Rapid Collection Research Articles

Analysis of Growth Variation in Maize Leaf Area Index Based on Time-Series Multispectral Images and Random Forest Models

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R2 = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R2 = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding.

Westminster Menswear Archive

The Westminster Menswear Archive (WMA) was established in 2016 as a specialized teaching and research collection focused on menswear. Addressing the historical underrepresentation of menswear in fashion archives, the WMA has rapidly grown into a vital resource for both academic and industry research. This article explores the archive’s origins, collection strategy, and its pivotal role in supporting design education, research, and industry engagement. Through key acquisitions, including garments from designers like Alexander McQueen and Massimo Osti, the WMA highlights menswear’s diverse evolution, from military uniforms to high fashion. The archive has also contributed to significant exhibitions, including Invisible Men (2019) and Umbro 100: Sportswear x Fashion (2024). This paper outlines the challenges the WMA faces, such as managing rapid collection growth and expanding digital access, while underscoring its critical role in shaping the future of menswear studies.

A wearable exhaled breath condensate (EBC) collector with controllable condensation microfluidics and a branched hydrophilic film

In previous research, exhaled breath condensate (EBC) analysis has emerged as a promising noninvasive method for assessing human health, potentially increasing patient testing acceptance. However, current EBC collection devices require cooling equipment for temperature reduction, leading to issues such as increased size, increased energy consumption, and a lack of real-time collection capability. In this study, an EBC collector was developed that integrates controllable condensation microfluidics with a biomimetic water-collecting film. Microfluids induce an endothermic reaction by rapidly dissolving water and NH4NO3 within the chamber, reducing the surface temperature to 3.5 °C. When positioned inside a mask, exhaled air undergoes condensation and collection on the branched biomimetic film. Human tests were conducted to analyze caffeine and nicotine in the collected EBC samples using LC–MS, demonstrating the device’s advantages in terms of power-free operation, wearable design, and rapid sample collection for metabolite detection.

Bubble fragmentation in turbulent flow and the potential implications of shear structures for bubbles formed by breaking waves

Large bubbles (1–5 mm radius) are important in a wide range of situations, including air-sea gas transfer, aerosol production as they burst at water surfaces, and the aeration of liquids in bioreactors and other industrial processes. When rising through turbulent flow, these bubbles are commonly distorted and may fragment to form daughter bubbles if their radius exceeds the Hinze scale (at which the restoring force due to surface tension is equal to the forces causing bubble distortion). Here, we present the results of laboratory experiments with fragmentation resulting from bubbles rising through a sheared and turbulent flow. The effects of water temperature, surface tension, local shear rate, and viscous dissipation rate of turbulent kinetic energy were assessed. Passive acoustical methods produce robust measurements of bubble fragmentation processes, allowing for rapid data collection to generate large data sets. In our experiments, even for bubbles very close to the Hinze scale, the dominant fragmentation mechanism is the capillary-driven fragmentation of elongated bubble filaments. The probability distribution of daughter bubble sizes from a single fragmentation event was independent of temperature, surface tension, and rate of viscous dissipation of turbulent kinetic energy. The overwhelming majority of fragmentation events resulted in one very large and one very small bubble, even for Hinze-scale parent bubbles and low Weber numbers (We < 5.3). Our results suggest that in a turbulent flow, there may be a link between the shear induced by large scale structures and the size of the smallest bubbles produced underneath a breaking wave.

How effective crowdsourced data during crisis emergency? A case of the 2018 Palu-Donggala earthquake

In disaster situations, updated geographic data is crucial for disaster relief efforts. OpenStreetMap (OSM) has demonstrated significant value in disaster response scenarios due to its capacity for rapid data collection and dissemination, since the 2010 Haiti earthquake. This study investigates the quality of OSM data during the 2018 Palu-Donggala earthquake, focusing on how contributor expertise affects data reliability and how effectively OSM data supports decision-making in emergencies. The research highlights the critical role of OSM in providing timely geospatial information, with 205 contributors mapping roads and buildings in Palu City and Donggala Regency within just three days of the earthquake. Our findings show that while road data exhibited substantial topological errors—7,085 errors primarily due to overshoots—building data had considerably fewer errors, with only 76 recorded. This disparity suggests that OSM data for buildings was of higher quality during the crisis. The preference of eight out of nine mapper types for building data over road data further underscores the value of OSM in emergencies, as experienced mappers tended to focus on features that were less error-prone. The study also evaluates contributor behavior, revealing that while a significant portion of contributors were inactive, a majority of experienced contributors remained engaged. This finding indicates the potential for inactive expert mappers to return and contribute in future crises. Additionally, the study assesses the rapid collection of data by OSM and its impact on decision-making. The National Disaster Management Agency of Indonesia (BNPB) and the ASEAN Coordinating Centre for Humanitarian Assistance (AHA Centre) effectively utilized the data to provide updates on fatalities, injuries, and displacement, facilitating a swift and equitable distribution of aid.

Health in Germany – A new panel infrastructure for Public Health Research in Germany: study design

Abstract Background The Robert Koch Institute (RKI) is developing a new panel infrastructure, ‘Health in Germany,’ to enhance public health research through continuous, rapid data collection from questionnaire surveys, measurements, and laboratory analyses. Using online and offline collection methods (CAWI and PAPI), the project aims to have 30,000 registered panelists by Spring 2024. Methods The sample of the recruitment study is based on address data from Germany’s local registration offices, whereby a mixed-mode design is used for data collection. Individuals aged 16-69 receive an initial online-only invitation, followed by postal reminders that include a paper questionnaire. For those aged 70 and above, both online and paper options are available from the outset. Incentives include an unconditional €5 cash payment for all invitees and an additional €10 for successful registration into the panel infrastructure. Results The initial response rate of the recruitment study is 35%. Of the CAWI participants, approx. 90% expressed willingness to participate again, with approx. 80% completing the online registration. To date, over 42,000 panelists have been recruited. Further detailed results on response rates, non-response bias, and sample composition will be presented at the conference. Conclusions RKI’s ‘Health in Germany’ is one of the largest probability panels in public health research, effectively gathering and analyzing data. The recruitment success and strong participant engagement highlight the infrastructure’s efficiency and its significant contribution to epidemiological research in Germany. Key messages • Successful recruitment of over 42,000 panelists demonstrates robust engagement and effective implementation. • The push-to-web strategy (or web-push strategy) underscores the infrastructure’s adaptability and efficiency.

The Rutgers Omnibus Study: Protocol for Quarterly Web-Based Surveys to Promote Rapid Tobacco Research.

Rapid and flexible data collection efforts are necessary for effective monitoring and research on tobacco and nicotine product use in a constantly evolving marketplace. The Rutgers Omnibus Survey (1) provides timely data on awareness and use of new and emerging tobacco products among adults in a rapid manner, (2) provides a platform for measurement experiments to help develop and refine measures of tobacco use that reflect the current marketplace, and (3) generates pilot data for grant applications and scientific manuscripts. This study aims to document the first 2 years of the Rutgers Omnibus Study through the reporting of methodology, fielding summaries, and sample characteristics. Launched in February 2022 and fielded quarterly thereafter, we survey convenience samples of 2000 to 3000 US adults aged 18-45 years recruited from Amazon Mechanical Turk (MTurk) using the MTurk Toolkit by CloudResearch. The questionnaire includes core and rotating modules and is designed to take approximately 10 minutes to complete through Qualtrics. The fielding duration is approximately 10 days per wave. Each wave includes both unique and repeating participants, and responses can be linked across waves by an anonymous ID. Sample sizes ranged from 2082 (wave 8, December 2023) to 2989 (wave 1, February 2022), and the 8-wave longitudinal dataset included 10,334 participants, of whom 2477 had 3 or more data points. The cost per complete at each wave was low, ranging from US $2.46 to US $3.27 across waves. Key demographics were consistent across waves and similar to that of the general population, while tobacco product trial and past-30-day use were generally higher. The Rutgers Omnibus Study is a quarterly survey that is effective for rapidly assessing the use of emerging tobacco and nicotine products and can also be leveraged to conduct survey experiments, generate pilot data, and address both cross-sectional and longitudinal research questions. RR1-10.2196/58203.

Overcoming Barriers to Childhood and Adolescent Vaccination: Opportunities in Precision Health Promotion

Abstract Background Vaccination remains one of the greatest success stories in public health as cost-effective interventions to reduce morbidity and mortality from infectious diseases at the population level. However, the COVID-19 pandemic created unprecedented challenges to the immunization sector. Uptake of routine childhood and adolescent vaccinations was forestalled by challenges related to pandemic lockdowns/quarantine, reduced patient-provider interactions, parental reluctance to seek preventative services, poor access to health services, and loss of income and/or health insurance. Compared to pre-pandemic estimations, the pandemic era brought a consequential decline in coverage of all vaccines recommended by the Advisory Committee on Immunization Practices (ACIP). This study sought to address suboptimal adolescent vaccine delivery systems and bolster uptake in the post-pandemic era with the introduction of precision health promotion strategies centered around the use of Personal Health Libraries (PHL). Methods Leveraging artificial intelligence (AI), and semantic web technologies to integrate contextual knowledge (i.e., clinical, biomedical, and SDoH data) with patient preferences, proposed PHL platforms could aid in generating personalized evidence-based recommendations useful to both care provider promotion of vaccination and mitigating patient and caregiver hesitancy. The proposed application stands to provide customized digital health interventions by 1) facilitating remote knowledge acquisition, exchange, and visualization of the benefits of on-time vaccination, 2) improving health information-seeking behaviors to reduce hesitancy, 3) bolstering patient reminder and recall systems for follow-up and series completion, and 4) learning usage patterns to incorporate end-user preferences to empower patients, promote best practices, and enable them to make informed health decisions. Results The proposed PHL would serve as an ecosystem to manage and deliver tailored vaccine-related information to inform health decision-making regarding ACIP-recommended immunizations. Such a resource could administer health education and promotion that addresses common misconceptions regarding vaccination, alleviate safety, efficacy, and side effect concerns, and deliver evidence-based support of vaccines as primary protective measures (i.e., cancer prevention). Conclusion The use of PHL would enable rapid and real-time collection and interpretation of patient data beyond the traditional clinical setting, supplying care providers with tools for optimal vaccination recommendation to bolster uptake in the post-pandemic era. Moreover, the application would enable patients and caregivers to readily access vital health information and play a crucial role in addressing the obstacles encountered when seeking accurate and dependable vaccine-related information.

Performance of agricultural extension agents in Indonesia: evidence from a nationally representative survey

ABSTRACT Purpose This study analyses the performance of field extension agents in Indonesia using a multi-dimensional performance indicator. Methodology The study uses data from an online survey of a nationally representative sample of 1,974 field extension agents. Binary and ordinal probit models are utilised to analyse the performance of extension agents and the factors influencing it based on a multi-dimensional measurement of performance. Findings and practical implications Improved performance is associated with increased career progression, availability of external funds, the opportunity to collaborate, involvement in government programs, implementation of disciplinary measures and a diversified use of advisory methods. To promote performance, the government should encourage and improve the institutional arrangements. Theoretical implications This study introduces a multidimensional measurement that covers timeliness, targeting and content dimensions while capturing both the institutional arrangement and demographic characteristics in explaining performance. Originality/value This study introduces an online survey for extension agents that encompasses a nationally representative sample, thereby demonstrating this cost effective approach designed for rapid data collection and facilitating the measurement of performance during unforeseen events, such as the Covid-19 pandemic. Its broad applicability allows for a thorough analysis of agricultural extension agents’ performance in a variety of contexts.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

Essential Fluidics for a Flow Cytometer.

Flow cytometry is an inherently fluidic process that flows particles on a one-by-one basis through a sensing region to discretely measure their optical and physical properties. It can be used to analyze particles ranging in size from nanoparticles to whole organisms (e.g., zebrafish). It has particular value for blood analysis, and thus most instruments are fluidically optimized for particles that are comparable in size to a typical blood cell. The principles of fluid dynamics allow for particles of such size to be precisely positioned in flow as they pass through sensing regions that are tens of microns in length at linear velocities of meters per second. Such fluidic systems enable discrete analysis of cell-sized particles at rates approaching 100 kHz. For larger particles, the principles of fluidics greatly reduce the achievable rates, but such high rates of data acquisition for cell-sized particles allow rapid collection of information on many thousands to millions of cells and provides for research and clinical measurements of both rare and common cell populations with a high degree of statistical confidence. Additionally, flow cytometers can accurately count particles via the use of volumetric sample delivery and can be coupled with high-throughput sampling technologies to greatly increase the rate at which independent samples can be delivered to the system. Due to the combination of high analysis rates, sensitive multiparameter measurements, high-throughput sampling, and accurate counting, flow cytometry analysis is the gold standard for many critical applications in clinical, research, pharmaceutical, and environmental areas. Beyond the power of flow cytometry as an analytical technique, the fluidic pathway can be coupled with a sorting mechanism to collect particles based on desired properties. We present an overview of fluidic systems that enable flow cytometry-based analysis and sorting. We introduce historical approaches, explanations of commonly implemented fluidics, and brief discussions of potential future fluidics where appropriate. © 2024 Wiley Periodicals LLC.

Development of Robust Steel Alloys for Laser-Directed Energy Deposition via Analysis of Mechanical Property Sensitivities

To ensure consistent performance of additively manufactured metal parts, it is advantageous to identify alloys that are robust to process variations. This paper investigates the effect of steel alloy composition on mechanical property robustness in laser-directed energy deposition (L-DED). In situ blending of ultra-high-strength low-alloy steel (UHSLA) and pure iron powders produced 10 compositions containing 10–100 wt% UHSLA. Samples were deposited using a novel configuration that enabled rapid collection of hardness data. The Vickers hardness sensitivity of each alloy was evaluated with respect to laser power and interlayer delay time. Yield strength (YS) and ultimate tensile strength (UTS) sensitivities of five select alloys were investigated in a subsequent experiment. Microstructure analysis revealed that cooling rate-driven phase fluctuations between lath martensite and upper bainite were a key factor leading to high hardness sensitivity. By keeping the UHSLA content ≤20% or ≥70%, the microstructure transformed primarily to ferrite or martensite, respectively, which generally corresponded to improved robustness. Above 70% UHSLA, the YS sensitivity remained low while the UTS sensitivity increased. This finding, coupled with the observation of auto-tempered martensite at lower cooling rates, may suggest a strong response of the work hardening capability to auto-tempering at higher alloy contents. This work demonstrates a methodology for incorporating robust design into the development of alloys for additive manufacturing.

Logistics for Rapid Isolation of Viruses From Humans.

An important aspect of microbiological surveillance is the ability to access live viruses for microneutralization assays, which enables the study of viral characteristics and mechanisms in vitro and production of positive controls for diagnostic methods. During the COVID-19 pandemic, the Public Health Agency of Sweden established a protocol for the rapid collection of clinical samples and subsequent isolation of novel virus variants.

StreetTalk: exploring energy insecurity in New York City using a novel street intercept interview and social media dissemination method

This study introduces StreetTalk, an original qualitative research methodology inspired by social media influencers, to investigate perceptions and experiences of energy insecurity among New York City (NYC) residents. Briefly, energy insecurity is defined as difficulty meeting household energy needs due to affordability, housing quality, outages and coping strategies. This present study employs dynamic short-form interviews with 34 participants from all five NYC boroughs of diverse economic, and racial/ethnic backgrounds. Thematic analysis of video-recorded interviews revealed six major energy insecurity-related categories: (1) conservation and trade-offs, (2) physical inefficiencies, (3) thermal agency, (4) response to the bill, (5) disappointment and distrust in energy-related authorities, and (6) desire for and barriers to clean energy adoption. These themes provide insight into NYC residents’ experiences with energy insecurity and are consistent with prior research. Beyond new scholarly insights, this study introduces StreetTalk, an innovative qualitative research methodology emphasizing rapid data collection and dissemination through social media platforms, including TikTok, Instagram, Facebook, and YouTube (@hotandcold_nyc). Taking advantage of modern technology and modes of communication, the research team was able to effectively break down barriers to academic research consumption as the videos achieved substantial engagement, with almost 200,000 views and impressions within the first year of launching this novel street-based data collection and social media dissemination campaign.

A post-hurricane building debris estimation workflow enabled by uncertainty-aware AI and crowdsourcing

Climate disasters often result in large amounts of debris that need to be cleaned up in the event's aftermath. Effective post-disaster debris management poses unique challenges due to limited resources, high expenses, and the nonhomogeneous and hazardous debris content. Current practices of debris cleanup rely on time-consuming and error-prone field-based estimation, which impedes immediate response and accurate analysis. While new approaches such as artificial intelligence (AI) and crowdsourcing have shown promise in various domains, their potential for debris estimation remains underexplored. This study leverages a human-AI teaming workflow that estimates post-disaster debris volume and composition. The workflow begins with the utilization of drones to capture aerial views for efficient disaster infrastructure reconnaissance. Subsequently, an AI model and a crowdsourcing module are calibrated and work together to rapidly and reliably detect damaged buildings and classify the levels of damage based on aerial imagery. By establishing a connection between building damage states and the resulting generated debris, these damage level predictions serve as a foundation for the efficient and accurate estimation of debris. Overall, the proposed approach uses drones for rapid data collection and AI to automate damage detection and classification, which reduces the time required for debris estimation from days to a few hours as well as minimizes predictive uncertainty with crowdsourcing by up to 40%. A case study on Hurricane Laura in 2020 was conducted to validate the proposed approach. Findings of this study lead to predicting debris volume and composition with minimal errors, particularly for partially damaged buildings.

Development of a Novel Electrostatic-Based Bioaerosol Sampler.

On-site bioaerosol monitoring is essential for estimating microbial biomass and mitigating the risk of infection induced by aerosol transmission. This study introduces a novel electrostatic bioaerosol sampler, which is fabricated by the use of 3D printing, for rapid bioaerosol collection. Aerosol particles were charged and enriched in the sampler. Relationships between particle sizes and collection efficiencies under varying charging voltages were established using a charging model. The design of the sampler was optimized using commercial software, incorporating electrostatic field analysis, computational fluid dynamics (CFD), and particle trajectory simulations. To validate the sampler's collection efficiency, polystyrene (PS) spheres in an aerosol dispenser were atomized into an aerosol. The sampler collection efficiency exceeded 90% for particles larger than 1.2 μm under an applied voltage of 4.7 kV and an airflow rate of 2 L/min. The enrichment capacity was greater than 153,000 for particles larger than 1.2 μm under an applied voltage of 4.7 kV and an airflow rate of 8 L/min. With the merits of low cost, miniaturization, and high collection efficiency, the sampler can be used to collect samples on-site and in remote areas to verify the pathogens and reduce the risk of infection through aerosol transmission.

Rapid data collection and processing in dense urban edge computing networks with drone assistance

In the edge computing network systems for the Internet of Things (IoT), there is growing attention to utilizing drones for collecting data and maintaining the freshness of data processing. This study focuses on analyzing the problems related to trajectory planning and task scheduling in a single drone-assisted edge computing network within a dense, three-dimensional urban environment. We first design an edge computing network architecture and establish an air-to-ground channel model between the drone and ground mobile devices to address the blockage caused by buildings in urban environments. Subsequently, to provide effective edge computing services, we structure the problem as a Partially Observable Markov Decision Process (POMDP) and introduce an optimization framework based on reinforcement learning. This improves data timeliness and reduces energy consumption.

Organ-Specific Immune Setpoints Underlie Divergent Immune Profiles across Metastatic Sites in Breast Cancer.

Immune composition within the tumor microenvironment (TME) plays a central role in the propensity of cancer cells to metastasize and respond to therapy. Previous studies have suggested that the metastatic TME is immune-suppressed. However, limited accessibility to multiple metastatic sites within patients has made assessing the immune TME difficult in the context of multiorgan metastases. We utilized a rapid postmortem tissue collection protocol to assess the immune composition of numerous sites of breast cancer metastasis and paired tumor-free tissues. Metastases had comparable immune cell densities and compositions to paired tumor-free tissues of the same organ type. In contrast, immune cell densities in both metastatic and tumor-free tissues differed significantly between organ types, with lung immune infiltration being consistently greater than that in the liver. These immune profiling results were consistent between flow cytometry and multiplex immunofluorescence-based spatial analysis. Furthermore, we found that granulocytes were the predominant tumor-infiltrating immune cells in lung and liver metastases, and these granulocytes comprised most PD-L1-expressing cells in many tissue sites. We also identified distinct potential mechanisms of immunosuppression in lung and liver metastases, with the lung having increased expression of PD-L1+ antigen-presenting cells and the liver having higher numbers of activated regulatory T cells and HLA-DRlow monocytes. Together, these results demonstrate that the immune contexture of metastases is dictated by organ type and that immunotherapy strategies may benefit from unique tailoring to the tissue-specific features of the immune TME.

Rapid Online Data Collection and Reporting to Local Health Departments during the Pandemic: The COVID-19 Outbreak Public Evaluation (COPE) Initiative SARS-CoV-2 Case Surveys.

To develop and implement a pilot online data collection tool to help local health departments with their COVID-19 pandemic response efforts and inform health department actions. The COVID-19 Outbreak Public Evaluation (COPE) was an online survey and was distributed by participating sites to individuals who recently tested positive for SARS-CoV-2. Surveys recorded participant demographics and assessed recent infection risk behaviors (eg, mask use, air travel), vaccination status, sleep and exercise habits, social behaviors and beliefs, and physical and mental health. Seven health departments participated in the initiative, which took place during May 1 to September 30, 2022. Identical items were administered to demographically representative samples of adults nationally in the United States within a similar timeframe. A total of 38555 participants completed surveys. Responses from participants with recent SARS-CoV-2 infections were compared with respondents from the national surveys who did not have evidence or awareness of prior SARS-CoV-2 infections. To implement of a process that allows health departments to receive data from local cases and compare this information to national controls during the COVID-19 pandemic. Fifty-four biweekly reports were provided to public health departments between May and September 2022. Information and comparisons within the reports were updated in response to evolving public health priorities for the pandemic response. The initiative helped to guide public health response efforts during the COVID-19 pandemic. Moreover, the receptiveness by local health departments and participants provides evidence to support this data collection and reporting model as a component of the public health response to future emergencies. This project demonstrates the feasibility of a centralized, rapid, and adaptive data collection system for local health departments and provides evidence to advocate for data collection methods to help guide local health departments to respond in a timely and effective manner to future public health emergencies.

Non-typhoidal Salmonella in Nigeria: do outcomes of ‘multisectoral’ surveillance, treatment and control justify the intervention costs?

ABSTRACT Non-typhoidal salmonellosis (NTS) is significant and an economic burden in Nigeria. To determine whether investment in NTS control is economically justifiable, Outbreak Costing Tool (OCT) was used to estimate the robust funding of public and animal health systems for epidemio-surveillance and control of multisectoral NTS outbreaks in Nigeria. Health, production, and economic data were collected and used to populate the tool for evaluation. The multisectoral NTS burden for the year 2020 in Nigeria was US$ 930,887,379.00. Approximately 4,835 technical officers, and 3,700 non-technical staff (n = 8,535) were needed with an investment of >2.2 million work hours. The investment cost for NTS control was US$ 53,854,660.87. The non-labour-related cost was 89.21% of the total intervention costs. The overall intervention’s investment was 374.15% of the estimated national and subnational systems’ annual budget for diarrhoeal diseases, and the outbreak response period attracted the highest costs (53%) of the total intervention. In conclusion, intervention against NTS was beneficial (benefit – cost ratio: 17.29), hence justifying the need for multisectoral surveillance-response against NTS in Nigeria. Complex sectoral silos must give way to coordinated collaborations to optimize benefits; and over-centralization of health interventions’ associated delays must be removed through decentralized sub-national-focused framework that empowers rapid investigation, response, control, data collection, and analyses. It should assist anticipatory planning, and outbreak investigation and reduce critical response time. Anticipatory planning tools, when applied pre-emptively, can benefit budgeting, identify gaps, and assist in the delivery of cost-saving and effective measures against infectious disease.