bibliography:["COVID-19文库.bib"]

1 项目框架

2 传播动力学模型

2.1 群体模型：SIRS^[1]

$$\frac{dS}{dt}=-\beta SI+ \gamma R$$

$$\frac{dI}{dt}=\beta SI - \mu I$$

$$\frac{dR}{dt}=\mu I - \gamma R$$

变量	说明	备注
$\beta$	有效接触感染率
$\mu$	治愈率
$\gamma$	丧失免疫力率

2.1.1 Social contact^[2] ^[3] ^[4]

(1) location catagories

Home,Work,School,Others $\beta_i=\Sigma (\alpha^{sc}(1-\rho_j)+\alpha^c\rho_j)\frac{I_j}{N_j}C^l_{ij}$ 其中$(1-\rho_j)$ 对应asymptomatic group，$\rho_j$ 对应sub-clinical group

$C=\beta_hC^h+\beta_wC^w+\beta_wC^w+\beta_oC^o$

2.2 个体免疫应答模型

$$\frac{dT}{dt}=d(T_0-T)-\frac{k}{A\alpha} VT$$

$$\frac{dI}{dt}=\frac{k}{A\alpha} VT-\delta I$$

$$\frac{dV}{dt}=pI-cV$$

变量	说明	备注
$T$	T细胞数量
$T_0$	T细胞初始量
$V$	病毒量
$I$	被感染的T细胞
...	...	...

进度计划

gantt
        dateFormat  YYYY-MM-DD
        axisFormat   %b %d
        title Covid-19讨论小组计划
 
        section 文献
        文献追踪                             :active,    des1, 2022-04-21,2022-07-31
        略读                                :done,  des2, 2022-05-09, 21d
        精读                                :activate， des3, 2022-05-23, 2022-07-04
        文献综述                             :         des4, 2022-06-17, 2022-07-11
 
        section 数据
        历史数据整理                         :crit, done, 2022-03-16,2022-04-21
        Johns Hopkins数据更新                :crit, active, 2022-05-21,2022-07-21
        数据清洗与数据工程                    :crit, done, 2022-05-21,2022-06-26
        *航空数据补充                         :after a1, 2022-07-10,2022-07-17
 
        section 模型
        个体模型                             :crit, done, 2022-05-31,2022-06-10
        群体模型                             :crit, active, 2022-06-14,2022-07-03
        个体/群体模型整合                     :crit, after, 2022-06-21,2022-07-10
        参数率定                             :crit, after, 2022-07-10,2022-07-17
        情景分析                             :crit, after, 2022-07-17,2022-07-24

        section 文章
        整理成文                             :after a1, 2022-07-04,2022-08-04

REFERENCES

[1] Du, Xiangjun, et al. "Evolution-informed forecasting of seasonal influenza A (H3N2)." Science translational medicine 9.413 (2017): eaan5325. [2] Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. (2008) Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Med 5(3): e74. https://doi.org/10.1371/journal.pmed.0050074 [3] Du SQ, Yuan W. Mathematical modeling of interaction between innate and adaptive immune responses in COVID‐19 and implications for viral pathogenesis. J Med Virol. 2020;1–14. 10.1002/jmv.25866 [4] Prem K, Cook AR, Jit M (2017) Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Comput Biol 13(9): e1005697. https://doi.org/10.1371/journal.pcbi.1005697

muyang/covid19