Anatomy of a famine: using fuzzy cognitive mapping to understand the crisis in Ayod County, South Sudan, 2017 

Anatomy of a famine: using fuzzy cognitive mapping to understand the crisis in Ayod County, South Sudan, 2017 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Anatomy of a famine: using fuzzy cognitive mapping to understand the crisis in Ayod County, South Sudan, 2017 Matthew Day This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5172135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper explores the limitations of current famine analysis methodologies and introduces Fuzzy Cognitive Mapping (FCM) as a novel approach to understanding famine dynamics. Traditional methods, such as the Integrated Food Security Phase Classification (IPC), often fail to capture the complex, non-linear interactions among social, economic, political, and environmental factors that drive famine. These methods rely heavily on cross-sectional survey data and expert judgment, which are prone to errors and biases. This study demonstrates the utility of FCM in analyzing famine systems, using the 2017 Ayod County crisis in South Sudan as a case study. FCM integrates qualitative and quantitative data, enabling real-time analysis of dynamic systems with limited data availability. The results highlight how FCM can reveal critical linkages and feedback loops, offering a more nuanced understanding of famine trajectories and enhancing early warning systems. The paper argues for the adoption of FCM in humanitarian contexts to improve the timeliness and accuracy of famine response and prevention strategies. Famine Analysis Complex Systems Fuzzy Cognitive Mapping Humanitarian Response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key Messages Complexity of Famine Systems: Traditional famine analysis methods, such as the Integrated Food Security Phase Classification (IPC), inadequately capture the complex, non-linear interactions among social, economic, political, and environmental factors that drive famine. This limitation often results in incomplete and delayed recognition of famine conditions, exacerbating human suffering and mortality. Utility of Fuzzy Cognitive Mapping (FCM): Fuzzy Cognitive Mapping (FCM) offers a robust alternative for famine analysis by integrating both qualitative and quantitative data. FCM effectively models the dynamic interactions and feedback loops within famine systems, providing real-time insights into system behavior and enabling more accurate early warning and response planning. Case Study of Ayod County, South Sudan: The application of FCM to the 2017 Ayod County crisis in South Sudan demonstrates its efficacy in capturing the intricate interdependencies and emergent properties of famine systems. The study highlights how FCM can identify critical tipping points and intervention opportunities, thereby enhancing the effectiveness of humanitarian responses and contributing to more equitable and transparent famine prevention strategies. Introduction Famine is best understood as a complex dynamic system, in which social, economic, political, and environmental factors interact in unpredictable and non-linear ways ( 1 – 3 ). Both Howe ( 4 ) and Fortnam and Hailey ( 5 ) argue that famines share properties associated with complex dynamic systems, characterized by non-linearity, emergent properties, and self-organization. They illustrate how cascading effects and feedback loops cause complex and unpredictable interactions between factors that drive a population toward a famine state, characterized by starvation, destitution, wasting, disease outbreaks, and ultimately, mass non-trauma mortality ( 3 – 5 ). However, current approaches to analyzing famine have not fully incorporated this contemporary understanding of famines as complex systems. Traditional analytical methods and protocols often treat famine as a linear process, failing to capture the intricate interdependencies and non-linear dynamics among contributing factors ( 3 ). Critiques of the Integrated Food Security Phase Classification (IPC) protocols, for example, highlight the limitations of focusing solely on severity indicators without accounting for the magnitude, temporal, and spatial dimensions of famine crises, which often leads to incomplete analysis ( 3 , 6 , 7 ). The currently accepted protocols for classifying famine, the IPC, require two of three outcome indicators to surpass their associated thresholds for Famine (IPC Phase 5): 20% or more of the population is facing starvation (caloric deficits), 30% or more of the population is acutely malnourished, and 2 people per 10,000 are dying from non-trauma-related causes ( 8 ). In humanitarian contexts, where conflict, displacement, and poor infrastructure severely limit physical access to the population, conducting representative household surveys to collect the requisite data to measure these outcomes accurately and precisely is nearly impossible. Further, survey data is cross-sectional, reflecting conditions at a single point in time, and often limited in scope, rendering it incapable of capturing the evolving dynamics of a crisis ( 7 ). The IPC’s reliance on such data results in a failure to capture the real-time dynamics of an area’s trajectory toward a famine state. Additionally, the IPC’s famine protocol is time-consuming, with unnecessary procedural delays ( 9 ). The consequence of delays in recognizing the emergence of a famine state is non-trauma mass mortality and extreme human suffering. Limitations in Quantitive Modeling of AFI and Famine Recent developments in systems thinking, artificial intelligence, and advanced computational models have opened new avenues for famine modeling ( 3 , 6 , 7 , 10 , 11 ). However, models designed and validated around predicting IPC Phase classifications, particularly IPC Phase 5 (e.g. Famine), present a fundamental limitation. First, the IPC phase thresholds are not designed to incorporate the complex, dynamic interactions between factors such as livelihood collapse, market failure, water sanitation and hygiene (WASH) conditions, environmental shocks, conflict, and disease outbreaks—factors that collectively drive outcomes ( 10 ). Second, expert judgment determines IPC classifications, and as such is subject to errors, bias, and noise, based on imperfect information ( 12 – 14 ). As a result, quantitative models that are anchored around IPC classifications are often predicting the outcome of expert judgment rather than the objective manifestation of caloric deficits, wasting, and non-trauma mortality. Consequently, quantitative models trained and tested against IPC classifications are unlikely to reflect the full complexity of famine systems, which exhibit non-linear interactions, feedback loops, and tipping points that traditional models struggle to capture ( 10 ). Applying Fuzzy Cognitive Mapping to Famine Systems Building on Howe’s ( 4 ) conceptualization of famine systems and Fortnam and Hailey’s ( 5 ) framing of famine as a social-ecological system (SES), Fuzzy Cognitive Mapping (FCM) offers a novel approach to famine modeling. Despite the extensive study of famine, there is no publicly available literature that has applied FCM to famine system analysis. This paper seeks to address this gap by demonstrating the utility of FCM for analyzing famine dynamics in near real-time without the intensive data requirements typical of traditional methods. Originally developed by Kosko (1986), FCMs are quasi-quantitative models that combine cognitive mapping with fuzzy logic, making them suitable for analyzing complex systems characterized by uncertainty and limited data availability ( 15 ). FCMs integrate both qualitative and quantitative data, enabling them to represent systems in which precise quantitative measurements are sparse or unavailable, such as in humanitarian crises ( 15 – 22 ). FCMs are constructed through iterative engagement with stakeholders or experts who identify key variables and causal relationships that define the system ( 16 – 19 , 22 , 23 ). Key Steps in FCM Construction Establishing Factors : Identify and define relevant system factors based on expert input or stakeholder consensus. Factors should be selected to align with the objectives of the analysis and the specific system of interest ( 17 , 24 ). Mapping Causal Relationships : Establish and define the causal relationships between factors. For example, the impact of seasonal rainfall variability on crop yields or the influence of market access on food prices ( 24 ). Assigning Weights : Determine the strength and direction of the influence between factors, usually using values ranging from − 1 to + 1. Positive values indicate a reinforcing relationship, while negative values represent an inhibiting effect ( 16 , 18 , 20 , 21 ). Constructing the FCM : Develop a directed graph in which nodes represent factors and edges represent weighted causal links. This graphical representation provides a visual model of how different components of the system interact ( 25 ). Analyzing the FCM : Use casual and dynamic analyses to explore system behavior and interactions. Static analysis provides a snapshot of the system at a specific point in time, while dynamic analysis examines how changes in one factor propagate through the system over time ( 16 , 23 , 24 ). Application to the 2017 Ayod, South Sudan Famine The 2017 crisis in Ayod County, South Sudan, underscores the necessity for reimagining famine analysis, early warning, and classification protocols. Ayod, an agropastoral livelihood system situated within the Sudd in central South Sudan, experienced severe flooding, disease outbreaks, and conflict between 2016 and 2017. Through a complex interaction of exogenous factors, starvation and livelihood collapse resulted in a near-complete reliance on wild foods, widespread internal distress migration, disease outbreaks, widespread malnutrition (classified as Extremely Critical (IPC AMN Phase 5) in the May 2017 IPC Acute Malnutrition analysis workshop), and near famine levels non-trauma crude death rates ( 26 , 27 ). In FGDs conducted by the author in 2017, local Ayod population referred to the year as "The Year of the Thou," highlighting their total dependence on the leaves of the Thou tree. Yet, Ayod was never officially classified as a famine by the IPC due to complications arising from 1) data availability, logistical constraints in data collection, and survey bias, and 2) the IPC famine protocols themselves, which include issues related to timeliness, rigid survey data requirements, and a disconnection between previous and current data. Utility of FCM in Famine Systems Analysis This paper aims to demonstrate that FCM, when applied to famine systems, can capture the complexity of interactions between factors that contribute to a crisis and serve as a blueprint for a system's trajectory towards a famine state. Baseline FCM development helps illustrate the current state of the system, providing insights into complex relationships, often overlooked. Dynamic FCM analysis, on the other hand, allows for real-time system analysis through the integration of real-time monitoring data, qualitative insights, and expert knowledge ( 16 , 19 , 23 ). This approach offers a more nuanced understanding of famine dynamics than conventional linear models, making it a valuable tool for early warning and response planning in humanitarian contexts ( 17 , 21 ). Furthermore, the author hopes this novel application of FCM will encourage other humanitarian practitioners to explore alternative methods and analytical tools beyond the outdated humanitarian toolkit, in the pursuit of a better humanitarian information system that is equitable, accountable, transparent, and innovative. Data and Methods The FCM for Ayod was created through a two-step process: initially establishing a baseline map, followed by conducting four "what-if" scenario analyses using the Dynamic FCM methodology. The author employed Mental Modeler, an online tool selected for its user-friendly interface, low bandwidth demands, and collaborative advantages, which make it suitable for humanitarian contexts with diverse expertise and restricted technical capabilities ( 25 , 28 ). While it lacks advanced machine learning functions, it facilitates baseline mapping and straightforward scenario analysis, aiding stakeholders in comprehending complex systems ( 28 ). Method: Development of the Baseline FCM Map for Ayod The FCM framework corresponds to famine outcome criteria, emphasizing four key factors: caloric intake deficits, malnutrition prevalence, disease outbreaks, and mortality rates ( 3 – 5 , 29 ). The concept of destitution—understood as the collapse of livelihood systems, household assets, and coping mechanisms—is not separately included but inferred through considerable reductions in aspects such as a household’s asset base and coping capacity. Factors for the model were selected based on diverse sources, including the FEWS NET South Sudan livelihood zone report, grey literature, qualitative data from a rapid assessment conducted by the author in June 2017, and the author’s experience as a humanitarian analyst based in South Sudan from 2017–2020 ( 30 , 31 ). The baseline FCM included 21 factors, which were also integrated into the dynamic FCM scenario analysis (refer to Table 2 ). Relationships between factors were quantified by assigning weights to links within a range of -1.0 to 1.0 in 0.25 increments. Positive weights indicate a direct relationship (e.g., an increase in one factor results in an increase in another), whereas negative weights signify inverse relationships. The magnitude reflects the strength of the relationship, with larger absolute values denoting a stronger influence ( 15 , 16 , 19 , 28 , 32 ). A total of 74 causal relationships were defined, producing a model with a density of 0.176 and an average of 3.52 relationships per factor (see Table 3 ). The FCM’s complexity score, which evaluates the balance among drivers, receivers, and intermediate factors, was 0.25. The baseline FCM model is depicted in Fig. 3 (see supplementary material for the Mental Modeler project file and FCM matrix table), providing a comprehensive visual of the famine system in Ayod and serving as the basis for subsequent dynamic FCM analysis. Table 1 Factors of Interest for Tracking Risk of a Famine State Factor of Interest Description Caloric Intake Estimated food availability and consumption patterns, highlighting caloric deficits across different system states. Nutritional Status Assessed malnutrition prevalence, focusing particularly on vulnerable groups such as children under five. Health Status Tracked the potential for disease transmission, including cholera and acute watery diarrhea (AWD), alongside other health risks. Mortality Risk Evaluated the likelihood of increased mortality due to interactions between food insecurity, malnutrition, and disease outbreaks. Table 2 2016–2017 Ayod FCM Factor List and Descriptions Factor Description Atypical Flooding Presence, extent, and impact of flooding that is not typical for the area, affecting various aspects of life and livelihoods. Livestock Access Ownership and ability to access livestock for food, income, and milk. Crop production Seasonal crop production and its impact on food availability and income. Market Functionality Functionality of local markets, including the availability of goods and the ability to trade. Income Generation Income generated from selling own production, such as crops, livestock and fish, petty trade and labor. Fishing Access Ability to access and engage in fishing for food and income. WASH General access to clean water, and sanitation and hygiene conditions in the area. Cholera Cholera morbidity - proxy for likely cholera cases IDPs Number of internally displaced persons (IDPs) relative to the host population and their impact on resources and services. Caloric Intake The amount of calories consumed by individual. Nutritional Status Overall nutritional status of the population, notable likely malnutrition prevalence. Disease Morbidity Prevalence of diseases such as malaria, acute watery diarrhea (AWD), or others. Health Status Health status of individuals, influenced by disease prevalence, nutrition, and access to healthcare. Conflict - Internal Presence of direct conflict inside of Ayod. Conflict - External Presence of conflict in neighboring areas. HFA Populations access to Humanitarian food assistance inflow to the area. Health and Nutrition Services Availability and functionality of health and nutrition services in the area. Outward Migration Movement of people out of the area due to various factors such as conflict, lack of resources, or seeking better opportunities. Wild Food Consumption Availability and frequency of wild food consumption as a supplement to regular food sources. Mortality Risk Risk of death due to various factors such as starvations, disease, malnutrition, and lack of healthcare. Distress Migration Internal atypical movement of people in search of resources and life saving services Method: Dynamic FCM Analysis Dynamic FCM analysis employed scenario-based system modeling by adjusting factor activation levels in response to distinct scenario periods characterized by shifts in external pressures, seasonality, or significant events (refer to Table 3 ). Activation values, ranging from − 1 to 1 in 0.25 increments, represented relative changes in each factor based on a convergence of qualitative and quantitative evidence ( 16 , 16 , 18 , 22 , 28 , 32 – 34 ). For factors such as caloric intake, nutritional status, health status, and mortality risk, activation values were not directly applied, allowing the model to predict relative changes in these values based on interrelationships with other factors, including feedback loops. Factors lacking sufficient evidence were treated similarly. Separate analyses were conducted for each scenario to ensure context-specific accuracy. The FCM’s predictive performance was assessed by comparing model-predicted values of key outcome indicators—caloric intake, nutritional status, health status, and mortality risk—against retrospective data, contextual information, and observed outcomes. Table 3 Dynamic FCM System Scenarios - Time Period and Description Dynamic FCM Scenarios Period Typical Livelihood Season Events System State 1 July 2016 - November 2016 Harvest period, livestock return to the homestead Outbreak of national conflict and displacement into Ayod from Juba; atypical flooding in Ayod and additional internal displacement of populations in Ayod System State 2 December 2016 - February 2017 Dry season, some household food stocks remaining from harvest, livestock move away from the homestead Outbreak of conflict in Ayod, with large advances into Ayod by SPLA, displacing populations in central Ayod to northern (Pagil) and western Ayod (low-lying Sudd) System State 3 March to June 2017 Late dry period and early rainfall. Lean season, a traditional period when household consumption patterns decrease Conflict reduces slightly in Ayod; SPLA makes large advances in Canal/Pigi and Fangak counties, north of Ayod; cholera outbreak in Ayod; collapse of northern trade corridor—a critical food and livestock supply route into Ayod System State 4 July to September 2017 Early harvest, some green harvest available, livestock return to the homestead Significant upscale in humanitarian food and nutrition distributions Table 4 Dynamic FCM Factor Activation Scale. Factor Change during Scenario Period Value Significantly increase 1 Increase 0.75 Moderate increase 0.5 Slight increase 0.25 Slight decrease -0.25 Moderate decrease -0.5 Decrease -0.75 Significant decrease -1 Data: Dynamic FCM Analysis The evidence repository compiled for the dynamic FCM analysis included 242 unique entries categorized by date range, factor, source, and geographic location. This repository incorporated various data types, such as reports, assessments, datasets, and key informant interviews. It served as the foundational dataset for determining factor activation levels across scenarios (refer to Table 5 for summary). See supplementary material for full list of evidence and sources. Table 5 Data Sources for Dynamic FCM Model Analysis Data Type Source Date Annual Report CMD Annual Report 2017 2017 Assessment REACH FSL Assessment / REACH FSL Data / REACH MUAC Screening June - July 2017 Assessment Report WFP/CRS Rapid Needs Assessment February - April 2017 Focus Group Discussion Notes SSD FGD Notes IDPs Ayod April 2017 Memo Ayod Analysis Memo August 2017 Needs Assessment Report Ayod County Inter-Agency Floods Needs Assessment August - September 2016 Population Movement Research REACH Population Movement Baseline Research 2017 Project Proposal Ayod SMART Project Proposal August 2017 Report RMF SMART Survey Report August - September 2017 Report FEWS NET Livelihood Zone Report April 2017 Report Flooding Report CMD September 2016 Report Jonglei FSL Report / REACH Canal Fangak FSL Report April 2017 Report REACH FSL Report June – July 2017 Report WFP Rapid Assessments / WFP RRM January 2015 - September 2017 Report WFP - Planned HFA October 2016 - May 2017 Report Conflict and Displacement Report IOM December 2016 - January 2017 Report UNICEF SSD Situation Report May 2017 Report WFP/UNICEF RRM Mission Normanyang April 2017 Report Governemt of South Sudan Cholera Update May - June 2017 Report IOM Situation Report June 2017 Report IPC Worksheet / IPC May 2017 Update May - September 2017 Report Jonglei Situation Overview March 2017 Report REACH FGD - Nyal September 2017 Report REACH FGD - Duk Padiet March 2017 Report RMF Q1 2017 Project Report January - March 2017 Report Acute Malnutrition and Jonglei Report by RMF October - December 2016 Report Conflict Briefs (Jan - May 2017) January - May 2017 Report UNICEF Situation Update May 2017 Report UNICEF RRM Jan 2017 January 2017 Report WHO Article - Cholera July 11, 2017 FGDs REACH – Jonglei and Unity Area of Knowledge Bases December 2016 - March 2017 Research REACH Population Movement Research 2017 Quantitative Data WFP Actual HFA April - September 2017 Quantitative Data CFSAM August 2017 Quantitative Data FSNMS Round 20 July 2017 Quantitative Data REACH Area of Knowledge FGDs August 2017 Quantitative Data WFP RRM Data October 2016 - September 2017 Worksheet IPC September 2017 Analysis Worksheet September 2017 Results System State 1: Impact of Flooding and Livelihood Disruption (July–November 2016) Severe flooding led to significant environmental and socio-economic disruptions. Livestock access declined sharply due to displacement and animal mortality, while crop production declined because of widespread crop destruction ( 30 ). Market functionality deteriorated, resulting in a substantial decline in household caloric intake and increased reliance on wild foods ( 35 ). The FCM predicted declines in caloric intake (–0.88), nutritional status (–0.46), and health status (–0.38), with a corresponding increase in mortality risk (0.22) System State 2: Onset of Hold Conditions and Escalation of Pressures (December 2016–February 2017) Pressures intensified with continued flooding effects, escalating conflict, and the early onset of the lean season. Livestock access further deteriorated, markets remained non-functional, and households exhausted their food stocks. First reported cases of cholera, linked to poor WAS conditions and increased concentration of host communities and IDPs ( 36 ). The FCM indicated continued low caloric intake (–0.86), further deterioration in nutritional status (–0.60) and health status (–0.56), and a sharp increase in mortality risk (0.65). System State 3: Emergence of a Famine State (March–June 2017) This period marked the peak of the crisis, with prolonged food shortages, intensified disease outbreaks, and mass displacement converging to create famine conditions. Livestock access, crop production, and markets collapsed, and humanitarian assistance was severely constrained. Households relied almost entirely on nutritionally inadequate wild foods. The FCM predicted near-total depletion of caloric intake (–0.94), widespread malnutrition (nutritional status − 0.65), deteriorated health status (–0.61), and a significant rise in mortality risk (0.80). System State 4: Initial Signs of Rebalancing (July–September 2017) Signs of recovery emerged due to seasonal improvements, partial livelihood restoration, and expanded humanitarian interventions. Livestock began returning, small-scale crop production resumed, and markets showed modest recovery. A cholera vaccination campaign reduced disease transmission, and humanitarian food assistance scaled up. The FCM reflected improvements in caloric intake (–0.61), nutritional status (–0.17), and health status (–0.25), with a decrease in mortality risk (0.13). Figure 4 illustrates the changes in the four factors of interest in each scenario iteration, and Table 6 summarizes activation and predicted factor values. See supplementary material for full list of evidence used for each system state. Table 6 Results of System State FCM Analysis Factor System State 1 - July to Nov 2016 System State 2 - December 2016 to Feb 2017 System State 3 - March to June 2017 System State 4 - July to September 2017 Atypical Flooding 1 .75 .5 .75 Livestock Access -0.75 -0.75 -1 -1 Crop production -0.5 -0.75 -1 -0.5 Market Functionality -0.25 -0.25 -1 -0.75 Income Generation -0.25 -0.5 -1 -0.75 Fishing Access -0.5 0.25 -0.75 -0.5 WASH -0.5 -0.5 -1 -1 Cholera 0.15 0.5 1 0.75 IDPs 0.25 0.75 1 0.5 Disease Morbidity 0.25 0.5 1 0.75 Conflict - Internal 0.25 1 0.5 -0.25 Conflict - External 0.5 1 1 0.5 HFA -1 -0.5 -1 0.75 Health and Nutrition Services -0.25 -0.5 -1 0.25 Outward Migration 0.75 -0.5 -1 -0.75 Wild Food Consumption 0.25 0.5 0.75 0.25 Distress Migration 0.15 0.5 1 0.75 Caloric Intake* -0.88 -0.86 -0.94 -0.61 Nutritional Status* -0.46 -0.6 -0.65 -0.17 Health Status* -0.38 -0.56 -0.61 -0.25 Mortality Risk* 0.22 0.65 0.8 0.13 Discussion Value Add of FCM for Analyzing and Understanding Famine Systems Developing a baseline FCM significantly improved the ability to visualize and analyze the intricate interrelationships among factors that influenced the system's trajectory towards famine. The resultant map reveals critical linkages and feedback loops driving the system's trajectory, offering insights into the various traits described by Howe and Fortnam and Hailey. The baseline FCM acts as both an analytical framework and decision support tool. In Ayod, it showed how flooding's initial impact on food production and livestock access (System State 1) worsened due to conflict and deteriorating WASH conditions (System State 2). The model revealed self-reinforcing dynamics in System State 3, leading to a food system collapse (Shown by Fig. 5 ), distress migration, and a resultant spread of cholera, marking a critical tipping point ( 37 ). Figure 6 illustrates this tipping point, where prolonged pressures (downward arrows) and adaptive capacity collapse (upward arrows) accelerated self-reinforcing dynamics (diagonal arrow), resulting in systemic destabilization and famine. During this system state, the FCM indicated that a convergence of malnutrition, disease outbreaks, and widespread food scarcity led to rapid declines in caloric intake, nutritional status, and overall health, increasing mortality risk 0.8. By modeling these dynamics, FCM enables a clearer understanding of when and how famine conditions emerge, thereby enhancing early warning systems and informing the timing of interventions. The model also demonstrated how increased humanitarian food assistance (HFA) acted as a rebalancing mechanism, improving adaptive capacity in System State 4 and breaking the self-reinforcing dynamics. The rise in mitigating factors (upward arrows) gradually alleviated famine conditions, shifting the system's trajectory away from a famine state. This shift, visualized through the system map and captured in the FCM model, underscores the value of FCM in real-time analysis and its potential to inform effective humanitarian responses even under data limitations. Capturing Nonlinear and Emergecny Properties FCM effectively represented non-linear interactions and emergent properties characteristic of dynamic complex systems. Nonlinear dynamics were notable in the FCM results, particularly from February to July 2017, when self-reinforcing feedback loops accelerated the system's trajectory toward famine conditions. This nonlinearity was likely driven by significant consumption deficits and the rapid depletion of food reserves, leading to increased distressed migration within Ayod. The migration, in turn, exacerbated public health issues by contributing to the spread of cholera. Vulnerable populations, already weakened by hunger, congregated in unsanitary conditions, such as cattle camps and areas with stagnant water, which facilitated cholera transmission. The system’s adaptive capacity was further constrained by a corralling effect—caused by natural barriers and ongoing conflict—which limited outward migration. This restriction undermined the community’s ability to seek external assistance or resources, compelling them to depend on deteriorating local conditions. Consequently, the convergence of these stressors heightened the population’s susceptibility to malnutrition, disease outbreaks, and ultimately mortality. Three independent but important factors illustrate some of the emergent properties which shaped the systems trajectory towards a famine state: Atypical Flooding (August–October 2017) : Severe flooding led to extensive crop and livestock losses, reducing food availability and triggering an earlier onset of the lean season. This asset depletion left households without sufficient resources to implement traditional coping strategies, worsening food insecurity. Internal and External Conflict : Internal conflict within Ayod (January–March 2017) led to widespread displacement, while external hostilities restricted access to markets, humanitarian aid, and health services. Conflict curtailed migration, compelling households to rely on wild foods and natural resources as last-resort coping mechanisms. Cholera Outbreak : Beginning in July 2016, a cholera outbreak, driven by conflict-induced displacement, rapidly spread across South Sudan. By February 2017, cholera intensified in Ayod due to deteriorating food security. In search of food, people moved to areas with contaminated water sources, leading to over 3,000 probable cases by March 2017—the highest concentration of cholera in South Sudan in four years ( 37 ). These events were interdependent, with each exacerbating the impact of the others. For instance, flooding heightened cholera transmission, and conflict restricted access to assistance. The integration of FCM within a complex systems framework revealed these interconnections and demonstrated how flooding, conflict, and cholera collectively drove the famine's emergent properties. FCM proved instrumental in mapping these dynamics, offering valuable insights into famine trajectories and identifying potential intervention points. Methodological Limitations in IPC - Ayod County, 2017 The IPC analytical protocols faced significant limitations in accurately assessing famine conditions in Ayod County during 2017. Between May and July of that year, Ayod County exceeded two of the three critical thresholds for famine classification: acute malnutrition and acute food insecurity. The IPC Acute Malnutrition (AMN) Analysis Workshop, held in May 2017, classified the region as Extremely Critical (IPC Phase 5) for the duration of this period ( 26 ). This classification was substantiated by Mid-Upper Arm Circumference (MUAC) screening data, which indicated that the proxy Global Acute Malnutrition (GAM) prevalence exceeded the IPC threshold of 15% for Extremely Critical status. Multiple mass screenings conducted during early to mid-2017 revealed proxy GAM rates exceeding 30% ( 38 ). Moreover, two household-level surveys performed in June and July 2017 demonstrated that the food consumption threshold was also breached, with around 20% of the population experiencing catastrophic levels of acute food insecurity (IPC Phase 5). According to IPC protocols, surpassing these two thresholds should have warranted a likely declaration of famine. However, the IPC Acute Food Insecurity Analysis Update, conducted in May 2017, did not account for the data collected in June and July, thereby failing to incorporate evidence of escalating food insecurity. The third IPC threshold—the non-trauma crude death rate (CDR) of 2 deaths per 10,000 individuals per day—was exceeded according to a SMART survey conducted between August and September 2017. The initial report indicated a CDR of 2.03 (1.36–3.02) per 10,000 persons per day, surpassing the famine threshold ( 39 ). However, this figure was later adjusted downward to 1.89 (1.26–2.84) due to an extension of the recall period from 107 days to 126 days ( 27 ). This adjustment was justified by delays in data collection related to weather conditions, despite no new deaths being reported from the affected clusters. Such an extension artificially suppressed the CDR and obscured the true mortality distribution. The recall period began on May 16, 2017, coinciding with the IPC classification of Extremely Critical acute malnutrition and preceding the household survey data that indicated catastrophic food insecurity. The survey's design effect was calculated at 3.52, suggesting a high degree of clustering in mortality data, indicative of real-world dynamics where deaths are often concentrated in specific conditions, such as migration or displacement camps.( 27 , 39 ) Over half of reported deaths occurred during migration, suggesting that fatalities were often recorded at new household locations rather than their original sites. Lastly, despite some reported improvements in food consumption outcomes from the SMART survey, the timing of data collection in relation to humanitarian food and nutrition distributions indicated potential biases, as over 50% of surveyed clusters had received aid less than two weeks prior (see supplemental material for timeline). These methodological concerns underscore how delays, timing discrepancies, and biases can significantly distort assessments of famine conditions, complicating the understanding of the severity of the crisis in Ayod County. FCM Efficacy as Methodology The Ayod case study highlighted FCM’s efficacy as a methodology for mapping and analyzing famine systems. FCM demonstrated three primary strengths. Flexibility and Adaptability : FCM is highly adaptable in contexts with imperfect information, integrating diverse data sources, both qualitative and quantitative. It enables focused analysis on specific factors like market trends or rainfall forecasts without necessitating a comprehensive famine model. Moreover, FCM’s ability to combine qualitative insights and expert judgment allows for effective analysis even when data is scarce. This flexibility supports analytical discussions and scenario analyses, making FCM valuable in contexts where data availability is limited, while ensuring stronger alignment with objective reality as more evidence is incorporated. Scenario Analysis Capability : FCM supports robust scenario analysis by allowing analysts to modify factor values based on the available evidence. This capability enables exploration of factor interdependencies and responses under different scenarios, including plausible or worst-case conditions. It also facilitates assumption tracking, comparing new data against prior assumptions, which is critical for adjusting projections when new information arises or unexpected events occur. This functionality enhances the projection process for food insecurity and famine trajectories. Integration of Diverse Perspectives : FCM offers an intuitive platform for incorporating local knowledge and expert input through focused group discussions (FGDs). Its scalability enables detailed analysis of critical factors by decomposing them into sub-components, if necessary. Continuous iterations of FCM ensure that the model remains relevant and aligned with the evolving understanding of the system. Future efforts should focus on further exploration of FCM as a decision support tool for analyst working in famine analysis, integration of humanitarian stakeholders and local population in the FCM development and analysis process, and exploring integration with complementary tools to enhance famine analysis, including GIS, analysis of competing hypothesis and related standard analytical techniques tailored to mitigate expert judgement error within imperfect data environments. Embracing real-time system analysis through FCM has the potential to improve early warning systems, facilitate timely interventions, and contribute to more effective famine prevention strategies. Declarations Author Contribution The primary author developed, analyzed, wrote and reviewed all text in the manuscript by Matthew Day. Acknowledgement Paul Howe, Dan Maxwell, Anu Atre, Peter Hailey, Oliver Callaghan, Chris Newton, Tim Hoffine Data Availability Data is provided within the manuscript or supplementary information files. References Sen A. Poverty and famines: An essay on entitlement and deprivation [Internet]. Oxford: Clarendon Press / Clarendon Press; 1981. Available from: http://www.amazon.com/Poverty-Famines-Essay-Entitlement-Deprivation/dp/0198284632/ref=sr_1_1?s=books &ie=UTF8&qid=1310678684&sr=1-1 Devereux S. The impact of droughts and floods on food security and policy options to alleviate negative effects. Agricultural Economics. 2007;37(s1):47–58. Maxwell D, Khalif A, Hailey P, Checchi F. Viewpoint: Determining famine: Multi-dimensional analysis for the twenty-first century. Food Policy. 2020;92:101832. Howe P. Famine systems: A new model for understanding the development of famines. World Development. 2018;105:144–55. Fortnam M, Hailey P. Insights from social-ecological systems thinking for understanding and preventing famine. Disasters. 2024;48(3):e12621. Maxwell D, Hailey P. Towards Anticipatory Information Systems and Action: Notes on Early Warning and Early Action in East Africa. 2020; Lentz E, Gottlieb G, Simmons C, Maxwell D. The Ecosystem of Humanitarian Diagnostics and Its Application to Anticipatory Action. 2020;24. PC Global Partners. Integrated Food Security Phase Classification Technical Manual Version 3.1. Evidence and Standards for Better Food Security and Nutrition Decisions. Rome; 2021. Maxwell D, Hailey P. Synthesis of Findings from Six Case Studies. 2020; Howe PW, Naumova EN. Poverty and famines 2.0: the opportunities and challenges of crisis modeling and forecasting. J Public Health Pol. 2022;43(3):329–34. Weiss S, Zouaoui C. What is the impact of AI on the decision-making process in Humanitarian disaster management ? 2021 [cited 2024 Sep 26]; Available from: http://rgdoi.net/10.13140/RG .2.2.19567.76960 Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. 1974;185. Heuer RJ. Psychology of intelligence analysis. Washington, D.C.: Center for the Study of Intelligence, Central Intelligence Agency; 1999. 184 p. Kahneman D, Sibony O, Sunstein CR. Noise: a flaw in human judgment. First Little, Brown Spark paperback edition. New York, NY Boston London: Little, Brown Spark; 2022. 452 p. Kosko B. Fuzzy cognitive maps. International Journal of Man-Machine Studies. 1986;24(1):65–75. Blacketer MP, Brownlee MTJ, Baldwin ED, Bowen BB. Fuzzy Cognitive Maps of Social-Ecological Complexity: Applying Mental Modeler to the Bonneville Salt Flats. Ecological Complexity. 2021;47:100950. Gray S, Chan A, Clark D, Jordan R. Modeling the integration of stakeholder knowledge in social–ecological decision-making: Benefits and limitations to knowledge diversity. Ecological Modelling. 2012;229:88–96. Gray SA, Zanre E, Gray SRJ. Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs. In: Papageorgiou EI, editor. Fuzzy Cognitive Maps for Applied Sciences and Engineering [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014 [cited 2024 Sep 9]. p. 29–48. (Intelligent Systems Reference Library; vol. 54). Available from: https://link.springer.com/ 10.1007/978-3-642-39739-4_2 Nyaki A, Gray SA, Lepczyk CA, Skibins JC, Rentsch D. Local-Scale Dynamics and Local Drivers of Bushmeat Trade: Participatory Modeling in Conservation. Conservation Biology. 2014;28(5):1403–14. Papageorgiou EI, editor. Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014 [cited 2022 Jun 24]. (Intelligent Systems Reference Library; vol. 54). Available from: http://link.springer.com/ 10.1007/978-3-642-39739-4 Papageorgiou E, Kontogianni A. Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management: A Methodological Primer and an Application. In: Young S, editor. International Perspectives on Global Environmental Change [Internet]. InTech; 2012 [cited 2024 Sep 9]. Available from: http://www.intechopen.com/books/international-perspectives-on-global-environmental-change/using-fuzzy-cognitive-mapping-in-environmental-decision-making-and-management-a-methodological-prime Khodadadi M, Shayanfar H, Maghooli K, Hooshang Mazinan A. Fuzzy cognitive map based approach for determining the risk of ischemic stroke. IET Syst Biol. 2019;13(6):297–304. Papageorgiou EI, Salmeron JL. A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Trans Fuzzy Syst. 2013;21(1):66–79. Barbrook-Johnson P, Penn AS. Systems Mapping: How to build and use causal models of systems [Internet]. Cham: Springer International Publishing; 2022 [cited 2023 Mar 1]. Available from: https://link.springer.com/ 10.1007/978-3-031-01919-7 Gray. Mental Modeler [Internet]. 2012. Available from: https://www.mentalmodeler.com/#about South Sudan: Acute Food Insecurity Situation for May 2017 and Projection for June - July 2017 | IPC - Integrated Food Security Phase Classification [Internet]. [cited 2024 Sep 28]. Available from: https://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1026196/?iso3=SSD RMF SMART Report Sept 2017. Felix G, Nápoles G, Falcon R, Froelich W, Vanhoof K, Bello R. A review on methods and software for fuzzy cognitive maps. Artif Intell Rev. 2019;52(3):1707–37. Xun. Surviving the Famine. 2023; 2016-09_SSD_Ayod_Flooding Report_CMD.pdf. FEWS NET. Livelihoods Zone Map and Descriptions for South Sudan [Internet]. Washington D.C.; 2018 Aug. Available from: https://fews.net/sites/default/files/documents/reports/Livelihoods%20Zone%20Map%20and%20Descriptions%20for%20South%20Sudan.pdf Mourhir A. Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management. Environmental Modelling & Software. 2021;135:104891. Aravindakshan S, Krupnik TJ, Shahrin S, Tittonell P, Siddique KHM, Ditzler L, et al. Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh. Environ Dev Sustain. 2021;23(11):16588–616. Van Vliet M, Kok K, Veldkamp T. Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures. 2010;42(1):1–14. REACH. Situation Overview - Jonglei State. 2016. Government of South Sudan, World Health Organization. South Sudan Cholera Outbreak Update. 2017. Jones FK, Wamala JF, Rumunu J, Mawien PN, Kol MT, Wohl S, et al. Successive epidemic waves of cholera in South Sudan between 2014 and 2017: a descriptive epidemiological study. The Lancet Planetary Health. 2020;4(12):e577–87. WFP. Rapid Response Mechanism - MUAC Screening Report Normanyang. 2017. Ayod RMF Presentation. Additional Declarations No competing interests reported. Supplementary Files 2017SSDAyodGFDsd1.csv AyodEvidenceRepository.xlsx AyodEvidenceTablewithDescriptions.docx AyodRealTimeSystemAnalysis.docx AyodFCMMentalModelProject20240907v2.mmp TimelineofSMARTSurveyandHFA.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Mar, 2025 Reviews received at journal 02 Mar, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviews received at journal 10 Dec, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers invited by journal 26 Nov, 2024 Editor assigned by journal 16 Nov, 2024 Submission checks completed at journal 29 Oct, 2024 First submitted to journal 28 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5172135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371657322,"identity":"09cebbf5-ece9-4134-8eea-c8e4e2cbc2a2","order_by":0,"name":"Matthew Day","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJACZjB5gIHxAQODHJgtQUADYzNUC7PBAQZjCZK0sEkQpUW3/fzxxwUVhxn4jp8xq/7YZlAn38B88DYPHi1mZ5IZm2ecOcwgeSbH7MbBNgMJgwNsydZ4tRwAauFtu81gcACs5Y+EAQOPmTReLecfA7X8A2o5/8asAGSLfAP/N/xaboBsaQBquZFjxgDSwnCAh42AlseGs3mO/eeRvPGsWOLMOQPJDYfZjC3n4HVY4oPPPDVpcnznkzd+qCgz4Jdvb3544w0eLTAAdAmHAYTJTIRyKGB/QLzaUTAKRsEoGFEAAC3lT1tejqtSAAAAAElFTkSuQmCC","orcid":"","institution":"Tufts University","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Day","suffix":""}],"badges":[],"createdAt":"2024-09-28 22:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5172135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5172135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69435945,"identity":"f29dd977-072b-4533-a639-4be24a8f4d2c","added_by":"auto","created_at":"2024-11-20 10:33:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":268615,"visible":true,"origin":"","legend":"\u003cp\u003eExample Fuzzy Cognitive Map\u003c/p\u003e","description":"","filename":"BasicFCMDiagram20240922194002.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/0cd0c1789b746f1a001e4ddb.png"},{"id":69436927,"identity":"6622bbad-e50b-4b11-a5c0-8a7fd56928f9","added_by":"auto","created_at":"2024-11-20 10:41:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":277848,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Workflow - FCM Baseline and Scenario Analysis\u003c/p\u003e","description":"","filename":"FCMWorkflowDiagram20240922193344.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/421711ebd6c8059d9a417832.png"},{"id":69436931,"identity":"659205a0-5ccb-4e2f-a3b2-618257daa7ba","added_by":"auto","created_at":"2024-11-20 10:41:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":535421,"visible":true,"origin":"","legend":"\u003cp\u003eAyod Static (Baseline) Fuzzy Cognitive Map\u003c/p\u003e","description":"","filename":"AyodFCM20240928065246.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/8457b5345a15a5402bbada08.png"},{"id":69437545,"identity":"016e72c2-47ac-4065-9ae8-08a1cf9118db","added_by":"auto","created_at":"2024-11-20 10:49:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32528,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic FCM Results 2016 - 2017\u003c/p\u003e","description":"","filename":"AyodFactorofConcernBarChartPAPERVerison.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/7fab272df70f92f9be151bc1.png"},{"id":69437543,"identity":"315f75f2-e866-439b-a793-6a9ea163ed25","added_by":"auto","created_at":"2024-11-20 10:49:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54527,"visible":true,"origin":"","legend":"\u003cp\u003eExample of Expanded FCM on Caloric Intake for System State 3. Grey Boxes indicate complete exhaustion. Dark Red indicates near exhaustion. Width of Arrows Represent Proportion of Kcal Intake.\u003c/p\u003e","description":"","filename":"floatimage523.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/0f7e6928896fb539c1253fa5.png"},{"id":69436925,"identity":"a94c3341-3f6a-47f8-8574-bdb1336fce93","added_by":"auto","created_at":"2024-11-20 10:41:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69742,"visible":true,"origin":"","legend":"\u003cp\u003eAyod Famine System Trajectory\u003c/p\u003e","description":"","filename":"floatimage612.png","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/0a906d7999eb505fd70d7900.png"},{"id":69439372,"identity":"f77126d5-1878-4170-b7fc-212c23e751a9","added_by":"auto","created_at":"2024-11-20 11:05:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1998199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/f7cbe9db-ad6d-4187-8d6b-5622319af2d9.pdf"},{"id":69435943,"identity":"27ac2513-f5b3-43b5-8806-045055095429","added_by":"auto","created_at":"2024-11-20 10:33:16","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2640,"visible":true,"origin":"","legend":"","description":"","filename":"2017SSDAyodGFDsd1.csv","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/5288145800a32f5acd5f9893.csv"},{"id":69435944,"identity":"60126f25-c922-4485-88d4-21a5ea23a16e","added_by":"auto","created_at":"2024-11-20 10:33:16","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38166,"visible":true,"origin":"","legend":"","description":"","filename":"AyodEvidenceRepository.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/1f88a2778fb6362b289b747b.xlsx"},{"id":69435948,"identity":"7ae6fc5d-32e0-4228-a3a2-0d50d19c50a6","added_by":"auto","created_at":"2024-11-20 10:33:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19895,"visible":true,"origin":"","legend":"","description":"","filename":"AyodEvidenceTablewithDescriptions.docx","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/eb466ea2de5a09b4abdf5496.docx"},{"id":69436928,"identity":"ec818ee6-9856-4601-97dd-a0b55231d691","added_by":"auto","created_at":"2024-11-20 10:41:17","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19859,"visible":true,"origin":"","legend":"","description":"","filename":"AyodRealTimeSystemAnalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/bed9fbfd85eb14a2cdf2a358.docx"},{"id":69438961,"identity":"d2c4790f-62b2-40a6-b788-e8d8d81130d9","added_by":"auto","created_at":"2024-11-20 10:57:16","extension":"mmp","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":52260,"visible":true,"origin":"","legend":"","description":"","filename":"AyodFCMMentalModelProject20240907v2.mmp","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/70ba8fc061e1e4a05ffa2104.mmp"},{"id":69435952,"identity":"baf25693-b57a-4523-bf10-9cb21bf7adf6","added_by":"auto","created_at":"2024-11-20 10:33:17","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17226,"visible":true,"origin":"","legend":"","description":"","filename":"TimelineofSMARTSurveyandHFA.docx","url":"https://assets-eu.researchsquare.com/files/rs-5172135/v1/6c601eea16fb6e72d690f93c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anatomy of a famine: using fuzzy cognitive mapping to understand the crisis in Ayod County, South Sudan, 2017 ","fulltext":[{"header":"Key Messages","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eComplexity of Famine Systems:\u0026nbsp;\u003c/strong\u003eTraditional famine analysis methods, such as the Integrated Food Security Phase Classification (IPC), inadequately capture the complex, non-linear interactions among social, economic, political, and environmental factors that drive famine. This limitation often results in incomplete and delayed recognition of famine conditions, exacerbating human suffering and mortality.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUtility of Fuzzy Cognitive Mapping (FCM):\u003c/strong\u003e Fuzzy Cognitive Mapping (FCM) offers a robust alternative for famine analysis by integrating both qualitative and quantitative data. FCM effectively models the dynamic interactions and feedback loops within famine systems, providing real-time insights into system behavior and enabling more accurate early warning and response planning.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCase Study of Ayod County, South Sudan:\u003c/strong\u003e The application of FCM to the 2017 Ayod County crisis in South Sudan demonstrates its efficacy in capturing the intricate interdependencies and emergent properties of famine systems. The study highlights how FCM can identify critical tipping points and intervention opportunities, thereby enhancing the effectiveness of humanitarian responses and contributing to more equitable and transparent famine prevention strategies.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eFamine is best understood as a complex dynamic system, in which social, economic, political, and environmental factors interact in unpredictable and non-linear ways (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Both Howe (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and Fortnam and Hailey (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) argue that famines share properties associated with complex dynamic systems, characterized by non-linearity, emergent properties, and self-organization. They illustrate how cascading effects and feedback loops cause complex and unpredictable interactions between factors that drive a population toward a famine state, characterized by starvation, destitution, wasting, disease outbreaks, and ultimately, mass non-trauma mortality (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, current approaches to analyzing famine have not fully incorporated this contemporary understanding of famines as complex systems. Traditional analytical methods and protocols often treat famine as a linear process, failing to capture the intricate interdependencies and non-linear dynamics among contributing factors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Critiques of the Integrated Food Security Phase Classification (IPC) protocols, for example, highlight the limitations of focusing solely on severity indicators without accounting for the magnitude, temporal, and spatial dimensions of famine crises, which often leads to incomplete analysis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe currently accepted protocols for classifying famine, the IPC, require two of three outcome indicators to surpass their associated thresholds for Famine (IPC Phase 5): 20% or more of the population is facing starvation (caloric deficits), 30% or more of the population is acutely malnourished, and 2 people per 10,000 are dying from non-trauma-related causes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn humanitarian contexts, where conflict, displacement, and poor infrastructure severely limit physical access to the population, conducting representative household surveys to collect the requisite data to measure these outcomes accurately and precisely is nearly impossible. Further, survey data is cross-sectional, reflecting conditions at a single point in time, and often limited in scope, rendering it incapable of capturing the evolving dynamics of a crisis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The IPC\u0026rsquo;s reliance on such data results in a failure to capture the real-time dynamics of an area\u0026rsquo;s trajectory toward a famine state.\u003c/p\u003e \u003cp\u003eAdditionally, the IPC\u0026rsquo;s famine protocol is time-consuming, with unnecessary procedural delays (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The consequence of delays in recognizing the emergence of a famine state is non-trauma mass mortality and extreme human suffering.\u003c/p\u003e\n\u003ch3\u003eLimitations in Quantitive Modeling of AFI and Famine\u003c/h3\u003e\n\u003cp\u003eRecent developments in systems thinking, artificial intelligence, and advanced computational models have opened new avenues for famine modeling (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, models designed and validated around predicting IPC Phase classifications, particularly IPC Phase 5 (e.g. Famine), present a fundamental limitation. First, the IPC phase thresholds are not designed to incorporate the complex, dynamic interactions between factors such as livelihood collapse, market failure, water sanitation and hygiene (WASH) conditions, environmental shocks, conflict, and disease outbreaks\u0026mdash;factors that collectively drive outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, expert judgment determines IPC classifications, and as such is subject to errors, bias, and noise, based on imperfect information (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). As a result, quantitative models that are anchored around IPC classifications are often predicting the outcome of expert judgment rather than the objective manifestation of caloric deficits, wasting, and non-trauma mortality.\u003c/p\u003e \u003cp\u003eConsequently, quantitative models trained and tested against IPC classifications are unlikely to reflect the full complexity of famine systems, which exhibit non-linear interactions, feedback loops, and tipping points that traditional models struggle to capture (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eApplying Fuzzy Cognitive Mapping to Famine Systems\u003c/h2\u003e \u003cp\u003eBuilding on Howe\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) conceptualization of famine systems and Fortnam and Hailey\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) framing of famine as a social-ecological system (SES), Fuzzy Cognitive Mapping (FCM) offers a novel approach to famine modeling. Despite the extensive study of famine, there is no publicly available literature that has applied FCM to famine system analysis. This paper seeks to address this gap by demonstrating the utility of FCM for analyzing famine dynamics in near real-time without the intensive data requirements typical of traditional methods.\u003c/p\u003e \u003cp\u003eOriginally developed by Kosko (1986), FCMs are quasi-quantitative models that combine cognitive mapping with fuzzy logic, making them suitable for analyzing complex systems characterized by uncertainty and limited data availability (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). FCMs integrate both qualitative and quantitative data, enabling them to represent systems in which precise quantitative measurements are sparse or unavailable, such as in humanitarian crises (\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). FCMs are constructed through iterative engagement with stakeholders or experts who identify key variables and causal relationships that define the system (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eKey Steps in FCM Construction\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEstablishing Factors\u003c/b\u003e: Identify and define relevant system factors based on expert input or stakeholder consensus. Factors should be selected to align with the objectives of the analysis and the specific system of interest (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMapping Causal Relationships\u003c/b\u003e: Establish and define the causal relationships between factors. For example, the impact of seasonal rainfall variability on crop yields or the influence of market access on food prices (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAssigning Weights\u003c/b\u003e: Determine the strength and direction of the influence between factors, usually using values ranging from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. Positive values indicate a reinforcing relationship, while negative values represent an inhibiting effect (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConstructing the FCM\u003c/b\u003e: Develop a directed graph in which nodes represent factors and edges represent weighted causal links. This graphical representation provides a visual model of how different components of the system interact (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAnalyzing the FCM\u003c/b\u003e: Use casual and dynamic analyses to explore system behavior and interactions. Static analysis provides a snapshot of the system at a specific point in time, while dynamic analysis examines how changes in one factor propagate through the system over time (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApplication to the 2017 Ayod, South Sudan Famine\u003c/h3\u003e\n\u003cp\u003eThe 2017 crisis in Ayod County, South Sudan, underscores the necessity for reimagining famine analysis, early warning, and classification protocols. Ayod, an agropastoral livelihood system situated within the Sudd in central South Sudan, experienced severe flooding, disease outbreaks, and conflict between 2016 and 2017. Through a complex interaction of exogenous factors, starvation and livelihood collapse resulted in a near-complete reliance on wild foods, widespread internal distress migration, disease outbreaks, widespread malnutrition (classified as Extremely Critical (IPC AMN Phase 5) in the May 2017 IPC Acute Malnutrition analysis workshop), and near famine levels non-trauma crude death rates (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In FGDs conducted by the author in 2017, local Ayod population referred to the year as \"The Year of the Thou,\" highlighting their total dependence on the leaves of the Thou tree. Yet, Ayod was never officially classified as a famine by the IPC due to complications arising from 1) data availability, logistical constraints in data collection, and survey bias, and 2) the IPC famine protocols themselves, which include issues related to timeliness, rigid survey data requirements, and a disconnection between previous and current data.\u003c/p\u003e\n\u003ch3\u003eUtility of FCM in Famine Systems Analysis\u003c/h3\u003e\n\u003cp\u003eThis paper aims to demonstrate that FCM, when applied to famine systems, can capture the complexity of interactions between factors that contribute to a crisis and serve as a blueprint for a system's trajectory towards a famine state. Baseline FCM development helps illustrate the current state of the system, providing insights into complex relationships, often overlooked. Dynamic FCM analysis, on the other hand, allows for real-time system analysis through the integration of real-time monitoring data, qualitative insights, and expert knowledge (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis approach offers a more nuanced understanding of famine dynamics than conventional linear models, making it a valuable tool for early warning and response planning in humanitarian contexts (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, the author hopes this novel application of FCM will encourage other humanitarian practitioners to explore alternative methods and analytical tools beyond the outdated humanitarian toolkit, in the pursuit of a better humanitarian information system that is equitable, accountable, transparent, and innovative.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eThe FCM for Ayod was created through a two-step process: initially establishing a baseline map, followed by conducting four \"what-if\" scenario analyses using the Dynamic FCM methodology. The author employed Mental Modeler, an online tool selected for its user-friendly interface, low bandwidth demands, and collaborative advantages, which make it suitable for humanitarian contexts with diverse expertise and restricted technical capabilities (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While it lacks advanced machine learning functions, it facilitates baseline mapping and straightforward scenario analysis, aiding stakeholders in comprehending complex systems (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMethod: Development of the Baseline FCM Map for Ayod\u003c/h3\u003e\n\u003cp\u003eThe FCM framework corresponds to famine outcome criteria, emphasizing four key factors: caloric intake deficits, malnutrition prevalence, disease outbreaks, and mortality rates (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The concept of destitution\u0026mdash;understood as the collapse of livelihood systems, household assets, and coping mechanisms\u0026mdash;is not separately included but inferred through considerable reductions in aspects such as a household\u0026rsquo;s asset base and coping capacity. Factors for the model were selected based on diverse sources, including the FEWS NET South Sudan livelihood zone report, grey literature, qualitative data from a rapid assessment conducted by the author in June 2017, and the author\u0026rsquo;s experience as a humanitarian analyst based in South Sudan from 2017\u0026ndash;2020 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The baseline FCM included 21 factors, which were also integrated into the dynamic FCM scenario analysis (refer to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelationships between factors were quantified by assigning weights to links within a range of -1.0 to 1.0 in 0.25 increments. Positive weights indicate a direct relationship (e.g., an increase in one factor results in an increase in another), whereas negative weights signify inverse relationships. The magnitude reflects the strength of the relationship, with larger absolute values denoting a stronger influence (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). A total of 74 causal relationships were defined, producing a model with a density of 0.176 and an average of 3.52 relationships per factor (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The FCM\u0026rsquo;s complexity score, which evaluates the balance among drivers, receivers, and intermediate factors, was 0.25. The baseline FCM model is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (see supplementary material for the Mental Modeler project file and FCM matrix table), providing a comprehensive visual of the famine system in Ayod and serving as the basis for subsequent dynamic FCM analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors of Interest for Tracking Risk of a Famine State\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor of Interest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaloric Intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated food availability and consumption patterns, highlighting caloric deficits across different system states.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssessed malnutrition prevalence, focusing particularly on vulnerable groups such as children under five.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTracked the potential for disease transmission, including cholera and acute watery diarrhea (AWD), alongside other health risks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaluated the likelihood of increased mortality due to interactions between food insecurity, malnutrition, and disease outbreaks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e2016\u0026ndash;2017 Ayod FCM Factor List and Descriptions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical Flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence, extent, and impact of flooding that is not typical for the area, affecting various aspects of life and livelihoods.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership and ability to access livestock for food, income, and milk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeasonal crop production and its impact on food availability and income.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Functionality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctionality of local markets, including the availability of goods and the ability to trade.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome generated from selling own production, such as crops, livestock and fish, petty trade and labor.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishing Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to access and engage in fishing for food and income.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral access to clean water, and sanitation and hygiene conditions in the area.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholera morbidity - proxy for likely cholera cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of internally displaced persons (IDPs) relative to the host population and their impact on resources and services.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaloric Intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe amount of calories consumed by individual.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall nutritional status of the population, notable likely malnutrition prevalence.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Morbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence of diseases such as malaria, acute watery diarrhea (AWD), or others.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth status of individuals, influenced by disease prevalence, nutrition, and access to healthcare.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict - Internal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence of direct conflict inside of Ayod.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict - External\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence of conflict in neighboring areas.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulations access to Humanitarian food assistance inflow to the area.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth and Nutrition Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability and functionality of health and nutrition services in the area.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutward Migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMovement of people out of the area due to various factors such as conflict, lack of resources, or seeking better opportunities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild Food Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability and frequency of wild food consumption as a supplement to regular food sources.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk of death due to various factors such as starvations, disease, malnutrition, and lack of healthcare.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistress Migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal atypical movement of people in search of resources and life saving services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethod: Dynamic FCM Analysis\u003c/h2\u003e \u003cp\u003eDynamic FCM analysis employed scenario-based system modeling by adjusting factor activation levels in response to distinct scenario periods characterized by shifts in external pressures, seasonality, or significant events (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Activation values, ranging from \u0026minus;\u0026thinsp;1 to 1 in 0.25 increments, represented relative changes in each factor based on a convergence of qualitative and quantitative evidence (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). For factors such as caloric intake, nutritional status, health status, and mortality risk, activation values were not directly applied, allowing the model to predict relative changes in these values based on interrelationships with other factors, including feedback loops. Factors lacking sufficient evidence were treated similarly. Separate analyses were conducted for each scenario to ensure context-specific accuracy. The FCM\u0026rsquo;s predictive performance was assessed by comparing model-predicted values of key outcome indicators\u0026mdash;caloric intake, nutritional status, health status, and mortality risk\u0026mdash;against retrospective data, contextual information, and observed outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDynamic FCM System Scenarios - Time Period and Description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic FCM Scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypical Livelihood Season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem State 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly 2016 - November 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHarvest period, livestock return to the homestead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutbreak of national conflict and displacement into Ayod from Juba; atypical flooding in Ayod and additional internal displacement of populations in Ayod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem State 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecember 2016 - February 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry season, some household food stocks remaining from harvest, livestock move away from the homestead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutbreak of conflict in Ayod, with large advances into Ayod by SPLA, displacing populations in central Ayod to northern (Pagil) and western Ayod (low-lying Sudd)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem State 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch to June 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLate dry period and early rainfall. Lean season, a traditional period when household consumption patterns decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConflict reduces slightly in Ayod; SPLA makes large advances in Canal/Pigi and Fangak counties, north of Ayod; cholera outbreak in Ayod; collapse of northern trade corridor\u0026mdash;a critical food and livestock supply route into Ayod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem State 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly to September 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly harvest, some green harvest available, livestock return to the homestead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant upscale in humanitarian food and nutrition distributions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDynamic FCM Factor Activation Scale.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor Change during Scenario Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificantly increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData: Dynamic FCM Analysis\u003c/h3\u003e\n\u003cp\u003eThe evidence repository compiled for the dynamic FCM analysis included 242 unique entries categorized by date range, factor, source, and geographic location. This repository incorporated various data types, such as reports, assessments, datasets, and key informant interviews. It served as the foundational dataset for determining factor activation levels across scenarios (refer to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for summary). See supplementary material for full list of evidence and sources.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Sources for Dynamic FCM Model Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMD Annual Report 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH FSL Assessment / REACH FSL Data / REACH MUAC Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJune - July 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessment Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP/CRS Rapid Needs Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFebruary - April 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFocus Group Discussion Notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSD FGD Notes IDPs Ayod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAyod Analysis Memo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeeds Assessment Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAyod County Inter-Agency Floods Needs Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust - September 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Movement Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH Population Movement Baseline Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject Proposal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAyod SMART Project Proposal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMF SMART Survey Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust - September 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEWS NET Livelihood Zone Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlooding Report CMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeptember 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJonglei FSL Report / REACH Canal Fangak FSL Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH FSL Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJune \u0026ndash; July 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP Rapid Assessments / WFP RRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJanuary 2015 - September 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP - Planned HFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOctober 2016 - May 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConflict and Displacement Report IOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecember 2016 - January 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNICEF SSD Situation Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP/UNICEF RRM Mission Normanyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernemt of South Sudan Cholera Update\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay - June 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIOM Situation Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJune 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPC Worksheet / IPC May 2017 Update\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay - September 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJonglei Situation Overview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH FGD - Nyal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeptember 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH FGD - Duk Padiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMF Q1 2017 Project Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJanuary - March 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute Malnutrition and Jonglei Report by RMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOctober - December 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConflict Briefs (Jan - May 2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJanuary - May 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNICEF Situation Update\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNICEF RRM Jan 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJanuary 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHO Article - Cholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly 11, 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH \u0026ndash; Jonglei and Unity Area of Knowledge Bases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecember 2016 - March 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH Population Movement Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP Actual HFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril - September 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFSAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFSNMS Round 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACH Area of Knowledge FGDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAugust 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWFP RRM Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOctober 2016 - September 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorksheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPC September 2017 Analysis Worksheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeptember 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSystem State 1: Impact of Flooding and Livelihood Disruption (July\u0026ndash;November 2016)\u003c/h2\u003e \u003cp\u003eSevere flooding led to significant environmental and socio-economic disruptions. Livestock access declined sharply due to displacement and animal mortality, while crop production declined because of widespread crop destruction (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Market functionality deteriorated, resulting in a substantial decline in household caloric intake and increased reliance on wild foods (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The FCM predicted declines in caloric intake (\u0026ndash;0.88), nutritional status (\u0026ndash;0.46), and health status (\u0026ndash;0.38), with a corresponding increase in mortality risk (0.22)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSystem State 2: Onset of Hold Conditions and Escalation of Pressures (December 2016\u0026ndash;February 2017)\u003c/h2\u003e \u003cp\u003ePressures intensified with continued flooding effects, escalating conflict, and the early onset of the lean season. Livestock access further deteriorated, markets remained non-functional, and households exhausted their food stocks. First reported cases of cholera, linked to poor WAS conditions and increased concentration of host communities and IDPs (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The FCM indicated continued low caloric intake (\u0026ndash;0.86), further deterioration in nutritional status (\u0026ndash;0.60) and health status (\u0026ndash;0.56), and a sharp increase in mortality risk (0.65).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSystem State 3: Emergence of a Famine State (March\u0026ndash;June 2017)\u003c/h2\u003e \u003cp\u003eThis period marked the peak of the crisis, with prolonged food shortages, intensified disease outbreaks, and mass displacement converging to create famine conditions. Livestock access, crop production, and markets collapsed, and humanitarian assistance was severely constrained. Households relied almost entirely on nutritionally inadequate wild foods. The FCM predicted near-total depletion of caloric intake (\u0026ndash;0.94), widespread malnutrition (nutritional status \u0026minus;\u0026thinsp;0.65), deteriorated health status (\u0026ndash;0.61), and a significant rise in mortality risk (0.80).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSystem State 4: Initial Signs of Rebalancing (July\u0026ndash;September 2017)\u003c/h2\u003e \u003cp\u003eSigns of recovery emerged due to seasonal improvements, partial livelihood restoration, and expanded humanitarian interventions. Livestock began returning, small-scale crop production resumed, and markets showed modest recovery. A cholera vaccination campaign reduced disease transmission, and humanitarian food assistance scaled up. The FCM reflected improvements in caloric intake (\u0026ndash;0.61), nutritional status (\u0026ndash;0.17), and health status (\u0026ndash;0.25), with a decrease in mortality risk (0.13).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the changes in the four factors of interest in each scenario iteration, and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes activation and predicted factor values. See supplementary material for full list of evidence used for each system state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of System State FCM Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystem State 1 - July to Nov 2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSystem State 2 - December 2016 to Feb 2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystem State 3 - March to June 2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSystem State 4 - July to September 2017\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical Flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Functionality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishing Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Morbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict - Internal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict - External\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth and Nutrition Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutward Migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild Food Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistress Migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCaloric Intake*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.88\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.86\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.94\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNutritional Status*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.46\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.65\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHealth Status*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMortality Risk*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.65\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eValue Add of FCM for Analyzing and Understanding Famine Systems\u003c/h2\u003e \u003cp\u003eDeveloping a baseline FCM significantly improved the ability to visualize and analyze the intricate interrelationships among factors that influenced the system's trajectory towards famine. The resultant map reveals critical linkages and feedback loops driving the system's trajectory, offering insights into the various traits described by Howe and Fortnam and Hailey.\u003c/p\u003e \u003cp\u003eThe baseline FCM acts as both an analytical framework and decision support tool. In Ayod, it showed how flooding's initial impact on food production and livestock access (System State 1) worsened due to conflict and deteriorating WASH conditions (System State 2). The model revealed self-reinforcing dynamics in System State 3, leading to a food system collapse (Shown by Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), distress migration, and a resultant spread of cholera, marking a critical tipping point (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates this tipping point, where prolonged pressures (downward arrows) and adaptive capacity collapse (upward arrows) accelerated self-reinforcing dynamics (diagonal arrow), resulting in systemic destabilization and famine. During this system state, the FCM indicated that a convergence of malnutrition, disease outbreaks, and widespread food scarcity led to rapid declines in caloric intake, nutritional status, and overall health, increasing mortality risk 0.8. By modeling these dynamics, FCM enables a clearer understanding of when and how famine conditions emerge, thereby enhancing early warning systems and informing the timing of interventions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model also demonstrated how increased humanitarian food assistance (HFA) acted as a rebalancing mechanism, improving adaptive capacity in System State 4 and breaking the self-reinforcing dynamics. The rise in mitigating factors (upward arrows) gradually alleviated famine conditions, shifting the system's trajectory away from a famine state. This shift, visualized through the system map and captured in the FCM model, underscores the value of FCM in real-time analysis and its potential to inform effective humanitarian responses even under data limitations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCapturing Nonlinear and Emergecny Properties\u003c/h2\u003e \u003cp\u003eFCM effectively represented non-linear interactions and emergent properties characteristic of dynamic complex systems. Nonlinear dynamics were notable in the FCM results, particularly from February to July 2017, when self-reinforcing feedback loops accelerated the system's trajectory toward famine conditions. This nonlinearity was likely driven by significant consumption deficits and the rapid depletion of food reserves, leading to increased distressed migration within Ayod. The migration, in turn, exacerbated public health issues by contributing to the spread of cholera. Vulnerable populations, already weakened by hunger, congregated in unsanitary conditions, such as cattle camps and areas with stagnant water, which facilitated cholera transmission.\u003c/p\u003e \u003cp\u003eThe system\u0026rsquo;s adaptive capacity was further constrained by a corralling effect\u0026mdash;caused by natural barriers and ongoing conflict\u0026mdash;which limited outward migration. This restriction undermined the community\u0026rsquo;s ability to seek external assistance or resources, compelling them to depend on deteriorating local conditions. Consequently, the convergence of these stressors heightened the population\u0026rsquo;s susceptibility to malnutrition, disease outbreaks, and ultimately mortality.\u003c/p\u003e \u003cp\u003eThree independent but important factors illustrate some of the emergent properties which shaped the systems trajectory towards a famine state:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAtypical Flooding (August\u0026ndash;October 2017)\u003c/b\u003e: Severe flooding led to extensive crop and livestock losses, reducing food availability and triggering an earlier onset of the lean season. This asset depletion left households without sufficient resources to implement traditional coping strategies, worsening food insecurity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInternal and External Conflict\u003c/b\u003e: Internal conflict within Ayod (January\u0026ndash;March 2017) led to widespread displacement, while external hostilities restricted access to markets, humanitarian aid, and health services. Conflict curtailed migration, compelling households to rely on wild foods and natural resources as last-resort coping mechanisms.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCholera Outbreak\u003c/b\u003e: Beginning in July 2016, a cholera outbreak, driven by conflict-induced displacement, rapidly spread across South Sudan. By February 2017, cholera intensified in Ayod due to deteriorating food security. In search of food, people moved to areas with contaminated water sources, leading to over 3,000 probable cases by March 2017\u0026mdash;the highest concentration of cholera in South Sudan in four years (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese events were interdependent, with each exacerbating the impact of the others. For instance, flooding heightened cholera transmission, and conflict restricted access to assistance. The integration of FCM within a complex systems framework revealed these interconnections and demonstrated how flooding, conflict, and cholera collectively drove the famine's emergent properties. FCM proved instrumental in mapping these dynamics, offering valuable insights into famine trajectories and identifying potential intervention points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Limitations in IPC - Ayod County, 2017\u003c/h2\u003e \u003cp\u003eThe IPC analytical protocols faced significant limitations in accurately assessing famine conditions in Ayod County during 2017. Between May and July of that year, Ayod County exceeded two of the three critical thresholds for famine classification: acute malnutrition and acute food insecurity. The IPC Acute Malnutrition (AMN) Analysis Workshop, held in May 2017, classified the region as Extremely Critical (IPC Phase 5) for the duration of this period (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This classification was substantiated by Mid-Upper Arm Circumference (MUAC) screening data, which indicated that the proxy Global Acute Malnutrition (GAM) prevalence exceeded the IPC threshold of 15% for Extremely Critical status. Multiple mass screenings conducted during early to mid-2017 revealed proxy GAM rates exceeding 30% (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, two household-level surveys performed in June and July 2017 demonstrated that the food consumption threshold was also breached, with around 20% of the population experiencing catastrophic levels of acute food insecurity (IPC Phase 5). According to IPC protocols, surpassing these two thresholds should have warranted a likely declaration of famine. However, the IPC Acute Food Insecurity Analysis Update, conducted in May 2017, did not account for the data collected in June and July, thereby failing to incorporate evidence of escalating food insecurity.\u003c/p\u003e \u003cp\u003eThe third IPC threshold\u0026mdash;the non-trauma crude death rate (CDR) of 2 deaths per 10,000 individuals per day\u0026mdash;was exceeded according to a SMART survey conducted between August and September 2017. The initial report indicated a CDR of 2.03 (1.36\u0026ndash;3.02) per 10,000 persons per day, surpassing the famine threshold (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). However, this figure was later adjusted downward to 1.89 (1.26\u0026ndash;2.84) due to an extension of the recall period from 107 days to 126 days (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This adjustment was justified by delays in data collection related to weather conditions, despite no new deaths being reported from the affected clusters. Such an extension artificially suppressed the CDR and obscured the true mortality distribution.\u003c/p\u003e \u003cp\u003eThe recall period began on May 16, 2017, coinciding with the IPC classification of Extremely Critical acute malnutrition and preceding the household survey data that indicated catastrophic food insecurity. The survey's design effect was calculated at 3.52, suggesting a high degree of clustering in mortality data, indicative of real-world dynamics where deaths are often concentrated in specific conditions, such as migration or displacement camps.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) Over half of reported deaths occurred during migration, suggesting that fatalities were often recorded at new household locations rather than their original sites.\u003c/p\u003e \u003cp\u003eLastly, despite some reported improvements in food consumption outcomes from the SMART survey, the timing of data collection in relation to humanitarian food and nutrition distributions indicated potential biases, as over 50% of surveyed clusters had received aid less than two weeks prior (see supplemental material for timeline). These methodological concerns underscore how delays, timing discrepancies, and biases can significantly distort assessments of famine conditions, complicating the understanding of the severity of the crisis in Ayod County.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFCM Efficacy as Methodology\u003c/h2\u003e \u003cp\u003eThe Ayod case study highlighted FCM\u0026rsquo;s efficacy as a methodology for mapping and analyzing famine systems. FCM demonstrated three primary strengths.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFlexibility and Adaptability\u003c/b\u003e: FCM is highly adaptable in contexts with imperfect information, integrating diverse data sources, both qualitative and quantitative. It enables focused analysis on specific factors like market trends or rainfall forecasts without necessitating a comprehensive famine model. Moreover, FCM\u0026rsquo;s ability to combine qualitative insights and expert judgment allows for effective analysis even when data is scarce. This flexibility supports analytical discussions and scenario analyses, making FCM valuable in contexts where data availability is limited, while ensuring stronger alignment with objective reality as more evidence is incorporated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eScenario Analysis Capability\u003c/b\u003e: FCM supports robust scenario analysis by allowing analysts to modify factor values based on the available evidence. This capability enables exploration of factor interdependencies and responses under different scenarios, including plausible or worst-case conditions. It also facilitates assumption tracking, comparing new data against prior assumptions, which is critical for adjusting projections when new information arises or unexpected events occur. This functionality enhances the projection process for food insecurity and famine trajectories.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegration of Diverse Perspectives\u003c/b\u003e: FCM offers an intuitive platform for incorporating local knowledge and expert input through focused group discussions (FGDs). Its scalability enables detailed analysis of critical factors by decomposing them into sub-components, if necessary. Continuous iterations of FCM ensure that the model remains relevant and aligned with the evolving understanding of the system.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFuture efforts should focus on further exploration of FCM as a decision support tool for analyst working in famine analysis, integration of humanitarian stakeholders and local population in the FCM development and analysis process, and exploring integration with complementary tools to enhance famine analysis, including GIS, analysis of competing hypothesis and related standard analytical techniques tailored to mitigate expert judgement error within imperfect data environments. Embracing real-time system analysis through FCM has the potential to improve early warning systems, facilitate timely interventions, and contribute to more effective famine prevention strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe primary author developed, analyzed, wrote and reviewed all text in the manuscript by Matthew Day.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003ePaul Howe, Dan Maxwell, Anu Atre, Peter Hailey, Oliver Callaghan, Chris Newton, Tim Hoffine\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSen A. Poverty and famines: An essay on entitlement and deprivation [Internet]. Oxford: Clarendon Press / Clarendon Press; 1981. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.amazon.com/Poverty-Famines-Essay-Entitlement-Deprivation/dp/0198284632/ref=sr_1_1?s=books\u003c/span\u003e\u003cspan address=\"http://www.amazon.com/Poverty-Famines-Essay-Entitlement-Deprivation/dp/0198284632/ref=sr_1_1?s=books\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;ie=UTF8\u0026amp;qid=1310678684\u0026amp;sr=1-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevereux S. The impact of droughts and floods on food security and policy options to alleviate negative effects. Agricultural Economics. 2007;37(s1):47\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell D, Khalif A, Hailey P, Checchi F. Viewpoint: Determining famine: Multi-dimensional analysis for the twenty-first century. Food Policy. 2020;92:101832.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe P. Famine systems: A new model for understanding the development of famines. World Development. 2018;105:144\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFortnam M, Hailey P. Insights from social-ecological systems thinking for understanding and preventing famine. Disasters. 2024;48(3):e12621.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell D, Hailey P. Towards Anticipatory Information Systems and Action: Notes on Early Warning and Early Action in East Africa. 2020;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLentz E, Gottlieb G, Simmons C, Maxwell D. The Ecosystem of Humanitarian Diagnostics and Its Application to Anticipatory Action. 2020;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePC Global Partners. Integrated Food Security Phase Classification Technical Manual Version 3.1. Evidence and Standards for Better Food Security and Nutrition Decisions. Rome; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell D, Hailey P. Synthesis of Findings from Six Case Studies. 2020;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe PW, Naumova EN. Poverty and famines 2.0: the opportunities and challenges of crisis modeling and forecasting. J Public Health Pol. 2022;43(3):329\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss S, Zouaoui C. What is the impact of AI on the decision-making process in Humanitarian disaster management ? 2021 [cited 2024 Sep 26]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rgdoi.net/10.13140/RG\u003c/span\u003e\u003cspan address=\"http://rgdoi.net/10.13140/RG\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2.2.19567.76960\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. 1974;185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeuer RJ. Psychology of intelligence analysis. Washington, D.C.: Center for the Study of Intelligence, Central Intelligence Agency; 1999. 184 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahneman D, Sibony O, Sunstein CR. Noise: a flaw in human judgment. First Little, Brown Spark paperback edition. New York, NY Boston London: Little, Brown Spark; 2022. 452 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosko B. Fuzzy cognitive maps. International Journal of Man-Machine Studies. 1986;24(1):65\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlacketer MP, Brownlee MTJ, Baldwin ED, Bowen BB. Fuzzy Cognitive Maps of Social-Ecological Complexity: Applying Mental Modeler to the Bonneville Salt Flats. Ecological Complexity. 2021;47:100950.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray S, Chan A, Clark D, Jordan R. Modeling the integration of stakeholder knowledge in social\u0026ndash;ecological decision-making: Benefits and limitations to knowledge diversity. Ecological Modelling. 2012;229:88\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray SA, Zanre E, Gray SRJ. Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs. In: Papageorgiou EI, editor. Fuzzy Cognitive Maps for Applied Sciences and Engineering [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014 [cited 2024 Sep 9]. p. 29\u0026ndash;48. (Intelligent Systems Reference Library; vol. 54). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-642-39739-4_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-39739-4_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyaki A, Gray SA, Lepczyk CA, Skibins JC, Rentsch D. Local-Scale Dynamics and Local Drivers of Bushmeat Trade: Participatory Modeling in Conservation. Conservation Biology. 2014;28(5):1403\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapageorgiou EI, editor. Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014 [cited 2022 Jun 24]. (Intelligent Systems Reference Library; vol. 54). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://link.springer.com/\u003c/span\u003e\u003cspan address=\"http://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-642-39739-4\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-39739-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapageorgiou E, Kontogianni A. Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management: A Methodological Primer and an Application. In: Young S, editor. International Perspectives on Global Environmental Change [Internet]. InTech; 2012 [cited 2024 Sep 9]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.intechopen.com/books/international-perspectives-on-global-environmental-change/using-fuzzy-cognitive-mapping-in-environmental-decision-making-and-management-a-methodological-prime\u003c/span\u003e\u003cspan address=\"http://www.intechopen.com/books/international-perspectives-on-global-environmental-change/using-fuzzy-cognitive-mapping-in-environmental-decision-making-and-management-a-methodological-prime\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhodadadi M, Shayanfar H, Maghooli K, Hooshang Mazinan A. Fuzzy cognitive map based approach for determining the risk of ischemic stroke. IET Syst Biol. 2019;13(6):297\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapageorgiou EI, Salmeron JL. A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Trans Fuzzy Syst. 2013;21(1):66\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbrook-Johnson P, Penn AS. Systems Mapping: How to build and use causal models of systems [Internet]. Cham: Springer International Publishing; 2022 [cited 2023 Mar 1]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-01919-7\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-01919-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray. Mental Modeler [Internet]. 2012. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mentalmodeler.com/#about\u003c/span\u003e\u003cspan address=\"https://www.mentalmodeler.com/#about\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouth Sudan: Acute Food Insecurity Situation for May 2017 and Projection for June - July 2017 | IPC - Integrated Food Security Phase Classification [Internet]. [cited 2024 Sep 28]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1026196/?iso3=SSD\u003c/span\u003e\u003cspan address=\"https://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1026196/?iso3=SSD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRMF SMART Report Sept 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelix G, N\u0026aacute;poles G, Falcon R, Froelich W, Vanhoof K, Bello R. A review on methods and software for fuzzy cognitive maps. Artif Intell Rev. 2019;52(3):1707\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXun. Surviving the Famine. 2023;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2016-09_SSD_Ayod_Flooding Report_CMD.pdf.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFEWS NET. Livelihoods Zone Map and Descriptions for South Sudan [Internet]. Washington D.C.; 2018 Aug. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fews.net/sites/default/files/documents/reports/Livelihoods%20Zone%20Map%20and%20Descriptions%20for%20South%20Sudan.pdf\u003c/span\u003e\u003cspan address=\"https://fews.net/sites/default/files/documents/reports/Livelihoods%20Zone%20Map%20and%20Descriptions%20for%20South%20Sudan.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMourhir A. Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management. Environmental Modelling \u0026amp; Software. 2021;135:104891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAravindakshan S, Krupnik TJ, Shahrin S, Tittonell P, Siddique KHM, Ditzler L, et al. Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh. Environ Dev Sustain. 2021;23(11):16588\u0026ndash;616.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Vliet M, Kok K, Veldkamp T. Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures. 2010;42(1):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eREACH. Situation Overview - Jonglei State. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovernment of South Sudan, World Health Organization. South Sudan Cholera Outbreak Update. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones FK, Wamala JF, Rumunu J, Mawien PN, Kol MT, Wohl S, et al. Successive epidemic waves of cholera in South Sudan between 2014 and 2017: a descriptive epidemiological study. The Lancet Planetary Health. 2020;4(12):e577\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWFP. Rapid Response Mechanism - MUAC Screening Report Normanyang. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyod RMF Presentation.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-public-health-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jphp","sideBox":"Learn more about [Journal of Public Health Policy](http://link.springer.com/journal/41271)","snPcode":"41271","submissionUrl":"https://submission.springernature.com/new-submission/41271/3?","title":"Journal of Public Health Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Famine Analysis, Complex Systems, Fuzzy Cognitive Mapping, Humanitarian Response","lastPublishedDoi":"10.21203/rs.3.rs-5172135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5172135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the limitations of current famine analysis methodologies and introduces Fuzzy Cognitive Mapping (FCM) as a novel approach to understanding famine dynamics. Traditional methods, such as the Integrated Food Security Phase Classification (IPC), often fail to capture the complex, non-linear interactions among social, economic, political, and environmental factors that drive famine. These methods rely heavily on cross-sectional survey data and expert judgment, which are prone to errors and biases. This study demonstrates the utility of FCM in analyzing famine systems, using the 2017 Ayod County crisis in South Sudan as a case study. FCM integrates qualitative and quantitative data, enabling real-time analysis of dynamic systems with limited data availability. The results highlight how FCM can reveal critical linkages and feedback loops, offering a more nuanced understanding of famine trajectories and enhancing early warning systems. The paper argues for the adoption of FCM in humanitarian contexts to improve the timeliness and accuracy of famine response and prevention strategies.\u003c/p\u003e","manuscriptTitle":"Anatomy of a famine: using fuzzy cognitive mapping to understand the crisis in Ayod County, South Sudan, 2017 ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:33:12","doi":"10.21203/rs.3.rs-5172135/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-17T15:44:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-02T20:33:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54154300075537578441924901392189988359","date":"2025-02-12T19:07:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T08:25:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13642762809188966321615232371791401971","date":"2024-11-27T04:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-27T03:48:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-16T17:19:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-29T09:18:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Public Health Policy","date":"2024-09-28T22:31:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-public-health-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jphp","sideBox":"Learn more about [Journal of Public Health Policy](http://link.springer.com/journal/41271)","snPcode":"41271","submissionUrl":"https://submission.springernature.com/new-submission/41271/3?","title":"Journal of Public Health Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"062a578f-6fca-4762-9339-7178d3866fb7","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-21T21:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 10:33:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5172135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5172135","identity":"rs-5172135","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}