The Important Foundations of Modern Epidemiology

The “Modeling 101” session, part of the MIDSEA Summer School series of activities, was successfully held at the Arjuna Room, Alana Hotel Yogyakarta. The session featured three main speakers: Dr. Hannah Clapham from the Saw Swee Hock School of Public Health, Prof. Jomar Fajardo Rabajante from the University of the Philippines Los Baños, and Dr. Wirichada Pan-Ngum from the Mahidol-Oxford Tropical Medicine Research Unit (MORU). The three speakers presented complementary materials on the fundamentals of modeling in the context of infectious disease epidemiology and its application in public health policy. ... 

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Exploring Reinforcement Learning for Infectious Disease Intervention

On the fourth day of the Track AI MIDSEA Summer School 2025 series of activities, participants explored the application of Reinforcement Learning (RL) in the context of infectious disease intervention. The first session began with an introduction to the basic concepts of RL, where participants learned about the main structure of RL agents, including the state, action, reward, and policy components. This explanation was presented visually using interactive simulations to facilitate understanding. ... 

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Dynamic Network Model: Changes in Network Graphs Over Time

Following the discussion of static network models on Wednesday, June 25, 2025, the fourth meeting of the Simulation Track MIDSEA Summer School 2025 at the Bima Room, MICC Building, The Alana Yogyakarta Hotel and Convention Center focused on dynamic network models for the spread of infectious diseases. The track began with a discussion of the program code for the static network model problem provided the previous day by Prof. Alexander Richard Cook, Deputy Dean for Global Health at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). Following that, as an introduction to the fourth meeting’s material, Prof. Cook explained that the fundamental difference between this model and the static network model is the change in the vertices or edges of the network graph. ... 

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The MIDSEA Summer School 2025 Excursion Takes Participants on a Journey from Borobudur to Ramayana

MIDSEA Summer School 2025 not only offers academic activities in the classroom, but also broadens participants’ horizons through cultural excursions held on Thursday (June 26). This excursion invites more than 80 participants from various countries to explore the rich history and performing arts of Javanese culture, from Borobudur to the Ramayana Ballet at Prambanan. ... 

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Statis Network Model

In contrast to the first and second days of the MIDSEA Summer School 2025 at the MICC Building, The Alana Yogyakarta Hotel and Convention Center, which focused on the standard SIR infectious disease model, the third Simulation Track meeting on June 25, 2025, focused on the static network model. The track, attended by 14 participants from various countries including Indonesia, the Philippines, Thailand, and Singapore, began with a discussion of the homogeneous mixing assumption used in the standard SIR model, which states that everyone has an infection risk dependent on the number of infected individuals in a population. ... 

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Infectious Disease Modeling: A Crucial Foundation of Modern Epidemiology

Yogyakarta, June 25, 2025 — The “Modelling 101” session, part of the MIDSEA Summer School series, was successfully held in the Arjuna Room at Alana Hotel Yogyakarta. The event was attended by 13 participants from various countries, including Indonesia, the Philippines, Japan, and Thailand. Participants came from diverse backgrounds, ranging from lecturers, researchers, WHO representatives, to students. ... 

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Exploring Bayesian Inference and MCMC in Complex Statistical Models: Perspectives from MIDSEA Summer School 2025

On the third day of the MIDSEA Summer School 2025, Dr. Akira Endo, Assistant Professor from the Saw Swee Hock School of Public Health, discussed Bayesian Inference and Markov Chain Monte Carlo (MCMC) as key topics. Dr. Endo explained the fundamentals of Bayesian inference, which is the process of updating knowledge or beliefs based on new data. Using Bayes’ Theorem, this approach combines prior knowledge about parameters (the prior) with observed data to produce a posterior distribution, which describes the likely values of the parameters after accounting for the data. ... 

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Exploring Satellite Foundation Models and Machine Learning for Health: Day Three of AI Track is Interactive

Yogyakarta, June 25, 2025 — The Summer School Track AI activities at Alana Hotel Yogyakarta continue with high enthusiasm. Entering its third day, the session titled “Satellite FM Day 3” featured two speakers who guided participants in exploring foundation models for satellite imagery and the application of data mining in the healthcare sector. ... 

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Implementing Maximum Likelihood Estimation (MLE): Parameter Estimation and Confidence Intervals from Data

The second day of the Inference Track at the MIDSEA Summer School 2025, held on June 24, 2025, in the Yudhistira Room at the MICC, The Alana Yogyakarta Hotel and Convention Center, was a success. The session, titled “Implementing MLE,” discussed the challenges of applying Maximum Likelihood Estimation (MLE) to complex statistical models. One issue explored was the uncertainty in parameter estimation, especially when dealing with models that have many variables and parameters. In this session, participants learned how numerical and optimization methods can be used to find parameters that maximize the likelihood function, even though the models used often involve uncertainty and imperfect data. ... 

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Realistic Simulation of Infectious Disease Models: Duration of Disease Phases and Uncertainty

The second day of the Simulation Track at the MIDSEA Summer School 2025 on June 24, 2025, proceeded smoothly. Held in the Bima Room at the MICC Building, The Alana Yogyakarta Hotel and Convention Center, the session titled “Sojourn Times and Uncertainty” discussed the unrealistic nature of the standard infectious disease model (SIR), which consists of three compartments or phases: the susceptible phase (S), the infected phase (I), and the recovered phase (R). This is due to the fact that the population size in each phase is not an integer, and the deterministic model does not account for the duration of time an individual spends in a particular phase. ... 

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