Code vs Covid19 Zürich Hackathon (March 28–30, 2020)

Simulation of Hospital Supply and Demand for COVID-19

This project was developed during the CodevsCovid19 Zürich Hackathon to explore data-driven approaches for forecasting hospital supply and demand during the COVID-19 pandemic.

The repository contains an inventory management simulation and an interactive Shiny dashboard designed to model demand forecasting, safety stock estimation, and reorder point calculations for essential medical supplies.

---

Project Overview

During the COVID-19 pandemic, healthcare systems faced major challenges in managing shortages of critical medical supplies. This project simulates an inventory management system capable of:

• Forecasting future product demand
• Estimating safety stock requirements
• Calculating reorder points
• Simulating supply and demand dynamics
• Supporting decision-making for hospital inventory planning

The initial phase of the project focused on generating synthetic supply-demand datasets. Later iterations expanded toward dashboard visualization and real-world applicability.

---

Repository Structure

.
├── setup.R                     # Loads required libraries and dependencies
├── simulation.R                # Generates simulated supply-demand datasets
├── shiny_data.R                # Prepares datasets for the Shiny dashboard
├── codevscovid_supply_demand/
│   ├── ui.R                    # Shiny user interface
│   └── server.R                # Shiny server logic
├── supply_demand_simulation.Rproj
└── README.md

---

Features

• Inventory demand forecasting
• Safety stock estimation
• Reorder point calculations
• Interactive Shiny dashboard
• Simulated pandemic supply-demand datasets
• Prototype decision-support workflow for healthcare logistics

---

Technologies Used

• R
• Shiny
• Data Simulation
• Inventory Modeling
• Statistical Forecasting

---

Getting Started

Prerequisites

Install R and the required packages.

Run the Project

1. Clone the repository:

git clone https://github.com/NoushinN/codevscovid19.git

1. Open the project in RStudio.

2. Run the setup script:

source("setup.R")

1. Generate simulation data:

source("simulation.R")

1. Prepare Shiny data:

source("shiny_data.R")

1. Launch the Shiny app:

shiny::runApp("codevscovid_supply_demand")

---

Live Demo

Shiny Application

Live Shiny App

Hackathon Submission

Devpost Project Page

Project Videos

• Demo Video 1
• Demo Video 2

---

Future Improvements

Potential future enhancements include:

• Integration of real-world hospital inventory datasets
• Geographic and regional demand modeling
• Machine learning forecasting approaches
• Priority scoring of incoming demands
• Automated recommendation systems
• Scenario-based pandemic simulations
• Time-series forecasting methods

---

Research and Practical Relevance

This project demonstrates how data simulation and interactive analytics can support healthcare logistics and emergency preparedness during large-scale public health crises.

The work also highlights the value of rapid interdisciplinary collaboration during hackathon environments.

---

Contributing

Contributions, suggestions, and improvements are welcome.

Please feel free to:

• Open an issue
• Submit a pull request
• Share feedback or ideas

---

Team

Supply and Demand Team — CodevsCovid19 Zürich Hackathon

• Noushin Nabavi
• Runzhi Yang
• Emilien Davaud
• Bettsina Walkinson
• Dorothee Brumann
• Judith Vornberger
• Adriatik Dushica
• Tim Fuhrmann
• Nikhil Mahendran
• Michael Lew

---

License

This project is licensed under the Apache 2.0 License.