Quantitative early warning models for mixed migration towards the EU.
Date: 2025-09-22
Institutions: Joint Research Centre, Pingsdorf, Julia
Subjects: EU alert system, European Union, emergency aid, food security, migration, report, risk management, scientific calculation, war

ISSN 1831-9424 

Quantitative early warning models for 
mixed migration towards the EU 

Pingsdorf, J., Minora, U., Di Salvo, M., Belmonte, M. 

2025 

 EUR 40441

 
  
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 
publication. For information on the methodology and quality underlying the data used in this publication for which the 
source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations 
employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the 
part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or 
concerning the delimitation of its frontiers or boundaries. 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC142976 

EUR 40441 

PDF  

ISBN 978-92-68-31270-4   ISSN 1831-9424   doi:10.2760/6820414  

KJ-01-25-446-EN-N 

Luxembourg: Publications Office of the European Union, 2025  

© European Union, 2025  

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 
on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised 
under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that 
reuse is allowed provided appropriate credit is given and any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from 
the copyright holders. 

How to cite this report: Pingsdorf, J., Minora, U., Di Salvo, M. and Belmonte, M., Quantitative early warning models for mixed migration towards 
the EU, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/6820414, JRC142976. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ...................................................................................................................................................................................................... 3 

Acknowledgements .............................................................................................................................................................................. 4 

1  Introduction ....................................................................................................................................................................................... 5 

1.1  Definitions ................................................................................................................................................................................ 5 

1.2  Scope and categories of events .................................................................................................................................. 6 

1.3  Sources ...................................................................................................................................................................................... 9 

1.4  Objectives ................................................................................................................................................................................. 9 

2  Methodologies for early warning ....................................................................................................................................... 11 

2.1  Anomaly detection in migration studies .............................................................................................................. 11 

2.2  Early warning algorithms in the policy context ............................................................................................... 12 

2.2.1  Migratory events in the EU ............................................................................................................................ 13 

2.2.2  Events in third countries ................................................................................................................................. 14 

2.2.2.1  The Science4Peace Portal: early warning on conflict risks ............................................... 15 

2.2.2.2  The Integrated Food Security Phase Classification: early warning on food 
insecurity ............................................................................................................................................................................ 16 

2.2.2.3  HDX Signals: automatic notifications on early warning ..................................................... 17 

2.3  Advantages, limitations and alternative approaches ................................................................................... 18 

3  Early warning for migration policy. Key requirements........................................................................................... 20 

3.1  Key criteria ........................................................................................................................................................................... 20 

3.2  Use cases .............................................................................................................................................................................. 21 

4  Algorithm proposals................................................................................................................................................................... 25 

4.1  A multi-criteria algorithm ............................................................................................................................................ 25 

4.1.1  Criterion 1: Percentage change ................................................................................................................... 25 

4.1.2  Criterion 2: Percentile distribution ............................................................................................................. 26 

4.1.3  Criterion 3: Rolling standard deviation ................................................................................................... 26 

4.1.4  Thresholds and scoring.................................................................................................................................... 26 

4.1.5  A flexible algorithm ........................................................................................................................................... 27 

4.2  Cumulative sum (CUSUM) control chart .............................................................................................................. 28 

4.2.1  CUSUM in the context of migration .......................................................................................................... 31 

1 

 
 
 
 
4.2.2  Dealing with seasonality, trends, autocorrelation and stationarity in time series ........ 31 

4.2.3  Three levels of warnings based on severity scores ........................................................................ 33 

5  Validation ......................................................................................................................................................................................... 34 

5.1  Asylum applications from Turkish nationals, 2020-2023......................................................................... 35 

5.2  Asylum applications from Egyptian nationals, 2020-2023 ..................................................................... 36 

6  Conclusions ..................................................................................................................................................................................... 39 

References ............................................................................................................................................................................................. 41 

List of abbreviations and definitions ...................................................................................................................................... 47 

List of figures ....................................................................................................................................................................................... 50 

List of tables ......................................................................................................................................................................................... 51 

Annexes ................................................................................................................................................................................................... 52 

Annex 1. IPC Scales .................................................................................................................................................................... 52 

Annex 2. Nationality distribution ........................................................................................................................................ 52 

Annex 3. Number of months signalled as anomalies ............................................................................................. 53 

Annex 4. Model parameters for the validation exercise ....................................................................................... 56 

Annex 5. Performance comparison: multi-criteria (strict and loose) vs. single-criteria ...................... 57 

Annex 6. Results of Early Warning methods for the selected use cases .................................................... 63 

2 

 
 
 
 
Abstract  

Anticipation and preparedness are key objectives of migration management policies at the EU level. 
The Migration Preparedness and Crisis Blueprint (Blueprint Network), overseen by DG Migration and 
Home Affairs, provides an operational framework for monitoring and anticipating migration flows, 
with the primary goal of supporting the EU’s emergency and crisis response. Within this framework, 
the development of an early warning system is seen as an essential step to support preparedness 
and effective, coordinated responses to emerging migration situations. The European Commission is 
in the process of developing its own early warning methodology to monitor mixed migration flows. 
This report contributes to this development by proposing an early warning methodology grounded in 
an  empirical  analysis  of  existing  approaches  to  anomaly  detection  in  the  policy  context  and  the 
literature.  

The proposed approach features two different early warning models which have been tested on two 
case studies, selected based on their potential relevance to policy makers. These are the number of 
asylum  applications lodged by  Turkish and Egyptian nationals.  Results show that both models  are 
effective in detecting early signals of important changes in the series, with one model limiting the 
rate of false alarms at the expense of missing a few real anomalies, and the other detecting a higher 
proportion of real anomalies but generating more false alarms. 

The work suggests that these methods can provide a robust foundation for an early warning system 
to support preparedness at the policy level. Policy experts would likely benefit from such a system, 
as  it  enables timely  monitoring  and  proactive  responses  to  events  that  can  impact  the  migration 
management system. 

3 

 
 
 
 
 
Acknowledgements  

The  authors  would  like  to  thank  Fabiana  Scapolo,  Dario  Tarchi  and  Alexander  Bossakov  for  their 
insightful comments and discussions during the development of this report. 

The  authors  would  also  like  to  thank  participants  to  the  technical  workshop  “Early  warning 
methodologies  for  mixed  migration  flows”,  co-organised  with  DG  HOME  in  February  2025.  In 
particular, they would like to express their gratitute towards: Martin Taschner, Magnus Ovilius, Lorenzo 
Franceschelli, Mathieu Tartar, Alessio Baccari, Brunoro Calderico Buzzaccarini, Elena Fiore, Francesca 
Pavano, Ilker Aydin, Jonas Begemann, Luca Barani, Stella Riggi, Viviana Trinchi. Their expertise and 
feedback have been instrumental in shaping the content of this report. 

Authors 

Julia Pingsdorf 

Umberto Minora 

Mattia di Salvo 

Martina Belmonte 

4 

 
 
 
 
1 

Introduction  

Anticipation and preparedness are key objectives of migration management policies at the EU level. 
The Migration Preparedness and Crisis Blueprint (Blueprint Network), overseen by DG Migration and 
Home  Affairs  (DG  HOME),  provides  an  operational  framework  for  monitoring  and  anticipating 
migration flows, with the primary goal of supporting the EU’s emergency and crisis response.1 Within 
this framework, the development of an early warning system is seen as an essential step to 
support  preparedness  and  effective,  coordinated  responses  to  emerging  migration 
situations.  This system should facilitate the timely and comprehensive understanding of events and 
emerging  trends  by  relevant  stakeholders,  triggering  the  monitoring  of  changing  situations  and 
enhancing proactive responses.  

To contribute to this objective, the European Commission is developing its own early warning system 
to monitor mixed migration flows. This report describes an early warning methodology that supports 
the  development  of  such  a  system,  grounded  in  an  empirical  analysis  of  existing  approaches  to 
anomaly detection in the policy context and the literature.  

This  report  focuses  on  an  early  warning  methodology  for  quantitative  indicators;  however  before 
delving into the specifics of the proposed methodology, it is important to first define the key concepts 
of a more general early warning system that underpin this work. 

1.1  Definitions 

Early warning systems (EWSs) are processes designed to analyse and disseminate timely and relevant 
data  and information, enabling individuals, communities, and organisations to anticipate  risks  and 
take proactive preparedness measures (UNDRR, 2007). There are many areas such as hydrological 
risks,  food  security,  earthquake  and  tsunami  events,  or  humanitarian  crises  in  general,  that  are 
addressed with early warning systems at various levels of administration.2 Their primary goal is to 
detect changes early enough to allow for the implementation of adjustments or corrective 
measures, while minimising the number of false alarms (Napierała et al., 2022).  

In  the  context  of  the  Blueprint  Network’s  activities,  an  Early  Warning  System  refers  to  a 
mechanism designed to support preparedness efforts by anticipating crises or challenges 
through  the  identification  of  changing  patterns  and  anomalies  that  point  to  potential 
migration. It involves the collection, analysis, and interpretation of data and information related to 
migration trends, patterns, and drivers, as well as factors that may exacerbate migration flows, such 
as conflict, environmental disasters, or economic instability. 

Anomalies are patterns in data that do not conform to a well-defined notion of normal 
behaviour. Anomalies can be contextual: this is the case when data is only unusual in a specific 
situation or context but would be considered normal in other contexts.  The contextual attributes 
are  used  to  determine  the  context  (or  neighbourhood)  for  that  instance.  In  time-series  data,  for 

1 See: Commission Recommendation (EU) 2020/1366 of 23 September 2020 on an EU mechanism for preparedness and 

management of crises related to migration.  

2 For a review, see: Early Warning System—An overview | ScienceDirect Topics. (n.d.). 
https://www.sciencedirect.com/topics/social-sciences/early-warning-system 

5 

 
 
 
 
 
instance, time is a contextual attribute that determines the position of an instance within the larger 
sequence  (Chandola  et  al.,  2009).  Examples  of  contextual  attributes  include  seasonal  or  monthly 
patterns, where a value might be normal during one period but unusual during another.  

Early  warning  notifications, in turn,  result  from  a  formalised  process  of  applying  a  predefined 
methodology to identify anomalies, in order to identify and flag these unusual patterns.  

The process of early warning (Figure 1) starts from being aware of what is happening in the EU and 
in third countries. Building on this first situational awareness step, however, early warning involves 
an analytical component informing about the significance of some events for migration. 
The significance is defined in relationship to warning thresholds or criticality levels. These can be pre-
defined by analysts based on user needs or dynamically adjusted by the chosen anomaly detection 
technique. When the threshold or a criticality level is reached, the event under monitoring 
becomes an anomaly, and an early warning notification is issued to users of the system.  

Figure 1. The early warning process 

Source: JRC elaboration 

1.2  Scope and categories of events 

An Early Warning system for mixed-migration flows3 in the EU aims to identify significant changes in 
events that are relevant for migration to the EU. Events are of two broad categories: 

3 According to the EMN Asylum and Migration Glossary, mixed migration flows refer to “complex migratory population 
movement including refugees, asylum seekers, economic migrants and other types of migrants as opposed to 
migratory population movements that consist entirely of one category of migrants […] IOM defines mixed movements 
as a movement in which a number of people are travelling together, generally in an irregular manner, using the same 
routes and means of transport, but for different reasons”. See: EMN. Mixed Migration Flow. https://home-
affairs.ec.europa.eu/networks/european-migration-network-emn/emn-asylum-and-migration-
glossary/glossary/mixed-migration-flow_en   

6 

 
 
 
 
 
 
1.  Events in third countries, that are linked to migration in the EU, either because they concern 

drivers of migration (such as conflict, environmental disasters, or economic instability);4 or 
because they indicate mobility outside the EU (e.g. migrants registered in traditional transit 
countries).  

2.  Migratory events in the EU – including events related to inflows (e.g., asylum applications or 
irregular border-crossings [IBC]); migration patterns within the EU (e.g., secondary move-
ments) or the impact on migration management system (e.g., changes in the asylum back-
log).  

In the second case, early warning is about migration phenomena in the EU that are – to some ex-
tent – already occurring; in the first case, early warning is about migration in the EU that might oc-
cur. 

There is a trade-off between how early anomalies are identified, and the certainty with which these 
are linked to migration events to the EU (Figure 2). 

4 The term “drivers” refers to factors or elements that influence particular outcomes, processes or phenomena. Analysing 
drivers of migration can provide insights on the complex and multi-dimensional factors that contribute to migration 
processes. By analysing anomalies in driver-related indicators, early warning systems can identify potential migration 
risks and facilitate timely interventions.  For more information see: Rosinska A., Drivers of migration: dimensions and 
indicators. A policy-oriented literature review. Publications Office of the European Union, Luxembourg, 2024. 

7 

 
 
 
 
 
Figure 2. Examples of events plotted on a ‘certainty of impact-time between the identification of changes and the 
migratory outcome’ matrix. 

Trade-off between timeliness and uncertainty 

The  plot  displays  some  events  along  two  main  axes:  the  time  between  the  identification  of  changes  and  the 
occurrence  of  migration  outcome  in  the  EU  (horizontal  axes),  and  the  certainty  of  the  impact  of  the  event  on 
migration flows in the EU (vertical). Below there is a list of some events that show the trade-off between timelines 
and uncertainty, from the top-left corner to the bottom-right cornet.  

 

The identification of significant increases in asylum applications in the EU from one particular nationality 
is a case of a certain ‘anomaly’ in migratory flows, yet it notifies about a migratory event (i.e. asylum 
applications) to the EU that is already occurring.  

  A significant increase in irregular border crossings along a specific route is a certain migration event that 
may  anticipate  an  increase  in  the  number  of  asylum  applications  in  country  along  the  route  (another 
migration event).  

 

 

The identification of changes in border management practices in countries of transit neighbouring the EU, 
or changes in the number of migrants registered in these countries, are less certain to generate migratory 
flows to the EU but provide more time to prepare early for such possible changes.  

The identification of changes in the security situation (e.g. the increase in the number of conflict events) 
of  a  country  of  origin  is  even  less  certain  to  generate  migratory  flows  into  the  EU  (especially  if  it  is 
geographically distant), but provides even more time to prepare.  

Source: JRC elaboration 

8 

 
 
 
 
 
1.3  Sources 

Early warning systems can benefit from a multi-faceted approach combining both quantitative and 
qualitative sources of information. 

  Algorithms on quantitative indicators can provide a set of early warnings triggered once 
thresholds are reached. For example, a warning could be automatically triggered once the 
number of irregular-border crossings within a given period of time reaches a record high. 

  At  the  same  time,  early  warnings  can  also  be  issued  on  the  basis  of  qualitative 
information  as  primary  sources.  These  qualitative  inputs  should  be  assessed  and 
classified  according  to  their  level  of  criticality  (in  terms  of  their  potential  impact  on 
migration), on the basis of pre-defined criteria. For instance, qualitative information on 
events in third countries could be classified according to the likelihood that the event will 
lead to a change in mobility (low to high) and the expected timeframe for the impact to 
materialise. 

A  comprehensive  assessment  of  a  situation  thus  requires  the  integration  of  both 
quantitative and qualitative sources. Findings from quantitative analyses can be validated and 
supplemented by qualitative insights, and vice versa. By combining multiple sources and perspectives, 
a more nuanced understanding of a given situation can be gained, including a deeper understanding 
of the underlying context and potential drivers of instability. Notifications on anomalous data patterns 
can  point  analysts’  attention  to  specific  countries  or  situations,  prompting  them  to  monitor  these 
countries  or  situations  by  looking  at  developments  from  a  qualitative  angle  to  gain  a  deeper 
understanding  of  the  underlying  factors  driving  the  quantitative  changes.  Although  this  report 
focuses on quantitative analysis, using algorithms and quantitative indicators to identify 
potential  early  warnings,  it  is  essential  to  acknowledge  the  importance  of  qualitative 
sources in providing contextual insights and validating quantitative findings.  

1.4  Objectives 

This  report  defines  an  early  warning  methodology  for  quantitative  indicators  related  to migration 
based  on  an  empirical  analysis  of  two  main  algorithms.  It  features  the  introduction  of  a  newly 
developed algorithm, in conjunction with a well-established anomaly detection technique.  
The performance of these methods is validated against public historical data on asylum applications 
– a key indicator for mixed migration flows, highlighted in exchanges with policy experts as a priority 
area  for  early  warning.  Through  the  testing  against  historical  data,  this  report  assesses  these 
methods’ performance and provides insights into their implementation, paving the way for further 
testing and refinement of the methodology.  

The report is structured as follows: 

  Section 2 provides an overview of quantitative early warning methodologies currently in 

use in the literature and in the policy context. 

  Based on this literature review and on consultations with experts, Section 3 outlines the 
key criteria that an EWS for migration should follow, and presents the two use cases that 
will be used to evaluate the proposed algorithms’ performance.  

9 

 
 
 
  Section 4 outlines the proposal of two main methodologies for early warning: the multi-

criteria algorithm and the CUSUM model. 

  Section 5 evaluates the performance of the proposed algorithms by analysing anomaly 

detection in two case-studies: Türkiye and Egypt.  

10 

 
 
 
2  Methodologies for early warning 

The definition of methods for anomaly detection is a crucial step in the development of an 
Early  Warning  System.  A  review  of  approaches  to  early  warning  currently  implemented  in  the 
literature (Section 2.1) and in the policy context (Section 2.2.) is a logical first step for the development 
of a tailored solution within the Migration and Crisis Blueprint. On the basis of this review, Section 3 
presents the key requirements of an EWS for migration, and Section 4 introduces a proposal for an 
early warning methodology.  

2.1  Anomaly detection in migration studies 

Anomaly detection in Early Warning Systems is an issue that has been researched and applied within 
diverse research areas and application domains (Chandola et al., 2009). There are many formulations 
of anomaly detection in the literature, each with its own advantages and disadvantages, and different 
fields of application, including migration. 

Anomaly  detection  techniques  can  operate  in  one  of  three  different  modes:  supervised,  semi-
supervised, or unsupervised. The choice of one mode over the other depends on the availability of 
data  labels,  that  is,  a  tag  that  classify  a  data instance as  normal  or anomalous  (Chandola  et  al., 
2009), usually provided by humans. In summary: 

  Supervised  Anomaly  Detection:  data  instances  in  a  training  set  are  labelled  as  either 
“anomaly” or “not-anomaly”, and an anomaly detection model is trained on these labels to 
learn how to distinguish between the two classes on new (unlabelled) instances. This approach 
can be seen as a classification problem, since its aim is to replicate the way the labels were 
defined (e.g. by human assignment, Hyndman, 2024). 

  Semi-supervised  Anomaly  Detection:  similar  to  the  Supervised  mode,  the  Semi-
supervised  mode  uses  data  labels,  but  only  one  type  of  label  is  available  (either  the 
anomalous class or the normal class). 

  Unsupervised Anomaly Detection: it does not require labelled training data and it is based 
on the assumption that normal instances are far more frequent than anomalies in the test 
data. 

In general, unsupervised techniques are most widely applicable, since obtaining quality labelled 
data is often very expensive. 

According  to  Gordenker  (1984)  an  EWS  on  forced  migration  should  aim  at  both  prevention  and 
efficient advance preparations for a possible emergency. Bijak et al. (2017) and later Napierała et al. 
(2022)  applied  an  unsupervised  early  warning  model  based  on  change-point  detection  to  asylum 
applications data, inspired by statistical control theory (the “CUSUM” model set out by Page [1954]). 
Their proposed approach enables a shift of perspective, from a purely reactive towards a proactive 
approach for increased preparedness against asylum's potentially high societal impacts on receiving 
countries  and  the  resource  implications  of  asylum  processes,  providing  a  tool  for  policy  and 
operational support, which necessitates exchange between analysists, decision-makers and modelers. 
A preliminary application of this technique was employed by the European Union Agency for Asylum 
(EUAA)  to  monitor  the  number  of  asylum  applications  lodged  by  different  nationalities  in  EU+ 
countries (Melachrinos et al., 2023). 

11 

 
 
 
Carammia et al. (2022) developed an entirely data-driven EWS on a mix of administrative statistics 
and  non-traditional  migration-related  data sources to  effectively  capture  early warning  signals  of 
asylum-related  migration.  It  is  based  on  unsupervised  techniques  borrowed  from  financial 
econometrics, namely change point analysis,5 the momentum approach and Lead-Lag correlations. 
Their system sets dynamic thresholds to trigger alerts, based on a moving average window of the 
latest data available, and works on a selection of 70 dyads of origin and destination countries (though 
it can be extended to include more if needed). The signals generated by this early warning model are 
then fed to a machine learning forecasting model aimed at estimating the future number of asylum 
applications in European countries of destination, aggregated by applicants’ nationalities. 

Sfyridis et al., (2017) used a semi-supervised One-Class Support Vector Machine (Scholkopf et al., 
1999)  trained  with  labelled  instances  of  anomaly  classes  to  detect  potential  smuggling  vessels 
carrying  migrants.  In  their  work,  the  authors  analysed  over  6.7  million  points,  representing 
approximately 6,000 vessels of which three were labelled as "migrant vessel". These were used as 
labels for anomalies, expecting that the three vessels exhibit abnormal behaviour at some part of 
their trajectory compared to the other vessels, although normality was not defined. 

Finally, Macis et al. (2024) developed an EWS to prevent conflict and unrest (which can be related to 
migration (Crippa et al., 2024), using an unsupervised anomaly detection method based on machine 
learning, specifically an Autoencoder model. They made use of the Armed Conflict Location and Event 
Data Project (ACLED) and Global Database of Events, Language, and Tone (GDELT) datasets due to 
their  availability,  coverage,  frequency  and  update,  and  prioritized  the  identification  of  sudden 
outbreaks over precise numerical predictions of conflicts. 

To effectively design an EWS for migration, it is also crucial to first understand the policy landscape 
in  which  it  will  operate.  This  includes  examining  the  existing  early  warning  methods  currently 
informing policy decisions. 

2.2  Early warning algorithms in the policy context 

While  the  European  Commission  is  still  in  the  process  of  developing  its  own  early  warning 
methodology,  it  currently  utilises  the  expertise  and  resources  available  through  the  Migration 
Preparedness  and  Crisis  Blueprint  (Blueprint  Network).6  Within  this  framework,  the  European 
Commission  relies  on  insights  and  analysis  provided  by  members  of  the  Network,  including  the 
European  External  Action  Service,  selected  Justice  and  Home  Affairs  (JHA)  Agencies  such  the 
European Border and Coast Guard Agency (Frontex), the European Union Agency for Asylum (EUAA), 
the  European  Agency  for  the  operational  management  of  large-scale  IT  systems  in  the  area  of 
freedom, security and justice (eu-LISA), the European Union Agency for Fundamental Rights (FRA), 
and  the  European  Police  Office  (Europol),  in  addition  to  contributions  from  Member  States.  The 
European Commission also takes into consideration information from key international organisations, 

5 Change point analysis aims at detecting where the probability distribution of a series or a process change based on 

some criteria (e.g. mean variance); for a description of the momentum approach and the Lead-Lag analysis refer to 
Murphy (1999) and Hoffmann et al. (2013) respectively. 

6  Commission Recommendation (EU) 2020/1366, of 23 September 2020 on an EU mechanism for preparedness and 

management of crises related to migration (Migration and Preparedness Blueprint). 

12 

 
 
 
 
 
such as the International Organisation for Migration (IOM) and the United Nations High Commissioner 
for Refugees (UNHCR). 

In order to identify how data related to migration trends and patterns is currently processed, the focus 
was placed on publicly available outputs from key stakeholders.  

The examples explored in the following sections are intended to provide an overview of 
different  approaches employed or  explored  in  the policy context,  and are  not meant  to 
represent a comprehensive list of all early warning algorithms in use.  

2.2.1  Migratory events in the EU 

This  section  provides  an  overview  of  publicly  available  analyses  from  key  stakeholders, 
highlighting methodologies, algorithms, and thresholds used to monitor changes in asylum 
and  migration  trends  within  the  EU  for  early  warning  purposes.  It  focuses  on  the  outputs  of 
Frontex  and  the  EUAA,  the  primary  entities  responsible  for  analysing  illegal  border  crossings  and 
asylum applications (respectively). 

Most of the reviewed analyses found on official and public reports rely on a single criterion 
(typically percentage increases) to detect anomalies. Their reference periods can be month-
on-month, quarter-on-quarter or focus on comparisons against a given period of time in the previous 
year. The level of disaggregation also varies depending on the indicator used, with some algorithms 
breaking  down  data  by  route,  nationality,  or  Member  State.  Notably,  these  algorithms  are  static, 
meaning they do not adapt to changing patterns or trends over time. Table 1 summarises the analysis 
identified  for two  of the main indicators  used to report migratory  events in  the  EU:  illegal  border 
crossings (IBC) and asylum applications. 

Table 1 Early warning algorithms for migratory events in the EU 

Indicator 

Source  

General 
description 

Irregular border crossings (IBC) 

Total asylum applications 

Frontex7 

EUAA8 

Percentage changes in the total number 
of IBC during a period in the current 
year, compared to the same period in 
the previous year.  

Percentage changes in monthly/yearly 
asylum applications, by nationality and 
Member State, compared to the previous 
month/year. 

Reference 
period 

Same period (cumulated) in the previous 
year 

Previous month or year 

Disaggregation 

Route  

Nationality, Member State. 

Condition for 
warning 

Formula 

Positive/negative changes could 
generate a warning 

Positive/negative changes could generate a 
warning 

Condition for ‘warning’ on IBC: 

Condition for ‘warning’ on asylum applications:

7 European Border and Coast Guard Agency (Frontex). (n.d.). Risk analysis for 2023/2024. Retrieved December 14, 2024, 

from https://www.frontex.europa.eu/assets/Publications/General/ARA_2023.pdf  

8 European Union Agency for Asylum (EUAA). Latest Asylum Trends. Retrieved December 14, 2024, from 

https://euaa.europa.eu/latest-asylum-trends-asylum; Asylum Report 2024. Retrieved December 14, 2024, from 
https://euaa.europa.eu/publications/asylum-report-2024  

13 

 
 
 
 
 
 𝑊 =

((cid:3010)(cid:3003)(cid:3004)(cid:3263),(cid:3293),(cid:3269)(cid:3300)(cid:3127)(cid:3010)(cid:3003)(cid:3004)(cid:3263),(cid:3293),(cid:3269)(cid:3300)(cid:3127)(cid:3117))
(cid:3010)(cid:3003)(cid:3004)(cid:3263),(cid:3293),(cid:3269)(cid:3289)(cid:3127)(cid:3117)

> 1% 

Where:  
W = warning  
N= All nationalities  
R = route;  
Ty=period of time in the reporting year, 
e.g. from January to March 2024 
Ty-1= same period of time in the 
previous year, e.g. from January to 
March 2023. 

𝑊 =

(𝐴𝑠𝑦(cid:3041),(cid:3014)(cid:3020),(cid:3047)(cid:2879)𝐴𝑠𝑦(cid:3041),(cid:3014)(cid:3020),(cid:3047)(cid:2879)(cid:2869))
𝐴𝑠𝑦(cid:3041),(cid:3014)(cid:3020),(cid:3047)(cid:2879)(cid:2869)

> 1% 

Where: 
W=warning 
n=nationality 
MS= Member State 
t= month or year of reporting 
t-1= previous month or year 

Source: JRC elaboration based on Frontex (2024) and EUAA (2024). 

In addition, the EUAA also developed the Push Factor Index (PFI), which is based on the elaboration 
of  event-based  data  from  the  GDELT  Project9  and  provides  important  insights  into  migration 
processes in origin countries, bearing the potential for early warning purposes. This is part of a broader 
early warning and forecasting system (EPS–Forecasting)10 that integrates administrative and non-
traditional data at scale to effectively capture early warning signals of asylum-related migration, and 
delivers  short-term  forecasts  of  asylum  applications  from  any  country  of  origin  to  any  European 
Union Member State (Carammia et al., 2022).  

Finally, the EUAA also explored other approaches for monitoring time series such as combining the 
cumulative  sum  (or  “CUSUM”)  method  with  the  analysis  of  short-term  and  medium-term  moving 
averages in the series borrowed from financial econometrics (EUAA, 2023). The CUSUM method is 
one of the two approaches for automatic early warning generation proposed in this report and it is 
detailed in Section 4.2. 

2.2.2  Events in third countries 

When it comes to information on the external dimension, the European Commission primarily relies 
on  intelligence  and  analysis  provided  by  staff  seconded  in  EU  Delegations  (such  as  European 
Migration  Liaison Officers [EMLO]), 11 the EEAS, and JHA Agencies,  but also from IOM and UNHCR. 
Information related to developments in third countries frequently consists in analytical considerations 
provided directly by stakeholders in the field, which may concern displacement in third countries, but 
also other events linked to drivers of migration. This type of analysis encompasses a broad range of 
topics, spanning political, security, economic, and human development dimensions and may include 
information retrieved in local news reports or direct inputs from local counterparts and interlocutors 
in third countries.  

To complement these insights, quantitative measures or indexes focused on potential drivers 
of migration are sometimes used. While changes in indicators related to migration drivers may 

9 https://www.gdeltproject.org/ 
10 ’EPS‘ stands for Early warning and Preparedness System, a database containing provisional data exchanged between EU 
Member States and associate countries with which arrangements are in place pursuant to Article 34 of the EUAA 
Regulation and the EUAA, intended to give an estimation of the latest asylum trends across EU+ countries in as near 
to real time as possible (https://euaa.europa.eu/sites/default/files/2024-
10/MBD_153_2023_EPS_databases_access_policy_FINAL.pdf). 

11 EMN Glossary, European Migration Liaison Officer, https://home-affairs.ec.europa.eu/networks/european-migration-
network-emn/emn-asylum-and-migration-glossary/glossary/european-migration-liaison-officer-emlo_en  

14 

 
 
 
 
 
not be  immediately reflected in migratory flows (Figure 1),  identifying  anomalies  can  support 
preparedness by pointing towards changing situations in countries of origin or transit. 

The sections that follow draw on a review of publicly available information, to illustrate the variety 
of  indicators and methodologies  for early warning that could be integrated into an Early  Warning 
System for mixed migration flows. These methodologies focus primarily on drivers of international 
migration, including conflict and political instability, humanitarian crisis, internal displacement.    

2.2.2.1  The Science4Peace Portal: early warning on conflict risks 

Science4Peace is a restricted resource managed by the JRC, which provides four analytical tools for 
early warning and crisis management:  

  The  Global  Conflict  Risk  Index  (GCRI)  and  the  Dynamic  Conflict  Risk  Model  (DCRM)  are 
quantitative models which evaluate on a yearly or monthly basis the risk of internal conflict 
in third countries and feed into the EU’s conflict Early Warning System12, 

  Conflict Event Trends (CET) offers a monthly analysis on conflict and protest events, 

  The  Global  Crisis  Atlas  (GCA)  is  a  catalogue  of  conflict  and  crisis  maps,  which  supports 

situational awareness and preparedness. 

  The platform also features country profiles which integrate data from these analytical tools 

for 140 countries (Schvitz, G. et al, 2023).  

Due to their relevance for early warning, methodologies for the GCRI and the DCRM are summarised 
in Table 4.  

Table 2 The Global Conflict Risk Index & the Dynamic Conflict Risk Model 

Tool  

General 
description 

Conflict risk 
definition 

Output 

GCRI 

DCRM 

Estimates the risk of violent conflict in 
140 countries over the next four years.13 

Estimates the monthly risk of violent conflict 
in sub-national regions over the next six 
months 

Probability and potential intensity of 
violent conflict (expressed in battle-related 
deaths).  

Likelihood of either the presence or absence 
of conflict in each sub-national 
administrative unit. 

Total conflict risk score (derived by 
multiplying probability and intensity 
estimates). 

Risk probability score. 

Variables in the 
model 

22 variables representing structural risk 
factors of conflict, divided into 6 
categories: political, security, social, 

1. Event data, including past conflict 
events, protests, and related incidents. 

2. Structural variables, which include 
local demographic and geographical data, as 

12 The EU’s conflict Early Warning System is a tool managed by the European External Action Service (EEAS) to identify 
and assess the risk of violent conflict in third countries, enabling timely preventive measures before situations 
deteriorate into crises. For more information see: EEAS (2022). Early Warning System. Fact sheet. 
https://www.eeas.europa.eu/sites/default/files/documents/Factsheet%20-%20EWS.pdf  

13 For example, the 2024 update of the GCRI estimates conflict risks from 2024 to 2027. 

15 

 
 
 
 
 
economy, geographical/environmental and 
demographic. 

well as political and economic variables at 
the country level. 

Conflict 
categories 

State-based conflict (SBC): Armed conflict between a state government and at least 
one organized group. 

Non-state conflict (NSC): Armed conflict between two groups, neither of which is 
directly affiliated with a state government. 

One-sided violence (OSV): Direct and deliberate killing of civilians, carried out either by 
a state government or a non-state armed group. 

Any conflict (ANY): includes all instances of internal conflict that belong to the three 
categories above. 

Data source 

Forecasting 
window  

Update 
frequency 

4 years 

Yearly 

Uppsala Conflict Data Program (UCDP) 

6 months 

Monthly 

Disaggregation 

National level 

Sub-national level 

Source: JRC elaboration 

2.2.2.2  The Integrated Food Security Phase Classification: early warning on food 

insecurity 

The  Integrated  Food  Security  Phase  Classification  (IPC)  is  a  tool  developed  by  various  partners 
including national administrations,  UN agencies and civil society organisations.14 The classification 
proposes common global scales to analyse food security and nutrition conditions, considering factors 
such as food availability, access and utilisation. By providing a common language (i.e., a standard 
classification  system  for  the  food  security  and  nutrition  situation),  the  IPC  allows  to  compare 
situations across countries and over time. The methodology is applied to three different scales15: IPC 
Acute Food Insecurity, IPC Chronic Food Insecurity, and IPC Acute Malnutrition and proposes severity 
phases for each of them, with specific assessment criteria or thresholds applied (See Annex 1). 

IPC  data is  publicly  available  and  regularly  updated  (typically  every  6 months).  It  aims  to  provide 
timely pictures of the food security situation across countries to inform policy-making and prompt 
both early responses and long-term programming (IPC, 2024).  

14 See: IPC Overview and Classification System | IPC - Integrated Food Security Phase Classification. (n.d.). Retrieved June 

17, 2024, from https://www.ipcinfo.org/ipcinfo-website/ipc-overview-and-classification-system/en/ 

15 Acute Food Insecurity refers to a sudden or short-term crisis that puts lives or livelihoods at risk, regardless of the 

cause. Chronic Food Insecurity is a persistent or recurring problem that prevents people from accessing adequate 
diets, often due to underlying structural issues. Acute Malnutrition that affects children, characterised by a high 
prevalence of malnutrition, illness, and inadequate food consumption. The distinction between these three categories 
is based on the recognition that each situation demands a unique response and affects distinct populations, 
necessitating tailored interventions to address their specific needs. For example, while actions targeting acute food 
insecurity should aim at urgently reducing food gaps, measures targeting chronic food insecurity should focus on the 
underlying factors. For more information see: Understanding the IPC Scales. 
https://reliefweb.int/report/world/understanding-ipc-scales  

16 

 
 
 
 
 
2.2.2.3  HDX Signals: automatic notifications on early warning 

In 2024, the United Nations Office for the Coordination of Humanitarian Affairs (OCHA)’s Centre for 
Humanitarian  Data  launched  an  early  warning  service:  the  Humanitarian  Data  Exchange  (HDX) 
Signals.  The  service  monitors  a selection  of  humanitarian  datasets  and  sends  automated  e-mails 
when specific criteria or thresholds are reached.  

Table 3 HDX Signals. Datasets and early warning criteria16 

Dataset 

Condition for warning 

Frequency 

Anomaly Hotspots of 
Agricultural Production 
(ASAP) - European 
Commission – Joint 
Research Centre  

Signals  are  triggered  when  new  agricultural 
anomaly hotspots17 are detected. 

Monthly 

Armed Conflict - Armed 
Conflict Location & Event 
Data Project (ACLED) 

Signals  are  triggered  when  monthly  fatalities 
reach  levels  not  seen  in  the  past  one  or  three 
years, and are higher than 100.  

Monthly18 

Acute food insecurity, from 
two sources: 

 

 

Integrated Food 
Security Phase 
Classification (IPC) 

Cadre Harmonisé 
(CH)19 

Signals are triggered when: 

Every six months 

 

 

There is an increase in populations in 
Phase  3  and  at  least  20%  of  the 
population are in phase 3 or above, or 
at  least  5%  of  the  population  are  in 
Phase 4. 

Any populations are estimated to be in 
catastrophic/famine conditions (Phase 
5). 

Conflict and disaster-driven 
population displacement, 

Signals 
triggered  when  monthly 
displacements reach levels not seen in the past 

are 

Monthly20  

16 United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for Humanitarian Data. (2024, June 

12). Datasets | HDX Signals. https://un-ocha-centre-for-humanitarian.gitbook.io/hdx-signals/datasets   

17 Anomaly hotspots refer to areas that experience anomalous agricultural production patterns (both in terms of crops and 

livestock). Through monitoring timely information on crop growing conditions, ASAP allows for the early detection of 
production deficits. For more information see: Meroni M. et al., (2019). The warning classification scheme of ASAP – 
Anomaly hot Spots of Agricultural Production, v4.0, https://agricultural-production-
hotspots.ec.europa.eu/files/asap_warning_classification_v_4_0.pdf  

18 ACLED datasets are transformed into time series of events and fatalities. Monthly fatalities are calculated as the rolling 
sum of fatalities in the previous 30 days. Armed conflict | HDX Signals. (2024, June 12). https://un-ocha-centre-for-
humanitarian.gitbook.io/hdx-signals/datasets/armed-conflict  

19 Cadre Harmonisè is a tool for analysing and identifying food and nutrition insecurity risks in the Sahel and West Africa 
produced by the Aghrymet Regional Centre of the of the Permanent Interstate Committee for Drought Control in the 
Sahel (CILSS). The CH uses the same analytical framework and severity scales as the IPC. Cadre Harmonisé. (n.d.). 
Manuel Version 3.0. Analyse et identification des zones à risque et des populations en  insécurité alimentaire et 
nutritionnelle. Retrieved July 23, 2024, from http://cadreharmonise.org/  

20 IDMC data is transformed by removing duplicate events based on the event’s ID. Data is then transformed into time 
series and monthly displacements are calculated as the rolling sum of displacements during the previous 30 days. 

17 

 
 
 
 
 
 
Internal Displacement 
Monitoring Centre's (IDMC) 

one  or  three years, and  are higher  than  5,000 
for conflict driven events or 50,000 for disaster-
driven events. 

Global Market Monitor 
dataset, World Food 
Programme (WFP). 

Signals  are  triggered  when  month-to-month 
price  increases  in  a  given  location  reach  the 
“high”  or 
lower 
classification in the previous month, indicating a 
worsening situation.21 

from  a 

“severe” 

level 

Monthly 

Source: JRC elaboration based on OCHA (2024) 

The  system  complements  its  automated  signals  with  AI-generated  background  information,  using 
Open  AI's  GPT-4.0  to  produce  headlines  and  summaries  for  signals  emerging  from  datasets 
containing publicly available text-based information (OCHA, 2024).  

Users can subscribe to the service’s e-mail notifications by selecting datasets and regions of interest. 
HDX Signals e-mails include a headline, location of the signal, information on the signal’s trend, links 
to further information, and contextual information. A signals’ map is also available on HDX’s website.22  

2.3  Advantages, limitations and alternative approaches 

As  shown in Section  2.2, early  warning  algorithms used in the  policy  context related to  migratory 
events into the EU often rely on one criterion, and tend to compare figures with either the same period 
in the previous year or with the previous period. These methods can provide immediate inputs for 
analysts working in the policy context to monitor trends and anomalous patterns in data, facilitating 
their work. Additional straightforward algorithms to support policymakers could include identifying 
the highest or lowest value within a given timeframe (e.g., the past quarter or year). The simplicity of 
these approaches can be an added value, as they can be easily understood and interpreted by policy 
officials. 

However,  these  types  of  algorithms  have  inherent  limitations.  Using  only  growth  rates  to  define 
anomalies, for example, might undermine the identification of different types of evolving situations: 
they are sensitive to changes at lower numbers, yet they might fail to detect anomalies when numbers 
are  high  but  not  sufficiently  growing  compared  to  the  reference  period  used  (e.g.,  previous  year, 
previous  month  -  Barker  and  Bijak,  2023).  In  other  words,  growth  rates  can  be  helpful  to  detect 
significant changes in the numbers compared to previous levels, but less reliable in capturing whether 
numbers represent a high volume or not.   

Evaluating figures in terms of standard deviations23 above the historical average is an approach used 
in the literature to assess the volume of current trends. However, historical standard deviations can 
be affected by outliers in the time series. Analysing the case of Syria, for instance, Barker and Bijak 

Internal displacements | HDX Signals. (2024, July 22). https://un-ocha-centre-for-humanitarian.gitbook.io/hdx-
signals/datasets/internal-displacement  

21 The Global Market Monitor of the WFP monitors market food prices across a range of countries. Price changes are 

classified as  ‘normal’ (between 0%-3%), ‘moderate (3%-10%), ‘high’ (10%-25%) or severe (≥25%). 

22 See: United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for Humanitarian Data. (n.d.). 

HDX Signals Map. Retrieved July 26, 2024, from https://data.humdata.org/visualization/signals/   

23 The standard deviation is a measure of how spread out a set of numbers is from its average value. It shows how much 

individual values tend to differ from the average, with a higher standard deviation meaning more variation.  

18 

 
 
 
 
 
(2023) show that due to the 2015 peak, an important outlier in the overall historical trend, the number 
of monthly asylum applications in the following years was falling below one standard deviation from 
the  historical  average  despite  often  exceeding  7,500,  which  is  a  rather  high  number  of  monthly 
applications.  In  addition,  these  types  of  algorithms  may  require  more  time  and  expertise  to  be 
interpreted, which may hinder their usability in policy environments, where timely decision-making is 
crucial.  

In summary, different methods are better suited to meet certain needs at the policy level 
or to capture different features in the time series (whether these are significant changes 
or high volumes). Some flexibility is necessary when defining the time frame used to assess current 
figures. On the one hand, algorithms need to avoid detecting too many changes that seem significant 
in the short term but are not when put into context: for instance, IBCs and asylum applications in 
2021 might report a large percentage change compared to 2020, but only because of generally very 
low arrivals during 2020 due to the COVID-19 pandemic. On the other hand, algorithms should avoid 
missing changes that would be relevant but are not signalled because of significant outliers in the 
past: for instance, considering the historical standard deviation, as often done in the literature, can 
hide new developments due to significant variations in outlier years (e.g., “migration crisis” of 2015 
and 2016). 

Additionally,  single-criteria  algorithms  tend  to  produce  binomial  "yes"  or  "no"  warnings, 
which  may  fail  to  capture  the  nuances  and  complexities  of  evolving  situations  across 
different  contexts,  potentially  leading  to  oversimplification  and  reduced  interpretability  of  the 
anomalies.  An  algorithm  which  results  in  different  warning  levels  could  allow  for  more 
meaningful insights.  

In addition, the early warning methodology proposed in this report (see Section 4) opts for 
an unsupervised approach for two key reasons: 

 

 

It can be applied without the need for data labels, making it more adaptable to different sets 
of indicators; 

It  is  not  prone  to  bias  due  to  misclassification  of  the  data  instances  (i.e.  when  the  label 
“anomalous” is not anomalous in a statistical sense). 

Data labels are used, however, to evaluate the performance of our approach, comparing the output 
of  our  models  to  labels  from  human  analysts  to  ensure  the  results  meet  user  expectations.  This 
evaluation will be discussed further in Section 5. 

Before presenting in detail the proposed early warning methodologies, the next chapter identifies the 
key criteria that such a system should meet. Building on the literature review conducted in this section, 
it integrates insights from policy experts to further refine these criteria. 

19 

 
 
 
 
3  Early warning for migration policy. Key requirements 

Building on the review of early warning methodologies presented in Chapter 2, the JRC consulted with 
European  Commission  policy  experts  specialising  in  migration  and  asylum.  The  objective  of  the 
consultations was to gather feedback from potential end-users to inform the development of an early 
warning system for mixed migration flows. Through both the methodology review and the exchange 
with  policy  analysts,  key  criteria  were  identified  that  a  quantitative  methodology  should  fulfil  to 
effectively support policy stakeholders. Additionally, the discussions revealed specific use cases where 
policy officers would have benefited from early alerts. 

3.1  Key criteria 

To effectively support policy stakeholders, an early warning system for mixed migration flows should 
be designed with the following key criteria in mind: 

1.  Simplicity:  the  early  warning  system  should  be  grounded  on  a  methodology  that  is 
transparent and easy to interpret. The system should provide clear and concise explanations 
for  why  particular  values  are  flagged  as  anomalies,  allowing  system  users  to  effectively 
analyse its outputs and use them for informed decision-making.  

2.  Timeliness: by definition (see Section 1.1), the early warning system should detect anomalies 
early enough to allow for corrective actions. Users of the system should be made aware of 
anomalous situations before they escalate into crisis or pose serious challenges. The selected 
use cases (see Section 3.2) illustrate anomalous situations in which policy analysts would 
have wanted to have received an early warning notification.    

3.  Relevance: indicators covered by the system should be closely related to migration, providing 
a logical link between the data analysed and potential inflows to the EU. To achieve this, the 
focus  should  be  placed  on  indicators  that  capture  both  mobility  patterns  and  drivers  of 
migration. While the early warning methodology itself does not allow to establish causal links 
between these indicators, the system should provide a coherent narrative that supports users 
in understanding the relationship between the monitored indicators and emerging situations 
related to migration into the EU.  

4.  Flexibility: the early warning methodology should be flexible and adapt to: 

(a)  Specificities of different data series: the system should be able to cater for different 
datasets, with different orders of magnitude, fluctuations and seasonality patterns.  

(b)  Uncertainty:  an  early  warning  system  for  migration  operates  in  a  dynamic  and 
unpredictable  environment,  where  sudden,  unexpected  changes  in  data  cannot  be 
excluded.  To  navigate  this  uncertainty,  the  system’s  parameters  for  anomaly 
detection  and  the  selected  thresholds  should  be  designed  to  be  flexible  and 
adaptable, allowing for adjustments when faced with rapidly changing situations.   

(c)  Benchmarks: in line with the inherent uncertainty mentioned above, it is important 
that the benchmarks used to define “normal behaviour” in the data can be adjusted 
to reflect changing circumstances.  

20 

 
 
 
5.  Anomaly detection goal: all early warning systems should be designed to minimise false 
positives (that is, pointing at anomalies that aren’t there) and false negatives (failing to detect 
genuine anomalies). On the one hand, excessive false positives may erode confidence in an 
Early Warning System. On the other hand, false negatives could result in delayed responses 
to real challenging situations or crisis. For instance, if an early warning system failed to detect 
a surge in asylum applications from a specific country (false negative), it would fail to alert 
policy  stakeholders  about  a  potential  vulnerability,  which  could  lead  to  untimely  resource 
allocation and response planning. If the system incorrectly identified a period of stability as 
anomalous  (false  positive),  this  could  lead  to  unnecessary  resource  mobilisation  and 
preparedness. It is also crucial that users of the system are made aware that all anomalies 
may be isolated incidents that may not necessarily translate into an emerging situation. The 
role  of  contextualising  these  anomalies  and  take  appropriate  responses  is  therefore 
delegated to the final users, not to the Early Warning System in itself. 

3.2  Use cases 

Two use cases were selected, focusing on recent migration trends involving specific nationalities for 
which the issuance of early warning notifications could have been useful. These cases were selected 
based on their potential relevance to policy makers and were discussed with European Commission 
policy  analysts  during  consultations  to  gather  their  input  and  perspectives.  The  focus  on  asylum 
applications was also informed by these exchanges, who highlighted the need and usefulness of early 
warning on this indicator. 

Asylum applications from Turkish nationals  

According to Eurostat data, in 2023, Turkish nationals submitted a record 94,000 asylum applications 
in the EU, making Türkiye the third country of origin that year. This was the second consecutive year 
that  Turkish  applications  nearly  doubled,  continuing a  steady  upward trend  since  2020.  Given  the 
relevance and steadiness of this increase, and Türkiye’s proximity to the EU, it can be safely assumed 
that policy analysts could have benefitted from a warning on anomalous data patterns in applications 
from Turkish nationals, especially prior to the all-time high of 15,600 in October 2023. This 
peak,  which  far  exceeded  historical  levels,  could  have  been  anticipated  to  some  extent  by 
analysing the trends in the previous two years (2021-2022) and considering qualitative 
information on events and developments linked to migration drivers in Türkiye. 

21 

 
 
 
Figure 3. Asylum applications in the EU by Turkish nationals, 2014-2024 

Source: JRC elaboration based on Eurostat migr_asyappctzm 

22 

 
 
 
 
 
These insights further reinforce the value of using the country as a case study and highlight how 
analysts  could  have  benefited  from  early  warnings  on  anomalous  asylum  application  trends.  For 
example, economic instability, particularly rising inflation since mid-2020, has been a recurring theme 
in  news  reports.  The  Turkish  lira  was  severely  impacted  during  the  Covid-19  pandemic, 
underperforming compared to other emerging market currencies.24 Economic turbulence was followed 
by  social  unrest  and  increasing  protests,  some  of  them  including  police  confrontation  with 
protesters.25 At the beginning of 2023, the country was hit by two strong earthquakes which resulted 
in over 50 thousand fatalities and hundreds of thousands of people displaced.26 This non-exhaustive 
overview  serves  to  illustrate  that  by  combining  early  warnings  on  anomalous  asylum 
application numbers with qualitative assessments of events linked to drivers of migration, 
analysts  could  have  been  better  equipped  to  anticipate  and  interpret  the  surge  in 
applications earlier on. 

Asylum applications from Egyptian nationals:   

Although  smaller in  scale compared  to Turkish  applicants,  applications  by Egyptian  nationals  also 
showed unprecedented numbers in the EU in both 2022 (15,600) and 2023 (26,600). Historically, 
Egyptian applications to the EU averaged around 4,800 per year from 2014 to 2019, but declined to 
3,360 in 2020 due to the Covid-19 pandemic. However, this trend reversed in 2021, with applications 
reaching a record high of 6,700, paving the way for the substantial increases seen in the following 2 
years.  First-time  applications  from  Egyptian  nationals  reached  several  monthly  all-time  highs 
during this period, the most significant peaks being March (1,315) and November (1,730) 2022, 
and January (2,165), March (2,405) and October (2,585) 2023. As was the case with Türkiye, policy 
experts  could  have  anticipated  this  trend  to  some  extent  had  they  been  alerted  of 
anomalous data patterns as early was 2021, potentially allowing for earlier intervention and a 
more proactive response. 

Similarly to the Turkish case, early alerts on anomalies in applications from Egyptian applicants could 
have been cross-checked against developments related to drivers of migration in this country. For 
instance, Egypt's economic instability, including a significant depreciation of the currency and rising 
inflation since 2021, have been widely reported. The country's economic issues were exacerbated by 
the Russian invasion of Ukraine, which led to disruptions in wheat imports and further exacerbated 
food price increases and water scarcity (EUAA, 2022). Additionally, the human rights situation in Egypt 
has been a subject of concern.27This is another case in which early alerts on anomalous data 

24 See: DW, “Why the Turkish currency is in free fall”, 17th August 2020. https://www.dw.com/en/erdogans-credit-binge-

fuels-lira-depreciation/a-54524078 

25 See: Focus - Inflation in Turkey: Soaring prices, housing crisis and student protests. (2021, October 18). France 24. 

https://www.france24.com/en/tv-shows/focus/20211018-inflation-in-turkey-soaring-prices-housing-crisis-and-
student-protests; Turkish police tear-gas women protesting over violence. (2021, November 25). AP News. 
https://apnews.com/article/europe-middle-east-violence-turkey-recep-tayyip-erdogan-
37dc9465b1f867b00465f8006a74d733; Protests in Turkey’s cities after lira plunges to new historic low. (2021, 
November 25). Euronews. https://www.euronews.com/next/2021/11/24/turkish-lira-plunges-to-a-historic-low-and-
protests-erupt-after-erdogan-defends-rates-cuts;   

26 See: Human Rights Watch, World Report 2024: Türkiye. Retrieved 28 March 2025 from: https://www.hrw.org/world-

report/2024/country-chapters/turkey  

27 See: 46th session of the United Nations Human Rights Council: General Debate Item 4: Human Rights Situations that 

require the Council’s attention, Joint Statement on Egypt. 12 March 2021. https://reliefweb.int/report/egypt/46th-
session-united-nations-human-rights-council-general-debate-item-4-human-rights  

23 

 
 
 
 
 
points,  combined with  insights  on relevant  developments in the country of origin, could 
have supported policy analysts in the monitoring of growing situations. 

Figure 4. Asylum applications by Egyptian nationals in the EU, 2014-2024 

Source: JRC elaboration based on Eurostat migr_asyappctzm 

Having outlined the essential requirements for an early warning system and case studies that can 
support the testing of the proposed methodology, Chapter 4 places the focus on two algorithms that 
can underpin the development of a methodology for an EWS for migration. 

24 

 
 
 
 
 
4  Algorithm proposals 

This section begins with a proposal for an algorithm that considers multiple criteria at the same 
time with the aim of providing a more complete picture of anomalies compared to single-criterion 
algorithms (e.g., percentage changes). The proposed algorithm also adopts different thresholds for 
each  criterion,  so  to  provide  further  flexibility  in  detecting  new  trends  as  well  as  mitigate  the 
uncertainty  that  comes  with  defining  strict  thresholds  that  need  to  be  applied  across  different 
contexts.  Finally,  the  three  proposed  criteria  combine  different  time  frames  to  assess  current 
figures against previous values.  

In  addition,  Section  4.2  proposes  a  complementary  approach  based  on  cumulative  sum 
(CUSUM) control charts, a statistical process control method capable of identifying changes in the 
data generating process and issuing timely warnings of emerging anomalies. 

4.1  A multi-criteria algorithm 

The proposed multi-criteria algorithm aims, on the one hand, at capturing anomalies that reflect 
both high volumes and significant changes and, on the other hand, at better contextualising current 
figures with respect to recent developments and historical trends.28 

The multi-criteria algorithm uses the following three criteria: 

  Monthly year-on-year percentage change. 

  Percentiles of the same month in the previous three years. 

  Rolling standard deviations above the mean of the previous twelve months. 

The proposed algorithm uses  a set of thresholds for each criterion to assign an overall "intensity" 
score to each observation, allowing for a nuanced interpretation of evolving situations across different 
contexts and mitigating the uncertainty of pre-defining strict thresholds. 

4.1.1  Criterion 1: Percentage change 

The first criterion, the percentage change29 compared to the previous year, is among the most used 
in methodologies applied in the policy context. It captures the significance of a change in a given 
month  compared  to  the  same  month  of  the  previous  year.  Among  the  three  criteria  used  in  the 
proposed methodology, it is the one with a medium-term reference period (i.e., last year), comparing 
figures in the same month to account for possible seasonality in the data.  

28 As shown by Barker and Bijak (2023) for the Syrian case, using single criterion on entire time-series can affect 

detection of anomalies due to past outliers. 

29 (Xn,m - Xn,m-12) / Xn,m-12 ; where X is the data series, n the nationality and m the month. 

25 

 
 
 
 
 
4.1.2  Criterion 2: Percentile distribution 

The second criterion, the percentile distribution30 of the same month in the last three years, instead, 
aims  at  capturing  the  size of  current  figures  compared  to  the  historical  trend,  thus  adopting  a 
long-term reference period. A ‘high’ volume is relative to each context (e.g., nationality, route, etc.) 
and,  thus,  absolute  values  might  not  be  the  best  option  to  define  thresholds.  For  instance,  when 
analysing the case of Syria and Ukraine, Barker and Bijak (2023) applied two different thresholds of 
monthly  asylum  applications  (300  for  Syria  and  50  for  Ukraine)  in  their  model  depending  on  the 
historical trend of each nationality. In other words, there cannot be a one-solution-fits-all using 
absolute values. The use of the percentile distribution (e.g., above the 80% of previous values) thus 
tries to overcome this issue: assessing current figures against historical consolidated values should 
help evaluate whether a figure is ‘high’ enough given each specific context.  Of course, on a case-by-
case evaluation, systems can be tailored to exclude values considered to be low by using additional 
filters  on  absolute  figures.  Similarly, while the tested  approach  compares  the  same  month in  the 
previous three years to account for seasonality of data, alternative specifications considering more 
months could be tested in future applications to evaluate possible differences in anomalies detected. 

4.1.3  Criterion 3: Rolling standard deviation 

Finally, the third criterion, the rolling standard deviation31 above the mean over the previous year, 
aims at evaluating current figures against recent developments. Among the three criteria, in 
fact, it is the one with the shortest reference period,32 thus adding information not considered in 
the other two criteria. Significant deviations above the mean of the past year can help understand 
whether figures are starting to be high compared to the recent past, thus capturing emerging shifts 
in time series. In addition to account for recent developments, using the previous twelve months as 
reference period helps eliminating the bias introduced by past outliers with exceptional variation (e.g., 
2015 for the case of Syria). 

4.1.4  Thresholds and scoring 

Besides  relying  on  three  criteria,  the  proposed  algorithm  also  uses  a  set  of  thresholds  for  each 
criterion, resulting in an overall score reflecting the “intensity” of anomalies detected (Table 4). This 
approach tries to overcome binomial “yes” or “no” types of warnings and to add nuances that might 
help  the  interpretation  of  evolving  situations  across  different  contexts.  Moreover,  using  a  set  of 
thresholds can help mitigate the uncertainty that comes with defining strict thresholds that need to 
be applied across different contexts and, possibly, types of data.  

Table 4 Criterion and thresholds of the multi-criteria algorithm 

criterion \ score 
% change t-12 

1 
25% 

2 
50% 

3 
75% 

30 Xn,m above Nth percentile of Xn,m-12, Xn,m-24 and Xn,m-36 ; where X is the data series, n the nationality and m the month. 
31 Xn,m above N standard deviations above the mean of Xn,m-1 to Xn,m-12; where X is the data series, n the nationality and m 

the month 

32 The rolling standard deviation criterion relies on data from the previous 12 months, meaning information up to the 

previous month are taken in consideration, therefore looking at the short-term developments.  

26 

 
 
 
 
 
percentile t-12, t-24, t-36 
rolling sd t-1 to t-12 

70th 
1 

80th 
1.5 

90th 
2 

Source: JRC elaboration. Note: for each threshold the condition is strictly above, not equal or above 

For each criterion, three increasing thresholds are defined and assigned a score from one to three 
(Table 4). For instance, a month recording a percentage change with the previous year above 75% 
(3rd threshold, +3) could be signalled as an anomaly with a score of 7 when either of the other two 
criteria overcomes its third threshold (+3) and the other one its first one (+1). For instance, figures 
can be above the 90th percentile (thus in the top 10%) of the same month in the past three years 
(3rd  threshold,  +3)  and  above  one  standard  deviation  from  the  last  12  months’  mean  (thus  top 
16%)33 (1st threshold, +1).  

Hence, the algorithm assigns an overall “intensity” score from 0 to 9 to each observation 
summing up each criterion’s score, with the observation being flagged as anomaly if the 
overall score is at least three (Figure 5). 

Figure 5 Intensity score and anomaly detection – multi-criteria algorithm 

Score 

0  1  2  3  4  5  6  7  8  9 

Anomaly  No 

Yes 

Source: JRC elaboration 

The decision to set a minimum value of three for the algorithm to signal an anomaly is driven by the 
need to balance, on the one side, the potential of the system to detect very different situations at 
early stages with, on the other side, avoiding too many anomalies.34  

4.1.5  A flexible algorithm 

In  order  to  provide  further  flexibility  in  defining  anomalies,  a  final  distinction  is  made  between  a 
“loose” and a “strict” algorithm. While for the “loose” only the condition of an overall score of at least 
three is necessary, for a “strict” signal each of the three criterion must overcome one of its thresholds. 
In other words, an overall score of three due to one of the three criterion overcoming its third threshold 
would  be  flagged  as  anomaly  under  the  “loose”  algorithm  but  not  under  the  “strict”  one.  On  the 
contrary, if each criterion has overcome at least its first threshold, then the overall score of three 
would trigger a signal in both the “loose” and the “strict” multi-criteria algorithms. 

33 In a normal distribution, 68% of observations fall within + or - one standard deviation from the mean (84% within one 
and a half, and 95% within two standard deviations). Looking only at the right side of the distribution where values 
are higher than the mean, observations falling more than one standard deviation above the mean belong to the 
highest 16% (i.e., (100-68)/2). While these specific percentages are valid for data with a normal distribution, the 
standard deviation remains a measure of the deviance from the mean, thus indicating how spread out are the data 
from the mean (and thus how ‘exceptional’ a value is) regardless from the specific distribution of each data series.  

34 As for defining thresholds, some degree of uncertainty applies also when selecting a cut-off for the intensity scale. The 
design choice of one third of the intensity scale in this approach could be further validated in future research to 
ensure that the number of anomalies detected reflect users’ needs.  

27 

 
 
 
 
 
The  combination  of  “loose”  and  “strict”  versions  can  be  helpful  in  monitoring  different  types  of 
situations evolving as well as changes in a situation that is already detected as being of relevance. 
For instance, the “loose” version can be more helpful in detecting early on signs of changes while the 
“strict” can be used to understand whether a situation is further intensifying since it was first detected 
or  if  it is  losing  momentum  over  time.  A  performance  comparison of  the  loose  and  strict  models 
against single-criteria algorithms can be consulted in Annex 5. 

4.2  Cumulative sum (CUSUM) control chart 

The cumulative sum (CUSUM) control chart is a Statistical process control (SPC) originally set out 
by Page (1954). This tool plots the cumulative sums of the deviations (scores) of the sample values 
from  a  target  value,  and  an  early  warning  can  be  triggered  when  the  scores  pass  a  certain  limit 
(threshold). The CUSUM is effective for change-point detection of regime shifts at an early stage of 
the  process  being  monitored,  because  it  combines  information  from  several  samples  (cumulative 
sums) rather than looking at each sample individually as some more basic SPCs do (e.g. Shewhart 
control charts).35 It is used, for instance, to monitor whether a process is drifting away from its mean, 
and  it  is  being  applied  in  many  different  domains  and  applications,  such  as  surveillance,  quality 
control, and clinical-care processes, to name only a few. 

The  scores  of  a  CUSUM  model  are  calculated  as  cumulative  deviations  from  a  target  value.  For 
instance, if we are interested in understanding if there was a positive shift in the mean of a normally 
distributed process, we can set our target value to the mean (or median, µ0) and the deviations are 
calculated  as  the  difference  between  each  ith  sample  and  the  mean  (or  median,  yi  =  xi  -  µ0).  A 
cumulative sum of such scores (si) would fluctuate around a central line (i.e. a random walk with 
mean  or  median  zero)  if  the  process  is  in  control.  If,  instead,  the  process  shifts  upward,  then  an 
upward drift will develop in the cumulative sums, and a signal is emitted when the score exceeds the 
decision interval (H). In this case the process is said to be out of control. It is also common to reset 
the scores to zero (vi) if they become negative because they are not relevant for determining when 
action  should  be  taken.  Figure  6  shows  an  example  of  the  workflow  to  obtain  a  one-sided  upper 
CUSUM chart from a time series X with a target value µ0 = 5, and an upper control limit (threshold) 
h+ = 6. Points within the red area in the last plot are the signal that the process is out of control. 

35 Family of models often used for on-line process surveillance to monitor if the sample values fall within some control 

limits and do not exhibit any systematic pattern. 

28 

 
 
 
 
 
Figure 6. One-sided upper CUSUM control chart applied to data with a change point at time 30 (vertical 
dashed line). (A) input time series X; (B) score function Y; (C) cumulative sum S; (D) cumulative sum V, 
restarted at negative values, and upper control limit h = 6. 

Source: Figure 1 in Napierała et al. (2022). 

The crucial aspects for designing an effective CUSUM control chart are the choice of the target value 
and the setup of the control limits, because they will affect how sensitive (or not) the model will be 
to detect important change in the pattern of the data. In the previous example, we used the mean as 
the target value. Note that any value can be used as target as long as it is meaningful for the shift 
we are interested to monitor. However, when dealing with migration flows data and related indicators 
like the ones this report is focusing on, the idea of a constant and clear “target value” becomes vague 
as  these  processes  are  often  not  normally  distributed  and  their  values  do  not  fluctuate  around  a 
constant mean or with a constant variance (i.e. they are “non-stationary”). To understand this concept 
better, let’s consider the number of first-time asylum applications lodged in the European Union since 
January 2008 (Figure 7). 

29 

 
 
 
 
Figure 7. Total number of first-time asylum application in the European Union (monthly data). The vertical 
dashed lines represent key critical past or ongoing events. 

Source: Eurostat. 

In the figure above we see that the number of monthly asylum applications lodged in the EU has a 
complex  and  far-from-linear  pattern:  it  started  with a constant  and  steady  increase  in  the  period 
2008 to 2014; saw a sudden surge between 2015 and 2016, a period knows as the “refugee crisis"; 
a rather “stable” period between 2018 and 2019, followed by the COVID-19 pandemic, that caused 
a marked drop in the series in early 2020, followed by an increase of the values in the subsequent 
months  which  most  likely  reflect  the  activity  of  the  relevant  asylum  authorities  that  process  the 
applications; finally, in February 2022, there is an increase due to the start of the war in Ukraine. 
Given the great variability of this time series, it would be very hard to set a static and clear threshold 
for the CUSUM model. There are various strategies to address these issues. First, even if the target 
value is unknown, it is still possible to estimate it “on the fly” using the samples in the data. Second, 
it is common to adjust it using a reference value K as multiplier (also known as the allowance, or the 
slack value). For instance, we can design a CUSUM control chart which monitors shifts that are of a 
distance of some multiple of the standard deviation (or the MAD) σ of the process from the mean (or 
median). As said, these values can be calculated on the fly using the historical data. Third, it is often 
advisable  to  restrict  the  information  used  for  calibration–the  data  used  to  estimate  the  CUSUM 
parameters  on  the  fly–to periods  prior  to  the  points  we  need  to  monitor.  On  the  one  side,  longer 
calibrations allow for better understanding past developments and detect possible features such as 
seasonality. On the other hand, however, observations that are too far from the present might not 
reflect the current situation and can add important bias to the system. For this reason, it is always 
important to have a good understanding of the data being monitored and the context in which they 
are used to find an ideal calibration period. 

30 

 
 
 
4.2.1  CUSUM in the context of migration 

Bijak et al. (2017) and later Napierała et al. (2022) assessed the potential of the CUSUM model in 
aiding the management of mixed migration flows, and a preliminary application of this technique was 
employed by the EUAA to monitor the number of asylum applications lodged by different nationalities 
in EU+ countries (Melachrinos et al., 2023). In this work, we are using the CUSUM control chart in a 
similar way, but with some modifications concerning the way we select the calibration period and the 
transformation of the data before they enter the model. 

The way the CUSUM model is used in the context of this study is a real-time change-point detection 
system.  In  other  words,  we  are  interested  in monitoring  a  continuous  stream  of  data  and 
detect  possible  signals  of  change  in  pseudo  real-time.  Therefore,  the  focus  is  not  on  the 
historical data, but in understanding if the most recent observation is in control or not. For 
this reason, we implement a moving window CUSUM model, where for each indicator to be monitored 
we set a fixed number of calibration samples which precede the most recent observation in the time 
series, and we move this window one step ahead each time a new observation is made available to 
the system. Each time this is done, we estimate new parameters on the fly from the calibration set. 
The parameters used in this report are reported in Annex 5. Finally, we reset the CUSUM value to zero 
every time it exceeds h±. By doing so, we focus on providing a timely alert of a potential regime shift. 

4.2.2  Dealing with seasonality, trends, autocorrelation and stationarity in time 

series 

One  important  aspect  common  to  many  migration  time  series  is  that  of  seasonality.  A  seasonal 
pattern occurs when a time series is affected by seasonal factors such as the time of the year or the 
day of the week. If we consider the number of asylum applications as an example, there is usually a 
drop at the end of each year when the processing capacity of asylum authorities tends to be limited, 
followed by a subsequent increase (Carammia et al., 2022). Another example is given by the asylum 
flow from the Western Balkan to EU+ countries, where the highest levels generally occur in October 
of each year, plus, usually, lows in applications during June or July (EUAA, 2015). Therefore, when 
seasonality is an important and known feature of a time series, it is extremely important that an Early 
Warning model account for it, otherwise we risk triggering alerts which are false positives due to the 
seasonal effect. 

One  strategy to deal with seasonality is to remove the seasonal component from the time  series 
before passing it to the Early Warning model. The “Seasonal and Trend decomposition using Loess” 
(STL) is  a versatile and robust method for decomposing time series developed  by Cleveland  et  al. 
(1990) and later extended to handle multiple seasonal patterns by Bandara et al. (2025). If the data 
has a clear seasonal pattern (e.g. with peaks and droughts occurring each year between Winter and 
Summer), an Early Warning model would probably trigger many alerts if we were not to account for 
these expected seasonal patterns. By adjusting for the seasonality effect, we can instead focus on 
the other types of variations in the data. 

As the name suggests, STL decomposition also provides a trend component using the LOESS method, 
a locally-weighted regression for estimating nonlinear relationships. LOESS fits a smoothed function 
across  the  data  points  representing  the  low  frequency  variation  in  the  data  together  with 
nonstationary, long-term changes (Cleveland at al., 1990). In anomaly detection, we are not really 
interested in these low frequency and long-term variations, rather in the sharp and short-term ones. 
Therefore, we can remove this component from the time series to improve the performance of the 
CUSUM model (and of other anomaly detection algorithms in general). By doing this, we also account 

31 

 
 
 
for the issue of autocorrelation to some degree, which is the linear relationship between lagged values 
of a time series, that may be the result of dynamics that are inherent to the process. 

Positive  autocorrelation  can  produce severe  negative  bias  in  traditional  estimators  of  the  process 
standard deviation, and this bias produces control limits that are much tighter than desired. This is 
because in presence of autocorrelation, closer observations tend to form clusters which mask the 
dispersion of the distribution around the mean, making it look smaller than it really is. This in turn 
would imply the estimation of a smaller standard deviation, on which the control limits are calculated. 
For these reasons, the presence of autocorrelation can result in an average false alarm rate much 
higher  than  expected  or  desired  (Lu  et  al.,  2001).  In  fact,  the  performance  of  CUSUM  charts 
deteriorates  when  the  process  observations  are  autocorrelated.  The  popular  way  of  monitoring 
changes in mean of an auto correlated series is to apply the CUSUM on the residuals of a chosen 
time-series model that best explains the dynamics of the process observations (Runger et al., 1995; 
Atienza et al., 2002). The LOESS method provided within the STL decomposition can be used in this 
regard, as it fits a time-series model and the residuals from the prediction of this model can be used 
for process monitoring. 

A time series may present either a seasonal and long-trend component, just one, or neither of these 
two. It’s up to the modellers and the analysts to find the best combination of parameters that would 
eventually  lead  to  the  desired  anomaly  detection  results.  In  Section  5,  we  provide  the  results  of 
applying the CUSUM model on a selection of use cases as identified in Section 3.2. 

Finally, if the time series is not stationary (even after removing the seasonal and trend component, 
which  should  help  in  this  regard),  one  common  strategy  to  make  it  stationary  is  to  compute  the 
differences  between  consecutive  observations  (a  technique  known  as  differencing),  which  helps 
stabilise the mean of a time series (Hyndman and Athanasopoulos, 2021), essential condition for the 
CUSUM model. An example of the use of this application is given by Figure 8. 

Figure 8. (A)  Asylum applications in the EU; (B) Monthly change in asylum applications in the EU. 

In Figure 8 A, the asylum applications in the EU series represents a non-stationary series, with various 
trends  and  changing  levels  throughout  the  period.  In  Figure  8  B,  the  monthly  change  in  asylum 

32 

 
 
 
 
applications is calculated as the difference between the asylum applications at month t minus the 
asylum applications in the previous month (t-1). The result in this case is a stationary series, where 
the values are centred around a constant mean, and the variance is rather stable. 

All the features described in this section are not only related to the CUSUM model, but they have 
implications for early warning methods more generally. We have implemented specific parameters 
to account for them and provide meaningful and unbiased results (see also Annex 5 for a list of these 
parameters). 

4.2.3  Three levels of warnings based on severity scores 

As mentioned in Section 4.1.4, intensity scores or severity levels can enhance the effectiveness of the 
EWS.  A  three-tiered  scale  classifying  anomalies  as  “moderate”,  “serious”  or  “critical”  provides  a 
straightforward reference to convey the severity of the warning. To operationalise this, we assigned 
different scores to each CUSUM value: if the CUSUM value is beyond 1σ, we mark this as an early 
warning with score 1 corresponding to the “moderate” level in the EWS; if it is beyond 2σ the score is 
2 corresponding to the “serious” level in the EWS; if it is beyond 3σ (which is also the control limit h±), 
the score is 3 corresponding to the “critical” level in the EWS. 

The next chapter places the focus on the validation of the effectiveness of the algorithms presented 
in this section. In order to do so, the focus will be placed on asylum applications by two key countries 
of origin: Türkiye and Egypt.  

33 

 
 
 
5  Validation 

The development of the proposed methodology was informed by consultations with policy experts in 
DG HOME, including through an exercise that required them to label anomalies in selected quantitative 
and univariate time series (as described in 3.2). 

By comparing the results of an anomaly detection model with the labels provided by human 
experts,  we  can  measure  the  performance  of  the  algorithms  proposed  in  this  report. 
However, it must be noted that this only works when the human has correctly and completely labelled 
the anomalies, and it also requires that what a human considers an anomaly is also anomalous in a 
statistical sense (Hyndman, 2024). For this reason, the exercise was done after the algorithms were 
presented  to  the  respondents.  This  allowed  us  to  gather  a  ground  truth  dataset36  to  test  the 
performance of our models. 

Since the idea of anomaly can vary amongst respondents, it can happen that the same observation 
is marked as anomaly by some respondents but not by others. For this reason, and for the sake of 
testing the performance of the EW models, we consider an instance to be “significant” when it 
has  gathered  more  consensus  among  the  respondents,  indicating  broader  agreement 
among experts. We can therefore define a consensus score, which is calculated as the sum of the 
number of respondents who marked an observation as an early warning (i.e. the number of votes). 
The total number of respondents for this exercise was five. Finally, we assume that instances with no 
votes are implicitly not anomalous. 

Using  the  ground  truth  and  the  outcome  of  the models,  we  can  build  a  confusion  matrix37  and 
calculate the confusion metrics of the models, which allows us to evaluate certain performances. 
In particular, by looking at the rate of true positives and false positives—respectively, how often the 
model identified a real anomaly as such or not—we can measure the Precision of an algorithm using 
the formula Precision=TP/(TP+FP), where TP and FP are respectively true and false positives (the latter 
also known as Type I error). The higher the Precision value, the better the algorithm is in limiting the 
occurrence of false positives, the maximum being 1 and the minimum 0. This is of great interest since 
false positives can jeopardise the credibility of an EWS by issuing false alarm notifications to the 
users. 

Similarly, we look into the Recall metrics, which measure how well the model identifies all 
actual positive cases. Its formula is Recall=TP/(TP+FN), where FN stands for false negative, namely 
the case in which the model has flagged an anomaly as not anomalous (also known as Type II error). 
A higher Recall value means a more performant algorithm, a lower value means the opposite, the 
maximum being 1 and the minimum 0. Contrary to Precision, this metrics focuses on missing positive 
cases and can be combined with the consensus score, since ideally we want our models not to miss 
any true anomaly, and in particular those with a high consensus. 

In the following sections, we present the consensus plots and the results of the model validation for 
each use case identified in 3.2 using the parameter configuration as reported in  Annex 4  and  the 
results reported in Annex 6. In general, we can see that there are only a few instances where the 

36 Verified and trusted data used for validation purposes. 
37 A confusion matrix is a table that is used to define the performance of a classification algorithm. A confusion matrix 

visualizes and summarizes the performance of a classification algorithm. (Singh et al., 2021) 

34 

 
 
 
 
 
respondents' votes match. This could indicate that each expert has a personal idea of what is an early 
warning that differs from the others. Even so, there are still observations with high scores (3 or more 
votes). 

Finally, since each EW model assigns a certain score to each anomaly which is different from the 
consensus  score,  the  error  metrics  are  derived  without  taking  these  scores  into  account  but  only 
considering the dichotomy between anomaly and normality. However, in describing the results of the 
validation, we reintroduce the concept of scores when looking at the anomalies that the model failed 
to detect, if any (false negative). This is because we want to highlight cases where a possible missed 
anomaly is also a serious one, which ideally should not happen with a well-performing EWS. 

5.1  Asylum applications from Turkish nationals, 2020-2023  

Figure 9 shows the number of first-time asylum applications lodged by Turkish nationals since 2020 
in the EU, along with the information on the consensus of what is an anomaly for DG HOME experts 
who participated in the interactive session of the workshop (consensus). 

Figure 9. Validation: asylum applications from Turkish nationals 2020-2023 

Source: JRC elaboration 

The figure shows that the highest score was 3, meaning that 3 respondents out of 5 labelled the 
same observations as anomalous. These are June 2022, and August and October 2023. No instance 
received neither 4 nor 5 votes, while the rest of the selected anomalies have low values (between 1 
and 2). 

Table 5 is the confusion matrix for the CUSUM and multi-criteria models. 

Table 5. Confusion matrix for the CUSUM and multi-criteria models on the Asylum applications in the EU by 
Turkish nationals. P: positive; N: negative; PP: predicted positive; PN: predicted negative; TP: true positive; TN: 
true negative; FP: false positive; FN: false negative. 

Predicted 

35 

 
 
 
 
 
 
N+P=49 

CUSUM 

Multi-criteria 

Anomaly 
(PP) 
12 

Not anomaly 
(PN) 
37 

Anomaly 
(PP) 
15 

Not anomaly 
(PN) 
34 

Actual 

Anomaly (P) 
16 
Not anomaly (N) 
33 

Source: JRC elaboration 

11 (TP) 

5 (FN) 

12 (TP) 

4 (FN) 

1 (FP) 

32 (TN) 

3 (FP) 

30 (TN) 

Table 6 shows the performance of the models, displaying the Precision and Recall error metrics. 

Table 6. Performance of the models 

Precision 
Recall 

Source: JRC elaboration 

CUSUM 
0.9 
0.7 

Multi-criteria 
0.8 
0.8 

Both models have very high Precision values, with the CUSUM scoring 0.9 and the multi-criteria 
0.8 respectively (both close to the maximum score which is 1). This means that they both trigger 
very few false alarms. This is crucial, since an EW model that throws too many false alarms is 
unreliable and users may end up not trusting its results. 

The Recall metrics, namely the probability of catching a real positive case, is also high in both models 
(0.7  and  0.8  for  the  CUSUM  and  the  multi-criteria  respectively).  This  means  that  the  models 
detected  70-80%  of  the  actual  anomalies  as  identified  by  HOME  experts.  Of  the  few 
anomalies not detected (false negatives), nearly all had a consensus score of 1 (the lowest score). 
The only exception was one anomaly in the CUSUM method, which had a score of 2. Not only did 
the  models  successfully  identify  most  of  the  actual  anomalies,  but  they  also  correctly 
detected the most significant ones, which had high consensus scores. The few anomalies that 
were missed were relatively minor, with low consensus scores. Overall, for this specific use case we 
can see that the CUSUM tends to give better results in terms of limiting the amount of false 
alarms, whereas the multi-criteria is better for detecting the real anomalies, although the 
difference between the two is very subtle. 

5.2  Asylum applications from Egyptian nationals, 2020-2023 

Figure 10 shows the number of first-time asylum applications lodged by Egyptian nationals since 
2020 in the EU, along with the information on the consensus of what is an anomaly for HOME experts 
who participated in the interactive session of the workshop (consensus). 

36 

 
 
 
 
 
Figure 10. Validation: asylum applications from Egyptian nationals 2020-2023 

Source: JRC elaboration 

The figure shows that the observation with the largest consensus was March 2023, with 4 votes out 
of 5, followed by September 2022 and October 2023, both with 3 votes each. It also shows that there 
was no anomaly with unanimous vote (level 5). The rest of the anomalies have low values (between 
1 and 2). 

Table 7 is the confusion matrix for the CUSUM and multi-criteria models. 

Table 7. Confusion matrix for the CUSUM and multi-criteria models on the Asylum applications in the EU by 
Egyptian nationals. P: positive; N: negative; PP: predicted positive; PN: predicted negative; TP: true positive; TN: 
true negative; FP: false positive; FN: false negative. 

N+P=49 

Actual 

Anomaly (P) 
17 
Not anomaly (N) 
32 

Source: JRC elaboration 

Predicted 

CUSUM 

Multi-criteria 

Anomaly 
(PP) 
12 

Not anomaly 
(PN) 
37 

Anomaly 
(PP) 
36 

Not anomaly 
(PN) 
13 

11 (TP) 

6 (FN) 

14 (TP) 

3 (FN) 

1 (FP) 

31 (TN) 

8 (FP) 

24 (TN) 

Table 8 shows the performance of the models, displaying the Precision and Recall error metrics. 

Table 8. Performance of the models 

Precision 
Recall 

Source: JRC elaboration 

CUSUM 
0.9 
0.6 

Multi-criteria 
0.6 
0.8 

37 

 
 
 
 
 
 
 
In this use case, the Precision metrics differ to some degree between the two models. On the one 
hand, the CUSUM model scored 0.9, close to the maximum score of 1. This means that it generated 
very few false alarms, with only one instance of incorrectly identifying an event as an anomaly, as 
was the case with the Turkish nationals. As explained previously, minimising false alarms is a key 
element of an EWS, as excessive false alarms can impact the credibility of the model. In contrast, the 
Precision for the multi-criteria is rather low instead (0.6), suggesting a rather high rate of false alarms. 
This might be due to the fact that using multiple criteria can increase the chances of over-detecting 
anomalies, even though we limited this possibility using the “strict” version of the algorithm (see also 
Annex 5 for a complete list of its parameters).  

The scores for the Recall metrics show the opposite scenario, with the multi-criteria scoring 0.8, while 
the  CUSUM  reached  0.6,  meaning  that  60%  of  the  actual  anomalies  were  detected  by  the  latter. 
Although the CUSUM model missed 40% of the anomalies (a total of 6 cases), it is notable that all 
of these undetected anomalies had very low consensus scores, with 5 having the minimum score of 
1 and 1 having a score of 2. Therefore, all the important anomalies (those with a high consensus 
score) were still correctly detected by the model. 

Overall, for this specific use case we can conclude that using the CUSUM model limits the 
rate of false alarms at the expense of missing real anomalies  (although these were of 
relatively  minor  importance).  On  the  other  hand,  the  multi-criteria  model  is  more 
successful  in  detecting  a  higher  proportion  of  real  anomalies,  but  this  comes  at  the 
expense of generating more false alarms. Ultimately, the choice between these two models 
depends on the users’ priorities. 

38 

 
 
 
6  Conclusions  

This report proposed an early warning methodology stemming from an empirical analysis of anomaly 
detection  methods  for  mixed  migration  flows  found  in  both  the  policy  context  and  the  academic 
literature.  

The proposed approach features two algorithms: 

  The multi-criteria algorithm, which allows to capture different patterns in the data 
and to contextualise data points by taking into consideration different time-spans. This 
approach allows for a nuanced interpretation of anomalies, accounting for both short-
term and long-term trends, and can be tailored to specific contexts. 

  The CUSUM method, a statistical process control technique capable of identifying early 

changes in data which had been previously tested in migration studies.  

Both methods are tailored to assign an overall "intensity" score or warning level to each observation, 
based  on  a  set  of  thresholds.  This  allows  to  better  capture  the  severity  of  changes  detected  for 
evolving situations across different contexts. 

The performance of these algorithms was evaluated through two case studies, selected according to 
their potential relevance to policy experts and discussed with European Commission experts: asylum 
applications from Turkish and Egyptian nationals. The validation was done by comparing the results 
of the models with the labels provided by human experts, who were asked to identify anomalies in 
the data. 

The experts' labels were used to create a ground truth dataset, which was then used to evaluate the 
performance of the models. The performance was measured using two metrics: Precision and Recall. 
Precision  measures  the  proportion  of  true  positives  (correctly  identified  anomalies)  among  all 
predicted  anomalies,  while  Recall  measures  the  proportion  of  true  positives  among  all  actual 
anomalies. 

The results show that: 

  For asylum applications from Turkish nationals, both models performed well, with high 
Precision and Recall values. The CUSUM model had a slightly higher Precision value, 
indicating that it generated fewer false alarms. 

  For asylum applications from Egyptian nationals, the CUSUM model had a high Precision 
value, but a lower Recall value, indicating that it missed some real anomalies. The 
multi-criteria model had a lower Precision value, but a higher Recall value, indicating 
that it detected more anomalies, but also generated more “false alarms”. 

Overall, the results suggest that, for the specific use cases presented in this report and possibly other 
indicators with similar distributions, the choice between the two models depends on the users' 
priorities.  If  minimising  false  alarms  is  more  important,  the  CUSUM  model  may  be  preferred.  If 
detecting a higher proportion of real anomalies is more important, the multi-criteria model may be 
preferred.  

To further develop and refine these methodologies, it is essential to test them on a broader range 
of  indicators,  including  those  related  to  mobility  (outside  or  inside  the  EU)  or  those  concerning 
drivers of migration or triggering factors. 

39 

 
 
 
These methods can provide the basis for the development of an Early Warning System that follows 
the key principles outlined in Section 3: 

  Simplicity:  the multi-criteria  algorithm  and the  CUSUM method  are transparent  and their 
results can be interpreted in a straight-forward manner by policy analysts, allowing them to 
analyse their outputs to inform decision-making. 

  Timeliness: the algorithms’ ability to detect anomalies early on, allowing for close to real-
time monitoring, combined with the use of intensity scores and warning levels, can allow for 
timely interventions. 

  Relevance: the methodology  proposed  can  be  applied to indicators that  are essential  for 
monitoring migration flows, ensuring relevance to the policy context, even if this report has 
placed the focus on asylum applications. This also relates to the next key criterion: 

  Flexibility:  the  proposed  methodology  offers  flexibility  in  several  aspects,  including  the 
option to choose an algorithm according to specific priorities, to assign intensity scores and 
warning levels which allow to prioritise different types of anomalies, or to adjust thresholds. 

  Accuracy: both methods proved effective in detecting significant anomalies, and can satisfy 
different users’ needs: while the CUSUM model limits the rate of false alarms at the expense 
of missing (few and relatively unimportant) real anomalies, the multi-criteria model detects 
a higher proportion of real anomalies but generates more false alarms. 

In  addition  to  the  above,  users  of  an  EWS  based  on  these  algorithms  should  be  encouraged  to 
integrate information on quantitative anomalies with qualitative sources. The latter can significantly 
enhance the effectiveness of early warning algorithms by providing valuable contextual background 
information.  

Moreover, these inputs can identify emerging trends and developments that warrant attention, even 
if quantitative indicators do not trigger alerts. Insights and judgements of experienced analysts are 
crucial for anticipatory decision-making in migration policy, as they allow to leverage both specialised 
knowledge and different perspectives on potentially interlinked developments. Techniques that allow 
to detect emerging issues and first signals of change, such as horizon scanning, could be a suitable 
solution for tapping into this expertise.  

40 

 
 
 
 
 
 
References  

Studies 

  Atienza, O. O., Tang, L. C., & Ang, B. W. (2002). A CUSUM Scheme for Autocorrelated 

Observations. Journal of Quality Technology, 34(2), 187–199. 
https://doi.org/10.1080/00224065.2002.11980145 

  Bandara, K., Hyndman, R. J., & Bergmeir, C. (2025). MSTL: A seasonal-trend 

decomposition algorithm for time series with multiple seasonal patterns. International 
Journal of Operational Research. 

  Barker, E., & Bijak, J. (2023). Could We Have Seen it Coming? Towards an Early Warning 

System for Asylum Applications in the EU (SSRN Scholarly Paper 4605897). 
https://doi.org/10.2139/ssrn.4605897 

  Bijak, J., Hilton, J. & Forster, J. (2017). Quantitative assessment of asylum-related 

migration: a survey of methodology, Publications Office. 
https://data.europa.eu/doi/10.2847/642161 

  Carammia, M., Iacus, S. M., & Wilkin, T. (2022). Forecasting asylum-related migration 
flows with machine learning and data at scale. Scientific Reports, 12(1), 1457. 
https://doi.org/10.1038/s41598-022-05241-8 

  Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM 
Comput. Surv., 41(3), 15:1-15:58. https://doi.org/10.1145/1541880.1541882 

  Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. J. (1990). STL: A seasonal-
trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–33. 
http://bit.ly/stl1990 

  Constantinos Melachrinos, Marcello Carammia, & Teddy Wilkin. (2023). An innovative 
framework for analysing asylum-related migration. In Harnessing data innovation for 
migration policy: A handbook for practitioners. 
https://euaa.europa.eu/publications/innovative-framework-analysing-asylum-related-
migration 

  Crippa, A., d’Agostino, G., Dunne, J. P., & Pieroni, L. (2024). Conflict as a cause of 
migration. Oxford Economic Papers, 1–23. https://doi.org/10.1093/oep/gpae037 

  European Union Agency for Asylum, Bijak, J., Hilton, J., & Forster, J. J. (2017). 

Quantitative assessment of asylum-related migration: A survey of methodology. 
Publications Office of the European Union. https://data.europa.eu/doi/10.2847/642161   

  Gordenker, L. (1984). Early Warning of Forced Migration. In Defense of the Alien, 7, 68–

77. 

  Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. & Stahel, W.A. (1986). Robust Statistics: 
The Approach Based on Influence Functions. Wiley Series in Probability and Statistics. 
10.1002/9781118186435 

  Hoffmann, M., Rosenbaum, M. & Yoshida, N. Estimation of the lead-lag parameter from 

non-synchronous data. Bernoulli 19, 426–461 (2013). 

41 

 
 
 
  Human Rights Watch (2024), World Report 2024: Türkiye. https://www.hrw.org/world-

report/2024/country-chapters/turkey  

  Hyndman, R.J. & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd 
edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 20th November 
2024. 

  Hyndman, R.J. (2023) fpp3: Data for "Forecasting: Principles and Practice" (3rd Edition). 

R package version 0.5. https://CRAN.R-project.org/package=fpp3 

  Hyndman, R.J. (2024). That's Weird: Anomaly Detection Using R. OTexts, Melbourne, 

Australia. In preparation, https://OTexts.com/weird/ 

  Lu, C. W. & Reynolds, M. R. (2001). CUSUM Charts for Monitoring an Autocorrelated 

Process. Journal of Quality Technology, 33(3), 316–334. 
https://doi.org/10.1080/00224065.2001.11980082 

  Macis, L., Tagliapietra, M., Meo, R., & Pisano, P. (2024). Breaking the trend: Anomaly 

detection models for early warning of socio-political unrest. Technological Forecasting 
and Social Change, 206, 123495. https://doi.org/10.1016/j.techfore.2024.123495 

  Melachrinos, C., Carammia, M. & Wilkin, T. (2023). An Innovative Framework for 

Analysing Asylum-Related Migration. International Organization for Migration (IOM), 
Harnessing Data Innovation for Migration Policy. A Handbook for Practitioners, Geneva: 
International Organization for Migration, pp. 54-65.Marcucci, S., & Verhulst, S. (n.d.). 
Towards a Taxonomy of Anticipatory Methods: Integrating Traditional and Innovative 
methods for Migration Policy. Big Data for Migration Alliance. Retrieved July 30, 2024, 
from https://www.data4migration.org//articles/blog-series-part-3-towards-a-taxonomy-
of-anticipatory-methods-integrating-traditional-and-innovative-methods-for-migration-
policy/ 

  Murphy, J. J. Technical Analysis of the Financial Markets: A Comprehensive Guide to 

Trading Methods and Applications (New York Institute of Finance, 1999) 

  Napierała, J., Hilton, J., Forster, J. J., Carammia, M., & Bijak, J. (2022). Toward an Early 

Warning System for Monitoring Asylum-Related Migration Flows in Europe. International 
Migration Review, 56(1), 33–62. https://doi.org/10.1177/01979183211035736 

  Page, E. S. (1954). Continuous Inspection Schemes. Biometrika, 41(1/2), 100–115. 

https://doi.org/10.2307/2333009 

  Runger, G. C., Wlllemain, T. R., & Prabhu, S. (1995). Average run lengths for CUSUM 

control charts applied to residuals. Communications in Statistics - Theory and Methods, 
24(1), 273–282. https://doi.org/10.1080/03610929508831487 

  Schölkopf, B., Williamson, R. C., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support 
Vector Method for Novelty Detection. Advances in Neural Information Processing 
Systems, 12. 
https://papers.nips.cc/paper_files/paper/1999/hash/8725fb777f25776ffa9076e44fcfd7
76-Abstract.html 

  Science Direct. (n.d.). Early Warning System—An overview. Retrieved June 11, 2024, 
from https://www.sciencedirect.com/topics/social-sciences/early-warning-system   

42 

 
 
 
  Schvitz, G., Weiler, S. M., Van, D. M.-S., & Bardelli, G. (2023). Science4Peace: A 

quantitative toolkit for conflict early warning and crisis management. JRC Publications 
Repository. https://publications.jrc.ec.europa.eu/repository/handle/JRC135125 

  Sfyridis, A., Cheng, T., & Vespe, M. (2017). Detecting vessels carrying migrants using 
machine learning. https://publications.jrc.ec.europa.eu/repository/handle/JRC106832 

  Sharifi, A., Pathak, M., Joshi, C., & He, B.-J. (2021). A systematic review of the health co-

benefits of urban climate change adaptation. Sustainable Cities and Society, 74, 
103190. https://doi.org/10.1016/j.scs.2021.103190 

  Singh, P., Singh, N., Singh, K. K., & Singh, A. (2021). Diagnosing of disease using machine 
learning. In Machine learning and the internet of medical things in healthcare (pp. 89-
111). Academic Press. https://doi.org/10.1016/B978-0-12-821229-5.00003-3 

Policy and Technical Documents 

  Agrhymet. (n.d.). Cadre Harmonisé. Retrieved July 23, 2024, from 

http://cadreharmonise.org/ 

  Aghrymet. (n.d.). Cadre Harmonisé. Manuel Version 3.0. Analyse et identification des 

zones à risque et des populations en  insécurité alimentaire et nutritionnelle. Retrieved 
July 23, 2024, from 
https://www.cadreharmonise.org/web/content/6811?unique=961409fc03685a9362c916
1ec92eb2f6c0a0bbee&download=true 

  Council of the European Union. (2016). The EU Integrated Political Crisis Response – 

IPCR– Arrangements. In Brief. 
https://www.consilium.europa.eu/media/29699/web_ipcr.pdf 

  European Border and Coast Guard Agency (Frontex). (n.d.). Risk analysis for 2023/2024. 

Retrieved December 14, 2024, from 
https://www.frontex.europa.eu/assets/Publications/General/ARA_2023.pdf  

  European Commission. (n.d.-a). Horizon Scanning. Retrieved July 30, 2024, from 
https://knowledge4policy.ec.europa.eu/foresight/topic/horizon-scanning_en  

  European Commission. (n.d.-b). Strategic foresight. Retrieved September 25, 2024, from 

https://commission.europa.eu/strategy-and-policy/strategic-foresight_en  

  European Commission, Directorate-General for International Partnerships (INTPA). (n.d.-

a). INTPA Strategic Foresight. Retrieved September 25, 2024, from 
https://capacity4dev.europa.eu/resources/INTPA-Foresight_en  

  European Commission, Directorate-General for International Partnerships (INTPA). (n.d.-

b). Youth4Foresight Toolkit | Capacity4dev. Retrieved September 25, 2024, from 
https://capacity4dev.europa.eu/resources/INTPA-Foresight/info/youth4foresight-
toolkit_en  

  European External Action Service (EEAS). (2022). Early Warning System. Fact sheet. 

https://www.eeas.europa.eu/sites/default/files/documents/Factsheet%20-%20EWS.pdf  

43 

 
 
 
  European Migration Network (EMN). (n.d.). Mixed Migration Flow. Retrieved December 16, 
2024, from https://home-affairs.ec.europa.eu/networks/european-migration-network-
emn/emn-asylum-and-migration-glossary/glossary/mixed-migration-flow_en  

  European Parliament, Committee on Foreign Affairs. (2022). REPORT on the 2021 

Commission Report on Turkey | A9-0149/2022. 
https://www.europarl.europa.eu/doceo/document/A-9-2022-0149_EN.html 

  European Strategy and Policy Analysis System (ESPAS). (n.d.). Horizon Scanning. 

Retrieved September 25, 2024, from https://espas.eu/horizon.html 

  European Union Agency for Asylum (EUAA) (2015). Asylum Applicants from the Western 
Balkans: comparative analysis of trends, push-pull factors and responses – Update. 
https://euaa.europa.eu/sites/default/files/public/Asylum-Applicants-from-the-Western-
Balkans_Update_r.pdf.  

  European Union Agency for Asylum (EUAA). (2023). An innovative framework for 

analysing asylum-related migration, May 
2023.https://euaa.europa.eu/sites/default/files/publications/2023-
05/An_innovative_framework_analysing_asylum-related_migration.pdf 

  European Union Agency for Asylum (EUAA). (2024a). Asylum Report 2024. Retrieved 
December 14, 2024, from https://euaa.europa.eu/publications/asylum-report-2024  

  European Union Agency for Asylum (EUAA). (2024b). Data analysis of decisions on 
asylum applications and recognition rates at second or higher instances in 2023. 
https://euaa.europa.eu/publications/data-analysis-decisions-asylum-applications-and-
recognition-rates-second-or-higher-instances-2023 

  European Union Agency for Asylum (EUAA). (2024c). Sudan: Country focus : security 
situation in selected areas and selected profiles affected by the conflict : April 2024. 
Publications Office. https://data.europa.eu/doi/10.2847/759057 

  European Union Agency for Asylum (EUAA). (2022). Migration Drivers Report: Egypt as a 
Country of Origin. https://euaa.europa.eu/publications/migration-drivers-report-egypt-
country-origin  

  European Union Agency for Asylum (EUAA). (n.d.-b). Data Analysis and Research. 

Retrieved December 11, 2024, from https://euaa.europa.eu/asylum-knowledge/data-
analysis-and-research  

  European Union Agency for Asylum (EUAA). (n.d.-c). EASO Asylum Report 2020. 

Retrieved December 14, 2024, from https://euaa.europa.eu/asylum-report-2020/43-
push-factors-third-countries  

  European Union Agency for Asylum (EUAA). (n.d.-d). Latest Asylum Trends. Retrieved 
December 14, 2024, from https://euaa.europa.eu/latest-asylum-trends-asylum  

  Food and Agriculture Organization of the United Nations (FAO). (n.d.). Integrated Food 

Security Phase Classification—IPC. Retrieved June 17, 2024, from 
https://www.fao.org/policy-support/mechanisms/mechanisms-details/en/c/428641/ 

44 

 
 
 
 

 

 

 

 

Integrated Food Security Phase Classification (IPC). (n.d.-a). IPC Overview and 
Classification System. Retrieved June 17, 2024, from https://www.ipcinfo.org/ipcinfo-
website/ipc-overview-and-classification-system/en/ 

Integrated Food Security Phase Classification (IPC). (n.d.-b). Population Tracking Tool. 
Retrieved June 17, 2024, from https://www.ipcinfo.org/ipc-country-analysis/population-
tracking-tool/en/ 

Integrated Food Security Phase Classification (IPC). (2022). Understanding the IPC 
Scales. https://reliefweb.int/report/world/understanding-ipc-scales 

Integrated Food Security Phase Classification (IPC). (2024). Food Security Cluster 
Handbook. https://handbook.fscluster.org/docs/671-integrated-phase-classification 

International Organisation for Migrations (IOM). (2023). Report of the 33rd session of 
the Standing Committee on Programmes and Finance. Delivering impact through data 
for action, insight and foresight. 
https://governingbodies.iom.int/system/files/en/scpf/33rd/s-33-8-delivering-impact-
through-data.pdf  

  United Nations Development Programme (UNDP). (2022). Foresight Playbook. Overview 
of Foresight Tools. https://www.undp.org/sites/g/files/zskgke326/files/2022-07/UNDP-
RBAP-Foresight-Playbook-Appendix-2022_0.pdf 

  United Nations Environment Programme (UNEP), (2023). Strategic Foresight @ UNEP. 

https://www.unep.org/ocs/strategicforesightatunep 

  United Nations Human Rights Council: General Debate Item 4: Human Rights Situations 

that require the Council’s attention, Joint Statement on Egypt. 12 March 2021. 
https://reliefweb.int/report/egypt/46th-session-united-nations-human-rights-council-
general-debate-item-4-human-rights  

  United Nations High Commissioner for Refugees (UNHCR). (2023a). Digital 

Transformation Strategy 2022-2026. https://www.unhcr.org/digitalstrategy/wp-
content/uploads/sites/161/2023/07/Digital-Transformation-Strategy-2022-2026-
UNHCR-Web.pdf 

  United Nations High Commissioner for Refugees (UNHCR). (2023b). Emergency 
preparedness and response. https://www.unhcr.org/sites/default/files/legacy-
pdf/640889b14.pdf 

  United Nations Office for Disaster Risk Reduction (UNDRR). (2007). Early warning 

system. https://www.undrr.org/terminology/early-warning-system 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 

Humanitarian Data. (n.d.-a). HDX Signals Map. Retrieved July 26, 2024, from 
https://data.humdata.org/visualization/signals/ 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 
Humanitarian Data. (n.d.-b). Intro to HDX Signals .pdf. Google Docs. Retrieved July 26, 
2024, from 
https://drive.google.com/file/d/1tcnhGDqlF_1tq4Q4SFkcXGLXpMRhM4bv/view?usp=embe
d_facebook 

45 

 
 
 
  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 
Humanitarian Data. (2024a). Armed conflict | HDX Signals. https://un-ocha-centre-for-
humanitarian.gitbook.io/hdx-signals/datasets/armed-conflict 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 
Humanitarian Data. (2024b). Datasets | HDX Signals. https://un-ocha-centre-for-
humanitarian.gitbook.io/hdx-signals/datasets 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 

Humanitarian Data. (2024c). Agricultural hotspots | HDX Signals. https://un-ocha-centre-
for-humanitarian.gitbook.io/hdx-signals/datasets/agricultural-hotspots 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 

Humanitarian Data. (2024d). Prompts | HDX Signals. https://un-ocha-centre-for-
humanitarian.gitbook.io/hdx-signals/prompts 

  United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Centre for 
Humanitarian Data. (2024e). Internal displacements | HDX Signals. https://un-ocha-
centre-for-humanitarian.gitbook.io/hdx-signals/datasets/internal-displacement 

Newspaper Articles 

  Focus - Inflation in Turkey: Soaring prices, housing crisis and student protests. (2021, 
October 18). France 24. https://www.france24.com/en/tv-shows/focus/20211018-
inflation-in-turkey-soaring-prices-housing-crisis-and-student-protests 

  Protests in Turkey’s cities after lira plunges to new historic low. (2021, November 25). 
Euronews. https://www.euronews.com/next/2021/11/24/turkish-lira-plunges-to-a-
historic-low-and-protests-erupt-after-erdogan-defends-rates-cuts 

  Turkish police tear-gas women protesting over violence. (2021, November 25). AP News. 
https://apnews.com/article/europe-middle-east-violence-turkey-recep-tayyip-erdogan-
37dc9465b1f867b00465f8006a74d733 

 

 “Why the Turkish currency is in free fall” (2020, August 17). DW. 
https://www.dw.com/en/erdogans-credit-binge-fuels-lira-depreciation/a-54524078  

46 

 
 
 
List of abbreviations and definitions  

Abbreviations 

Definitions 

ACLED 

Armed Conflict Location & Event Data Project 

AFI 

AMN 

ASAP 

CET 

CFI 

CH 

CUSUM 

DCRM 

Acute food insecurity 

Acute malnutrition 

Anomaly Hotspots of Agricultural Production 

Conflict event trends 

Chronic food insecurity 

Cadre Harmonisé 

Cumulated sum control chart 

Dynamic Conflict Risk Model 

DG HOME 

Directorate-General for Migration and Home Affairs 

EC 

EEAS 

EMLO 

EMN 

EPS 

EU 

Eu-LISA 

EUAA 

Europol 

Eurostat 

European Commission 

European External Action Service 

European Migration Liaison Officer 

European Migration Network 

EUAA Early Warning and Preparedness System 

European Union 

European Union Agency for the Operational Management of 
Large-Scale IT Systems 

European Union Agency for Asylum 

European Union Agency for Law Enforcement Cooperation 

Statistical Office of the European Union 

47 

 
 
 
Abbreviations 

Definitions 

EWS 

FN 

FP 

FRA 

Frontex 

GCA 

GCRI 

GDELT 

HDX 

IBC 

IDMC 

IOM 

IPC 

JHA 

LOESS 

MAD 

NSC 

N 

OCHA 

OSV 

P 

Early Warning System 

False negative 

False positive 

European Union Agency for Fundamental Rights 

European Border and Coast Guard Agency 

Global Crisis Atlas 

Global Conflict Risk Index 

Global Database of Events, Language, and Tone 

Humanitarian Data Exchange 

Irregular border-crossing 

Internal Displacement Monitoring Centre 

International Organization for Migration 

Integrated Food Security Phase Classification 

Justice and Home Affairs 

Locally estimated scatterplot smoothing 

Median absolute deviation 

Non-state conflict 

Negative 

United Nations Office for the Coordination of Humanitarian 
Affairs 

One-sided violence 

Positive 

48 

 
 
 
Abbreviations 

Definitions 

PN 

PP 

SBC 

SPC 

STL 

TN 

TP 

UCDP 

UNDRR 

UNCHR 

WFP 

Predictive negative 

Predictive positive 

State-based conflict 

Statistical control process 

Seasonal and trend decomposition using Loess 

True negative 

True positive 

Uppsala Conflict Data Program 

United Nations Office for Disaster Risk Reduction 

United Nations High Commissioner for Refugees 

World Food Programme 

49 

 
 
 
 
 
 
 
List of figures  

Figure 1. The early warning process ......................................................................................................................................... 6 

Figure 2. Examples of events plotted on a ‘certainty of impact-time between the identification of 
changes and the migratory outcome’ matrix. ........................................................................................................................ 8 

Figure 3. Asylum applications in the EU by Turkish nationals, 2014-2024 .................................................... 22 

Figure 4. Asylum applications by Egyptian nationals in the EU, 2014-2024 ................................................. 24 

Figure 5 Intensity score and anomaly detection – multi-criteria algorithm.................................................... 27 

Figure 6. One-sided upper CUSUM control chart applied to data with a change point at time 30 
(vertical dashed line). (A) input time series X; (B) score function Y; (C) cumulative sum S; (D) 
cumulative sum V, restarted at negative values, and upper control limit h = 6. ........................................... 29 

Figure 7. Total number of first-time asylum application in the European Union (monthly data). The 
vertical dashed lines represent key critical past or ongoing events. ..................................................................... 30 

Figure 8. (A)  Asylum applications in the EU; (B) Monthly change in asylum applications in the EU. 32 

Figure 9. Validation: asylum applications from Turkish nationals 2020-2023 ............................................. 35 

Figure 10. Validation: asylum applications from Egyptian nationals 2020-2023 ...................................... 37 

Figure 11 Distribution of nationalities by number of consecutive months with signals – different 
algorithms ............................................................................................................................................................................................... 52 

Figure 12 Average number of signals per month and per nationality across algorithms ...................... 58 

Figure 13 Distribution of nationalities by detected months: 1 vs at least 3 consecutive months .... 59 

Figure 14 Number of signalled months in 2023 (first month in brackets)- selection of nationalities 
with significant IBCs .......................................................................................................................................................................... 61 

Figure 15 Anomalies by criteria and overall intensity score – “loose” algorithm on months with at 
least 10 IBCs ......................................................................................................................................................................................... 62 

Figure 16. Warnings detected by the CUSUM model for first-time asylum applications in the EU 
lodged by Egyptian nationals. The fill colours represent the three severity level of the warning: 
“moderate” (yellow), “serious” (orange), and “critical” (red). ....................................................................................... 63 

Figure 17. Warnings detected by the multi-criteria model for first-time asylum applications in the 
EU lodged by Egyptian nationals. The fill colours represent the intensity score of the warning. ......... 64 

Figure 18. Warnings detected by the CUSUM model for first-time asylum applications in the EU 
lodged by Turkish nationals. The fill colours represent the three severity level of the warning: 
“moderate” (yellow), “serious” (orange), and “critical” (red). ....................................................................................... 64 

Figure 19. Warnings detected by the multi-criteria model for first-time asylum applications in the 
EU lodged by Turkish nationals. The fill colours represent the intensity score of the warning. ............ 65 

50 

 
 
 
 
List of tables  

Table 1 Early warning algorithms for migratory events in the EU ....................................................................... 13 

Table 2 The Global Conflict Risk Index & the Dynamic Conflict Risk Model .................................................... 15 

Table 3 HDX Signals. Datasets and early warning criteria ........................................................................................ 17 

Table 4 Criterion and thresholds of the multi-criteria algorithm .......................................................................... 26 

Table 5. Confusion matrix for the CUSUM and multi-criteria models on the Asylum applications in 
the EU by Turkish nationals. P: positive; N: negative; PP: predicted positive; PN: predicted negative; 
TP: true positive; TN: true negative; FP: false positive; FN: false negative. ........................................................ 35 

Table 6. Performance of the models ..................................................................................................................................... 36 

Table 7. Confusion matrix for the CUSUM and multi-criteria models on the Asylum applications in 
the EU by Egyptian nationals. P: positive; N: negative; PP: predicted positive; PN: predicted negative; 
TP: true positive; TN: true negative; FP: false positive; FN: false negative. ........................................................ 37 

Table 8. Performance of the models ..................................................................................................................................... 37 

Table 9. IPC scales ............................................................................................................................................................................ 52 

Table 10 Number of months signalled as anomaly for IBCs in 2023 under the four algorithms for 
detected nationalities ....................................................................................................................................................................... 53 

Table 11. Validation parameters in the CUSUM model ............................................................................................... 56 

Table 12. Criterion and thresholds of the multi-criteria algorithm ...................................................................... 56 

Table 13 Number of signals and nationalities detected across algorithms ................................................... 57 

51 

 
 
 
 
 
Annexes 

Annex 1. IPC Scales38 

Table 9. IPC scales 

Scale 

Acute Food 
Insecurity (AFI) 

Minimal/None 

Stressed 

Crisis 

Emergency 

Catastrophe 

Severity phase 

Essential needs met 
without atypical 
and unsustainable 
methods to access 
food and income. 

Minimally adequate 
food consumption – 
inability to afford 
essential non-food 
expenditures 

Significant food 
gaps, leading to 
malnutrition, or 
minimum food 
needs met by 
depleting assets or 
using crisis-coping 
strategies. 

Large consumption 
gaps, leading to 
very high acute 
malnutrition and 
excess mortality; or 
mitigation of food 
consumption gaps 
through emergency 
strategies and 
asset liquidation.  

Extreme lack of 
food or other basic 
needs. Evident 
levels of starvation, 
destitution and 
extremely critical 
acute malnutrition. 

Chronic Food 
Insecurity (CFI) 

No/Minimal Chronic 
Food Insecurity 

Mild Chronic Food 
Insecurity 

Moderate Chronic 
Food Insecurity 

Severe Chronic 
Food Insecurity 

Access and 
consumption of a 
diet of acceptable 
quantity and 
quality, sustainable 
livelihoods, 
resilience to shocks 
and low risk of 
stunted children.  

Access to enough 
food, but not 
always of 
acceptable quality. 
Livelihoods are 
borderline 
sustainable and 
limited resilience to 
shocks, but risk of 
stunting in children 
is low.  

Mild deficits in food 
quantity for 2-4 
months each year, 
consistent lack of 
food quality. 
Livelihoods are 
marginally 
sustainable, with 
limited resilience to 
shock. Moderate 
risk of stunting in 
children.  

Seasonal deficits in 
food quantity for 
more than 4 
months per year, 
consistent lack of 
food quality. Very 
marginal 
livelihoods which 
are not resilient. 
Likeliness of severe 
stunting in children. 

Acute 
Malnutrition 
(AMN) 

Acceptable 

Alert 

Serious 

Critical 

Extremely critical 

Less than 5% of 
children acutely 
malnourished. 

5-9.9% of children 
acutely 
malnourished. 

10.14-14.9% 
children acutely 
malnourished. 

15-29.9% children 
acutely 
malnourished. 

30% or more 
children acutely 
malnourished.  

Source: JRC elaboration based on IPC (2022). 

Annex 2. Nationality distribution 

Figure 11 Distribution of nationalities by number of consecutive months with signals – different algorithms 

All months 

10 IBCs min 

25 IBCs min 

38 Integrated Food Security Phase Classification (IPC). (2022, February 23). Understanding the IPC Scales. 

https://reliefweb.int/report/world/understanding-ipc-scales  

52 

 
 
 
 
 
 
 
 
 
 
 
M
o
M
-

.
r
e
t
i
r
c

e
l
g
n
S

i

1
-
Y
M
2

–

.
r
e
t
i
r
c

e
l
g
n
S

i

t
c
i
r
t
S

–

a
i
r
e
t
i
r
c
i
t
l
u
M

e
s
o
o
L

–

a

i
r
e
t
i
r
c
i
t
l
u
M

61,0%

36,6%

55,6
%

41,7
%

62,9
%

34,3
%

2,4%

2,8%

2,9%

2

3-5

6-8

9-12

2

3-5

6-8

9-12

2

3-5

6-8

9-12

30,9%

29,4%

22,1%

17,6%

29,6
%

22,2
%

25,9
%

22,2
%

29,2
%

25,0
%

25,0
%

20,8
%

2

3-5

6-8

9-12

2

3-5

6-8

9-12

2

3-5

6-8

9-12

54,8%

25,8%

58,6%

55,2%

19,4%

20,7%

20,7%

24,1%

20,7%

2

3-5

6-8

9-12

2

3-5

6-8

9-12

2

3-5

6-8

9-12

32,8%

29,7%

26,6%

32,7%

30,6%

26,5%

31,1%

28,9%

26,7%

10,9%

10,2%

13,3%

2

3-5

6-8

9-12

2

3-5

6-8

9-12

2

3-5

6-8

9-12

Number of consecutive months with signals 

Annex 3. Number of months signalled as anomalies 

Table 10 Number of months signalled as anomaly for IBCs in 2023 under the four algorithms for detected 
nationalities 

Syria 
Senegal 
Tunisia 
Ivory Coast 

Afghanistan 
Guinea 

Multicriteria 
Strict 

Multicriteria 
Loose 

Single crit. 
MoM 

Single crit. 
2M Y-1 

All four 

2 

6 

5 

6 

9 

11 
6 
8 
1 

10 

53 

4 

7 
6 
4 
4 

3 

5 

11 
2 
9 

10 

Yes 

Yes 
No 
Yes 
No 

Yes 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Mali 

Morocco 
Turkey 

Pakistan 
Sudan 

Gambia 
Bangladesh 

Burkina Faso 
Egypt 

Palestine 
Algeria 

Cameroon 
Eritrea 

Iraq 
Ukraine 

Somalia 
Sierra Leone 

Benin 
Nigeria 

Democratic Republic of 
the Congo 
Iran 
Ethiopia 

Cuba 
Mauritania 

Yemen 
Ghana 

Republic of the Congo 
Comoros 

Liberia 
Albania 

China 
Guinea-Bissau 

South Sudan 
Nepal 

India 
Libya 

Togo 
Sri Lanka 

Burundi 
Chad 

Mongolia 

8 
1 

2 
1 

5 
7 

1 
7 

3 

4 

4 
1 

3 

5 
7 

2 

1 

5 
2 

4 
3 

5 
2 

4 
5 

6 

6 
4 

3 

5 

3 

4 
1 

6 
4 

4 
5 

5 
5 

4 
5 

3 
6 

5 
4 

5 
4 

4 
5 

5 
5 

3 

5 

4 

6 
4 

6 
4 

5 
4 

6 
4 

4 
4 

5 
4 

4 
4 

4 
6 

6 
5 

4 
4 

12 
6 

4 
4 

9 
12 

3 
12 

4 
7 

2 
9 

11 
4 

2 
4 

10 
12 

3 

2 

1 

8 
6 

7 
8 

8 
9 

6 
10 

1 
11 

10 
11 

10 
1 

11 

8 
3 

9 
5 

Yes 
Yes 

Yes 
Yes 

Yes 
Yes 

Yes 
Yes 

No 
Yes 

No 
Yes 

Yes 
Yes 

No 
Yes 

Yes 
Yes 

Yes 

Yes 

No 

Yes 
Yes 

Yes 
Yes 

Yes 
Yes 

Yes 
Yes 

No 
Yes 

Yes 
Yes 

Yes 
No 

No 
Yes 

Yes 
No 

Yes 
Yes 

12 
6 

4 
5 

9 
11 

7 
11 

3 
9 

4 
8 

10 
6 

6 
4 

9 
12 

4 

4 

2 

9 
6 

10 
11 

10 
10 

11 
8 

1 
12 

10 
10 

12 

8 

8 
5 

10 
7 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
3 
2 

1 

2 
1 

1 

2 
3 

1 
1 

1 
3 

1 

1 

2 

1 
1 

1 
1 

1 

1 

Niger 

Russia 
Kosovo* 

Lebanon 
Kuwait 

Haiti 
Djibouti 

Belarus 
Central African Republic 

Angola 
Rwanda 

Serbia 
Moldova 

Azerbaijan 
Uganda 

Bosnia and Herzegovina 
Kenya 

Turkmenistan 
Gabon 

Uzbekistan 
North Macedonia 

Tajikistan 
Montenegro 

Jordan 
Armenia 

Kazakhstan 
Bolivia 

Kyrgyzstan 
South Africa 

United Kingdom 
Georgia 

Zimbabwe 
Equatorial Guinea 

Ecuador 
North Korea 

Dominican Republic 
Tanzania 

Mozambique 
Malawi 

Myanmar 
Saudi Arabia 

4 
4 

3 
3 

2 
2 

3 
4 

3 
2 

3 
3 

3 
3 

2 
3 

2 
3 

2 
2 

1 
1 

1 
1 

9 
6 

1 
2 

7 
6 

5 

9 
7 

4 
4 

1 
10 

3 
3 

6 

2 
3 

3 
2 

6 

2 
3 

3 
2 

1 
2 

3 

Yes 
Yes 

No 
Yes 

Yes 
Yes 

Yes 
No 

Yes 
Yes 

Yes 
Yes 

Yes 
Yes 

No 
Yes 

No 
Yes 

No 
No 

No 
No 

No 
Yes 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 

7 
6 

1 
3 

8 
6 

5 

7 
6 

6 
2 

3 
10 

3 
5 

5 
6 

8 
3 

4 
1 

2 
8 

2 
3 

4 
4 

1 
2 

2 
5 

1 
2 

1 
4 

2 
2 

2 
3 

2 

55 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Colombia 

Papua New Guinea 
South Korea 

Qatar 
Cape Verde 

Singapore 
Japan 

Thailand 
Brazil 

Argentina 
Macao 

Venezuela 
Tonga 

Jamaica 
Mauritius 

Namibia 

1 
1 

1 
1 

1 
1 

1 
1 

1 
1 

1 
1 

1 
1 

1 
1 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

No 
No 

Note: For nationalities with extremely low historical IBCs, some months can be flagged as anomalies just because figures 

are not null for a short period of time  

Annex 4. Model parameters for the validation exercise 

Table 11 shows the parameters used for the CUSUM model used to calculate the performance in the 
validation exercise.39 

Table 11. Validation parameters in the CUSUM model 

parameter 
seasonal adjustment 
residual 
stationary 
robust (σ = MAD) 
window 
reference value (K) 
control limits (h±) 

value 
false 
false 
true 
true 
25 
σ/2 
σ*3 

Table 12 shows the parameters used for the multi-criteria model used to calculate the performance 
in the validation exercise (“strict” version). 

Table 12. Criterion and thresholds of the multi-criteria algorithm 

criterion \ score 

1 

2 

3 

39 All time series considered in this report do not have any missing data. It is possible to deal with missing data in the time 

series, but this is not discussed here as it is out of the scope of this work. 

56 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
% change t-12 

percentile t-12, t-24, t-36 

rolling sd t-1 to t-12 

25% 
70th 

1 

50% 
80th 

1.5 

75% 
90th 

2 

Note: for each threshold the condition is strictly above, not equal or above 

Annex 5. Performance comparison: multi-criteria (strict and loose) vs. single-
criteria 

This section presents the results on total IBCs by nationality in 2023 using the two versions of the 
multi-criteria  algorithm  (i.e.,  “loose”  and  “strict”)  and  two  algorithms  relying  on  a  single  indicator. 
Following the general approach in the policy context, the two single-criterion algorithms tested are: 

  Month on month percentage change40  
  Two-months cumulated year-on-year percentage change41 

In both cases, the single-criterion algorithm launches a warning if the change is above 25%. The main 
difference lies in the reference period used: the month on month looks at very recent developments, 
while the two-month cumulated change contextualises current figures with  respect to past trends 
(i.e., previous year). 

Number of anomalies and nationalities detected 

With a similar number of nationalities identified, the multi-criteria loose algorithm detects on average more 
anomalies when a situation is evolving and worthy of attention than single-criterion algorithms. By posing 
more restrictions, instead, the multi-criteria strict detects on average fewer anomalies, which can help 
understand when a new detected trend under the loose version is intensifying. 

The first dimension to compare the four algorithms is the number of months signalled as anomalies 
and the number of nationalities detected (Figure 13). When looking at all observations, the multi-
criteria algorithm applying the “loose” condition reports a considerably higher number of anomalies.  
When  months  with  very  few  IBCs  per  nationality  are  excluded  (e.g.,  less  than  10  IBCs),  however, 
differences between algorithms became clearer.  

While the number of nationalities detected becomes quite similar across the four algorithms,42 the 
number of months signalled as anomalies still differs considerably, despite a significant drop for both 
the “loose” multi-criteria and the single criterion two-month year-on-year change.  

Table 13 Number of signals and nationalities detected across algorithms  

All months 

10 IBCs min 
Signals  Nationalities  Signals  Nationalities  Signals  Nationalities 

25 IBCs min 

Single criterion MoM 
Single criterion 2M Y-1 
Multi-criteria Strict 

277 
447 
174 

71 
76 
58 

231 
328 
153 

56 
58 
45 

201 
282 
139 

51 
51 
40 

40 (IBCn,m - IBCn,m-1) / IBCn,m-1 ; where n=nationality and m=month of crossing 
41 [(IBCn,m + IBCn,m-1) - (IBCn,m-12 + IBCn,m-13)] / (IBCn,m-12 + IBCn,m-13); where n=nationality and m=month of crossing 
42 The number of nationalities detected does not decrease significantly when the threshold of IBCs is raised from 10 to 

25, thus pointing to stability of results at low level of IBCs. 

57 

 
 
 
 
 
 
 
Multi-criteria Loose 

522 

101 

357 

56 

304 

49 

With a similar number of nationalities covered, the multi-criteria algorithms tend to send more 
signals. This means that, on average, more months are detected when a situation is evolving and it 
is  worthy  of  attention.  Moreover,  the  multi-criteria  algorithms  do  not  require  significantly 
more signals per month to detect anomalies across nationalities (Figure 12). For instance, 
the “loose” multi-criteria reports 6.4 signals per nationality with 29.75 signals per month, compared 
to 5.6 per nationality and 27.3 per month of the two-month year-on-year change.43 

Figure 12 Average number of signals per month and per nationality across algorithms 

Average signals per month 

Average signals per nationality 

50

40

30

20

10

0

7
6
5
4
3
2
1
0

Single crit.
MoM

Single crit.
2M Y-1

Multicriteria
Strict

Multicriteria
Loose

Single crit.
MoM

Single crit.
2M Y-1

Multicriteria
Strict

Multicriteria
Loose

All

10 IBCs min

25 IBCs min

All

10 IBCs min

25 IBCs min

Distribution of anomalies 

The multi-criteria algorithm in the "loose" version tends to perform better at detecting and following 
emerging trends than single-criterion algorithms, especially those using the month-over-month criterion. 

When focusing on nationalities with consecutive months detected, both versions of the multi-criteria 
algorithm show a more normal distribution than single-criterion algorithms.  70% of nationalities with 
consecutive anomalies lie between 3 and 8 months under the “strict” version, compared, on the one hand, 
to an even distribution between 2 and 12 months under the two-months year-on-year change algorithm 
(i.e., not clearly differentiating) and, on the other hand, a concentration in the 2 months group under the 
month-over-month algorithm (i.e., not following trend). 

While averages can help evaluate algorithms’ efficiency in identifying anomalies, the distribution 
of anomalies detected is relevant to understand what kind of situations are captured by 
the algorithms, and how early emerging situations are identified and for how long.  

Figure 13 compares the four algorithms checking the percentage of nationalities that are detected 
only  for  one  month  (Y-axis)  and  the  percentage  of  those  detected  for  at  least  three  consecutive 
months (X-axis), thus indicating a sustained trend. Ideally, algorithm should lie low on the y axis and 
high on the x-axis. 

43 Figures refer to the models considering only months with at least 10 IBCs recorded. 

58 

 
 
 
 
 
 
 
Among  the  four,  the  month-over-month  algorithm  reports  very  low  shares  of  both  nationalities 
detected only for one month (7%), and detected for three (or more) consecutive months (29%) (Figure 
13, purple circle). In other words, this algorithm detects mostly for two months, thus meaning that 
can easily lead to an anomaly, but can hardly detect and follow emerging trends.44 In fact, among the 
nationalities with consecutive months with this algorithm, 60% have only two consecutive months 
(Figure 11 in Annex 2). 

The “loose” multi-criteria and the two-months year-on-year change both result in 16% of nationalities 
detected only for one month, with the former reporting 79% of nationalities detected for three (or 
more) consecutive months and the latter 72% (Figure 13). Hence, differently from the month-over-
month, both approaches manage to detect as well as to follow trends for different months. 
When  looking  in  details  at  the  distribution  of  nationalities  with  consecutive  months,  the  “loose” 
multi-criteria seems to perform better in following emerging trends: nationalities detected 
only for two consecutive months are 10-13% using this algorithm, compared to 20-22% in the two-
month year-on-year change (Figure 11 in Annex).   

Figure 13 Distribution of nationalities by detected months: 1 vs at least 3 consecutive months 

h
t
n
o
m
1
y
l

n
o

h
t
i

w
s
e
i
t
i
l

a
n
o
i
t
a
n
%

30%

25%

20%

15%

10%

5%

20%

30%

40%

50%

60%

70%

80%

% nationalities with at least 3 consecutive months

Multicriteria Strict Multicriteria Loose

Single crit. MoM Single crit. 2M Y-1

Note: Months with at least 10 IBCs recorded 

Finally,  the  “strict”  multi-criteria,  given  the  additional  imposed  conditions,  reports  the 
highest share of nationalities detected only in one month (24%), but with still 50% signalled 
for three (or more) consecutive months (Figure 14), thus better than the single-criterion algorithm 
month-over-month (29%). Moreover, the distribution of nationalities with consecutive months shows 
that  70%  are  detected  between  3  and  8  months  in  the  “strict”  multi-criteria,  compared  to 
approximately 40% in the month-over-month (Figure 11 in Annex 2).  

44 It is in fact hard to imagine a trend that progressively increases every month by a given percentage, whatever this is. 
Or, if this happen, then the trend would already been clearly identified as an anomaly without the need of the 
algorithm. 

59 

 
 
 
 
 
 
 
 
 
 
 
Hence,  from the  distribution  of  nationalities  by  consecutive  months  detected,  it  appears  that  the 
multi-criteria  algorithms  tend  to  better  differentiate  among  situations  and  capturing 
growing trends than single-criterion algorithms (Figure 11 in Annex 2). Finally, between the two 
multi-criteria, the “loose” shows a more right-skewed distribution towards more months reported (i.e., 
nationalities with 9 to 12 months detected) than the “strict” as it is less restrictive in the conditions 
necessary to flag an anomaly. As explained, the two versions of the multi-criteria algorithm can be 
used compared to understand whether a new emerging situation detected with the “loose” version is 
intensifying further or slowing down by applying the “strict” version. 

Number of anomalous months in main nationalities detected 

The multi-criteria algorithm can detect anomalies as early or even earlier than the single-criterion 
algorithms tested, especially the month over month. The "loose" version identifies potential changes worthy 
of attention while the "strict" version adds information on the severity of the change. 

Differences between the four algorithms can be seen also from the number of months signalled as 
anomalies for a selection of nationalities with significant IBCs in 2023. The single-criterion month-
on-month  algorithm  detects  more  or  less  5  months,  considerably  below  the  other  single-criterion 
algorithm and the “loose” multi-criteria (Figure 14). This seems to confirm challenges that come with 
this approach in following situations over time and/or detect them early on.  The “loose” multi-criteria 
reports the same number of anomalies (or more) than the month-over-month approach in 22 out of 
25 nationalities, with larger differences especially in the top eight.  The “strict” multi-criteria reports 
similar numbers, and in some cases even more, than the month-over-month for the main nationalities 
detected (e.g., Burkina Faso, Guinea, Mali). 

The  comparison  with  the  single-criterion  two-month  year-on-year  change  can  help 
highlight  the  potential  of  multi-criteria  algorithms  in  identifying  different  types  of 
situation that might be either missed or detected only later on using a single indicator. 
While the number of signalled months in the “loose” and in the two-month year-on-year change is 
the same in the majority of cases, differences appear for nationalities with lower number of signals. 
When there  are four or less months signalled by the two-month year-on-year change,  the “loose” 
multi-criteria  tends  to have  more  signals,  even  four  more  in  the  case of  Tunisia,  Bangladesh  and 
Ukraine. Hence, it seems that the multi-criteria “loose” detects a new emerging trend earlier on, with 
differences shrinking as situations develop, with more and more months being signalled under the 
two-month year-on-year change too.  

The  comparison  between  the  “strict”  multi-criteria  and  the  “loose”  further  allows  to  evaluate  the 
different anomalies, with the “loose” detecting early on changes possibly worthy of attention, 
and the “strict” adding information on the severity of the change. For instance, Afghanistan, 
Algeria  and  Tunisia,  are  all  detected  under  the  “loose”  but  not  the  “strict”  algorithm,  possibly 
identifying changes that might be relevant but are not yet quite significant across all dimensions and 
respect to each nationality’s trend (Figure 14). For these three nationalities, for instance, the algorithm 
using two-month year-on-year change detects either two anomalies (Algeria and Tunisia)  or  none 
(Afghanistan). Similarly, the “strict” version can help understand when a situation is slowing down 
in intensity. For instance, in the cases of Guinea, Gambia and Senegal, the “strict” version detects 
the first anomaly either one month later or in the same month of the “loose” and the two-month 
year-on-year change, but then identify less months as anomalies. 

60 

 
 
 
Figure 14 Number of signalled months in 2023 (first month in brackets)- selection of nationalities with 
significant IBCs 

Multicriteria 
Strict 

Multicriteria 
Loose 

Single crit. 
MoM 

Single crit. 2M Y-
1 

Syria 

Senegal 

Guinea 

Ivory 
Coast 

Mali 

Gambia 

Burkina 
Faso 

Sudan 

Tunisia 

Morocco 

Turkey 

Afghanis
tan 
Banglade
sh 

Pakistan 

Cameroo
n 

Egypt 

Palestine 

Eritrea 

Iraq 

Sierra 
Leone 

Somalia 

Benin 

Algeria 

Ukraine 

Nigeria 

2 
(Sep) 
6  
(Feb) 
6  
(Feb) 
5  
(Feb) 
8  
(Feb) 
7  
(Feb) 
7  
(Jan) 
5  
(Apr) 

1  
(Aug) 
2  
(Aug) 

1  
(Dec) 
1  
(Mar) 
4  
(Feb) 

3  
(Jul) 
4  
(Apr) 
1  
(Jun) 
5  
(Jan) 
3  
(Jul) 
7  
(Jan) 

2  
(Jul) 

4 
(Mar) 
7  
(Jan) 
3  
(Feb) 
4  
(Feb) 
6  
(Feb) 
5  
(Feb) 
5  
(Feb) 
5  
(Jun) 
6  
(Feb) 
4  
(Apr) 
4  
(Mar) 
4  
(May) 
4  
(Feb) 
5  
(Feb) 
4  
(Feb) 
3  
(Mar) 
6  
(Mar) 
5  
(Jan) 
4  
(Mar) 
5  
(Mar) 
5  
(Mar) 
5  
(Jan) 
5  
(Apr) 
4  
(Jan) 
3  
(Mar) 

9  
(Jan) 
11  
(Feb) 
10  
(Jan) 
8  
(Jan) 
12  
(Jan) 
11  
(Jan) 
11  
(Jan) 
9  
(Feb) 
6  
(Feb) 
6  
(Mar) 
4  
(Aug) 
1  
(Sep) 
7  
(Feb) 
5  
(Jan) 
8  
(Jan) 
3  
(Mar) 
9  
(Jan) 
10  
(Jan) 
6  
(Jan) 
9  
(Jan) 
4  
(Apr) 
12  
(Jan) 
4  
(Mar) 
6  
(Jan) 
4  
(Feb) 

61 

5  
(Jan) 
11  
(Feb) 
10  
(Jan) 
9  
(Jan) 
12  
(Jan) 
12  
(Jan) 
12  
(Jan) 
9  
(Feb) 
2  
(Mar) 
6  
(Apr) 
4  
(Sep) 

3  
(Mar) 
4  
(Jan) 
9  
(Jan) 
4  
(Jan) 
7  
(Jan) 
11  
(Feb) 
4  
(Jan) 
10  
(Jan) 
4  
(May) 
12  
(Jan) 
2  
(Mar) 
2  
(Jan) 
3  
(Jul) 

 
 
 
 
 
 
 
 
 
 
Applying intensity scores 

The multi-criteria algorithm provides further flexibility in evaluating anomalies by using an overall intensity 
score, which allows for a more nuanced interpretation and tries to overcome binomial “yes” or “no” types of 
anomalies. 

The  overall  intensity  score  obtained  by  the combination  of  the  three criteria,  and  their 
thresholds (see Section 4.1.4), used in multi-criteria algorithms provides further flexibility 
in evaluating the anomalies detected. The matrix below reports the distribution of months with 
at least 10 IBCs that were signalled as an anomaly under the “loose” multi-criteria algorithm. While 
the  majority  of  anomalies  is  clustered  in  the  lower-right-hand  corner  of  the  matrix  reporting  the 
higher overall intensity scores, several observations lie over intermediate scores given by different 
combinations across the three criteria.  

Figure 15 Anomalies by criteria and overall intensity score – “loose” algorithm on months with at least 10 
IBCs 

e
g
n
a
h
c
%

  No 

r
a
e
y
t
s
a

l

L 

M 

H 

15 

No 

Percentile same month last 3 years 

No 

M 

H 

2 

1 

45 

11 

14 

86 

5 

3 

1 

9 

2 

1 

19 

24 

2 

1 

2 

1 

1 

L  M 

H  No 

L  No 

L  M 

Rolling standard deviation previous 12 months 
intensity score    

3 

4 

5 

6 

7 

8 

9 

10 

3 

1 

98 

H 

Source: JRC elaboration. Note: The three thresholds for each criterion are reported as Low (L, score 1), Medium (M, score 2) 
and High (H, score 3). When anomalies are not overcoming the first threshold for a given criterion, they are listed under 
the “No” category.  

Finally,  the  distribution  of  anomalies  under  the  “loose”  multi-criteria  algorithm  highlights  the 
importance of looking at different trends at the same time in order to avoid missing potential 
important changes. About 20% of anomalies are reported in the first row, which means the year-on-
year percentage change was below the first threshold (i.e., 25%). Hence, these months would have 
not  been  signalled  under  the  “strict”  multi-criteria  algorithm,  which  imposes  that  at  least  one 
threshold is overcome for each criterion. Similarly, they would not be identified under a single-criterion 
algorithm  using  year-on-year  change  with  the  same  threshold. However,  since  these  observations 
overcame thresholds in either the percentile and standard deviation criteria (or both),  they  might 
indicate very first signs of change and thus possibly requiring further attention in the following 
months, and qualitative information to better contextualize situation-specific developments.  

Key takeaways 

—  Multi-criteria algorithms offer flexibility in defining anomalies compared to single-criterion algorithms. 

—  There is no one-size-fits-all approach to defining thresholds across nationalities or data series. 

—  Flexibility should be prioritised to minimise false signals while considering different types of data. 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This  overview  has  shown  the flexibility of  multi-criteria algorithms  in  defining possible 
anomalies compared to single-criterion algorithms, while at the same time being able to 
better capture the evolution of situations. Moreover, the comparison between the “loose” and 
the “strict” multi-criteria further highlights the importance of considering different dimensions at once 
to lower the risk of exclusion due to excessively high thresholds. As explained, different observations 
marked as anomalies under the “loose” are not under the “strict” as they do not overcome the first 
threshold  for  the  year-on-year  criterion,  and  would  have  not  been  detected  in  a  single  criterion 
algorithm.  Flexibility  can  thus  be  introduced,  for  instance,  in  terms  of  lowering  thresholds,  or 
considering two out of three criteria to be necessary to launch a “strict” signal instead of all three. 

There  is  not  a  clear-cut  approach  to  define  thresholds  across  nationalities  and,  even  less,  across 
different types of data series (e.g., migration flows, drivers and triggering factors). Hence, flexibility 
should  be prioritized,  for  instance  through  the  use of  “loose” and “strict” models,  intensity  scores, 
and/or changing thresholds, without compromising the need to minimise false signals. 

Annex 6. Results of Early Warning methods for the selected use cases 

In this section, the anomalies and relative scores detected by the multi-criteria and CUSUM models 
for the two use cases are presented. Figures 16 and 17 show the results for the first-time asylum 
applications  in  the  EU  lodged  by  Egyptian  nationals  (for  the  CUSUM  and  multi-criteria  models, 
respectively). Figures 18 and 19 show the results for the first-time asylum applications in  the  EU 
lodged by Turkish nationals (for the CUSUM and multi-criteria models, respectively). 

Figure 16. Warnings detected by the CUSUM model for first-time asylum applications in the EU lodged by 
Egyptian nationals. The fill colours represent the three severity level of the warning: “moderate” (yellow), 
“serious” (orange), and “critical” (red). 

63 

 
 
 
 
Figure 17. Warnings detected by the multi-criteria model for first-time asylum applications in the EU lodged 
by Egyptian nationals. The fill colours represent the intensity score of the warning. 

Figure 18. Warnings detected by the CUSUM model for first-time asylum applications in the EU lodged by 
Turkish nationals. The fill colours represent the three severity level of the warning: “moderate” (yellow), 
“serious” (orange), and “critical” (red). 

64 

 
 
 
 
 
Figure 19. Warnings detected by the multi-criteria model for first-time asylum applications in the EU lodged 
by Turkish nationals. The fill colours represent the intensity score of the warning. 

65