Decoding Infection and Transmission最新文献

{"title":"Construction of a Yersinia pestis BAC library and evidence that a 33 kb prophage fragment is dispensable for virulence","authors":"Bin Ni,&nbsp;Qiang Zhang,&nbsp;Hailian Wu,&nbsp;Yongshun Wang,&nbsp;Xiaoyan Yang,&nbsp;Wentao Guo,&nbsp;Youquan Xin,&nbsp;Yiquan Zhang,&nbsp;Qingwen Zhang,&nbsp;Haisheng Wu","doi":"10.1016/j.dcit.2026.100088","DOIUrl":"10.1016/j.dcit.2026.100088","url":null,"abstract":"<div><h3>Objective</h3><div>To determine whether the 33 kb prophage-like DNA fragment (YPO2095-YPO2135), which is present in virulent <em>Yersinia pestis</em> biovars but absent in the human-avirulent microtus biovar, is required for virulence in a larger mammal host, and to construct a BAC library as a genomic resource for functional genomics in this pathogen</div></div><div><h3>Methods</h3><div>A BAC library was constructed from high-molecular-weight genomic DNA of the virulent <em>Y. pestis</em> antiqua strain 910706 using <em>Hind</em> III partial digestion and the CopyControl pCC1BAC vector. The library was characterized by insert size analysis, stability testing, and BAC-end sequencing. A 121 kb BAC clone containing the entire 33 kb prophage fragment was transferred into the avirulent microtus strain 91001 to generate a large genomic fragment complementary strain (LGFCS). Prophage gene expression was verified by RT-PCR and Western blot. Virulence was assessed in guinea pigs via intraperitoneal challenge with ∼10⁸ CFU, with survival monitored for 14 days.</div></div><div><h3>Results</h3><div>The BAC library comprised 2624 clones with an average insert size of 105 kb, providing approximately 7.4- to 8.3-fold coverage of the <em>Y. pestis</em> genome. BAC-end sequences showed &gt;97% homology to reference genomes of strains 91001, CO92, and KIM. The LGFCS successfully transcribed and expressed prophage genes (YPO2131 and YPO2134). Guinea pig infection assays revealed no significant difference in survival between groups infected with strain 91001 and LGFCS, indicating that the prophage fragment did not restore virulence.</div></div><div><h3>Conclusion</h3><div>The 33 kb prophage-like fragment, although conserved in virulent biovars, is not individually sufficient to confer virulence in a guinea pig model. The BAC library constructed in this study serves as a valuable resource for future genetic mapping, comparative genomics, and functional discovery in <em>Y. pestis</em>.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of artificial intelligence and machine learning in the prediction of infectious diseases: A comprehensive review of bacterial, viral, and fungal pathogens","authors":"Akmal Zubair,&nbsp;Ghousia Sayeed,&nbsp;Mohammad Saquib Ashraf,&nbsp;Gunesh Kumar,&nbsp;Abdul Haseeb,&nbsp;Yasir Waheed","doi":"10.1016/j.dcit.2026.100089","DOIUrl":"10.1016/j.dcit.2026.100089","url":null,"abstract":"<div><div>Infectious diseases are still a significant cause of morbidity and mortality in the world and are very costly in terms of health and socio-economic burden. Innovative surveillance, diagnostic and predictive strategies are needed, given the increasing risk of antimicrobial resistance (AMR), the emergence of new pandemics, and the re-emergence of neglected pathogens. Artificial Intelligence and Machine Learning are revolutionizing technologies that can harness actionable insights from complex biological, clinical, genomic, imaging and epidemiological datasets. The existing Artificial Intelligence and Machine Learning applications were classified into three core pathogen areas of bacterial, viral, and fungal infectious diseases. Several studies reported that artificial intelligence and machine learning models achieved superior predictive performance compared to conventional approaches within their respective datasets and evaluation settings. Supervised learning approaches, such as random forest and gradient boosting algorithms, performed very well in many bacterial infections, potentially with area-under-the-curve values up to &gt;0.95 for predicting AMR from Whole Genome Sequencing data and predicting sepsis in patients. In the field of viral diseases, deep learning architectures such as long short-term memory, transformers, and graph neural networks were used to facilitate swift predictions of outbreaks, real-time surveillance of mutations, and support for vaccine development. In fungal infections, microscopic images were used to obtain classification accuracy &gt;99% for species-level identification using graph neural networks, and early detection of invasive candidemia in intensive care units was improved.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148950967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trend, pattern, and outcome of meningitis cases in Northwest, Nigeria: A 10 Year review of case-based surveillance data","authors":"Usman Muhammad Ibrahim ,&nbsp;Boateng Kofi ,&nbsp;Salisu Muazu Babura ,&nbsp;Sunday Audu ,&nbsp;Abba Ahmed Danzomo ,&nbsp;Salihu Abdullahi Ahmed ,&nbsp;Faruk Abdullahi Namadi ,&nbsp;Musa Mahdi Wade ,&nbsp;Saidu Yusuf ,&nbsp;Nuruddeen Muhammad ,&nbsp;Usman L. Shehu ,&nbsp;Rayyan Muhammad Garba ,&nbsp;Kabiru Abdulsalam ,&nbsp;Abubakar Mayana Sanusi ,&nbsp;Abubakar Mohammed Jibo","doi":"10.1016/j.dcit.2026.100077","DOIUrl":"10.1016/j.dcit.2026.100077","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to describe the epidemiological pattern of Cerebrospinal meningititis (CSM) cases, and related mortality in Jigawa State, Northwest Nigeria, over a 10-year period.</div></div><div><h3>Methods</h3><div>We conducted a retrospective descriptive cross-sectional study of 1943 line-listed CSM cases spanning 2015–2024, in line with the Integrated Disease Surveillance and Response (IDSR) guidelines. Data were analyzed using SPSS version 22 at a 5 % level of significance.</div></div><div><h3>Results</h3><div>Patient ages ranged from 1 to 70 years, with a median of 12 years (IQR 6–17). Adolescents aged 10–19 years constituted the largest proportion of cases (39.7 %). The highest burden of CSM occurred in 2023 (71.6 % of cases). Overall, case fatality was 8 %. Unlike previous years, when outbreaks peaked in the early weeks, the 2023 outbreak extended from epidemiological week 50 through week 1 of 2024. The overall attack rate at the state level was 25.8 per 100,000 population. Mortality was significantly higher among cases not admitted for care; admitted patients were 50 % less likely to die (aOR = 0.5; 95 % CI: 0.4–0.8). Laboratory results from the National Reference Laboratory (NRL) showed 59.4 % positivity, with serogroup C (<em>Neisseria meningitidis</em> type C) accounting for 57.1 % of confirmed cases. All positive cultures correlated with PCR positivity (100 %, p &lt; 0.001), and nearly all (99.7 %, p &lt; 0.001) confirmed <em>N. meningitidis</em> type C. Other detected pathogens included <em>N. meningitidis</em> X, <em>Streptococcus pneumoniae</em>, and non-groupable <em>Neisseria</em>.</div></div><div><h3>Conclusions</h3><div>Recurrent CSM outbreaks in Jigawa State are linked with considerable illness and death, primarily affecting individuals aged 1–29 years, most of whom lacked prior vaccination against any meningococcal serotype. The predominant strains are not covered by the current national immunization program. There is an urgent need for government and partners to introduce the Multivalent Meningococcal Conjugate Vaccine (MMCV), in line with WHO recommendations.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and big data for decoding infectious disease transmission dynamics and outbreak prediction","authors":"Bellarmin Michael,&nbsp;Harish Veerasami,&nbsp;Nandhini Jayaprakash","doi":"10.1016/j.dcit.2026.100079","DOIUrl":"10.1016/j.dcit.2026.100079","url":null,"abstract":"<div><div>Understanding and predicting infectious disease transmission remains a central challenge in global health due to the complex interplay of pathogen evolution, host biology, environmental conditions, and human behavior. Traditional epidemiological models, while foundational, are often limited in their ability to integrate heterogeneous, high-dimensional data or capture nonlinear, cross-scale dynamics that govern real-world outbreaks. Recent advances in artificial intelligence and big data analytics are fundamentally reshaping this landscape. This review provides a comprehensive overview of recent progress in Artificial Intelligence (AI)-enabled infectious disease research, highlighting how Machine learning (ML), deep learning and network-based approaches integrate diverse data streams including epidemiological surveillance, pathogen genomics, multi-omics profiles, environmental variables, and human mobility to decode transmission dynamics across molecular, ecological, and population scales. Case studies spanning viral, vector-borne, bacterial and zoonotic pathogens illustrate how AI models enhance outbreak forecasting, identify high-risk populations, and link molecular variation with transmissibility and disease severity. Beyond predictive performance, review discuss about AI in driving a conceptual shift from parameter driven epidemiology toward mechanism-aware, multi-scale modeling of infection and spread.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a hybridized artificial neural network, fuzzy logic and particle swarm optimization model for improved diagnosis of tuberculosis","authors":"Benjamin Aribisala ,&nbsp;Oluwatosin Ogunbodede ,&nbsp;Babajide Adeyemi ,&nbsp;Oluwatoyin Enikuomehin","doi":"10.1016/j.dcit.2026.100083","DOIUrl":"10.1016/j.dcit.2026.100083","url":null,"abstract":"<div><h3>Background</h3><div>Tuberculosis (TB), a highly infectious disease caused by <em>Mycobacterium tuberculosis</em>, remains a leading cause of mortality, particularly in resource-limited regions. Limitations in sensitivity, specificity, and diagnostic delays often challenge traditional diagnostic methods. To address these, we propose a hybrid diagnostic model based on Particle Swarm Optimization- Aided Neuro-Fuzzy Model (PSOFUZ) which integrates fuzzy logic, artificial neural networks (ANN), and particle swarm optimization (PSO).</div></div><div><h3>Materials and methods</h3><div>The dataset used for the study was obtained from the Center for Disease Control and Prevention. The dataset contains 1200 records, of which 850 were TB-positive and 350 were TB-negative. The dataset included relevant clinical and demographic attributes such as smoking status, drug use, HIV status, body temperature, blood pressure, and prior history of tuberculosis. These inputs were fuzzified with Gaussian membership functions, which transform clinical data into fuzzy values that capture uncertainty and imprecision. The fuzzified data was then fed into an ANN, which analyzes patterns and detects potential TB symptoms. PSO was used to optimize both the ANN's connection weights and the parameters of the fuzzy membership functions</div></div><div><h3>Result</h3><div>The model gave an accuracy of 91.2 %, sensitivity of 93.4%, and specificity of 89.1%, outperforming conventional and other computational models (Neuro-Fuzzy: 88.9%, 83.5% and 86.2% of sensitivity, specificity and accuracy respectively). Our results hence show that PSOFUZ outperformed the baseline models in terms of accuracy, sensitivity, and specificity, as the experimental data show. When compared to non-optimized or single-method techniques, these improvements show that the joint optimization of neural and fuzzy parameters adds to improved generalization and more reliable tuberculosis diagnosis.</div></div><div><h3>Conclusion</h3><div>This study demonstrated the potential of intelligent hybrid models to enhance TB diagnosis, especially in settings where systems blend fuzzy logic interpretability with neural network adaptive learning capabilities, resulting in increased diagnostic accuracy and resilience with diagnostic resource constraints.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated review of ecology, epidemiology, and public health burden of anthrax in Africa","authors":"Adelekan Oluseyi Okunlade ,&nbsp;Ridwan Olamilekan Adesola ,&nbsp;Ibrahim Idris ,&nbsp;Favour Akinfemi Ajibade","doi":"10.1016/j.dcit.2026.100082","DOIUrl":"10.1016/j.dcit.2026.100082","url":null,"abstract":"<div><div>The emergence and re-emergence of anthrax across Africa remain a persistent public health and veterinary concern. In recent years, several African countries have experienced recurrent outbreaks, underscoring the need for sustained and coordinated interventions by national and international stakeholders. This review synthesizes past and current knowledge on the ecology, epidemiology, and public health challenges associated with anthrax in Africa. Anthrax is a zoonotic disease caused by <em>Bacillus anthracis</em>, affecting both humans and animals, with transmission primarily occurring through direct contact with infected animals or animal products, as well as inhalation of bacterial spores. Recent outbreaks reported in 2025 in Democratic Republic of Congo, Nigeria, Uganda, and Ethiopia highlight the continued circulation of the pathogen across diverse ecological settings. Notably, wildlife losses exceeding 50 hippopotamuses and buffalo were reported in the Democratic Republic of Congo, while Uganda documented 1165 human anthrax cases between 2017 and 2024. Key challenges identified include weak disease surveillance systems, reduced external funding—particularly the withdrawal of support from the United States Agency for International Development—limited vaccine coverage, underreporting, and inadequate laboratory diagnostic capacity. Addressing these constraints is critical to reducing the burden of anthrax and safeguarding livelihoods across the continent. Anthrax remains a disease of significant public health and economic importance in Africa, and its continued re-emergence emphasizes the urgent need for integrated prevention and control strategies. The adoption of a One Health approach, encompassing human, animal, and environmental health sectors, is essential for effective and sustainable anthrax control in Africa.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public education as a structural determinant of integrated mosquito management: A narrative synthesis of behavioral, institutional, and contextual evidence from Nigeria","authors":"Morenike Olufunmilayo Akpo ,&nbsp;Great Iruoghene Edo ,&nbsp;Tayser Sumer Gaaz ,&nbsp;Joshua Othuke Orogu ,&nbsp;Raghda S. Makia ,&nbsp;Ephraim Evi Alex Oghroro ,&nbsp;Joseph Oghenewogaga Owheruo ,&nbsp;Emad Yousif ,&nbsp;Agatha Ngukuran Jikah ,&nbsp;Ufuoma Augustina Igbuku ,&nbsp;Arthur Efeoghene Athan Essaghah ,&nbsp;Ibiyinka Agboola Fuwape ,&nbsp;Maryam Rabiu Aliyu ,&nbsp;Huzaifa Umar ,&nbsp;Dina S. Ahmed","doi":"10.1016/j.dcit.2026.100086","DOIUrl":"10.1016/j.dcit.2026.100086","url":null,"abstract":"<div><h3>Background</h3><div>Public education represents a critical yet under-synthesized component of Integrated Mosquito Management (IMM), particularly in low- and middle-income countries where mosquito-borne diseases pose substantial public health burdens. This narrative review systematically examines the role of public education within IMM frameworks in Nigeria, addressing a significant gap in the existing literature.</div></div><div><h3>Methods</h3><div>A comprehensive narrative review was conducted following PRISMA guidelines. Literature searches were performed across PubMed, Scopus, Web of Science, African Journals Online, and Google Scholar databases for publications between 2000 and 2025. Studies examining educational interventions within mosquito control programs in Nigeria and comparable settings were synthesized thematically.</div></div><div><h3>Results</h3><div>The review identified 87 eligible studies, demonstrating that public education significantly enhances knowledge, attitudes, and practices related to mosquito control. Educational interventions were most effective when integrated with environmental management, surveillance systems, and community mobilization approaches. Key outcomes included improved source reduction behaviors, enhanced community surveillance participation, and increased utilization of preventive measures. However, effectiveness varied substantially across urban, peri-urban, and rural contexts, influenced by literacy levels, gender dynamics, and infrastructural factors.</div></div><div><h3>Conclusions</h3><div>Public education constitutes an essential structural component of IMM rather than an ancillary activity. For Nigeria, institutionalizing education within national malaria elimination frameworks, developing standardized evaluation metrics, and ensuring context-specific adaptation are critical priorities. Future research should prioritize longitudinal designs, standardized outcome measures, and mixed-methods approaches to strengthen the evidence base for educational interventions in vector control.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic advances in Lassa fever: From ribavirin to mechanism-based antivirals and monoclonal antibody combinations","authors":"Yogitha Balasubramani,&nbsp;Gayathri Krishnakumar,&nbsp;Karthikeyan Elumalai","doi":"10.1016/j.dcit.2026.100087","DOIUrl":"10.1016/j.dcit.2026.100087","url":null,"abstract":"<div><div>Lassa fever is a zoonotic viral haemorrhagic disease that is a public health problem in West Africa, and has become a concern for global health security. The clinical efficacy of ribavirin is still unknown, although the drug has been used for over 40 years; evidence is mainly from observational studies, and efficacy seems to be highly dependent on its early use. Recent developments in <em>Lassa virus</em> (LASV) biology, structural virology, and translational pharmacology have led to rapid progress in the development of mechanism-based therapeutic agents targeting different stages of the viral life cycle. This review critically reassesses the role of ribavirin as a historical comparator, the ongoing need for optimized supportive care, and the evaluation of emerging candidates, particularly favipiravir, LHF-535, and 4′-fluorouridine (4′-FlU). We also discuss the emerging data for broadly neutralizing monoclonal antibody (mAb) therapies that have shown great promise in nonhuman primate models for protection from the onset of viremia and clinical disease. Special emphasis is placed on rational combination strategies that combine both anti-viral and monoclonal antibodies to maximize antiviral activity, increase the therapeutic window, and minimize the likelihood of treatment failure. We also address frameworks for stage-specific treatment, therapeutic issues in special populations, and barriers to translation in endemic settings. Overall, current evidence suggests a shift from empirical antiviral therapy to mechanism-driven therapeutic strategies according to the stage. Success will require an adaptive clinical trial platform, strong pharmacological evaluation of new drugs within varied patient demographics, and reinforcement of a healthcare system infrastructure equipped to provide advanced therapeutics in resource-limited settings.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge, attitudes and management practices related to urogenital schistosomiasis in Ebonyi State, Nigeria","authors":"Chioma Ogochukwu Anorue,&nbsp;Ikechukwu Oliver Onyali,&nbsp;Victor Oluebube Ozoekwo,&nbsp;Olalekan John Okesanya","doi":"10.1016/j.dcit.2026.100093","DOIUrl":"10.1016/j.dcit.2026.100093","url":null,"abstract":"<div><h3>Background</h3><div>Urogenital schistosomiasis remains a significant public health challenge in Nigeria, particularly in rural communities with high freshwater contact. This study assessed the knowledge, attitudes, and management practices (KAP) related to urogenital schistosomiasis among residents of three endemic Local Government Areas (LGAs) in Ebonyi State.</div></div><div><h3>Methods</h3><div>A descriptive cross-sectional study was conducted among 800 respondents selected through a multistage sampling technique. Data were collected using a structured, pretested questionnaire and analysed using frequencies and percentages. For questions permitting more than one response, each option was treated as a separate binary indicator and summarised as a percentage of eligible respondents.</div></div><div><h3>Results</h3><div>Of the 800 respondents, 578 (72.3%) reported awareness of urogenital schistosomiasis. Among respondents reporting awareness, health clinics or hospitals were the most frequently reported source of information (302/578; 52.2%). Swimming or bathing in infested water was selected by 260 respondents (32.5%) as a transmission route, while 213 (26.6%) identified haematuria as a symptom. Avoiding swimming in rivers was selected by 310 (38.8%) as a preventive measure. Among the 654 respondents with a recorded answer about preventability, 349 (53.4%) believed that the disease could not be prevented. Health-centre or hospital care was reported by 270 respondents (33.8%), while 226 (28.3%) reported using patent medicine stores. Multiple-response findings were interpreted descriptively<strong>.</strong></div></div><div><h3>Conclusion</h3><div>The findings demonstrate a gap between general awareness and accurate knowledge of urogenital schistosomiasis. Community-focused education, improved access to safe water and sanitation, and strengthened mass drug administration programmes are needed.</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the impact of climate variability on malaria transmission dynamics across the different ecological zones in Taraba State, Nigeria: A machine learning approach","authors":"Dupsy Akoma ,&nbsp;Yusuf Abdulakeem ,&nbsp;Ganiyat Eshikhena ,&nbsp;Obonganwan Akpabio ,&nbsp;Atinuke Arthur ,&nbsp;Zainab Abdullahi ,&nbsp;Nicholas Oyeh ,&nbsp;Abayomi Amire ,&nbsp;Christian Abba ,&nbsp;Charles Obi ,&nbsp;Gbenga Adegbite ,&nbsp;Adaeze Eche-George ,&nbsp;Ifeoma Ezenyi ,&nbsp;Ezra Gayawan ,&nbsp;Eze Nelson ,&nbsp;Chijioke Kaduru","doi":"10.1016/j.dcit.2026.100084","DOIUrl":"10.1016/j.dcit.2026.100084","url":null,"abstract":"<div><h3>Background</h3><div>Malaria transmission in Taraba State, Nigeria, remains a persistent public health challenge, shaped by climatic and ecological factors. Although the influence of weather patterns on malaria incidence has been widely studied, limited research has incorporated ecological zoning into predictive modelling frameworks.</div></div><div><h3>Methods</h3><div>We analysed malaria incidence data (2021–2023) from the District Health Information Software (DHIS2), alongside rainfall and temperature data from the Nigerian Meteorological Agency (NiMet). Data were cleaned, standardised, and encoded to include ecological and local government area (LGA) variables. Supervised machine learning models, including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost), were trained and validated using four-fold cross-validation. Model performance was assessed with mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Shapley Additive Explanations (SHAP) were applied to determine feature importance. Exploratory analyses employed one-way ANOVA to compare malaria incidence across ecological zones and Pearson correlation to test associations with climatic variables.</div></div><div><h3>Results</h3><div>Exploratory analysis revealed significant heterogeneity in malaria incidence across ecological zones (ANOVA: <em>F</em> = 5.21, <em>p</em> &lt; 0.01), with the Montane Forest reporting the highest burden, largely driven by Sardauna and Bali LGAs. Rainfall showed a positive correlation with malaria incidence (<em>r</em> = 0.42, <em>p</em> &lt; 0.01), while temperature was negatively correlated (<em>r</em> = −0.36, <em>p</em> &lt; 0.05). Across all models, ecological zone consistently emerged as the strongest predictor of malaria incidence, outweighing climatic factors. Random Forest achieved the best predictive accuracy (MAE = 452.6, RMSE = 675.5). SHAP analysis confirmed that geographic variables (zone and LGA) exerted the greatest influence on malaria predictions.</div></div><div><h3>Conclusion</h3><div>Machine learning approaches may provide a useful framework for exploring malaria transmission dynamics in climate-sensitive and ecologically diverse settings. The findings of this study suggest that ecological zones may be important determinants of malaria incidence in Taraba State. These results indicate that incorporating geographic heterogeneity into malaria surveillance and control strategies could help support more data-driven and locally tailored interventions</div></div>","PeriodicalId":100358,"journal":{"name":"Decoding Infection and Transmission","volume":"4 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}