anthonyzhang25 / covid_19_model

R code for: Optimal timing and effectiveness on control strategies for COVID19 outbreak in China: a modelling study

Optimal timing and effectiveness of COVID-19 outbreak responses in China: a modelling study

Anthony Zhenhuan Zhang ([email protected]) Eva A. Enns ([email protected])

University of Minnesota

Introduction

This Github repository contains Rscripts to simulation COVID-19 outbreak in major Chinese cities: Wuhan, Chongqing, Beijing, and Shanghai. Below, we first introduce the functionality of each script, and present the model calibration. Lastly, we demonstrate how to run simulation to generate results.

Scripts

1. functional scripts which simulates SARS-CoV-2 dynamics in Wuhan and other Chinese cities: wuhan_simulation_policy_by_age.R and other_city_simulaiton_policy_by_age.R
2. model inputs generation: model_inputs.R
3. Incremental Mixture Importance Sampling model calibration: model_calibration_ver_3.R
4. Generate status quo: decompose_economy_loss_probablistic.R
5. Generate model outcomes under different timing and duration of control policies: all scripts under "simulation_by_policy" folder.

Model Calibration

We calibrated our model using Incremental Mixture Importance Sampling methods, see model_calibration_ver_3.R

Status quo, and disease burden estimation

Run decompose_economy_loss_probablistic.R twice. In the first round, we estimate the mean disease burden, in the second round, we generate both the epidemiological and economic outcome

Results Generation

To generate model results, run scripts under "simulation_by_policy" folder.