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COVID-19 Model of Puerto Princesa City as of May 19,2023.
COVID-19 Model of Puerto Princesa City
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Simulasi Pemodelan Penyebaran COVID-19 di Indonesia + Prokes
Simulasi Pemodelan Penyebaran COVID-19 di Indonesia + Prokes
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Өзіндік жұмыс 1 (жүйелік динамика)
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covid 19 in itale пример
8 12 months ago
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Using the reading assignment from El-Taliawi and Hartley on using a SSM for COVID-19 follow the steps for SSM to include: 1) Describe the Problem (unstructured). 2) Develop a Root Definition for the COVID-19 problem space by identifying the three elements: what, how, why. A System to do X, by (means of) Y, in order to achieve Z. X - What the system does Y - How it does it Z - Why is it being done (see slide 33 in the Systems Thinking Workshop reading)Download Systems Thinking Workshop reading) 3) Identify the Perspectives (CATWOE) 4) Develop a basic Systemigram / Rich Picture to tell the story. Submit your assignment as a Word document or PDF that addresses #1-4.
1) Problem Situation (Unstructured)

The COVID‑19 pandemic represents a complex, ill‑structured problem characterized by uncertainty, rapidly changing conditions, and conflicting stakeholder perspectives. As El‑Taliawi and Hartley emphasize, COVID‑19 is not merely a biomedical crisis but a socio‑technical system failure involving public health, governance, economics, social behavior, and global interdependence. There was no single agreed‑upon definition of “the problem.” For some actors, the problem was viral transmission and mortality; for others, it was economic collapse, civil liberties, misinformation, or institutional trust.

Key features of the unstructured problem include:

  • High uncertainty about the virus’s behavior, transmission, and long‑term effects.

  • Multiple stakeholders with competing values and priorities (health vs. economy, freedom vs. safety).

  • Nonlinear dynamics, where interventions (lockdowns, travel bans, vaccination campaigns) produced unintended consequences.

  • Fragmented governance, with responses varying across nations, states, and institutions.

  • Information overload and misinformation, complicating sense‑making and public compliance.

This ambiguity and plurality make COVID‑19 unsuitable for purely “hard” systems approaches and well suited for Soft Systems Methodology (SSM), which focuses on learning, interpretation, and accommodation rather than optimization.

2) Root Definition (What–How–Why)

A system to coordinate societal responses to the COVID‑19 pandemic (X), by integrating public health expertise, policy decision‑making, communication, and stakeholder engagement under conditions of uncertainty (Y), in order to reduce harm to human life and societal functioning while maintaining legitimacy, trust, and resilience (Z).

  • What (X) — Coordinating societal responses to COVID‑19.

  • How (Y) — Through adaptive governance, expert input, communication, and stakeholder engagement.

  • Why (Z) — To minimize health, social, and economic harm while sustaining trust and resilience.

This root definition reflects SSM’s emphasis on purposeful human activity systems, not technical control systems.

3) Perspectives (CATWOE Analysis)
  • Customers — General public, vulnerable populations, healthcare workers, businesses, future generations affected by long‑term consequences.

  • Actors — Governments, public health agencies (e.g., WHO, CDC), healthcare providers, scientists, media organizations, community leaders.

  • Transformation — A society facing uncontrolled viral spread and systemic disruption is transformed into one that manages risk, adapts behavior, and mitigates impacts.

  • Worldview (Weltanschauung) — COVID‑19 is a complex, evolving crisis requiring learning, adaptation, and pluralistic decision‑making rather than linear control.

  • Owners — National governments, international bodies, and political authorities with the power to authorize or terminate interventions.

  • Environmental Constraints — Scientific uncertainty, political polarization, economic limitations, cultural norms, global interdependence, time pressure.

CATWOE highlights how different worldviews lead to different “valid” problem definitions, reinforcing the need for SSM.

4) Systemigram / Rich Picture (Narrative Description)

The rich picture would depict COVID‑19 at the center as a disruptive force interacting with multiple subsystems. Public health institutions generate evolving scientific knowledge, feeding into government decision‑making. Governments issue policies (lockdowns, mandates, economic relief), which influence public behavior. Public response is shaped by trust, media narratives, misinformation, and social norms. Economic systems react to restrictions, feeding back political pressure on leaders. Healthcare systems experience strain, influencing policy urgency and public fear.

Feedback loops dominate the picture:

  • Policy decisions affect public behavior, which affects transmission rates.

  • Media narratives influence trust, which affects compliance.

  • Economic impacts influence political decisions, sometimes undermining health goals.

The systemigram emphasizes learning loops, delays, conflicts, and unintended consequences rather than linear cause‑effect chains.

If you want, I can translate this directly into a Word‑ready format, tighten it to a specific page limit, or sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.

translate this directly into a Word‑ready format 5 pages and sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.
Soft Systems Methodology (SSM) Analysis of the COVID‑19 Pandemic

Based on El‑Taliawi and Hartley

Introduction

The COVID‑19 pandemic represents one of the most complex global crises of the modern era. Traditional “hard” systems approaches, which assume clearly defined problems and measurable objectives, proved insufficient for addressing the pandemic’s intertwined health, social, political, and economic dimensions. El‑Taliawi and Hartley argue that COVID‑19 is best understood as a messy, ill‑structured problem situation, making it well suited for analysis using Soft Systems Methodology (SSM).

SSM does not attempt to “solve” the problem in a technical sense. Instead, it provides a structured learning process to explore multiple perspectives, clarify assumptions, and identify purposeful human activity systems that can improve the situation. This document follows the core SSM steps required in the assignment: (1) describing the unstructured problem, (2) developing a root definition, (3) identifying perspectives using CATWOE, and (4) developing a systemigram / rich picture narrative.

1. Description of the Problem Situation (Unstructured)

The COVID‑19 pandemic emerged rapidly and evolved unpredictably, creating a situation characterized by ambiguity, disagreement, and uncertainty. At the outset, there was no shared understanding of the nature or scale of the threat. Scientific knowledge about transmission, severity, and long‑term effects developed incrementally, often changing public guidance and policy decisions. This uncertainty undermined confidence and complicated coordinated action.

The problem situation extended far beyond public health. Governments faced competing pressures to protect lives, preserve economic stability, and maintain civil liberties. Healthcare systems experienced surges in demand, shortages of personnel and equipment, and moral distress among frontline workers. Businesses and workers faced closures, unemployment, and financial insecurity. Social isolation measures disrupted education, mental health, and community cohesion.

Multiple stakeholders framed the “problem” differently. For public health officials, the primary concern was reducing transmission and mortality. For political leaders, the challenge included maintaining legitimacy and public compliance. For citizens, the problem often centered on personal risk, economic survival, and trust in institutions. Media organizations and social platforms amplified both accurate information and misinformation, shaping public perception and behavior.

Covid-19 HW
8 months ago
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COVID-19 agent based model
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This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.

This model is upgraded on the basis of the SIR model and adds more variables.

A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy

Government policy will help control the growth of COVID-19 cases by getting people tested.


BMA708 Model of COVID-19 Outbreak in Burnie island. Ming Liu 501335
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самостаятельная работа часть 2 Акилбеков Асет
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A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy though of higher detected cases though is negative. 




Clone of Burnie COVID-19 outbreak demo model version 2
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COVID-19 in Indonesia
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Өзіндік жұмыс 1 жүйелік динамика
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La meilleur simulation du marché à propos de la Covid-19
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Pada Tugas 3 mata kuliah Pemodelan Transportasi Laut, ditugaskan untuk membuat pemodelan penyebaran COVID-19 di negara yang dipilih, dan pada simulasi ini merupakan negara Indonesia

Dosen Pengampu : Dr.-Ing Ir Setyo Nugroho
Clone of Simulasi Pemodelan Penyebaran COVID-19 di Indonesia
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
SEIR Model for COVID-19 in Indonesia (Revised V2)
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COVID-19 Crisis Management
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Introduction
This model simulates the COVID-19 outbreaks in Burnie, the government reactions, as well as the economic impact. The government's strategy is based on the number of COVID-19 cases reported and testing rates and recovered.

Assumptions
In the same trend that government policy decreases infection, it also reduces economic growth.
When there are ten or fewer COVID-19 cases reported, government policy is triggered.
The economy suffers as a result of an increase in COVID-19 cases.

Interesting insights
The higher testing rates appear to result in a more quick government response, resulting in fewer infectious cases. However, it has a negative influence on the economy.
Model of COVID-19 outbreak in Burnie Tasmania - Xiaoqing Ren 525418
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Based on this particular model created by Lutfi Andriyanto and Aulia Nur Fajriyah: https://insightmaker.com/insight/2wxxIeiWJsHNFGNH6cf6ke/SEIR


Updated by (Kelompok 2):

Daffa Muhammad Romero 20/456363/TK/50493

Iskan Mustamir 20/456367/TK/50497

Tasya Nafisah Kamal 20/460569/TK/51158

Hervi Nur Rahmadien 20/463601/TK/51593

SEIR Model COVID-19 Updated - Kelompok 2
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Өзіндік жұмыс 1 германия Covid-19 2022ж
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Modelo SEIR in Covid-19
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Whitney_6550_Covid-19_GlobalCrisis
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My Insight 1 covid-19
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COVID-19 Модель
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COVID-19 in Kazakstan