As the world works to achieve the United Nations Sustainable Development Goal 1 (SDG1):
End poverty in all its forms everywhere, by 2030, it is critical to understand its
strong interrelationship with two other SDGs: End hunger, achieve food security and
improved nutrition, and promote sustainable agriculture (SDG2) and Reduce inequality
within and among countries (SDG10). Progress in one area depends on efforts in the
others, and all three goals are part of the global public health agenda. Famines,
which can be understood as “acute episodes of extreme hunger that result in excess
mortality due to starvation and hunger-induced diseases” [1, 2], represent one of
the most serious consequences of poverty and inequality, but they also contribute
to the further immiseration and marginalization of affected populations. Recognizing
these interrelationships has helped transform our approach to famine in the past and
could provide a starting point for exploring new advances for the future.
In his landmark 1981 book, Poverty and Famines: An Essay on Entitlement and Deprivation
[3], future Nobel Laureate Amartya Sen proposed a revolutionary shift in our understanding
of these crises. He argued that famines often occur not from a lack of availability
of food, but from the inability of certain populations to access it. While this insight
suggested that poverty was a principal reason that people experience famines, Sen
pressed for a more nuanced understanding of why certain groups are more at risk of
starvation than others during a crisis. He suggested that it was an inability to ‘command’
adequate food because of a failure of entitlements that led to mortality. His entitlements
approach laid the foundation for decades of research that have extended, contested,
and moved beyond his views by, for example, emphasizing the importance of the processes
that lead to famines [4, 5], highlighting the critical role of politics and power
in their causation and differential impacts [6], and addressing the global dimensions
of the crises [7].
Although in the early 2000s trends suggested that these crises were diminishing in
number and scale [2, 8], famines have killed hundreds of thousands [9] of people
in the first two decades of the twenty-first century and left a legacy of livelihood
damage, emotional trauma, physical impairment, and social disruption. In 2021 and
2022, there have been concerns about the risk of famine in Afghanistan, Ethiopia,
Nigeria, Somalia, South Sudan, and Yemen [10]. As of November 2021, it was estimated
that more than 45 million people in 43 countries were experiencing emergency food
security conditions [11]. With the looming threats of climate change, continued conflict,
and emerging pandemics, the world is likely to continue to face the risk of famine
in the years to come, making it critical to understand with greater certainty when
and where famines will occur, and which populations will be at greatest risk, and
thereby help to trigger appropriate early action [12].
Just as the risk of famine appears to be increasing again globally, three trends—related
to famine theory, measuring and modeling, and humanitarian practice—are converging
to offer an opportunity for a step-change in our ability to understand and forecast
these crises. While the focus in the discussion will be on ‘famine,’ the trends (and
the proposed initiative) apply to food and nutrition security crises more generally.
First, drawing on previous literature, academics have recently suggested that famines
can be understood as complex systems and have identified conceptual models that describe
their evolution from formation to collapse [13]. This systems approach to famine offers
new possibilities for understanding the dynamics of these crises and could help in
defining driving forces, characteristic milestones, and well-tailored metrics, which
would facilitate the translation of these concepts into quantitative and analytical
models and produce famine forecasts.
Second, there have been rapid developments in the fields of primary data collection
and computational, mathematical, and statistical modeling. Real-time data collection
capabilities, through electronic devices and crowdsourcing, have accelerated and changed
the possibilities for gathering information. Innovations in predictive analytics make
it possible to handle the large volume of complex data required to model and forecast
famines [14]. At the same time, a suite of different types of models—systems dynamics,
agent-based models, stochastic models, and regression time series models—offer a wide
range of approaches to challenging problems and have gained in sophistication, accuracy,
and applications. These developments have enabled progress on critical problems as
complex as climate change and the COVID-19 global pandemic. They offer hope for meaningful
advances to deepen our understanding of famines as systems and develop robust conceptual
and forecasting models.
Third, in terms of global reach and innovation, humanitarian practice has evolved
in ways that could both drive these efforts forward and translate them into significant,
real-world impact. Early warning analysts have continued to improve systems for predicting
food insecurity crises and famines through widespread monitoring that combines sophisticated
data analysis with on-the-ground insight. The Integrated Phase Classification (IPC)
platform, developed in 2004, offers comparable analyses of food insecurity and malnutrition
situations globally and provides a widely accepted process for determining whether
a famine has occurred based on an internationally agreed definition. Humanitarian
agencies are transforming responses through greater use of cash, integration into
social protection systems, and emphasis on early action. United Nations Security Council
Resolution 2417, which requests regular updates on crises and strongly condemns the
use of hunger as a weapon of war, has reinforced accountability for famine prevention.
In all these ways, it is clear that humanitarian agencies have the capability to create,
adopt, and apply innovations to address crises on a global scale.
The convergence of these trends could permit two important changes in our approach
to famine. The first is to help researchers create models of famine as complex systems
and describe their evolution from formation to collapse, as has been done for hurricanes
and infectious outbreaks. Such models could help us gain new insights into questions
such as: What are the components of a famine system? What are its spatial and temporal
dimensions? How do the various parts of the system interact to produce critical outcomes
such as malnutrition and mortality and why are certain populations especially vulnerable?
Based on these insights, the second change would be for experts to better identify
the signals of famine formation and therefore improve forecasts. If successful, these
forecasts could help (as part of a wider set of tools) reduce uncertainty about the
likely occurrence of food and nutrition security crises, contribute to timelier, more
targeted, and life-saving action, help deter famine creation, and perhaps spark new
fields of inquiry. The efforts to model and forecast hurricanes and infectious outbreaks
suggest these kinds of benefits are possible if there is an iterative process that
continually promotes learning from experience.
In moving toward a ‘Poverty and Famine 2.0 approach,’ we identify measuring, modeling,
and forecasting as three essential and interrelated processes for gathering insight
based on the goals and primary activities of each. Measuring aims to collect data
to create information and knowledge. Modeling aims to process data, information, and
knowledge to form casual paths, rules, and systems thinking, along with their uncertainties.
Forecasting aims to infer future unknown situations based on data, information, knowledge,
and system thinking. To describe a process of estimating future unknown situations,
the terms ‘forecasting,’ ‘prediction,’ ‘projection,’ and ‘prognosis’ are often used
by researchers and practitioners interchangeably. We are also making a distinction
between forecasting and predictions suggesting that forecasting is an extrapolation
of the past into the future, while predictions and projections are typically subjective
and judgmental in nature. While both approaches are useful in considering changes
that may take place in the future, ideally, forecasting is free from intuition and
personal forecasters’ biases, whereas prediction is based on judgment. In short, all
forecasts are predictions but not all predictions are forecasts.
Recognizing the potential of these three converging trends, especially the power of
accurate relevant data and sound analytical solutions, several actors have engaged
in pioneering efforts to use predictive analytics to improve food security and nutrition
forecasting based on data sources, machine learning, and novel algorithms [14, 15].
For example, the Famine Early Warnings System Network (FEWSNET) has partnered with
scientists to incorporate climate models into their scenario-building [16]; the World
Bank has developed sophisticated algorithms to forecast food crises globally [17,
18]; Consortiums have used models to understand the risk of malnutrition; and the
United States Agency for International Development (USAID) has used economic models
to analyze the benefits of investments in resilience [19]. To a large extent, however,
these efforts have been fragmented. The learnings from one are not always widely shared
to inform the efforts of others. They are also partial in that most do not try to
model famine itselfe, focusing instead on forecasting IPC phases, food insecurity
indicators, or other outcomes. Some incorporate climate science models but have not
yet found ways to bring in political and social dimensions to algorithmic forecasts.
Recognizing that we are in the early stages of an emerging field, the IPC and the
Global Network Against Food Crises have made famine forecasting a key component of
their long-term strategies.
However, we are cognizant of the daunting challenges and genuine risks associated
with pursuing famine modeling and forecasting. Modeling famine is complicated by the
complexities of interacting economic, social, environmental, and political systems
and the challenges of characterizing human action and decision-making. The track record
on forecasting social phenomena and conflict has sometimes been discouraging [20,
21]. These efforts also require large amounts of data from some of the most challenging
contexts in the world. As a result, there is a danger that the international community
will expend substantial resources and time on a venture with highly uncertain results.
Such an initiative may also inadvertently reinforce the notion that there is a technical
solution to famine and divert attention from pressing political issues central to
its prevention. Moreover, because of the complexity of the models, they may hide assumptions
and biases, reduce transparency, and perpetuate inequalities, creating ethical concerns
[14, 15]. Relatedly, the emphasis on data gathering activities and modeling exercises
could lead to data-driven products that are increasingly divorced from local realities,
and the experiences, views, and inputs of affected populations. Finally, while these
efforts may contribute to improved forecasts, they do not directly address the critical
challenge of translating early warning into early action [12].
We see potential responses to these concerns. For example, the cost of investing in
these efforts is substantially smaller than the resources that are required to address
current crises and that could be saved through the insights accurate forecasts could
provide. Moreover, modeling and forecasting should not be seen as a substitute for
social and political efforts (or existing early warning systems), but rather a complementary
tool. Deliberate, joined-up approaches could also help address ethical concerns, engage
affected populations, make the link to early action, and deal with data issues such
as the need for reliable data repositories, tools to abstract and examine data, intra-agency
and intergovernmental agreements, and data-curation standards and model-sharing protocols.
Considering these challenges and potential responses, we offer six principles that
could guide a famine modeling and forecasting initiative in terms of both content
and process.
Focus on both modeling famine itself and forecasting its occurrence. It is difficult
to forecast what is not well understood. It is also difficult to act when the forecast
is poorly focused and not well explained to end users. If we have better conceptual
and analytical models of the formation, evolution, and collapse of famines as complex
systems, it should help our attempts to better predict their occurrence. Likewise,
progress on forecasting will point to new areas to explore in understanding the dynamics
of these crises.
Integrate multiple dimensions. While we may be more advanced in the use of climate
and economic data in models, it will be important to think creatively about how political
and social dimensions can be better integrated. Human behavior at any stages of decision-making
could alter forecasts.
Take an inclusive approach with global scope. The initiative requires a joined-up
effort that brings together the insights of affected communities, humanitarian practitioners,
famine theorists, public health professionals, data holders, and modelers across the
globe. Similarly, a wide range of tools employed in science and practice—from machine
learning and predictive analytics to intervention strategies to participatory approaches
to simulation exercises—should be utilized.
Be realistic, learn, and invest for the long-term. The history of using models in
other fields—whether climate change or hurricanes or pandemics—suggests that they
can provide early warning and deeper understanding. But the process of developing
and deploying sophisticated, accurate models is challenging and the immediate payoffs
uncertain. Results from conceptual developments, data analysis, and modeling need
to be widely disseminated and built upon in an iterative process by a broader community.
Decades may be needed to achieve the initiative’s full potential.
Address ethical and other concerns. These models and forecasts entail a number of
potential ethical risks that could undermine their usefulness and inadvertently perpetuate
biases and inequalities. These concerns should be articulated and addressed upfront
by the wider community involved [12, 14, 15], perhaps through development of protocols
and procedures to guide the process.
Look beyond models and forecasts. Models and forecasts are a potential tool for better
understanding famines. But they should only be viewed as a part of a wider famine
and public health agenda involving theoretical insights, enhanced early warning and
action approaches, improved practice, and political efforts to prevent these crises
more effectively.
Forty years after the publication of Sen’s seminal work, famine studies have identified
its shortcomings and evolved in new directions, but his concern about the relationship
between poverty and famines remains and challenges the global community to take creative
approaches to more systematically address these crises that continue to threaten the
lives and well-being of humans. Given recent trends, we believe it is an opportune
moment to make a step-change in our efforts by investing in a 2.0 approach to crisis
modeling and forecasting––thereby also supporting the achievement of the interrelated
SDGs on poverty, hunger, and inequality.
The Journal of Public Health Policy is joining Springer in seeking submissions to
a new Collection on Reducing Poverty and Its Consequences, in support of the International
Day for the Eradication of Poverty. This multi-journal Collection aims to synthesize
and integrate social, behavioral, and public health perspectives on systemic structures
bolstering poverty and inequality, poverty-reduction interventions, as well as gaps
in our knowledge and future research directions. In promoting the UN Sustainable Development
Goals, we see the need for proactive dialogs across multiple stakeholders, forward-looking
intervention study designs, understanding of long-term consequences of hunger and
poverty, as well as the need for modeling and forecasting incorporating human behaviors
beyond the technical solutions. We invite readers and contributors to share your thoughts,
findings, and experiences through this new multi-journal Collection.