CERES: A Probabilistic Early Warning System for Acute Food Insecurity

CERES is a novel, open-access, and continuously running probabilistic forecasting system that fuses six global data streams to generate weekly, machine-readable 90-day ahead risk estimates for acute food insecurity across 43 high-risk countries, with a unique commitment to public prospective verification of all predictions.

The Gist

Imagine a world where we could predict a famine before the first person goes hungry, giving aid organizations enough time to send food before the crisis hits. That is the goal of CERES, a new computer system described in this paper.

Here is the story of CERES, explained simply with some everyday analogies.

🌪️ The Problem: The "Fire Alarm" is Too Slow

Right now, when a country starts running out of food, we usually find out after people are already suffering. The current system (called IPC) is like a team of firefighters who have to drive to a house, knock on the door, check the smoke, and then write a report. By the time they finish the report, the fire might have already burned the house down.

Other warning systems exist, but they are like weather forecasters who say, "It might rain next week," or "It might not." They don't give you a specific number, like "There is a 70% chance of rain." Without a specific number, it's hard for aid groups to decide exactly how much money to spend before the disaster happens.

🤖 The Solution: CERES (The "Weather App" for Hunger)

CERES is a computer program that acts like a super-smart, 24/7 weather app for food security. Instead of waiting for humans to check the fields, it automatically scans the globe every week.

How it works (The Ingredients):
Think of CERES as a chef making a soup. To know if the soup is going to be "spicy" (a crisis), it tastes six different ingredients:

1. Rain: Is it too dry? (Satellite data)
2. Plants: Are the crops turning brown? (Satellite data)
3. Fighting: Is there war nearby? (Conflict data)
4. Current Status: What do the experts say right now? (IPC data)
5. Wallets: Can people afford bread? (Food consumption surveys)
6. Prices: Are grain prices skyrocketing? (Market data)

It mixes all these ingredients together using a mathematical recipe (a logistic model) to spit out a probability score.

• Instead of saying "Famine is coming," it says, "There is a 65% chance of a severe food crisis in the next 90 days."

🎯 The "Tier" System: Traffic Lights

CERES doesn't just give a number; it gives you a traffic light to help you decide what to do:

• 🔴 TIER-1 (Emergency Alert): "Red Light." The chance of a major crisis is high (over 50%). Send help now.
• 🟡 TIER-2 (Warning): "Yellow Light." Things look bad. Keep your eyes on the road and prepare your resources.
• 🟢 TIER-3 (Watch): "Green Light, but watch out." Nothing is happening yet, but keep monitoring.

🔒 The "Black Box" Promise: Why This Paper is Different

This is the most exciting part of the paper. Usually, when a scientist makes a prediction, they keep their notes hidden. If they are wrong, no one knows. If they are right, they take credit.

CERES is different. The author, Tom Pedersen, has built a "Public Ledger" (like a permanent, unchangeable diary).

• The Promise: Every single prediction CERES makes is timestamped, locked with a digital seal (cryptography), and published online before the 90 days are up.
• The Test: In 90 days, the system checks the real world. Did the crisis happen?
  • If CERES said "70% chance" and a crisis happened, that's a good sign.
  • If CERES said "70% chance" and nothing happened, the system admits it was wrong.
• The Goal: By keeping a public record of every win and every loss, the system can learn from its mistakes and get better over time. It's like a student who keeps a public grade book so they can see exactly where they need to study harder.

⚠️ The "Honest" Limitations

The author is very honest about what CERES isn't yet:

• It's a "Beta" Version: The math recipe (the coefficients) was written by the author based on past studies, not by a committee of 50 experts. It's a "best guess" starting point.
• It's Not Perfect: The paper admits that the "sensitivity intervals" (the margin of error) might be too small because the computer doesn't fully understand how drought and plant health are linked.
• It's Not a Replacement: CERES doesn't replace the human experts on the ground. It's a tool to help them decide when to send the experts.

🚀 The Big Picture

Think of CERES as the first "Open Source" Famine Forecast.

• Old Way: A closed club of experts makes a guess, publishes it months later, and no one can check if they were right.
• CERES Way: A computer runs every week, gives a specific probability, publishes the guess immediately, and then waits 90 days to see if it was right.

The ultimate goal isn't just to predict the future; it's to build a system that gets smarter every time it makes a prediction, so that one day, we can stop famine before it ever starts.

In short: CERES is a transparent, automated "crisis radar" that gives us a percentage chance of hunger, publishes its guesses for the whole world to see, and promises to learn from its mistakes to save more lives in the future.

Technical Summary

Here is a detailed technical summary of the paper "CERES: A Probabilistic Early Warning System for Acute Food Insecurity" by Tom Danny S. Pedersen (Northflow Technologies, 2026).

1. Problem Statement

Acute food insecurity remains a major preventable humanitarian crisis. Existing early warning systems face critical limitations:

• Latency: Systems like the Integrated Food Security Phase Classification (IPC) rely on field assessments with 3–6 month cycles, often identifying crises only after resources could have been pre-positioned.
• Lack of Probabilistic Output: Current systems (e.g., FEWS NET, INFORM Risk Index) provide scenario-based outlooks or static risk scores rather than calibrated probability estimates (0–1). Without probabilities, decision-makers cannot allocate resources proportionally to risk.
• Absence of Verification: No existing system maintains a public, machine-readable, prospective record of every prediction made against actual outcomes. Without this "ground truth" archive, empirical calibration (ensuring a 70% forecast actually occurs 70% of the time) is impossible.

CERES aims to fill this gap by providing a fully automated, open-access, probabilistic forecasting system committed to public prospective verification.

2. Methodology

System Architecture

CERES is a seven-stage automated pipeline running weekly (Mondays, 06:00 UTC) on cloud infrastructure. It processes data for 43 high-risk countries and outputs structured FamineHypothesis JSON objects.

• Pipeline: Ingest $\rightarrow$ Normalize (0.25° grid) $\rightarrow$ Signal Extraction $\rightarrow$ Convergence Detection $\rightarrow$ Scoring $\rightarrow$ Hypothesis Ranking $\rightarrow$ Publish.
• Runtime: ~45–90 seconds per run.

Data Sources (Six Streams)

The system fuses six distinct data streams, normalized to a weekly cadence:

1. Precipitation: CHIRPS v2.0 (z-score anomalies).
2. Vegetation: MODIS NDVI (z-score anomalies).
3. Conflict: ACLED (events and fatalities, weighted 0.6/0.4).
4. IPC Status: Current and projected IPC phases.
5. Food Access: WFP Food Consumption Scores (FCS) and rCSI.
6. Market: FAO/WFP Cereal Price Indices.

Scoring Model

• Composite Stress Score (CSS): A weighted mean of normalized stress signals (IPC: 0.25, Conflict: 0.20, Drought: 0.20, Food Access: 0.15, Vegetation: 0.10, Price: 0.10).
• Convergence Detection: Flags multi-source anomalies. If independent pillars exceed thresholds, a convergence_score is generated (0.0 to 1.0).
• Logistic Regression: Three logistic equations estimate probabilities for:
  • $P(\text{IPC 3+})$ within 90 days.
  • $P(\text{IPC 4+})$ within 90 days.
  • $P(\text{IPC 5/Famine})$ within 90 days.
• Coefficients: Initial coefficients are author-specified based on literature and an 87-record historical IPC transition dataset. They are not yet empirically derived via machine learning on a held-out set.
• Constraints: Monotonicity constraints are enforced ($P4 \le 0.70 \times P3$; $P5 \le 0.45 \times P4$) to reflect historical transition probabilities.

Uncertainty Quantification

• Sensitivity Intervals: The system reports 90% sensitivity intervals (5th–95th percentiles) derived from parametric perturbation (2,000 Monte Carlo draws with Gaussian noise).
• Limitation: Current intervals treat features as independent, underestimating uncertainty due to known correlations (e.g., drought-vegetation). Future versions plan to use copula-based resampling.

Alert Tiers

• TIER-1 (Emergency): $P(\text{IPC 4+}) \ge 0.50$ OR Critical Convergence.
• TIER-2 (Warning): $P(\text{IPC 3+}) \ge 0.45$ OR Warning Convergence.
• TIER-3 (Watch): All other regions with significant stress.

3. Key Contributions

1. Public Prospective Verification Infrastructure: The most significant contribution is the cryptographically signed, write-once ledger. Every prediction is timestamped, hashed, and archived. At T+90 days, outcomes are automatically verified against IPC data. This creates the first machine-readable "track record" for famine forecasting.
2. Probabilistic Output: Unlike binary alerts, CERES outputs probability distributions, enabling Anticipatory Action frameworks (OCHA) to trigger funding based on risk thresholds rather than waiting for confirmed crises.
3. Open Access & Transparency:
   • All data, code, and predictions are open-source (CC BY 4.0).
   • A public REST API provides real-time access.
   • The "Track Record" dashboard allows independent verification of accuracy.
4. Calibration Commitment: The paper explicitly defines a pre-registered protocol (Table 1) to measure Brier Scores, CRPS, and calibration reliability once sufficient prospective data accumulates (targeting 2026–2027).

4. Results

Historical Back-Validation (Sanity Check)

The system was tested against four historical Phase 4–5 events (Somalia 2011, South Sudan 2017, Ethiopia 2022, Yemen 2021).

• Performance: CERES assigned TIER-1 classification to all four events.
• Probabilities: High confidence scores (e.g., $P(\text{IPC 4+})$ ranged from 0.53 to 0.74).
• Comparison: In-sample Brier scores were 0.0066 (CERES) vs. 0.0494 (Persistence baseline) and 0.2275 (Climatological baseline).
• Caveat: These are in-sample results. Coefficients were set with knowledge of these events; thus, they do not prove prospective skill. The author explicitly states these are "sanity checks" only.

Sensitivity Analysis

• Coefficient perturbation ($\pm 20\%$) resulted in stable tier classifications for 39 of 43 regions (91%), indicating robustness to parameter uncertainty.

5. Significance and Future Work

Significance:
CERES represents a paradigm shift from descriptive (what happened) to predictive (what will happen) and calibrated (how likely) forecasting in humanitarian aid. By committing to public verification, it addresses the "black box" nature of many AI models in critical sectors. It enables a feedback loop where the model can be empirically improved as real-world data accumulates.

Limitations & Future Work:

• Coefficient Initialization: Current weights are author-specified, not data-optimized. Future work involves Maximum Likelihood Estimation (MLE) once the prospective ledger matures.
• Uncertainty Modeling: Moving from independent perturbation to copula-based resampling to better capture correlated risks (e.g., drought and vegetation).
• Data Gaps: Handling irregular IPC cadences and missing survey data in specific countries.
• Validation Timeline: The author plans a full empirical validation paper (targeting Nature Food, late 2026) once 12–24 months of prospective data (approx. 2,500–5,000 pairs) are collected.

Conclusion:
CERES demonstrates that automated, probabilistic famine forecasting is technically feasible at a global scale. Its primary value lies not just in the current model accuracy, but in the architectural commitment to transparency and calibration, setting a new standard for accountability in humanitarian early warning systems.