#mathematical-modeling — Public Fediverse posts

From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

https://kemal.yaylali.uk/model90-football-prediction-engine/

From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

https://kemal.yaylali.uk/model90-football-prediction-engine/

The science of how (and when) we decide to self-censor The study’s main takeaway: https://s.faithcollapsing.com/alee1#censorship #human-behavior #mathematical-modeling #psychology #science #social-media

The science of how (and when) we decide to self-censor The study’s main takeaway: https://s.faithcollapsing.com/alee1#censorship #human-behavior #mathematical-modeling #psychology #science #social-media

The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

#Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

#Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

Grid-Free Approach to Partial Differential Equations on Volumetric Domains [pdf]

http://rohansawhney.io/RohanSawhneyPhDThesis.pdf

#HackerNews #GridFree #PDEs #VolumetricDomains #MathematicalModeling #ComputationalPhysics

Grid-Free Approach to Partial Differential Equations on Volumetric Domains [pdf]

http://rohansawhney.io/RohanSawhneyPhDThesis.pdf

#HackerNews #GridFree #PDEs #VolumetricDomains #MathematicalModeling #ComputationalPhysics

@HildegardUecker and I are excited to be running the second edition of our #EvolutionaryRescue workshop series at the #MaxPlanck Plön, June 30-July 3. This time the focus is on bridging theory and experiments.

Invited speakers: Helen Alexander, Lutz Becks, Robert D Holt, Laure Olazcuaga, Jitka Polechova.

Submit an abstract by March 15 and tell your friends.

More info: https://workshops.evolbio.mpg.de/event/128/

#Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin https://www.journals.uchicago.edu/doi/10.1086/734003

#mathematicalModeling #model

New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin https://www.journals.uchicago.edu/doi/10.1086/734003

#mathematicalModeling #model

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=81227

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=81227

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=81038

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=81038

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=79715

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=79715

#Journals | EPJ B
#CallForPapers for a #TopicalIssue on “Mathematical Modeling in Condensed Matter and Complex Systems: Limits and Pitfalls”
Guest Editors from #UniversitätSaarland
#HumboldtUniversity

📅June 30, 2024
➡️ bit.ly/3TZuu7z

#CondensedMatter #ComplexSystems #MathematicalModeling #Physics #AcademicResearch #ScientificResearch
#sciences #ScienceMastodon
@ScienceScholar @academicsunite @academicchatter

#Journals | EPJ B
#CallForPapers for a Topical Issue on “ #MathematicalModeling in #Epidemiology:
Limits and Pitfalls” Guest Editors from
#UniversitätSaarland
#HumboldtUniversitatZuBerlin

📅June 30, 2024
➡️ https://bit.ly/3TZuu7z
#EDPSciences
#TheEuropeanPhysicalJournal
#physics
@physics
@science
@academicsunite
@academicchatter
#ScienceMastodon

#Journals | EPJ B
#CallForPapers for a Topical Issue on
“ #MathematicalModeling in #epidemiology : Limits and Pitfalls” Guest Editors from
@saar_uni #HumboldtUniversitätzuBerlin

📅30 June 2024
➡️ https://bit.ly/3TZuu7z
#EDPSciences
#ScienceMastodon
@phdstudents
@academicchatter

#MathematicalBiology #evolution #evolutionarybiology #popgen #evolutionarydynamics #MathematicalModeling @Project_Telos

#MathematicalBiology #evolution #evolutionarybiology #popgen #evolutionarydynamics #MathematicalModeling @Project_Telos

Coaching and mentoring programs sometimes called “fellowships” have been upheld as the gold standard for developing leaders in global health.

For example, a fellowship in the field of immunization was recently advertised in the following manner.

• Develop your skills and become an advocate and leader: The fellowship will begin with two months of weekly mandatory live engagements led by [global] staff and immunization experts around topics relating to rebuilding routine immunization, including catch-up vaccination, integration and life course immunization. […]
• Craft an implementation plan: Throughout the live engagement series, fellows will develop, revise and submit a COVID-19 recovery strategic plan.
• Receive individualized mentoring: Participants with strong plans will be considered for a mentorship program to work 1:1 with experts in the field to further develop and implement their strategies and potentially publish their case studies.

We will not dwell here on the ‘live engagements’, which are expert-led presentations of technical knowledge. We already know that such ‘webinars’ have very limited learning efficacy, and unlikely impact on outcomes. (This may seem like a harsh statement to global health practitioners who have grown comfortable with webinars, but it is substantiated by decades of evidence from learning science research.)

On the surface, the rest of the model sounds highly effective, promising personalized attention and expert guidance.

The use of a project-based learning approach is promising, but it is unclear what support is provided once the implementation plan has been crafted.

It is when you consider the logistical aspects that the cracks begin to show.

The essence of traditional coaching lies in the quality of the one-to-one interaction, making it an inherently limited resource.

Take, for example, a fellowship programme where interest outstrips availability—say, 1,600 aspiring global health leaders are interested, but only 30 will be selected for one-on-one mentoring.

Tailored, one-on-one coaching can be incredibly effective in small, controlled environments.

While these 30 may receive an invaluable experience, what happens to those left behind?

There is an ‘elitist spiral’.

Coaching and mentoring, while intensive, remain exclusive by design, limited to the select few.

This not only restricts scale but also concentrates knowledge among the selected group, perpetuating hierarchies.

Those chosen gain invaluable support.

The majority left out are denied access and implicitly viewed as passive recipients rather than partners in a collective solution.

Doubling the number of ‘fellows’ only marginally improves this situation.

Even if the mentor pool were to grow exponentially, the personalized nature of the engagement limits the rate of diffusion.

When we step back and look at the big picture, we realize there is a problem: these programs are expensive and difficult to scale.

And, in global health, if it does not scale, it is not solving the problem.

How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

So while these programs can make a real difference for a small group of people, they are unlikely to move the needle on a global scale.

That is like trying to fill a swimming pool with a teaspoon—you might make some progress, but you will never get the job done.

The model creates a paradox: the attributes making it effective for individuals intrinsically limit systemic impact.

There is another paradox related to complexity.

Global health issues are inextricably tied to cultural, political and economic factors unique to each country and community.

Complex problems require nuanced solutions.

Yet coaching promotes generalized expertise from a few global, centralized institutions rather than fostering context-specific knowledge.

Even the most brilliant, experienced coach or mentor cannot single-handedly impart the multifaceted understanding needed to drive impact across diverse settings.

A ‘fellowship’ structure also subtly perpetuates the existing hierarchies within global health.

It operates on the tacit assumption that the necessary knowledge and expertise reside in certain centralized locations and among a select cadre of experts.

This sends an implicit message that knowledge flows unidirectionally—from the seasoned experts to the less-experienced practitioners who are perceived as needing to be “coached.”

Learn more: How does peer learning compare to expert-led coaching ‘fellowships’?

Peer learning: Collective wisdom, collective progress

In global health, no one individual or institution can be expected to possess solutions for all settings.

Sustainable change requires mobilizing collective intelligence, not just centralized expertise.

Learn more: The COVID-19 Peer Hub as an example of Collective Intelligence (CI) in practice

This means transitioning from hierarchical, top-down development models to flexible platforms amplifying practitioners’ contextual insights.

The gap between need and availability of quality training in global health is too vast for conventional approaches to ever bridge alone.

Instead of desperately chasing an asymptote of expanding elite access, we stand to gain more by embracing approaches that democratize development.

Complex challenges demand platforms unleashing collective wisdom through collaboration. The technologies exist.

In the “fellowship” example, less than five percent of participants were selected to receive feedback from global experts.

A peer learning platform can provide high-quality peer feedback for everyone.

• Such a platform democratizes access to knowledge and disrupts traditional hierarchies.
• It also moves away from the outdated notion that expertise is concentrated in specific geographical or institutional locations.

What learning science underpins peer learning for global health? Watch this 14-minute presentation at the 2023 annual meeting of the American Society for Tropical Medicine and Hygiene (ASTMH).

What about the perceived trade-off between quality and scale?

Effective digital peer learning platforms negate this zero-sum game.

Research on MOOCs (massive open online courses) has conclusively demonstrated that giving and receiving feedback to and from three peers through structured, rubric-based peer review, achieves reliability comparable, when properly supported, to that of expert feedback alone.

If we are going to make a dent in the global health crises we face, we have to shift from a model that relies on the expertise of the few to one that harnesses the collective wisdom of the many.

• Peer learning isn’t a Band-Aid. It is an innovative leap forward that disrupts the status quo, and it’s exactly what the global health sector needs.
• Peer learning is not just an incremental improvement. It is a seismic shift in the way we think about learning and capacity-building in global health.
• Peer learning is not a compromise. It is an upgrade. We move from a model of scarcity, bound by the limits of individual expertise, to one of collective wisdom.
• Peer learning is more than just a useful tool. It is a challenge to the traditional epistemology of global health education.

Read about a practical example: Movement for Immunization Agenda 2030 (IA2030): grounding action in local realities to reach the unreached

As we grapple with urgent issues in global health—from pandemic recovery to routine immunization—it is clear that we need collective intelligence and resource sharing on a massive scale.

And for that, we need to move beyond the selective, top-down models of the past.

The collective challenges we face in global health require collective solutions.

And collective solutions require us to question established norms, particularly when those norms serve to maintain existing hierarchies and power imbalances.

Now it is up to us to seize this opportunity and move beyond outmoded, hierarchical models.

There is a path – now, not tomorrow – to truly democratize knowledge, make meaningful progress, and tackle the global health challenges that confront us all.

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https://redasadki.me/2024/02/29/the-limitations-of-expert-led-fellowships-for-global-health/

#coaching #CollectiveIntelligence #fellowships #globalHealth #mathematicalModeling #peerLearning

By connecting practitioners to learn from each other, peer learning facilitates collaborative development.

How does it compare to expert-led coaching and mentoring “fellowships” that are seen as the ‘gold standard’ for professional development in global health?

Scalability in global health matters. (See this article for a comparison of other aspects.)

Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach K learners effectively:

For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

Expert coaching “fellowships” require the availability of experts, which is often optimistic in highly specialized fields.

The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

Scalability of one-to-one peer learning

By comparison, peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback.

This has significant advantages in scalability.

Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

In this context, the number of learning interactions scales quadratically with the number of learners. This means that if the number of learners doubles, the total number of learning interactions increases by a factor of four. This quadratic relationship highlights the significant increase in interactions (and potential scalability challenges) as more learners participate in the model.

However, this one-to-one model is difficult to implement: not every learner is going to interact with every other learner in meaningful ways.

A more practical ‘triangular’ peer learning model with no upper limit to scalability

In The Geneva Learning Foundation’s peer learning model, learners give feedback to three peers, and receive feedback from three peers. This is a structured, time-bound process of peer review, guided by an expert-designed rubric.

When each learner gives feedback to 3 different learners and receives feedback from 3 different learners, the model changes significantly from the one-to-one model where every learner could potentially interact with every other learner. In this specific configuration, the total number of interactions can be calculated based on the number of learners N, with each learner being involved in 6 interactions (3 given + 3 received).

The total number of interactions per learner is six. However, since each interaction involves two learners (the giver and the receiver of feedback), we do not need to double-count these interactions for the total count in the system. Hence, the total number of interactions for each learner is directly 6, without further adjustments for double-counting.

Therefore, the total number of learning interactions in the system can be represented as:

Given this setup, the complexity or scalability of the system in terms of learning interactions relative to the number of participants N is linear. This is because the total number of interactions increases directly in proportion to the number of learners. Thus, the Big O notation would be:

This indicates that the total number of learning interactions scales linearly with the number of learners. In this configuration, as the number of learners increases, the total number of interactions increases at a linear rate, which is more scalable and manageable than the quadratic rate seen in the peer-to-peer model where every learner interacts with every other learner. Learn more: There is no scale.

Illustration: The Geneva Learning Foundation © 2024

https://redasadki.me/2024/02/28/how-does-peer-learning-compare-to-expert-led-coaching-fellowships/

#coaching #CollectiveIntelligence #fellowships #globalHealth #mathematicalModeling #peerLearning

How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

By connecting practitioners to learn from each other, peer learning facilitates collaborative development. ow does it compare to expert-led coaching and mentoring “fellowships” that are seen as the ‘gold standard’ for professional development in global health?

Scalability in global health matters. (See this article for a comparison of other aspects.)

Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach K learners effectively:

For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

Expert coaching “fellowships” require the availability of experts, which is often optimistic in highly specialized fields.

The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

Scalability of one-to-one peer learning

By comparison, peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback.

This has significant advantages in scalability.

Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

In this context, the number of learning interactions scales quadratically with the number of learners. This means that if the number of learners doubles, the total number of learning interactions increases by a factor of four. This quadratic relationship highlights the significant increase in interactions (and potential scalability challenges) as more learners participate in the model.

However, this one-to-one model is difficult to implement: not every learner is going to interact with every other learner in meaningful ways.

A more practical ‘triangular’ peer learning model with no upper limit to scalability

In The Geneva Learning Foundation’s peer learning model, learners give feedback to three peers, and receive feedback from three peers. This is a structured, time-bound process of peer review, guided by an expert-designed rubric.

When each learner gives feedback to 3 different learners and receives feedback from 3 different learners, the model changes significantly from the one-to-one model where every learner could potentially interact with every other learner. In this specific configuration, the total number of interactions can be calculated based on the number of learners N, with each learner being involved in 6 interactions (3 given + 3 received).

The total number of interactions per learner is six. However, since each interaction involves two learners (the giver and the receiver of feedback), we do not need to double-count these interactions for the total count in the system. Hence, the total number of interactions for each learner is directly 6, without further adjustments for double-counting.

Therefore, the total number of learning interactions in the system can be represented as:

Given this setup, the complexity or scalability of the system in terms of learning interactions relative to the number of participants N is linear. This is because the total number of interactions increases directly in proportion to the number of learners. Thus, the Big O notation would be:

This indicates that the total number of learning interactions scales linearly with the number of learners. In this configuration, as the number of learners increases, the total number of interactions increases at a linear rate, which is more scalable and manageable than the quadratic rate seen in the peer-to-peer model where every learner interacts with every other learner. Learn more: There is no scale.

Illustration: The Geneva Learning Foundation © 2024

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#coaching #CollectiveIntelligence #fellowships #mathematicalModeling #peerLearning

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is:

This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning.

Weights for each variables are derived from empirical data and expert consensus.

All values are on a scale of 0-4, with a “4” representing the highest level.

Here is a summary table including all values for each criterion, learning efficacy calculated with weights, and Efficacy-Scale Score (ESS) for peer learning, cascade training, and expert coaching.

The Efficacy-Scale Score (ESS) can be calculated by multiplying the efficacy (E) of a learning method by the number of learners (N).

This table provides a detailed comparison of the values for each criterion across the different learning methods, the calculated learning efficacy values considering the specified weights, and the Efficacy-Scale Score (ESS) for each method.

Of course, there are many nuances in individual programmes that could affect the real-world effectiveness of this simple model. The model, grounded in empirical data and simplified to highlight core determinants of learning efficacy, leverages statistical weighting to prioritize key educational factors, acknowledging its abstraction from the multifaceted nature of educational effectiveness and assumptions may not capture all nuances of individual learning scenarios.

Peer learning

The calculated learning efficacy for peer learning, , is 3.20. This value reflects the weighted assessment of peer learning’s strengths and characteristics according to the provided criteria and their importance.

By virtue of scalability, ESS for peer learning is 24 times higher than expert coaching.

Cascade training

For Cascade Training, the calculated learning efficacy, , is approximately 1.40. This reflects the weighted assessment based on the provided criteria and their importance, indicating lower efficacy compared to peer learning.

Cascade training has a higher ESS than expert coaching, due to its ability to achieve scale.

Learn more: Why does cascade training fail?

Expert coaching

For Expert Coaching, the calculated learning efficacy, , is approximately 2.20. This value indicates higher efficacy than cascade training but lower than peer learning.

However, the ESS is the lowest of the three methods, primarily due to its inability to scale. Read this article for a scalability comparison between expert coaching and peer learning.

Image: The Geneva Learning Foundation Collection © 2024

https://redasadki.me/2024/02/27/calculating-the-relative-effectiveness-of-expert-coaching-peer-learning-and-cascade-training/

#cascadeTraining #expertCoaching #fellowship #mathematicalModeling #peerLearning

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is:

This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning.

Weights for each variables are derived from empirical data and expert consensus.

All values are on a scale of 0-4, with a “4” representing the highest level.

Here is a summary table including all values for each criterion, learning efficacy calculated with weights, and Efficacy-Scale Score (ESS) for peer learning, cascade training, and expert coaching.

The Efficacy-Scale Score (ESS) can be calculated by multiplying the efficacy (E) of a learning method by the number of learners (N).

This table provides a detailed comparison of the values for each criterion across the different learning methods, the calculated learning efficacy values considering the specified weights, and the Efficacy-Scale Score (ESS) for each method.

Of course, there are many nuances in individual programmes that could affect the real-world effectiveness of this simple model. The model, grounded in empirical data and simplified to highlight core determinants of learning efficacy, leverages statistical weighting to prioritize key educational factors, acknowledging its abstraction from the multifaceted nature of educational effectiveness and assumptions may not capture all nuances of individual learning scenarios.

Peer learning

The calculated learning efficacy for peer learning, , is 3.20. This value reflects the weighted assessment of peer learning’s strengths and characteristics according to the provided criteria and their importance.

By virtue of scalability, ESS for peer learning is 24 times higher than expert coaching.

Cascade training

For Cascade Training, the calculated learning efficacy, , is approximately 1.40. This reflects the weighted assessment based on the provided criteria and their importance, indicating lower efficacy compared to peer learning.

Cascade training has a higher ESS than expert coaching, due to its ability to achieve scale.

Learn more: Why does cascade training fail?

Expert coaching

For Expert Coaching, the calculated learning efficacy, , is approximately 2.20. This value indicates higher efficacy than cascade training but lower than peer learning.

However, the ESS is the lowest of the three methods, primarily due to its inability to scale. Read this article for a scalability comparison between expert coaching and peer learning.

Image: The Geneva Learning Foundation Collection © 2024

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#cascadeTraining #expertCoaching #fellowship #mathematicalModeling #peerLearning

Coaching and mentoring programs sometimes called “fellowships” have been upheld as the gold standard for developing leaders in global health.

For example, a fellowship in the field of immunization was recently advertised in the following manner.

• Develop your skills and become an advocate and leader: The fellowship will begin with two months of weekly mandatory live engagements led by [global] staff and immunization experts around topics relating to rebuilding routine immunization, including catch-up vaccination, integration and life course immunization. […]
• Craft an implementation plan: Throughout the live engagement series, fellows will develop, revise and submit a COVID-19 recovery strategic plan.
• Receive individualized mentoring: Participants with strong plans will be considered for a mentorship program to work 1:1 with experts in the field to further develop and implement their strategies and potentially publish their case studies.

We will not dwell here on the ‘live engagements’, which are expert-led presentations of technical knowledge. We already know that such ‘webinars’ have very limited learning efficacy, and unlikely impact on outcomes. (This may seem like a harsh statement to global health practitioners who rely on webinars, but it is substantiated by decades of evidence from learning science research.)

On the surface, the rest of the model sounds highly effective, promising personalized attention and expert guidance.

The use of a project-based learning approach is promising, but it is unclear what support is provided once the implementation plan has been crafted.

It is when you consider the logistical aspects that the cracks begin to show.

The essence of traditional coaching lies in the quality of the one-to-one interaction, making it an inherently limited resource.

Take, for example, a fellowship programme where interest outstrips availability—say, 1,600 aspiring global health leaders are interested, but only 30 will be selected for one-on-one mentoring.

Tailored, one-on-one coaching can be incredibly effective in small, controlled environments.

While these 30 may receive an invaluable experience, what happens to those left behind?

There is an ‘elitist spiral’.

Coaching and mentoring, while intensive, remain exclusive by design, limited to the select few.

This not only restricts scale but also concentrates knowledge among the selected group, perpetuating hierarchies.

Those chosen gain invaluable support.

The majority left out are denied access and implicitly viewed as passive recipients rather than partners in a collective solution.

Doubling the number of ‘fellows’ only marginally improves this situation.

Even if the mentor pool were to grow exponentially, the personalized nature of the engagement limits the rate of diffusion.

When we step back and look at the big picture, we realize there is a problem: these programs are expensive and difficult to scale.

And in global health, if it does not scale, it is not solving the problem.

So while these programs can make a real difference for a small group of people, they are unlikely to move the needle on a global scale.

That is like trying to fill a swimming pool with a teaspoon—you might make some progress, but you will never get the job done.

The model creates a paradox: the attributes making it effective for individuals intrinsically limit systemic impact.

There is another paradox related to complexity.

Global health issues are inextricably tied to cultural, political and economic factors unique to each region and community.

Complex problems require nuanced solutions.

Yet coaching promotes generalized expertise from a few global, centralized institutions rather than fostering context-specific knowledge.

Even the most brilliant coach cannot single-handedly impart the multifaceted understanding needed to drive impact across diverse settings.

A ‘fellowship’ structure also subtly perpetuates the existing hierarchies within global health.

It operates on the tacit assumption that the necessary knowledge and expertise reside in certain centralized locations and among a select cadre of experts.

This sends an implicit message that knowledge flows unidirectionally—from the seasoned experts to the less-experienced practitioners who are perceived as needing to be “coached.”

Illustration: The Geneva Learning Foundation Collection © 2024

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https://redasadki.me/2024/02/26/the-limitations-of-expert-led-fellowships-for-global-health/

#coaching #CollectiveIntelligence #fellowships #globalHealth #mathematicalModeling #peerLearning

By connecting practitioners to learn from each other, peer learning facilitates collaborative development.

Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach KK learners effectively:

For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

This model requires the availability of experts, which is often optimistic in highly specialized fields.

Peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback. This has significant advantages in scalability and self-directed learning.

Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

Collective wisdom, collective progress

In global health, no one individual or institution can be expected to possess solutions for all settings. Sustainable change requires mobilizing collective intelligence, not just centralized expertise. This means transitioning from hierarchical, top-down development models to flexible platforms amplifying practitioners’ contextual insights.

The gap between need and availability of quality training in global health is too vast for conventional approaches to ever bridge alone. Instead of desperately chasing an asymptote of expanding elite access, we stand to gain more by embracing approaches that democratize development. Complex challenges demand platforms unleashing collective wisdom through collaboration. The technologies exist. Now, global leaders and funders may need to renounce outdated mindsets.

Recall the “fellowship” example, in which only 60 participants were selected to receive feedback from global experts.

A peer learning platform can provide high-quality peer feedback all 1,500 interested participants. Such a platform democratizes access to knowledge and disrupts traditional hierarchies. It also moves away from the outdated notion that expertise is concentrated in specific geographical or institutional locations.

What about the perceived trade-off between quality and scale?

• Effective digital peer learning platforms negate this zero-sum game.
• Research on MOOCs (massive open online courses) has conclusively demonstrated that giving and receiving feedback from three peers through structured, rubric-based peer review, achieves reliability comparable to that of expert feedback.

If we are going to make a dent in the global health crises we face, we have to shift from a model that relies on the expertise of the few to one that harnesses the collective wisdom of the many.

• Peer learning isn’t a Band-Aid. It is an innovative leap forward that disrupts the status quo, and it’s exactly what the global health sector needs.
• Peer learning is not just an incremental improvement. It is a seismic shift in the way we think about learning and capacity-building in global health.
• Peer learning is not a compromise. It is an upgrade. We move from a model of scarcity, bound by the limits of individual expertise, to one of collective wisdom.
• Peer learning is more than just a useful tool. It is a challenge to the traditional epistemology of global health education.

As we grapple with urgent issues in global health—from pandemic recovery to routine immunization—it is clear that we need collective intelligence and resource sharing on a massive scale.

And for that, we need to move beyond the selective, top-down models of the past.

The collective challenges we face in global health require collective solutions. And collective solutions require us to question established norms, particularly when those norms serve to maintain existing hierarchies and power imbalances.

We are at a pivotal moment in global health education. The technological solutions are in place to facilitate a more egalitarian, effective form of professional development.

Now it is up to us to seize this opportunity and move beyond outmoded, hierarchical models. This is how we can truly democratize knowledge, make meaningful progress, and tackle the global health challenges that confront us all.

We are at a juncture where technological advancements allow for an inclusive, scalable model that also maintains quality. The programs enable continuous learning, based on real-world challenges and goals, matching peers dynamically to enhance the learning experience.

Illustration: The Geneva Learning Foundation © 2024

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https://redasadki.me/2024/02/26/how-does-peer-learning-compare-to-expert-led-coaching-fellowships/

#coaching #CollectiveIntelligence #fellowships #globalHealth #mathematicalModeling #peerLearning

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is:

This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning.

Weights for each variables are derived from empirical data and expert consensus.

All values are on a scale of 0-4, with a “4” representing the highest level.

Weights assigned

Here is a summary table including all values for each criterion, learning efficacy calculated with weights, and Efficacy-Scale Score (ESS) for peer learning, cascade training, and expert coaching.

This table provides a detailed comparison of the values for each criterion across the different learning methods, along with the calculated learning efficacy values considering the specified weights.

Of course, there are many nuances in individual programmes that could affect the real-world effectiveness of this simple model.

Peer learning

The calculated learning efficacy for peer learning, , is 3.20. This value reflects the weighted assessment of peer learning’s strengths and characteristics according to the provided criteria and their importance.

By virtue of scalability, ESS for peer learning is 24 times higher than expert coaching.

Cascade training

For Cascade Training, the calculated learning efficacy, , is approximately 1.40. This reflects the weighted assessment based on the provided criteria and their importance, indicating lower efficacy compared to peer learning.

Cascade training has a higher ESS than expert coaching, due to its ability to achieve scale.

Expert coaching

For Expert Coaching, the calculated learning efficacy, , is approximately 2.20. This value indicates higher efficacy than cascade training but lower than peer learning.

However, the ESS is the lowest of the three methods, primarily due to its inability to scale.

Image: The Geneva Learning Foundation © 2024

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https://redasadki.me/2024/02/26/calculating-the-relative-effectiveness-of-expert-coaching-peer-learning-and-cascade-training/

#cascadeTraining #expertCoaching #fellowship #mathematicalModeling #peerLearning

Mathematicians finally solved Feynman’s “reverse sprinkler” problem - Light-scattering microparticles reveal the flow pattern for the reve... - https://arstechnica.com/?p=2000600 #mathematicalmodeling #mathematicalphysics #reversesprinkler #richardfeynman #fluiddynamics #science #physics

Mathematicians finally solved Feynman’s “reverse sprinkler” problem - Light-scattering microparticles reveal the flow pattern for the reve... - https://arstechnica.com/?p=2000600 #mathematicalmodeling #mathematicalphysics #reversesprinkler #richardfeynman #fluiddynamics #science #physics

Don't be smart, be lucky.

#wealth #luck #MathematicalModeling

If You’re So Smart, Why Aren’t You Rich? Turns Out It’s Just Chance. https://getpocket.com/explore/item/if-you-re-so-smart-why-aren-t-you-rich-turns-out-it-s-just-chance?utm_source=pocket-newtab-en-us

Don't be smart, be lucky.

#wealth #luck #MathematicalModeling

If You’re So Smart, Why Aren’t You Rich? Turns Out It’s Just Chance. https://getpocket.com/explore/item/if-you-re-so-smart-why-aren-t-you-rich-turns-out-it-s-just-chance?utm_source=pocket-newtab-en-us

We have a fully funded (3 year) #PhD position available in #disease ecology at the University of Oslo, Norway. The topic is #Lyme disease with focus on modeling ecological interactions and processes influencing the disease dynamics.

Deadline February 29th (master students can also apply if they complete the degree before June 30).

Please help spread the word to potential applicants! #PhDposition #MathematicalModeling

For more information and to apply:

https://www.jobbnorge.no/en/available-jobs/job/257248/phd-research-fellow-in-disease-ecology

Balanced Boulders On San Andreas Fault Suggest The 'Big One' Won't Be As Destructive As Once Thought
--
https://www.livescience.com/planet-earth/earthquakes/balanced-boulders-on-san-andreas-fault-suggest-the-big-one-wont-be-as-destructive-as-once-thought <-- shared technical article
--
https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1303303 <-- shared abstract
--
#GIS #spatial #mapping #spatialanalysis #model #modeling #earthquake #faulting #prediction #reconstruction #dating #risk #hazard #hazardassessment #shaking #seismic #earthquakeengineering #LoveJoyButtes #hazardmodel #earthquakemodel #CA #California #geology #engineeringgeology #SanAndreasFault #SanAndreas #fault #groundmotion #history #holocene #earthquakehazard #Mojave #validation #returnperiod #magnitude #mathematicalmodeling

Balanced Boulders On San Andreas Fault Suggest The 'Big One' Won't Be As Destructive As Once Thought
--
https://www.livescience.com/planet-earth/earthquakes/balanced-boulders-on-san-andreas-fault-suggest-the-big-one-wont-be-as-destructive-as-once-thought <-- shared technical article
--
https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1303303 <-- shared abstract
--
#GIS #spatial #mapping #spatialanalysis #model #modeling #earthquake #faulting #prediction #reconstruction #dating #risk #hazard #hazardassessment #shaking #seismic #earthquakeengineering #LoveJoyButtes #hazardmodel #earthquakemodel #CA #California #geology #engineeringgeology #SanAndreasFault #SanAndreas #fault #groundmotion #history #holocene #earthquakehazard #Mojave #validation #returnperiod #magnitude #mathematicalmodeling

2023 was a very positive year regarding my Julia endeavours. In short, I gave 4 talks, I started doing more general Julia videos in English, and I started working a lot more with Julia at my current job. I give more details in this post: https://abelsiqueira.com/blog/2024-01-04-personal-julia-2023-retrospective/

2024 will also be a great year for Julia, but more on that later.

#JuliaLang #JuliaProgramming #Julia #talk #optimization #mathematicalmodeling #orms

Scientists Uncover Link Between Ocean Weather And Global Climate, Using Mechanical Rather Than Statistical Analysis
--
https://phys.org/news/2023-12-scientists-uncover-link-ocean-weather.html <-- shared technical article
--
https://doi.org/10.1126/sciadv.adi7420 <-- shared paper
--
#GIS #spatial #mapping #remotesensing #model #modeling #global #model #modeling #numericmodeling #ocean #weather #oceanweather #globalclimate #climatechange #atmosphere #mathematicalmodeling #energytransfer #climatemodel #satellite #earthobservation

Scientists Uncover Link Between Ocean Weather And Global Climate, Using Mechanical Rather Than Statistical Analysis
--
https://phys.org/news/2023-12-scientists-uncover-link-ocean-weather.html <-- shared technical article
--
https://doi.org/10.1126/sciadv.adi7420 <-- shared paper
--
#GIS #spatial #mapping #remotesensing #model #modeling #global #model #modeling #numericmodeling #ocean #weather #oceanweather #globalclimate #climatechange #atmosphere #mathematicalmodeling #energytransfer #climatemodel #satellite #earthobservation

Lately I have been working with an energy model over a graph, and it motivated me to create a new mathematical modeling video.

It is a much simpler video about a very traditional network flows problem, the maximum flow: https://youtu.be/AtfuShpbWEQ

Maybe we'll get to energy models in future videos?

The video also serves as a brief introduction to Graphs.jl, and how to use it in conjunction with JuMP.jl. Many more fun things can be done with this combination, so let me know in the comments if you're interested in that.

#JuliaLang #JuMP #Graphs #MaximumFlow #ORMS #NetworkFlows #MathematicalModeling #Optimization #Pluto

Lately I have been working with an energy model over a graph, and it motivated me to create a new mathematical modeling video.

It is a much simpler video about a very traditional network flows problem, the maximum flow: https://youtu.be/AtfuShpbWEQ

Maybe we'll get to energy models in future videos?

The video also serves as a brief introduction to Graphs.jl, and how to use it in conjunction with JuMP.jl. Many more fun things can be done with this combination, so let me know in the comments if you're interested in that.

#JuliaLang #JuMP #Graphs #MaximumFlow #ORMS #NetworkFlows #MathematicalModeling #Optimization #Pluto

Javier is back, now including the demand for his art in the production planning. Sorry for the long video, I hope it will at least be watched by the #Julia enthusiasts or the #orms people out there. Maybe #microeconomics people will enjoy it too. Once more I use #pluto and #jump.

When planning the production, in our last video, we ignored the demand for the products. In this video, we will evaluate two strategies to include the demand into the model.
The first strategy is to have a sale of excess products. In this case, we have a piecewise revenue function, and we use some modeling tricks to model it into a mixed-integer linear program.
The second strategy is to consider the price at which we sell things to be a decision variable as well and to model the demand as a function that depends on the price linearly. This case leads to a very interesting conclusion about the nature of demand satisfaction.

https://youtu.be/0HRAYGYrkIc

You check a blog post and other links at https://abelsiqueira.com/youtube

#mathematicalmodeling #mip #optimization

In Brazil, when we got overwhelmed with our studies, we would say that we were thinking about dropping out and selling art on the beach.
Well, if you did that, could you optimize your art production?

Check out how to model and solve that using #JuliaLang and #JuMP: https://youtu.be/IOUi1juD5HQ

You can find the #PlutoJL notebook for this code at https://abelsiqueira.com/youtube.

#Julia #orms #optimization #mathematicalprogramming #MathematicalModeling

Scientists May Have Solved The Mystery Of How The Andes Got So Big
--
https://www.sciencealert.com/scientists-may-have-solved-the-mystery-of-how-the-andes-got-so-big <-- shared article
--
https://doi.org/10.1016/j.epsl.2023.118009 <-- shared paper
--
[although my postgrad was in geology and I am licensed in the field, it has been a number of years since I worked directly in a geologic discipline; that said, I very much like to try and keep up with ‘things geology’…]
#GIS #spatial #mapping #geology #southamerica #data #modeling #model #mathematics #mathematicalmodeling #structuralgeology #andes #chile #peru #platetectonics #tectonics #tectonic #movement #mountain #mountainbuilding #remotesensing #geologists #orogeny #vulcanism #pointers #APM #RPM

Scientists May Have Solved The Mystery Of How The Andes Got So Big
--
https://www.sciencealert.com/scientists-may-have-solved-the-mystery-of-how-the-andes-got-so-big <-- shared article
--
https://doi.org/10.1016/j.epsl.2023.118009 <-- shared paper
--
[although my postgrad was in geology and I am licensed in the field, it has been a number of years since I worked directly in a geologic discipline; that said, I very much like to try and keep up with ‘things geology’…]
#GIS #spatial #mapping #geology #southamerica #data #modeling #model #mathematics #mathematicalmodeling #structuralgeology #andes #chile #peru #platetectonics #tectonics #tectonic #movement #mountain #mountainbuilding #remotesensing #geologists #orogeny #vulcanism #pointers #APM #RPM

Applications are rolling in! The Yale Department of Epidemiology of Microbial Diseases is looking for two new tenure track faculty. Yale EMD is a great place to work with wonderful colleagues and students. Please spread the word.

https://apply.interfolio.com/122824

#epidemiology #epiverse #causalinference #publichealth #yale #ysph #ecology #infectiousdiseases #microbiology #genomics #mathematicalmodeling #socialdeterminants

https://apply.interfolio.com/122824

all sneaky @openscience suddenly follows me while I'm reading a paper published in @PLOS i see being fully vaccinated is working

in all seriousness this is a really important paper that everyone should read especially considering #Wisconsin is currently developing its new wolf management plan

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259604

#OpenScience #Conservation #Wildlife #MathematicalModeling

Oxford scientists crack case of why ketchup splatters from near-empty bottle - Enlarge / Getting those few last dollops of ketchup out of the bottle c... - https://arstechnica.com/?p=1899879 #mathematicalmodeling #non-newtonianfluids #criticalthreshold #phasetransitions #appliedphysics #fluiddynamics #foodscience #condiments #rheology #science #physics