Disaster Risk Management Strategies Research Articles

Rainfall projections under different climate scenarios over the Kaduna River Basin, Nigeria

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Comprehensive evaluation and optimal management of extreme disaster risk in Chinese urban agglomerations by integrating resilience risk elements and set pair analysis

This study comprehensively assesses the extreme disaster risk faced by Chinese urban agglomerations and optimizes disaster risk management strategies to enhance their disaster resilience and sustainable development. The focus is specifically on droughts, floods, and earthquakes. The integration of resilience risk elements and set pair analysis (SPA) is employed to obtain risk assessment results for different urban agglomerations, including hazard, exposure, vulnerability, and disaster prevention and mitigation capabilities. The study explores the contributions of important indicators, such as exposure, vulnerability, and disaster prevention and mitigation capabilities, in disaster risk assessment, as well as their performance in various risk-prone urban areas. Comparative analysis of the assessment results for different urban agglomerations under various disaster types reveals varying levels of risk in droughts, floods, and earthquakes across different regions. Some areas exhibit higher hazard, exposure, or vulnerability levels, necessitating strengthened disaster prevention and mitigation measures. Regions with high exposure, low vulnerability, and strong disaster prevention and mitigation capabilities may face lower earthquake disaster risk, while regions with low exposure, high vulnerability, and weak disaster prevention and mitigation capabilities may face higher earthquake disaster risk. Moreover, the Comprehensive SPA model and Interval-valued SPA model outperform traditional SPA methods in assessing the existing flood risk level in Chinese urban agglomerations. The integrated model plays a significant role in predicting earthquake disasters, demonstrating the best predictive performance. This study provides essential reference data for urban disaster prevention and mitigation efforts, aiming to enhance urban resilience and disaster resilience capabilities.

Unveiling extensive and intensive disaster risks: an in-depth analysis of Asia’s vulnerability

ABSTRACT The Asian region has significant susceptibility to the convergence of environmental hazard-related disaster risks, resulting in the recurring occurrence of intensive and impactful disasters. This study conducts a comprehensive examination of the extensive risks, characterised by high frequency and low magnitude, and intensive risks, characterised by low frequency and high magnitude, encountered by the most impacted nations in Asia, identified based on their ranking in the Global Climate Risk Index. Utilising data from the EM-DAT disaster database spanning the years 1980 to 2022, the study finds that in terms of extensive, intensive, and combined risks India stands at the top, followed by the Philippines and Bangladesh. However, in terms of casualties, Bangladesh stood at the top followed by Myanmar and India. Furthermore, the study reveals that there is an inverse relationship between income level and casualties. Overall, the study underscores the critical importance of conducting risk assessments so that robust disaster risk management strategies can effectively reduce losses.

The Impacts of Multiple Tropical Cyclone Events and Associated Precipitation on Household Income and Expenditures

Tropical cyclones (TCs) are one of the most destructive natural hazards. Damages arise from strong winds, compounded by associated flood-inducing hazards such as heavy rainfall and storm surge. Recent papers have shown that the modelled TC damage estimates fall short of the observed estimates due to the use of wind speed as a sole damage proxy. Damage estimates may be further confounded by inaccurate representations of vulnerability of people and economic sectors, for example, calling for adjusted damage thresholds in less developed regions. This paper evaluates the impacts of compounded TC hazards on household income and expenditure in the Philippines, with adjustments in vulnerability representation drawn from local information. Our results show that the omission of TC-associated precipitation leads to an underestimation of impacts, as well as the number of areas and economic sectors affected by TCs. We find that households cope through a reallocation of budgets and reliance on alternative income sources. Despite extensive public and private disaster risk reduction and management strategies, we still find significant losses in income and expenditures at any number of TC exposure.

Anticipatory action for drought in the Sahel: an innovation for drought risk management or a buzzword?

Anticipatory action is an approach that combines early warning information with flexible, pre-positioned funds to trigger actions that mitigate the impact of predictable shocks on the most vulnerable people. Historically, drought is the climatic hazard that led to the highest and most severe humanitarian impacts in the Sahel. This region, according to climate projections, will be one of the most deeply affected by climate change in future years, leading to considerable changes to societies, economies, as well as impacting rural communities. While this negative projection may lead to further increases in humanitarian consequences, recent experiences from integrating anticipatory action for drought into humanitarian practice hold positive prospects. This article will review current experiences on anticipatory action for drought in the Sahel and shed light on whether this approach has brought innovation in local disaster risk management. Through the review of recent initiatives in Burkina Faso, Chad and Niger, this paper highlights key advancements as well as gaps and challenges pertaining to key components of anticipatory action and disaster risk management, namely: (1) data, risk analysis and early warning; (2) funding; (3) preparedness and community engagement; (4) learning, coordination and partnership; (5) policies and institutional frameworks. Even in challenging environments like in the Sahel, anticipatory action for drought can become an integral component of standard disaster risk management and financing strategies. However, this process will require more robust evidence about which ingredients of anticipatory action approaches lead to the desired result.

Religion and Strategic Disaster Risk Management in the Better Normal: The Case of the Pagoda sa Wawa Fluvial Festival in Bocaue, Bulacan, Philippines

Religion involves expressing beliefs, performing practices, and obeying norms about what is considered sacred and worthy of worship. While some argue that religion has become irrelevant due to the widespread influence of secularism and scientific reasoning, many still find comfort in the sacred. Scientific research has shown that religion can positively impact health and safety, especially during disasters. Accordingly, religion plays a crucial role in one’s wellbeing. In the Philippines, the sound of church bells calls for parishioners to gather and celebrate, and acts as a warning system for different types of danger, such as earthquakes, typhoons, floods, raids, uprisings, and fires. Filipinos are warned to leave their houses and come to the church to take shelter. Thus, churches have been considered evacuation centers and loci for disaster risk-reduction undertakings. The proponents conducted a qualitative study investigating the disaster risk management strategies developed and implemented by St. Martin of Tours Parish Church in Bocaue, Bulacan, Philippines, during the “Pagoda sa Wawa” fluvial festival, where safety measures and crowd control are essential in maintaining a prayerful and peaceful experience. The objective of the study was to investigate how festival organizers prioritize the safety of devotees after a tragedy occurred 30 years ago, which resulted in the deaths of 266 people. Furthermore, this study explores the precautionary measures taken during and after the COVID-19 pandemic, recognizing devotees’ compliance and resilience for the common good. This study utilized a tripartite method, including reviewing relevant literature, participating in a pagoda fluvial parade, and conducting semi-structured interviews. The results were presented in a format that consisted of context, content, and challenges for the sake of coherence.

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

This study delves into the application of machine learning (ML) and deep learning (DL) techniques for the analysis of seismic data, aiming to identify and categorize patterns and anomalies within seismic events. Using a robust dataset, we applied three distinct clustering approaches: K-Means, DBSCAN, and an Autoencoder-based method, each offering unique perspectives on the data. K-Means clustering provided a fundamental partitioning of the data into five predefined clusters, facilitating the identification of broad seismic patterns. DBSCAN, a density-based clustering algorithm, offered insights into the spatial distribution and density of seismic events, adeptly pinpointing anomalies and outliers that signify unusual seismic activity. The Autoencoder, leveraging deep learning, excelled in capturing complex and non-linear relationships within the data, revealing subtle patterns not immediately apparent through traditional methods. The effectiveness of these clustering techniques was quantitatively evaluated using the Silhouette Score and the Davies-Bouldin Score, alongside visual assessments through PCA and t-SNE for dimensionality reduction. The results indicated that while K-Means provided clear partitioning, DBSCAN excelled in outlier detection, and the Autoencoder offered a balanced approach with its nuanced analysis capabilities. Our comprehensive analysis underscores the significance of employing a multi-methodological approach in seismic data analysis, as each method contributes uniquely to the understanding of seismic events. The insights gained from this study are valuable for enhancing predictive models and improving disaster risk management strategies in seismology. Future research directions include the integration of additional seismic features, validation against larger datasets, and the development of hybrid models to further refine the predictive accuracy of seismic event analysis.

Estimating emergency costs for earthquakes and floods in Central Asia based on modelled losses

Abstract. Estimating the emergency costs for disasters is of high relevance in the design of any comprehensive disaster risk management strategy. These costs usually include the ones associated with the immediate response aimed to provide the required safety and emergency attention, and those of debris removal and disposal. Over the time, and in different regions of the world, several research efforts have been carried out for the quantification of the emergency costs and have been usually associated with those of the direct losses. Also, previous studies have been typically carried out in the aftermath of large disasters, and to the best of our knowledge, no specific quantification of the emergency costs has been carried out in Central Asia. This paper presents a methodology, which has been applied in five countries in Central Asia using historical and synthetic events, to estimate the emergency costs as a function of modelled direct losses for earthquakes and floods, taking into account the demographic and building characteristics. The methodology allows the prospective estimation of the total emergency costs, so that they can be considered in the planning and budgeting of the emergency and recovery phases, as well as in disaster risk financing initiatives. It was found that the average emergency response costs for earthquakes and floods in Central Asia show good agreement with those previously estimated at other locations as a function of the direct losses; however, this methodology allows differentiating between different types of events allowing a better description and understanding of these needs.

Uncertainty Analysis of the Prediction of Massive Ash Fallout From a Large Explosive Eruption at Sakurajima Volcano

AbstractVolcanic ash hazards present life‐threatening dangers to populations near volcanoes during large explosive eruptions. Vulnerable infrastructures demand a comprehensive disaster risk reduction strategy to protect residents from enormous ashfall accumulations. To prepare for the next large eruption of Sakurajima volcano, authorities in Kagoshima City are developing a countermeasure plan utilizing ash dispersal prediction 24 hr before an eruption. However, the absence of large eruptions in the last century makes it challenging to confirm the accuracy of predictions for hazard area designation. To ensure established protocols are effective, uncertainties in ashfall predictions must be addressed, enabling authorities to make appropriate responses. We simulated the previous large eruption of Sakurajima volcano using multiple wind forecast lead times as historical predictions from July 2018 to December 2022. The uncertainty in prediction results was evaluated by comparing simulation outputs with validation data from the ash dispersal data set. We examined uncertainty in hazard area assignment and its variation depending on risk items, wind field states, and the influences of seasonal patterns and events. Updating ashfall predictions with improved wind forecasts reduces uncertainty for all risk items and increases the precision of identified hazard zones. Furthermore, uncertainty levels are influenced by seasonality and can shift significantly with varying wind strengths controlling ash dispersal process. Providing uncertainty information is vital for decision‐making during ash fallout events, and it is recommended to update response decisions 12 hr after the initial prediction. This study's outcomes will aid in developing better disaster risk management strategies, focusing on volcanic ash hazards.

Analysis of Disaster Resilience Governance in Pancoh Tourist Village, Yogyakarta

Mount Merapi, an active volcano, poses constant threats to the surrounding communities. Pancoh, a village located 9.6 km away, has embraced resilience efforts in response to the eruption in 2010. This research aims to explore the community's preparedness, disaster risk management strategies, and investments in disaster risk reduction, focusing on the development of sustainable ecotourism. A qualitative descriptive approach was employed for this study. The research findings reveal that resilience initiatives in Pancoh Tourism Village commenced after the 2010 eruption of Mount Merapi. Disaster risk management is achieved through the promotion of sustainable ecotourism. The research concludes that Pancoh Tourism Village has made commendable progress in building resilience since the Mount Merapi eruption in 2010. The integration of sustainable ecotourism, community involvement, and investments in disaster risk reduction showcases a holistic approach to mitigating the impact of volcanic disasters.

Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis

Traditionally, nonlinear time history analysis (NLTHA) is used to assess the performance of structures under future hazards which is necessary to develop effective disaster risk management strategies. However, this method is computationally intensive and not suitable for analyzing a large number of structures on a city-wide scale. Surrogate models offer an efficient and reliable alternative and facilitate evaluating the performance of multiple structures under different hazard scenarios. However, creating a comprehensive database for surrogate modelling at the city level presents challenges. To overcome this, the present study proposes meta databases and a general framework for surrogate modelling of steel structures. The dataset includes 30,000 steel moment-resisting frame buildings, representing low-rise, mid-rise and high-rise buildings, with criteria for connections, beams, and columns. Pushover analysis is performed and structural parameters are extracted, and finally, incorporating two different machine learning algorithms, random forest and Shapley additive explanations, sensitivity and explainability analyses of the structural parameters are performed to identify the most significant factors in designing steel moment resisting frames. The framework and databases can be used as a validated source of surrogate modelling of steel frame structures in order for disaster risk management.

Traditional and local communities as key actors to identify climate-related disaster impacts: a citizen science approach in Southeast Brazilian coastal areas

The impacts of climate-related disasters can be estimated by climate models. However, climate models are frequently downscaled to specific settings to facilitate Disaster Risk Management (DRM) to better understand local impacts and avoid overlooking uncertainties. Several studies have registered the increasing importance of recognizing traditional knowledge, co-design, and collaboration with local communities in developing DRM strategies. The objective of this research was co-design local-scale observations with traditional and local communities to characterize their local context regarding the impacts of climate-related disasters. The citizen science approach coupled with participatory action research was conducted with two traditional communities in the Southeast of the Brazilian coast: Quilombo do Campinho da Independência in Paraty, Rio de Janeiro, and the Caiçara (artisanal fishing) community of Ubatumirim in Ubatuba, São Paulo. Working groups were organized with leaders to become community researchers, conducting interviews and actively mobilizing their communities. A structured questionnaire was developed, adapting 22 variables taken from the Protocol for the Collection of Cross-Cultural Comparative Data on Local Indicators of Climate Change Impacts—LICCI Protocol. A total of 366 impacts were analyzed, after combining the georeferencing form data collected—Survey123 (280 impacts) and the interviews with community leaders (86 impacts). The results showed a significant level of cohesion (α = 0.01) between the Caiçara (artisanal fishers) and Quilombola (Afro-descendants) perceptions of climate-related events associated with their subsistence practices and climate variability. These findings highlighting the importance of DRM proposals that recognize traditional peoples and local communities as frontline vulnerable populations while acknowledging their role as key actors in identifying impacts, collecting data on land use and territory, subsistence-oriented activities, and cosmovision. However, it is still necessary to address climate change challenges at different scales. To do this, it is crucial to promote cognitive justice though the recognition of the values of the memories, perceptions and local knowledge, by scaling up locally-driven observations that empower local communities to lead their own climate adaptation efforts.

Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management.

Wading out the storm: Exploring the effect of flooding on energy poverty amidst disaster management strategies in Dar es Salaam

Although flooding is well-thought-out as one of the deadliest natural disasters, there is scarce evidence on how such an environmental shock affect energy poverty prevelence in a developing country context amidst the implementation of disaster risk management strategies. Using the Disaster Poverty Household Survey in Tanzania’s capital Dar es Salaam, we examine the effect of flooding on multidimensional energy poverty while estimating the moderation effects of non-structural ex-ante risk management strategies. After employing a myriad of robust methods, we observe that flooding increases energy poverty prevalence by about 32 %. The mediation analysis shows that income reduction serves as a pathway through which flooding affects energy poverty. Furthermore, estimates from the interaction analysis reveal how effective fiscal non-structural ex-ante risk management methods are in reducing the incidence of energy poverty among flood victims. However, the defense strategies were noted to be inefficient. These results provide policymakers with the necessary tools to create policies that address energy-related needs in areas affected by natural disasters, especially in developing countries.

Differential ethnic approach in disaster risk reduction and management: The case of Colombia

Colombia is a multicultural country, with ethnic minorities comprising 13% of its population. These ethnic groups have high vulnerability to disasters, not only due to their geographic location, but also because of inequities that exacerbate the vicious cycle of poverty. However, they have developed knowledge and tools to face these disasters that are responsive to their specific needs and should be incorporated into national and sub-national disaster risk reduction and management (DRRM) policies and strategies to ensure inclusion and institutionalization. This article aims to understand the current incorporation of the differential ethnic approach in the DRRM system of Colombia. A literature review of journal articles and grey literature directly relevant to the differential ethnic approach in DRRM in Colombia was conducted on December 2022 and January 2023. Out of 1067 records, only 20 were eligible: 15 (75.0%) grey literature and 5 (15.0%) journal articles. Majority of the records (19, 95.5%) were on disaster prevention or mitigation. Results showed that official national DRRM policies and plans recognize the cultural diversity of Colombia, mandating several activities that require the inclusion of ethnic minorities. Narratives of DRRM activities where ethnic minorities were involved were found. However, challenges in adopting the guidelines at the national and local levels were found, highlighting the need to increase efforts to strengthen the integration of the differential ethnic approach in the DRRM system. The low number of scoped records also highlights the lack of documentation, monitoring, evaluation, or research that can provide insights that can contribute to this topic.

Implementation of Spatial and Regional Plan Policies and Disaster Resilient Villages in West Nusa Tenggara

West Nusa Tenggara is located on active fault line along the Lombok and Sumbawa Faults, which often causes high magnitude earthquakes and can cause major impact on society, environment and infrastructure. The purpose of this study is to determine the implementation of spatial and regional planning policies in earthquake risk reduction in West Nusa Tenggara. The method used in this research is descriptive qualitative research using purposive sampling technique and literature study. The informant of this research is the Planning Analyst of the Regional Planning and Development Agency of West Nusa Tenggara. The result of this research is that disaster risk management strategies are implemented collaboratively between agencies from various sectors. In the pre-disaster stage, the local government ensures the readiness of the region and the community against the potential threat of natural disasters, by forming disaster resilient villages. Spatial and regional plan policies in West Nusa Tenggara are directed at natural disaster mitigation and adaptation to reduce disaster risk.

Damage Risk Reduction using Indonesian Wooden Culture

Cultural Heritage can play a crucial role in fostering resilience by reducing vulnerabilities and providing precious assets for an affected region’s sustainable social and economic development during its recovery phase by attracting investment, creating employment, or providing renewable natural resources. Disaster Risk Management (DRM) strategies allow a systematic approach to identify, assess, and reduce disaster risks decreasing socioeconomic vulnerability. Extensive consultations with the different institutions involved in preserving and disseminating information often contribute to design mitigation measures that align the understanding of various risks among stakeholders and communities, particularly in risk monitoring and emergency response. This article shows a peculiar and exemplary context as that of Indonesia. We want to show that Indonesian wood, constructed following the vernacular tradition, is a source to reduce the risks in that framework. A brief introduction shows how disaster management in Indonesia’s plan is essential to protect the community and its tangible and intangible cultural heritage.

Putting the constructive ambiguity of climate change loss and damage into practice: The early work of the UNFCCC WIM ExCom

AbstractThe establishment within the United Nations Framework Convention on Climate Change of the Warsaw International Mechanism on Loss and Damage associated with Climate Change Impacts (L&D) resulted from a loose consensus that emerged based on a constructively ambiguous understanding of what climate change loss and damage is and how to best address this policy problem. Different actors have understood and advocated for divergent conceptualizations of L&D: some frame it through the lens of risk and see comprehensive disaster risk management strategies, insurance schemes and post hoc humanitarian approaches as most appropriate. Others understand it through the lens of climate justice, emphasizing the harms that arise because of climate change losses and damages and advocate for compensation as an appropriate policy response. How does this ambiguity embedded within the climate regime translate into practice during the implementation stage? This research shows that ideational contestation over L&D has specific implications for institutional development, including: (i) the composition and expertise of the governing Executive Committee (ExCom); and (ii) the practices of agenda‐setting and the development of the ExCom's workplan. Drawing on multi‐sited ethnographic data and interviews with key stakeholders, this analysis identifies some of the ways in which constructive ambiguity can become embodied and institutionalized in L&D governance. It also points to a paradox in international climate governance—that the very ambiguity that allowed for the institutional embedding of L&D is also the driver of continued contestation, facilitates the re‐negotiating of issues already agreed and explains institutional delays in effectively grappling with the losses and damages that are already taking place.

Climate Risk Analysis Using a High-Resolution Spatial Model in Costa Rica

Increasing extreme weather and climate events have led to recurrent disasters that cause significant harm to human populations. The aim of this present study is to complement Costa Rica’s National Meteorological Institute 2017 risk assessment methodology for extreme weather and climate events. This methodology uses different socio-spatial indicators related to vulnerability and hazards to extreme hydrometeorological events. However, in the methodology applied and presented in this document, an exposure component was added to the model to obtain a more detailed representation of climate-related risks. The presented methodology was implemented in the municipalities of Cartago and Turrialba, where the frequency and severity of weather events have been a major issue in the last few decades. The results showed in fact, there were considerable differences between both, whereby factoring in the exposure component it is possible to visualize more specific risk zones, which evidences the need to include exposure components in this type of model, as this may allow for more timely responses and disaster risk prevention according to the specific vulnerability conditions in these zones. The outcomes at the minimal geostatistical unit level can aid local decision makers in developing more effective disaster risk management and adaptation strategies to minimize the loss of life and property resulting from extreme weather and climate events in Costa Rica.

Optimisation-based integrated decision model for ambulance routing in response to pandemic outbreaks

Pandemics and sudden disease outbreaks place considerable stress on hospital resources. Their increasing numbers in recent years has necessitated investment in disaster risk management strategies, particularly in the healthcare sector. The sudden surge of patients, particularly in requesting ambulance services, overwhelms hospital systems and compromises health service delivery. Failure of health planners to respond immediately to a sudden disease outbreak can result in insufficient distribution of healthcare services and can thereby exacerbate the death toll dramatically. The current research aims to develop an optimisation-based integrated decision model to assist healthcare decision-makers with immediate and effective planning for ambulances to move critical patients from their residences to hospitals, considering the available capacities of each hospital. Several lemmas for the problem are proposed, and based on these; several local search methods are developed to improve the performance of the proposed optimisation method. To confirm the efficacy of the proposed approach, a comprehensive comparison is conducted. In conclusion, sensitivity analyses are performed to discuss some practical insights. The proposed models can be adopted to develop decision tools that enable hospital system managers to optimize their resources to changing healthcare needs in disease outbreaks.