A Potential Influenza Pandemic Modelled in Greek Refugee Camps

Searle, Christa; Sutherland, Gloria

doi:10.1093/jrs/feaa028

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…This disparity is evident in a study carried out by Adiga et al [18] where the spread of the influenza virus in the city of Delhi was modelled. The intricacies of disease modelling within an informal setting are further captured in the study by Searle and Sutherland [19] which modelled the spread of the influenza virus within a Greek refugee camp.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Spread of Tuberculosis in South African Informal Settlements

Udall

Searle

2023

Oper. Res. Forum

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Due to the high population density of informal settlements, the spread of communicable diseases is highly effective in this environment. South Africa has been on the World Health Organization’s list of 30 top tuberculosis-burdened countries in the world for the last two decades. The rate of individuals diseased with tuberculosis in informal settlements, however, is reported to be double that of the country. In order to succeed in reducing the number of tuberculosis cases, policy decision makers have a growing need to understand the risk factors surrounding tuberculosis and data which may inform their decision in a quantifiable manner. In this paper, agent-based simulation is employed in order to facilitate the understanding of the spread of tuberculosis in these environments. The simulation model developed models the personal characteristics, daily movements and interactions between informal settlement residents. This model is able, within a certain range of accuracy, to indicate the spread of tuberculosis given certain input parameters, such as population size, probability of treatment termination and percentage initially infected, amongst others. This may assist policy makers in making informed decisions based on quantitative targets. It is evident that population density has a great impact on the spread rate of the disease. This is supportive of the fact that 90% of residents in informal settlements have latent tuberculosis. Given that only 68% of people with the disease seek treatment, by increasing this likelihood through incentives, it is possible to drastically decrease the rate of death and unsuccessful treatment.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling the Spread of Tuberculosis in South African Informal Settlements

Udall

Searle

2023

Oper. Res. Forum

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“…These are very representative of the larger themes that simulation of forced migration tends to follow: policing, economics, and logistics. Two other major themes across the relatively small number of simulation-related articles on forced migration in all journals include health, such as those that look at communicable disease spread (Crooks and Hailegiorgis 2014;Searle and Sutherland 2020) or health and safety (Anderson, Chaturvedi, and Cibulskis 2007;Frydenlund and Earnest 2015), and migration patterns or predicted movement (Collins and Frydenlund 2016;Collmann et al 2016;Frydenlund et al 2018;Groen 2016;Sokolowski, Banks, and Hayes 2014;Suleimenova and Groen 2020;Suleimenova, Bell, andGroen 2017a, 2017b). Related to the prediction of migration, an emerging area at the intersection of forced migration and policy-relevant simulation is computer modeling of future climate-induced migration (Hassani-Mahmooei and Parris 2012; Kniveton, Smith, and Black 2012;Kniveton, Smith, and Wood 2011).…”

Section: Policy-centric Themes Of Mands In Forced Migration Studiesmentioning

confidence: 99%

Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies

Frydenlund

2021

Journal on Migration and Human Security

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Modeling and Simulation (M&S) is a relatively unused research approach in forced migration studies. In most of its application areas, M&S is applied in several broad thematic policy-oriented topics: predicting human movement, humanitarian logistics, communicable diseases, healthcare, policing, and economics. More recently, there has been increased use of M&S in predicting human movement and health impacts resulting from climate change. Computer modeling has benefits for both policy and theoretical advancements in the field. M&S is often associated as a predictive tool that appeals to policymakers and planners. But M&S has many other significant benefits. For policymakers and planners, M&S provides a simulated environment to explore “what-if” scenarios. These scenarios can be adjusted to reflect different assumptions and conditions in the field. M&S can provide a means for translating interpretivist research findings—themselves rich in descriptions of actors, relationships, and dynamics—into visual artifacts that can communicate results to policymakers. It also can help to test the assumptions of theoretical findings, explore generalizability to other cases and conditions, and inform future data collection efforts. On the other hand, because models can take a long time to construct and test, they cannot provide rapid insights about evolving situations to inform decision-making at the onset of a new emergency. Data dashboards, the staple of modern humanitarian operations, are better for identifying emergent needs and tracking efforts. However, modeling what we know from one situation allows us to use those insights in systematic and strategic ways to improve operations for the future. M&S can also provide researchers with an additional tool that allows improved integration of multiple data types and sources. These data, assumptions, and theoretical constructs can be linked together in a complex, nonlinear, dynamic system that can then be set in motion to see if the simulated world reveals phenomena that are witnessed in the real world. But interdisciplinary communication and collaboration is hard and finding modelers who value interpretivist research can be even harder. M&S is not a silver bullet but provides a unique approach that incorporates dynamics and is becoming increasingly accessible to policymakers, practitioners, and academics, including those without computer programing experience. For policy, it is important to invest the time and resources to develop simulation models—from simple to complex—that help when planning for the evolution of events, or for the next event. Models of current refugee situations should inform where to concentrate efforts to advance durable solutions and how to plan for potential scenarios of return to countries of origin. Policymakers must recognize the long-term value of modeling what we know now to use later. Researchers of migration should become familiar with M&S paradigms so they can become part of the model design, development, and testing phases—at least part of the conversation. Better integration of migration scholars in design and development has the potential to strengthen models for policymaking, but perhaps more importantly, to strengthen the field by serving as an extra tool for testing theories, assumptions, and generalizability of findings to other locations and situations.

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Big Data (R)evolution in Geography: Complexity Modelling in the Last Two Decades

Perez,

Sengupta

2024

Geography Compass

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The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent‐Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio‐temporal data. We define this ‘Big Data’ as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly.

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A Potential Influenza Pandemic Modelled in Greek Refugee Camps

Cited by 3 publications

References 24 publications

Modelling the Spread of Tuberculosis in South African Informal Settlements

Modelling the Spread of Tuberculosis in South African Informal Settlements

Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies

Big Data (R)evolution in Geography: Complexity Modelling in the Last Two Decades

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