Nelson Mandela African Institution of Science and Technology (NM-AIST) is a public institution in northern Tanzania based in Arusha City.The Nelson Mandela African Institution of Science and Technology in Arusha (NM-AIST Arusha) is part of the network of Pan-African Institutes of Science and Technology located across the continent. The NM-AIST Arusha, which is accredited by the Tanzania Commission for Universities (TCU), is being developed into a research intensive institution for postgraduate and postdoctoral studies and research in Science, Engineering and Technology (SET). The training in SET, however, incorporates appreciable doses of relevant humanities and business studies ingredients. Life sciences and bio-engineering are being developed to become specializations of the NM-AIST Arusha due to the bio-diversity in the region. The institution seeks to stimulate, catalyze and promote intensification of agricultural production. Value addition to the various natural products (agricultural, mineral, etc.) produced in Tanzania and the Eastern African region is also a primary focus. Other main thematic areas covered by NM AIST Arusha include Energy, ICT, Mining, Environment and Water..
IntroductionMass dog vaccination is the most effective approach for interrupting canine rabies transmission. However, current vaccination strategies are typically centralized and conducted annually, leaving some communities excluded and with few opportunities to vaccinate their dogs. A community-based continuous mass dog vaccination strategy was piloted in the Mara region of Tanzania. We investigate factors that influenced the delivery of this approach and how the processes were sustained over two years.MethodsWe employed mixed methods to explore what influenced vaccination delivery. We conducted in-depth interviews (n = 24) and focus group discussions (n = 12) with implementers and community members, and non-participant observation of vaccinations (n = 172 h). We documented time spent by dog owners attending campaigns (n = 610) and how dogs were handled (n = 696), and audited how components of the community-based continuous approach were delivered (n= 47). Qualitative data was analyzed thematically, and regression and descriptive statistics used to assess factors affecting delivery.Main findingsFactors that facilitated participation in the campaigns included delivering vaccination free of charge, more frequent availability of vaccinations, and co-implementation with communities. Limiting factors were distance to vaccination points, difficulties in handling dogs and vaccination schedules conflicting with local socioeconomic activities. Sub-village level campaigns were more accessible and required less time from dog owners.InterpretationInvolving community-based persons facilitated planning and advertising of campaigns. Mass dog vaccination campaigns can achieve and maintain herd immunity if organized at least twice a year and at subvillage levels. Educating vaccinators and communities on dog behavior and handling could improve participation in campaigns.
Background Maternal health service utilisation is poor in many low- and middle-income countries, contributing to high maternal mortality rates. This study aimed to identify the key factors influencing the use of maternal health services among reproductive-age women in Tabora, Tanzania. Methods Secondary data from a stepped-wedge clinical trial design were used. Baseline data were collected in November and December 2017 throughout Tabora. Data from 958 women aged 15–49 years in Tabora were analysed for this study. Variables analysed included maternal age, marital status, religion, household wealth status, household size, ethnicity, parity, antenatal care (ANC) visits, facility deliveries, facility distance and location (rural–urban). Descriptive analysis was used to assess the demographic characteristics of respondents and multivariate logistic regression was used to assess factors associated with the utilisation of maternal health services. Results Achieving four or more ANC visits (ANC4+), was associated with high maternal age category (36–50 years) as compared with those aged <20 years (OR=3.13 (95% CI 1.56 to 6.31)) and least poor household wealth status (OR=1.63 (95% CI 1.11 to 2.39)) as compared with the poorest household wealth status. Facility-based delivery was associated with married women (OR=1.76 (95% CI 1.15 to 2.71)) as compared with women who were not married, low parity (<5) (OR=0.85 (95% CI 0.78 to 0.93)) as compared with high parity (>5), ethnicity (Nyamwezi) (OR=2.01 (95% CI 1.34 to 3.11)) as compared with other ethnic groups in Tabora, ANC4+ (OR=1.96 (95% CI 1.44 to 2.67)) as compared with less than four ANC visits, household wealth status; least poor (OR=2.8 (95% CI 1.81 to 4.33)) as compared with poorest household wealth status and distance (<5 km) to facility (OR=0.91 (95% CI 0.89 to 0.94)) as compared with ≥5 km. Postnatal care visits were associated with facility-based delivery (OR=6.76 (95% CI 4.71 to 9.69)) as compared with non-facility-based deliveries. Increased antenatal visits were associated with higher likelihoods of facility deliveries, which in turn led to higher postnatal care attendance. Conclusions The findings highlight the need for targeted interventions to address the factors influencing service utilisation to improve overall maternal and child health outcomes.
Combining larviciding with insecticide treated nets (ITNs) can reduce malaria transmission. However, most modelling analyses use generalized scenarios rather than incorporating local epidemiological and ecological contexts. In Tanzania and other countries, larviciding is increasingly being prioritized in national strategies, with growing advocacy for its broader implementation, to achieving sustained malaria reduction. District-specific modelling is therefore essential to capture variation in transmission ecology, seasonality, and varying coverage levels, providing evidence that is both rigorous and actionable for malaria control programs. The Vector Control Optimization Model (VCOM) was adapted and extended to incorporate local seasonality, simulating the impact of larviciding across a range of coverage levels combined with ITNs. The model was parameterized using district-level field-data on mosquito mortality collected before (2016-2017) and after (2019-2021) larviciding implementation. Mosquito mortality rates were estimated using Bayesian inference. Outcomes were evaluated specifically for Anopheles gambiae s.l. including annual entomological inoculation rates (EIR) and mosquito density. Sensitivity analysis explored the influence of key parameters driving transmission in this scenario study. The immature mosquito mortality rate due to larviciding was 61% based on field data. VCOM simulation showed that, at 80%, ITNs coverage, larviciding substantially reduced mosquito densities and EIR. Specifically, combining ITNs at 80% and larviciding coverage ≥ 60% lowered EIR below 1 ib/p/year, the threshold required to interrupt malaria transmission. Sensitivity analyses highlighted the high impact of targeting immature mosquitoes, suggesting larviciding can effectively complement ITNs to control vectors, including invasive species like An. stephensi, regardless of feeding preference, resting, and biting behaviors, which hinder the effectiveness of most vector control tools. This study provides local evidence that larviciding is an effective complement to ITNs for interrupting malaria transmission. Implementation should leverage innovative approaches, such as drones for precise mapping and targeted application of biological larvicides, to maximize coverage, and scalability for district-level malaria control and elimination.
BackgroundMalaria transmission is highly sensitive to climatic variability, as changes in temperatures and rainfall, directly influence mosquito breeding, survival, and parasite development. Extreme climatic events, such as flooding, further exacerbate malaria risk by disrupting access to preventive, diagnostic and treatment services. However, there is limited evidence on how communities in malaria-endemic settings perceive and respond to the health impacts of climate variability and change. This study explored community knowledge, perceptions, and practices related to the relationship between climate variability and malaria transmission in south-eastern Tanzania.MethodsAn explanatory mixed-methods cross-sectional study was conducted in malaria-endemic villages in south-eastern Tanzania. Quantitative data were collected through structured questionnaires administered to 384 community members, while qualitative data were obtained through 11 key informant interviews and 12 focus group discussions involving 72 participants. Survey data were analysed descriptively, and qualitative data were analysed thematically.ResultsAmong survey respondents, 86% reported experiencing climate-related changes, including altered cropping seasons, increased flooding, and a perceived rise in vector-borne diseases. Approximately two-thirds (67.5%) recognized a link between climate change and malaria transmission. Perceived vulnerability was high, with 59.5% reporting increased risk of vector-borne diseases and 70% indicating higher malaria occurrence during the rainy season compared to the dry season. Access to timely climate and health information was limited, as only 26.6% regularly received updates, despite 96.6% expressing a desire for such information. Findings from focus group discussions and key informant interviews corroborated these perceptions and highlighted the need for targeted community awareness and education on climate-related malaria risks.ConclusionsCommunity members demonstrated awareness of climate change and its perceived impacts on malaria and livelihoods. These findings highlight the importance of integrating community perspectives and local knowledge into climate-adaptation and malaria-control strategies to enhance locally relevant and community-centered resilience.
The Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2026 workshop was held from 24 to 26 March 2026 in a hybrid format, combining onsite participation at the University of Buea Center for Drug Discovery with global virtual attendance. Building on previous editions, CAiSMD continues to promote the application of bio/cheminformatics and computational chemistry in natural product-based discovery, with a strong emphasis on capacity building for researchers in resource-limited settings. The scientific program featured three keynote lectures, seven oral presentations, two hands-on training sessions, two roundtable discussions, and a session dedicated to Young Investigators. Key themes included chemoinformatics resources (databases, webservers, packages, codes, etc.) for natural product research, antimicrobial drug discovery, integrated computational bioprospection, virtual screening, network pharmacology, and multi-target drug design. The hands-on sessions provided practical training in chemical space exploration and virtual screening using the Latin American and African natural products databases. A major highlight was the Young Investigator session, which showcased innovative research from emerging scientists (MSc students, PhD students, and early postdocs) across diverse therapeutic areas, emphasizing the growing adoption of computational approaches worldwide. Interactive discussions further addressed challenges related to data accessibility, interdisciplinary collaboration, and training needs. Complemented with relevant experimental models, the introduction of bioinformatics, and computational systems biology have accelerated therapeutic innovations through the elimination of time-consuming, expensive, challenging, and inefficient processes that are consistent with the conventional drug discovery systems. Overall, CAiSMD2026 successfully combined scientific exchange with practical training, reinforcing the role of computational tools in accelerating secondary metabolite research and empowering young and emerging scientists, particularly in developing countries, to contribute to global drug discovery efforts. This workshop particpates actively in capacity building fo next generation scientists for the use of computational technologies for secondary metabolite discovery with diverse applications.
