9512, a precision of 98.55%, and a recall of 92.85%. Spatial and temporal analyses confirmed that the model effectively predicted fire-prone zones and seasonal trends, with a spatial alignment of 93.2% to observed fire events. Expert review further affirmed the model’s practical applicability: 87% of experts acknowledged its accuracy, and 82% observed alignment with historical patterns. However, concerns were raised regarding data resolution, the need for real-time deployment capabilities, and local capacity for model use. These concerns were beyond the scope of the current study and are identified as important areas for future exploration. The findings demonstrate the model’s strength in delivering interpretable, location-specific fire risk predictions while addressing common limitations in existing systems. By merging scientific modeling with local data and expert feedback, the study offers a scalable, AI-powered framework for fire early warning systems in climate-vulnerable landscapes. Future research should explore integrating real-time meteorological feeds, uncertainty quantification, and adaptive features for broader deployment across different ecological regions.
Agent-based modelling to predict water rationing and allocation enhanced by machine learning techniques: case study of the pangani basin
(NM-AIST, 2026-05) Lyuba, Matimbila
Water scarcity and inequitable distribution remain major challenges in many river basins, including the Pangani Basin in Tanzania. Previous studies have applied machine learning techniques to predict water consumption and Agent-Based Modelling (ABM) to support water allocation. However, the machine learning approaches used were often not validated for robustness or evaluated for generalization to unseen environments. Similarly, ABM approaches used relies on generated datasets, which introduce uncertainties when models are deployed in real-world settings. This study developed an ABM for the Pangani Basin to simulate water allocation, rationing, and reallocation in response to these challenges. Water user characteristics were identified through validated multi-model characterization using K-means, Hierarchical Agglomerative and Fuzzy C-means algorithms; and further enriched through association rule mining with minimum support ranging from 0.5% to 5.0% and confidence levels above 50%. The identified characteristics were then modelled as agents in NetLogo to simulate socioeconomic activities and predict their water abstraction through allocation, rationing, and reallocation. The dataset was analyzed using Interquartile Range (IQR) with capping, mean imputation, Principal Component Analysis (PCA), standardization, and Hopkins test. Cluster validation was conducted using the Davies-Bouldin Index, Silhouette Score, Calinski-Harabasz Index, and Dunn Index, while clustered data were evaluated using logistic regression model to determine prediction accuracy. The ABM was validated using both a paired t-test and expert evaluation through a developed web tool. The study identified four distinct clusters of water users with varying behaviors influencing water abstraction. The ABM simulation revealed that predicted mean abstraction values generally decreased as the number of users increased, with predicted values remaining below actual abstractions across the four clusters. Paired t-test results showed that differences between predicted and actual mean values were not statistically significant. Expert validation further indicated that the ABM predictions reflected field realities and could generalize to other basins with different geographical settings. The findings demonstrate improved allocation efficiency, reduced user conflict, and enhanced system resilience under climate variability and rising demand. Overall, the study shows that integrating ABM with machine learning provides a robust, adaptive, and data-driven framework for optimizing water rationing and allocation in the river basins.
Mathematical modelling of cryptosporidiosis transmission dynamics and control in humans and cattle
(NM-AIST, 2025-12) Luhanda, Faraja
Cryptosporidiosis is a globally significant zoonotic disease caused by Cryptosporidium, posing a substantial concern for public health and veterinary medicine. Despite the significant contribution of cattle and human immune status in shaping the transmission dynamics and control of cryptosporidiosis, there is limited evidence of mathematical models that concurrently incorporate this aspect. This study aimed to investigate the transmission dynamics and control of cryptosporidiosis in humans and cattle using mathematical modelling. Non-linear ordinary differential equations were used to formulate deterministic models, from which the assumptions for the corresponding continuous-time Markov chain (CTMC) stochastic models were derived. Using the next-generation matrix technique, the basic and effective reproduction numbers were computed. Latin Hypercube Sampling was applied to compute Partial Rank Correlation Coefficients, allowing for a global sensitivity analysis of the parameters influencing disease transmission dynamics. The existence of disease-free and endemic equilibria was well established, and their stability was examined using the Routh-Hurwitz criterion and the Lyapunov function method, respectively. The multitype branching process was employed to compute the likelihood of disease extinction or outbreak in the CTMC stochastic models. Lastly, the optimal control theory was applied to determine the most effective strategy for controlling and preventing cryptosporidiosis in humans and cattle. The results revealed that cattle significantly influence the dynamics of the disease. Immunocompromised humans contribute more substantially to the dynamics of the disease than immunocompetent humans. The persistence of cryptosporidiosis is influenced by increased disease transmission rates and higher shedding rates of Cryptosporidium oocysts into the environment by infectious cattle and humans. Cryptosporidiosis is more likely to extinct if it emerges from infected immunocompetent human compartments than from infected immunocompromised compartments. Contrarily, a disease major outbreak is probable if it originates from either infected cattle compartments or Cryptosporidium oocysts in the environment. The optimal control model findings suggest that the best strategy for controlling and preventing cryptosporidiosis is the simultaneous implementation of all six control efforts, grouped into preventions, treatments, and environmental decontamination. Thus, efforts to prevent and control cryptosporidiosis should concentrate on eliminating Cryptosporidium oocysts from the environment, treating infectious cattle and humans, maintaining proper sanitation practices and sound cattle farm management and hygiene practices.