2022数学建模校赛.zip

Modeling transmission of SARS-CoV-2 Omicron in China: instructions for code running ================ Deng Xiaowei[1] 2022-05-10 This is the instruction for readers to repeat and understand the construction for the model simulating the transmission of SARS-CoV-2 Omicron in China and its according health impact. The details for the methodology and results refer to our paper published (https://www.nature.com/articles/s41591-022-01855-7). ## Running platform and software version If you want to repeat our demo, you must deploy **C** compiler and [**GSL**](https://www.gnu.org/software/gsl/) (a library for C and C++). The detialed version information refers to below. ## OS and hardware ## OS: Microsoft Windows 10 家庭中文版 ## OS Version: 10.0.19044 ## CPU: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz ## Memory size: 15.80 GB ## Soft version ## R version 4.1.0 (2021-05-18) ## cygwin (3.2.0-1) ## gcc-core (10.2.0-1) ## gsl (2.3-2) ## Linelist for **C** code, input and output We separate our code, input, and output into three folders named in “C”, “INPUT\_CN”, and “OUTPUT”. Codes in folder “C” consist of three files, func.c, head.h, main.c. “main.c” is the main body for the model, “func.c” includes all functions used to read files, to process transmission, to allocated vaccine, and “head.h” declares all relevant STRUCT and functions. Most parameters used in our model are deposited in folder “INPUT\_CN”. Detailed description refers to table below. | Folder | File | Dimentions(rows x columns) | Type | Description | Limits | |:------------|:----------------------|:---------------------------|:-------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------| | CM/baseline | cm\_{i} | 14x14 | double | Contact matrix in baseline scenario setting | {i} in 0-199 | | POP | contraindication\_{j} | 14x1 | double | Proportion for people cannot be vacccinated | {j}=YES indicates limits for limits in vaccinating elderly according to real-world data | | POP | {k}\_sus | 200x14 | double | Susceptibility for 200 max times simulation | {k}=hetero indicates unequle susceptibility for 14 age-group | | POP | population | 14x1 | int | Population number | | | PROB\_SA | p\_symp | 14x1 | double | Probability for developing symptom (adjusted)