First, the epidemic patterns in Wuhan are significantly different from those in other cities. We exclude Wuhan, the capital city of Hubei province, from our analysis for two reasons.

United Nations Development Programme Translation communicates words and meanings, but also includes culture, social norms and even politics. This is why translators are faced with the challenge of how to translate content in a professional manner, while respecting aspects of the target language and locale. The common effect on other cities is controlled for by the day fixed effect.Flu viruses are easier to survive in cold weather.

Some confirmed cases in Wuhan contracted the virus through direct exposure to Huanan Seafood Wholesale Market, which is the most probable origin of the virusTo model the spread of the virus, we consider within-city spread and between-city transmissions simultaneously (Adda To make it easier to interpret the coefficients, we assume that the transmission dynamics (We also consider more parsimonious model specifications, such as the model that only considers within-city transmissions, (COVID-19 case definitions were changed in Hubei province on February 12 and February 20. Also importantly, among patients who have died from COVID-19, the time from symptom onset to outcome ranges from 2 to 8 weeks (World Health Organization The COVID-19 epidemic is still ongoing at the time of writing, and the estimates are revised from time to time in the literature as new data become available. In Eq. (Our sample starts from January 19, when the first COVID-19 case was reported outside Wuhan. ©

That strategy involves city lockdowns and mandatory quarantines to ban or restrict traffic since January 23, social distance–encouraging strategies since January 28, and a centralized treatment and isolation strategy since February 2.This study estimates how the number of daily newly confirmed COVID-19 cases in a city is influenced by the number of new COVID-19 cases in the same city, nearby cities, and Wuhan during the preceding 2 weeks using the data on confirmed COVID-19 case counts in China from January 19 to February 29. The influence of weather conditions on policy adoption may be complicated, so we use the Cluster-Lasso method of Belloni et al.

In addition, all cities in Hubei province implemented the lockdown policy, and most Hubei cities had also adopted measures commensurate with class A infectious diseases by January 28Health facilities in Wuhan had been extremely overstretched with shortage in medical supplies and high rates of nosocomial infections until February 2 when (1) two new hospitals, i.e., Huoshenshan and Leishenshan, were built to treat patients of COVID-19 with severe symptoms, and (2) 14 makeshift health facilities were converted to isolate patients with mild symptoms and to quarantine people suspected of contracting COVID-19, patients with fever symptoms, and close contacts of confirmed patients. $$ y_{ct}=\sum\limits_{s=1}^{14}\alpha_{\text{within},s}y_{c,t-s}+\sum\limits_{s=1}^{14}\alpha_{\text{between},s}\sum\limits_{r\neq c}d_{cr}^{-1}y_{r,t-s}+\sum\limits_{s=1}^{14}\rho_{s}z_{t-s}+x_{ct}\upbeta+\epsilon_{ct}, $$\(\bar {y}_{ct}^{\tau }=\frac {1}{7}{\sum }_{s=1}^{7}y_{ct-7\left (\tau -1\right )-s}\)\(\bar {y}_{rt}^{\tau }=\frac {1}{7}{\sum }_{s=1}^{7}y_{rt-7\left (\tau -1\right )-s}\)\(\bar {z}_{t}^{\tau }=\frac {1}{7}{\sum }_{s=1}^{7}z_{t-7\left (\tau -1\right )-s}\)$$ y_{ct}=\sum\limits_{\tau=1}^{2}\alpha_{\text{within},\tau}\bar{y}_{ct}^{\tau}+\sum\limits_{\tau=1}^{2}\alpha_{\text{between},\tau}\sum\limits_{r\neq c}d_{cr}^{-1}\bar{y}_{rt}^{\tau}+\sum\limits_{\tau=1}^{2}\rho_{\tau}\bar{z}_{t}^{\tau}+x_{ct}\upbeta+\epsilon_{ct}.\quad\textbf{Model A} $$$$ y_{ct}=\sum\limits_{\tau=1}^{2}\alpha_{\text{within},\tau}\bar{y}_{ct}^{\tau}+x_{ct}\upbeta+\epsilon_{ct}, $$$$ y_{ct}=\alpha_{\text{within}}\frac{1}{14}\sum\limits_{s=1}^{14}y_{c,t-s}+\alpha_{\text{between}}\frac{1}{14}\sum\limits_{s=1}^{14} \sum\limits_{r\neq c}d_{cr}^{-1}y_{r,t-s}+\rho\frac{1}{14}\sum\limits_{s=1}^{14}z_{t-s}+x_{ct}\upbeta+\epsilon_{ct}.\quad\textbf{Model B} $$$$ \begin{array}{@{}rcl@{}} y_{ct}&= & \sum\limits_{\tau=1}^{2}{\sum}_{k=1}^{K_{\text{within}}}\alpha_{\text{within},\tau}^{k}\bar{h}_{ct}^{k\tau}\bar{y}_{ct}^{\tau}+\sum\limits_{\tau=1}^{2}{\sum}_{k=1}^{K_{\text{between}}}{\sum}_{r\neq c}\alpha_{\text{between},\tau}^{k}\bar{m}_{crt}^{k\tau}\bar{y}_{rt}^{\tau}+\sum\limits_{\tau=1}^{2}{\sum}_{k=1}^{K_{\text{Wuhan}}}\rho_{\tau}^{k}\bar{m}_{c,\text{Wuhan},t}^{k\tau}\bar{z}_{t}^{\tau}\\ && + x_{ct}\upbeta+\epsilon_{ct}, \end{array} $$\(\ddot {d}_{ct}=d_{ct}-\frac {1}{n}{\sum }_{c}d_{ct}-\frac {1}{T}{\sum }_{t}d_{ct}+\frac {1}{nT}{\sum }_{ct}d_{ct}\)$$ \frac{1}{nT}\sum\limits_{ct}\left( \ddot{d}_{ct}-\ddot{q}_{ct}^{\prime}b\right)^{2} + \frac{\lambda}{nT}\sum\limits_{k}\phi_{k}|b_{k}|.
The current estimates include the following. The transmission dynamics may change during the course of this epidemic because of improved medical treatments, more effective case isolation and contact tracing, increased public awareness, etc. Human-to-human transmission of COVID-19 is mostly through droplets and contacts (National Health Commission of the PRC We then regress the endogenous variables on the instrumental variables, contemporaneous weather controls, city, date, and city by week fixed effects.


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