metadata: authors: 0: author: Steve Penson
1: author: Lomme,Mathijs
2: author: Carmichael,Zacharey Austin
3: author: Manni,Alemu
4: author: Shrestha,Sudeep Kumar
5: author: Andree,Bo Pieter Johannes
count: Yemen Arab Republic
docna: 0: docna: A Data-Driven Approach for Early Detection
            of Food Insecurity in Yemen's Humanitarian Crisis
docty: Policy Research Working Paper
abstracts: cdata!: The Republic of Yemen is enduring the
            world's most severe protracted humanitarian crisis,
            compounded by conflict, economic collapse, and natural
            disasters. Current food insecurity assessments rely on
            expert evaluation of evidence with limited temporal
            frequency and foresight. This paper introduces a data-driven
            methodology for the early detection and diagnosis of food
            security emergencies. The approach optimizes for simplicity
            and transparency, and pairs quantitative indicators with
            data-driven optimal thresholds to generate early warnings of
            impending food security emergencies. Historical validation
            demonstrates that warnings can be reliably issued before
            sharp deterioration in food security occurs, using only a
            few critical indicators that capture inflation, conflict,
            and agricultural productivity shocks. These indicators
            signal deterioration most accurately at five months of lead
            time. The paper concludes that simple data-driven approaches
            show a strong capability to generate reliable food security
            warnings in Yemen, highlighting their potential to
            complement existing assessments and enhance lead time for
            effective intervention.
display_title: A Data-Driven Approach for Early
            Detection of Food Insecurity in Yemen's Humanitarian Crisis
listing_relative_url: /research/2024/05/34316081/data-driven-approach-early-detection-food-insecurity-yemens-humanitarian-crisis
topicv3: Agriculture and Food Security m1326205 435,Food Price Analysis m1326440 200
publishtoextweb_dt: 2024-05-09T00:00:00Z
dois: 10.1596/1813-9450-10768
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 42
author_keys: 0: author_key: NA
1: author_key: 2374836
2: author_key: 1422200
3: author_key: NA
4: author_key: 794708
5: author_key: 1539948
model_extractions: Policy Research Working Paper 10768 A Data-Driven Approach for Early Detection of Food Insecurity in Yemen’s Humanitarian Crisis _Steve Penson Mathijs Lomme Zacharey Carmichael Alemu Manni Sudeep Shrestha Bo Pieter Johannes Andrée_ Poverty and Equity Global Practice Development Data Group Agriculture and Food Global Practice May 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10768

# **Abstract**

The Republic of Yemen is enduring the world’s most severe protracted humanitarian crisis, compounded by conflict, economic collapse, and natural disasters. Current food insecurity assessments rely on expert evaluation of evidence with limited temporal frequency and foresight. This paper introduces a data-driven methodology for the early detection and diagnosis of food security emergencies. The approach optimizes for simplicity and transparency, and pairs quantitative indicators with data-driven optimal thresholds to generate early warnings of impending food security emergencies.

Historical validation demonstrates that warnings can be reliably issued before sharp deterioration in food security occurs, using only a few critical indicators that capture inflation, conflict, and agricultural productivity shocks. These indicators signal deterioration most accurately at five months of lead time. The paper concludes that simple data-driven approaches show a strong capability to generate reliable food security warnings in Yemen, highlighting their potential to complement existing assessments and enhance lead time for effective intervention.

This paper is a product of the Poverty and Equity Global Practice, the Development Data Group, and the Agriculture and Food Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at spenson@worldbank.org and bandree@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# A Data-Driven Approach for Early Detection of Food Insecurity in Yemen's Humanitarian Crisis

Steve Penson,<sup>a*</sup> Mathijs Lomme,<sup>a,b</sup> Zacharey Carmichael,<sup>a</sup> Alemu Manni,<sup>c</sup> Sudeep Shrestha,<sup>b</sup> Bo Pieter Johannes Andrée,<sup>a*1</sup>

**Keywords** : Agriculture and Food Security, Crisis, Early Warning Systems, Food Price Analysis, Vulnerability, Economic Monitoring

**JEL** : C01, C14, C25, C53, O10

1 a World Bank, b ACAPS, c FAO, *email: spenson@worldbank.org and bandree@worldbank.org. Funding by the World Bank’s Food Systems 2030 (FS2030) Multi-Donor Trust Fund program (grants TF0C0728 and TF0C0828) is gratefully acknowledged. This paper has been prepared as background to the Joint Monitoring Report (JMR), a multi-partner monitoring initiative in Yemen. We would like to thank the peer reviewers on the Quality Enhancement Review for the Yemen Data Driven Identification of Food Security Crises through the JMR, Alan Fuchs, Alexandra Christina Horst, and José Lopez; and the peer reviewers on the Decision Review meeting on Expansion of Real Time Food and Energy Price Monitor, Sergiy Zorya, Nick Haan, Kamau Wanjohi, whose comments helped improve this paper. The authors would like to thank members of JMR Core Development Team including teams at ACAPS, UNICEF, FAO, WFP, WHO and the World Bank. Thanks also to those who contributed to the contextual analysis and methodology development and review including Francesca Marini, Artavazd Hakobyan, Faiza Hesham Hael Ahmed, Nic Parham, Maliha Hussein, Oleg Bilukha, Elijah Odundo, Ismail Kassim, Peter Hailey, Dan Maxwell, Rebecca Semmes, Riham Abuismail, Fawad Raza, Gaurav Singhal, Hussein Gadain, Andres Chamorro, Alia Jane Aghajanian, Olaf De Groot, Emily Henderson, Patrick Vercammen, Felix Leger and Seb Fouquet. Finally, we would like to thank participants to the Joint Monitoring Report workshops including the Yemen Social Fund for Development (SFD) as well as humanitarian, development, and donor partners who have supported the research throughout. This paper reflects the views of the authors and does not reflect the official views of the World Bank, its Executive Directors, or the countries they represent.
<!-- Start of picture text --> Phase Classification (IPC) assessment of end of 2022 (IPC, 2022)<br>are severely impaired. The food security challenges faced by Yemen are significant, and the number of<br>intricate connections among conflict, poverty, extreme weather, climate, and food price shocks<br>physical and mental health consequences. Prior to the 2015 escalation of conflict, Yemen already had<br>one of the world’s highest malnutrition levels. The situation has been aggravated by escalating conflict<br>of life, severe food crises inflict lasting harm on the children of affected families, resulting in<br>enormous humanitarian aid. According to World Bank data on aid and official development assistance,<br>Yemen received 8 billion dollars in official development assistance alone in 2018. At the $1.90 dollar<br>poverty line, this was sufficient to pay for almost 90% of the annual expenses of the population FAO<br><!-- End of picture text -->
<!-- Start of picture text --> development programming, including FEWS NET and the Integrated Food Security Phase Classification<br>risks. These major analyses, however, require significant resources and time to conduct and are typically<br>into price signals and economic deterioration specifically were provided by Seaman and Holt (1980),<br>using data on food prices, agricultural productivity shocks, and conflict. Using the same data, Wang, et<br>a specific focus on parsimony, simplicity, and transparency. The proposed approach is a response to<br>detected before they occur, using only a few critical indicators to capture inflation, conflict, and<br><!-- End of picture text -->
This paper aims to predict transitions into critical states of food insecurity with sufficient lead time for Official IPC data is only available from 2018 to the present. Since the objective is to calibrate indicators July 2023. The data quantifies food insecurity using the IPC-compatible analytical framework Moloney (2012) provide a review of the process). The IPC scale distinguishes five phases of food food insecurity reaches crisis levels, the IPC scale advises a significant policy shift. Specifically, for 7% of IPC observations could be classified as IPC escalations when compared to the previous
The phase data is netted from humanitarian impacts, the values indicate the five phases of food insecurity: (1) minimal/none, 350 300 2 250 2 3 5 200 - ° ® 150 a = =] Zz 100 50 0 Tm MMM OO ORHRDDAHAA SeooNagagagaaqgqgggqgngqygqyganquqjnqygnqygnqygnquyugqygnqyngqyaguagngeaaqagqggqggqgaagaaaen ooclUmcrmUCcUclmUCOCOUCOUCOUCcOUUCcOUUCcOUCee|CDC OCCTOOUCOOUCOCOOCOCOOCOOCOOCOCOCAH TCCOH HH ANNA COC COCOYM EY ~~~~SauriaAcooeo~~~rtO ~~~AFOFR~~~AtOF—~~~GNQAKBCCoOGCCOKAGCKBoKAKECCKAKTCSCAKCSCAKCSAtoO~~Oo~At ~~~~atOoOO~~~~atTF ~~~~etOCUCOCUCUCOCUCUCOCUCUCNDCOCCUOCUCOCUCOCUCOUCUCCCOCUCOCUCOCUCOCUCOUCOlCloO ~~~aAFO~~~~rt ~~~~AOo —~F~At ~~FCO~tt ~~~ttGo ~~~~TtFO~~~~tt ~~8St ~~~~FTOt ~~~~Oohet wc~~CG —~~FOa ~~~~oo ~~~~ot8 —~~otFt A~~~oSao GGBOo~~~ox Date

FEWS NET IPC originalphase #2 m3 m4 m5 issuing alerts at two specific thresholds. These thresholds are adjusted to balance the trade-off between
Conflict conflict fatalities opportunities have significantly decreased purchasing power, leaving more people unable to meet their food prices across, filling in gaps where direct market data collection was not possible due to access worked better. In addition to the five food categories in the SMEB, prices for individual Real Time Food categories, specifically imported foods such as beans, millet, sorghum, sugar, and wheat flour. A 5-item
in our selection of the average price of these five food items as our target indicator. The optimal method on fuel imports makes it susceptible to fluctuations in international oil prices, impacting internal fuel signals from the data, a fuel basket was specified consisting of an equal part of petrol and diesel. The Exchange rate volatility significantly influences food price changes, and particularly in areas controlled Aden to finance the IRG's budget deficit has depreciated the YER against the USD, increasing the cost of pronounced inflation differential.

Drought significantly impacts food security in Yemen, exacerbating water scarcity, reducing crop yields humanitarian crisis. It can also cause displacement and resource conflicts, further destabilizing the the significance of cash crops like Qat on income and expenditure, affecting household purchasing power
<!-- Start of picture text --> crop months to fix the signals at 0 in the data. Z-scores considering a crop calendar resulted generally in<br>outperformed crop-growing only months. The predefined general thresholds recommend for the SPI to<br>Conflict affects general security and the movement of people, crucial for agricultural and fisheries<br>children lived in conflict-affected areas (FAO, IFAD, UNICEF, WHO, and WFP, 2017). Studies indicate that<br>conflict's impact on food security and nutrition worsens with prolonged conflict and weak institutional<br>ACLED data, tracking monthly conflict incidents and fatalities at the district level, was utilized. Conflict<br>violence, or violence against civilians. The analysis considered conflict at both the district and<br>surrounding district levels to capture exposure to nearby conflict, defined as occurring within either one<br>Methods tested for both direct and neighboring district conflict data included percentage deviations<br>from MA and EMA, and the RSI of these EMAs. For neighboring conflict, the averages over the<br>neighboring districts were constructed first, before calculating the moving averages over time. The<br>local/regional conflict is surging or easing. Of the indicators tested, neighboring conflict within one level<br>neighboring conflict fatalities using RSI over a 14-month period based on the EMAofthe last 12 months.<br>Displacement amplifies demand for food and services in host areas and often signifies severe livelihood<br>disruptions. Research indicates significant impacts of forced migration on wages, household income,<br>both were examined, using the same analytical methods as for conflict modeling.<br>an EMA identified as the optimal method. The analysis employed a 6-month time window for the EMA<br><!-- End of picture text -->
tracked. Figure 2 displays the data. Thresholds for each indicator were then fine-tuned to best front run <!-- Start of picture text --> 350<br>300<br>w 250<br>Fd 200<br>%5<br>5150150<br>E<br>2 100<br>50<br>0<br>SEEEEsdassasasasasgasgasasasagageaELLEsdassasasasasgasgasasasagageaELLEELLE EEL LEE EEE ELLE ELLE<br>Date<br>Targetindicator mO m1<br><!-- End of picture text --> <!-- Start of picture text --> 350<br>300<br>g 250<br>E 200<br>5<br>$ 150<br>E<br>2 100<br>50<br>0<br>Messsssssssssssssssasssssssss CETL ET EEEsssssssssssssssssasssssssss CETL ET EEE CETL ET EEE ET EEE EEE EETEEEEEE<br>Date<br>Target indicator MO M1<br><!-- End of picture text --> 350 350 300 300 w 250 g 250 Fd 200 E 200 %5 5150150 $ 150 E E 2 100 2 100 50 50 0 0 SEEEEsdassasasasasgasgasasasagageaELLEsdassasasasasgasgasasasagageaELLEELLE EEL LEE EEE ELLE ELLE Messsssssssssssssssasssssssss CETL ET EEEsssssssssssssssssasssssssss CETL ET EEE CETL ET EEE ET EEE EEE EETEEEEEE Date Date Targetindicator mO m1 Target indicator MO M1 Notation-wise, this can be detailed as follows. Specifically, the target variable takes the value 1 if a food security emergency is observed, defined as IPC categories 4, or 5, and 0 otherwise. For convenience, 0 was defined as a column vect r contain ng the class labels, defined as a corresponding vector withi dicator values used to generate arnings. was defined as the is defined as a numeric threshol . Warnings for time to front run tra sition into emergencies by validating a insta specific subset of data: district/month specifically, let be defined as the sub-vectors of ~~( *# a~~
<!-- Start of picture text --> = ¥% was used to find an optimal method and time-window.<br>The solution spaces per indicator ere t mmed down following several filtering steps. First, we filter<br>to avoid extremely unbalanced error profiles that may be undesirable for similar reasons. Finally, two<br>method-specific filtering steps were followed to remove implausible solutions. First, for RSI indicators,<br>filtering steps, the optimal performing method/time-window combination is selected, with which we<br>elevated risks) and alarms (indicating critical risks). Following the identification of optimized<br>validation underscores varied performance and differential sensitivity and specificity across indicators.<br><!-- End of picture text -->
of 0.33 for alerts, and a significantly reduced FPR of 0.05 for alarms, highlighting a successful calibration specificity at critical risk levels. Fuel prices produce similar alerts but emerge as the more predictive
significant over-triggering tendency, potentially leading to alarm fatigue. Nevertheless, its loss values, specificity. Our further analysis shows that the indicator performs much better when taking a high false negative rate (FNR) of 0.94 for alerts and 0.96 for alarms. This extreme conservatism, reflected more balanced error mixture), reaching similar loss values as the conflict and displacement indicators. Conflict and displacement indicators, employing RSI of the EMA of local and neighboring conflict fatalities, highlight a different challenge. With notably high FNRs at the alert level (0.49 for conflict and a propensity to under-predict emergencies. Their loss values reflect this, with a significant reduction in predictive error from alerts to alarms, for conflict (from 0.42 to 0.34) and displacements (from 0.42 to <!-- Start of picture text --> Food price index (Average of top 5 food items) Displacements index (Local displacements)<br>8 0.07<br>0.06<br>® 0.05<br>, 3 004<br>& & 0.03<br>2 0.02<br>° LAA . /\ PR<br>os 0.9 1.0 11 1.2 13 00 oO 20 40 60 80 100<br>Percentage of EMA RSI of EMA<br>Conflict index (Regional fatalities) Fuel price index (Average of diesel and petrol)<br>0.025<br>2.5<br>0.020<br>2.0<br>2 0.015 4 15<br>0.005 05<br>8 0.0100.000 I Pv) 4 100.0 eee<br>oO 20 40 60 80 100 0.5 1.0 LS 2.0 2.5 3.0<br>RSI of EMA Percentage of MA<br>Exchange rate index (YER to USD) Drought index (Standardized Precipation Index)<br>0.030 04 — Heightened threshold<br>— Critical threshold<br>0.025<br>0.3<br>2 0.020 2<br>0.010<br>0.1<br>0.005<br>Fi 0.015 i | | 5 0.2<br>0.000 [~~ 0.0<br>20 40 60 80 100 2 oO 2 -4 -6<br>RSI ‘SPI<br><!-- End of picture text -->
<!-- Start of picture text --> Alarms<br>100%<br>E<br>=3 50% \<br>E Me a. |<br>= \ WH j<br>7 RW auc, pad<br>on DR ApEE SA hn Mnotiar EAA<br>2010 2012 2014 2016 2018 2020 2022<br>Indicator —— Conflict —— Displacement —— Drought —— Exchange rate —— Food price —— Fuel price<br>Alerts<br>100%<br>2J<br>:2 50% i | i A<br>A a )<br>&° 0. NAMB4 r' QSShal \ NAvy LT \ainy’ ; ad<br>2010 2012 2014 2016 2018 2020 2022<br>Indicator —— Conflict —— Displacement —— Drought —— Exchange rate —— Food price —— Fuel price<br><!-- End of picture text -->

period of 2010-2011, marked by a sharp currency devaluation reflective of Yemen's severe internal conflict and the onset of the Arab Spring protests. The subsequent period from 2011 to 2014 appears displacement data only begins in 2014, and conflict data in 2015, implying that the full spectrum of and conflict data contribute to a stark rise in the issuance of alerts and alarms, with the food and fuel price data highlighting additional risks during the Saudi-led port blockade in 2015. A significant increase in currency devaluation and inflation from 2017, coupled with escalating conflict, culminated in a peak in alarm issuance in 2018. Lastly, a widespread inflationary surge across all price indicators triggered escalations in economic and conflict indicators, the drought alarms do not center on any specific year and the highest count of alarms occurs when economic and conflict risks coincide with periods of The univariate validation exercise confirmed the relevance of each indicator while also underscoring the
each other, whereas displacement and conflict warnings are more closely related. This indicates that combining either a conflict or displacement indicator with food prices may yield a more comprehensive influential indicator positioned first. We then forecasted the outcome, determining the optimal full set of indicators. The findings are depicted in Figure 5. The analysis indicates that sequentially drop beyond a certain point, indicating that combining multiple indicators may lead to over-fitting.

cut-off used of classification, were calculated.
<!-- Start of picture text --> 0.74<br>g 0.73<br>5§ 0.72<br>&<br>0.71<br>0.70<br>1 2 3 4 5 6<br>Number of indicators<br><!-- End of picture text --> <!-- Start of picture text --> 0.725<br>0.720<br>8 0.715<br>B5 0.710<br>3 0.705<br>0.700<br>1 2 3 4 5 6<br>Number of indicators<br><!-- End of picture text --> evolves as indicators are combined. From right to left, indicators are dropped in order of significance, the first indicator thus numberof indicators is identified when graph reached maximum balanced accuracy. Results are for Model (7) (left) and Model (8, final) (right).

<!-- Start of picture text --> 0.82<br>0.80<br>> 0.78<br>rc<br>g 0.76<br>=<<br>2 0.74<br>°<br>0.72<br>0.70<br>0.68<br>0 5 10 15 20 25<br>Number of indicators<br><!-- End of picture text --> <!-- Start of picture text --> 0.80<br>><br>rc 0.75<br>g<br><=<br>2 0.70<br>°<br>0.65<br>0 5 10 15 20 25<br>Number of indicators<br><!-- End of picture text --> controlled by IRG and AA, the study explored region-specific dynamics. Models (4) - (6) in Table 5 build upon the frameworks of Models (1) - (3), integrating region-specific interaction effects. Following this out-of-sample data. The findings highlight a significant interaction effect across the IRG/AA divide. For the models utilizing alerts and alarms, these interactions center on the inflation and drought indicators. driven by inflation, while food security in the AA areas is driven by droughts. This outcome aligns the
an oversimplified representation of true food security drivers.

and a weighted average of error types was calculated, optimizing the probability cut-off used of classification.

In our final comprehensive analysis, we combined both alerts and alarms and applied RFE across all in theory and is predictive on a univariate basis, optimal forecasting specifications typically result from in Figure 6, the model's prediction performance improves significantly with the inclusion of the initial set of predictors and then flattens. Model (7) identifies the optimal combination of predictors, while and are maintained. This approach ensures that all dimensions are kept in the model and is justified by
significance. Retaining a relationship with all indicators in the model, despite causing a potential Model (8) finally remained identical at the triple digit level when compared to the purely statistically optimized Model (7), yielding a significant improvement over models (1) - (6). This observation calculated and a weighted average of<sup>errortypeswascalculated,optimizingtheprobabilitycut-off usedof classification.</sup> Conflict Conflict rate with IRG/AA gives a probability between 0 and 1. Specifically, the total population per district can be multiplied by
The following notation clarifies the calculation. Let =B xL(CMA(P __ ,3),k) oper or. sion is fitted for is t to calculate the fi on that are predicted o be in s expe iencing IPC IPC assessments are contingent on sufficient evidence for high-confidence declarations, with no phases declared when information availability does not meet confidence criteria. The graph therefore highlights the likely access issues that may have <!-- Start of picture text --> 350<br>300<br>& 250<br>= 200<br>=<br>z<br>8 150<br>E<br>z 100<br>50<br>:<br>2019 2020 2021 2022 2023<br>Date<br>AA @IRG<br><!-- End of picture text --> This fin metric enables the monitoring of how alerts and alarms collectively correspond to the
<!-- Start of picture text --> important to note that IPC assessments are contingent on sufficient evidence for high-confidence<br>declarations, with no phases declared when information availability does not meet confidence criteria.<br>estimates line up closely to FEWS NET figures in the pre-COVID-19 period, while lining up more closely<br>finding the optimal lag value __to be 5. This suggests our final estimate can predict food security<br>conditions most reliably five months in advance, indicating that alerts and alarms can preemptively<br>signal deterioration into IPC Ph se 4 conditions by at least five months.<br>Figure 8 presents the detailed comparison of IPC Phase 4+ figures from IPC and FEWS NET sources<br>data-driven alerts and alarms. Notably, the trends in the data-driven visuals precede the official<br>declarations, aligning with the previously identified optimal lead-time of five months in IRG and four<br>(but not significantly different from five) in AA. Specifically, in areas controlled by AA, the model's results<br>confidence in the FEWS NET phases. The modeled results in turn showed a sharp increase in estimates<br>associated with the official IPC phase data not meeting confidence thresholds in both 2020 and 2022 as<br>official data. Finally, by 2023, the model estimates decreased, highlighting a period with notably fewer<br>ultimate population estimates from official or modeled sources but also monitoring the underlying alerts<br>Overall, the findings indicate that combining indicators can reasonably express IPC phase exposure,<br>data. While the final population estimate is a valuable meta-indicator, the results emphasize the<br>significance of evaluating the contributions of individual indicators to improve real-time food security<br>Comparison between official IPC Phase 4+ figures, FEWS NET and the calculated estimation of people at risk of experiencing a<br><!-- End of picture text -->
<!-- Start of picture text --> IRG<br>100%<br>80%<br>(eeccccocosooes<br>s<br>® 60%<br>Fa<br>&<br>x)<br>o<br>&<br>c<br>§ 40%<br>Fd<br>‘esetslo<br>0% | i I"| PUTT | !il| tla ! | | I | uhi| flI lMk.halaooo il fullecccces<br>2010 2012 2014 2016 2018 2020 2022<br>Date<br>@ Population % at risk of experiencing a deterioration into IPC Phase 4+ @% of pop in IPC Phase 4+ (FEWS) @ % of pop in IPC Phase 4+ (IPC) @% of pop in IPC Phase 4+ (FEWS and IPC averaged)<br><!-- End of picture text -->

<!-- Start of picture text --> AA<br>100%<br>80%<br>§<br>=s ecco<br>& 60%<br>2<br>g<br>x=)<br>g<br>£c<br>$ 40%<br>é (<br>20% oPecceees<br>2010 2012 2014 2016 2018 2020 2022<br>Date<br>@ Population % at risk of experiencing a deterioration into IPC Phase 4+ @% of pop in IPC Phase 4+ (FEWS) @% of pop in IPC Phase 4+ (IPC) @% of pop in IPC Phase 4+ (FEWS and IPC averaged)<br><!-- End of picture text --> a significant leap forward from traditional heavy loaded assessment techniques, which often suffer from insecurity emergencies. The selection of indicators—focusing on inflation, conflict, and agricultural alarms and detecting critical emergencies. In contrast, the conflict and displacement indicators, with a
<!-- Start of picture text --> level, suggesting a need for refined area-specific calibration to improve relevance.<br>The analysis highlights a complex scenario where the utility of each indicator is defined by its balance<br>between sensitivity and specificity. Proper threshold calibration is essential, with price signals showing<br>optimized thresholds they do provide additional predictive power. Specifically, when combining<br>indicators in a single model, results show that inflation data and drought data have area-specific impacts<br>The findings from historical validation underline the efficacy of the chosen indicators, with a notable<br>precision in forecasting emergencies five months ahead of time. This lead time is critical for mobilizing<br>The availability of nutrition outcome indicators in particular presents significant challenges, primarily<br>significantly enhance the accuracy and effectiveness of food insecurity predictions. Integrating these<br>The overall findings suggest that data-driven indicators successfully signal prevailing food security risks,<br>significant contribution to the field of food security assessment, offering a scalable and transparent<br>human and financial costs of food crises.<br>dependency on the quality and availability of data and the necessity for continuous refinement of model<br>In conclusion, while significant challenges remain, the promising results of this research highlight the<br><!-- End of picture text -->