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H.Y. has received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, and Shanghai Roche Pharmaceutical Company. A.V. reports grants from Metabiota inc., outside the submitted work. M.A. has received research funding from Seqirus. None of those research funding is related to COVID-19. All other authors report no competing interests.

‡ These authors are joint senior authors on this work.

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R_{0} at 2.4 (95% CI: 1.6–3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31–68 days), causing 9,216 more cases (95% CI: 1,317–25,545).

Since its emergence in Wuhan, SARS-CoV-2 rapidly started its spread across China. On January 21, 2020 the first COVID-19 case was detected in the Sichuan Province of China and led to an outbreak of local transmission. Less than two months later, the outbreak was over with the last reported case on March 4, 2020. In this study, we analyzed patient records for all laboratory-confirmed cases reported in Sichuan to provide an epidemiological characterization of the outbreak, to estimate SARS-CoV-2 transmission potential, and to assess the impact of the adopted interventions. We estimated that, during the initial exponential growth phase of the outbreak, each COVID-19 case has generated a mean of 2.4 secondary cases (95% CI: 1.6–3.7). Moreover, we estimated that, were the Sichuan strict containment measures implemented four weeks later, the outbreak would have caused 9,216 more cases (95% CI: 1,317–25,545). Our findings suggest the key role of a quick response to COVID-19 outbreaks and the importance of an adequate surveillance and monitoring system.

SARS-CoV-2 has been incredibly successful in spreading swiftly from Wuhan, Hubei Province in China. The number of cases rocketed and the disease spread through China, quickly entering an exponential growth phase [

Chinese provinces outside Hubei represent an important example of successful local containment of COVID-19 outbreaks and thus represent a valuable source of information for other countries as well. Sichuan is one of the largest provinces of China with a population size of about 83-million individuals and a major transport hub in Southwestern China. Sichuan was one of the first provinces outside Hubei to record COVID-19 cases with a first importation from Wuhan detected on January 21, 2020 [

The aim of this study is to describe the epidemiological characteristics of the COVID-19 outbreak in the Sichuan and to shed light on its successful local containment. By using a Bayesian approach based on the renewal equation [

The study was approved by the Clinical Trials and Biomedical Ethics Committee of West China Hospital, Sichuan University (No. 2020190). Data were deidentified, and informed consent was waived.

All cases were PCR confirmed. The severity of cases (paucisymptomatic, symptomatic, severe, and critical) is classified according to the fifth version of “Guideline on diagnosis and treatment of novel coronavirus infected pneumonia (NICP)” issued by China CDC on February 4, 2020 [_{2} (saturation of peripheral oxygen) ≤93% at rest; iii) PaO2/FiO2 ≤ 300mmHg (1mmHg = 0.133kPa). Critical cases have to meet one of the following conditions: i) respiratory failure and consequent needs of mechanical ventilation; ii) shock; iii) require intensive care because of multiple organ dysfunction.

Patient records were provided by Sichuan CDC [

All schools of Sichuan Province were closed on January 18, 2020. Sichuan declared the top-level public health emergency and established an emergency command center on January 24, 2020. Following this, gathering activities and entertainment (e.g., sports events) were suspended and public libraries closed. On January 26, the Sichuan government announced a strict set of measures to deal with the outbreak, including case isolation, tracing and screening of contacts of confirmed cases, quarantine of travelers from affected areas, and screening of people's temperature in public places. As of February 25, 2020, fourteen prefectures in Sichuan had no new confirmed cases for a week and the government decided to allocate provincial resources to deal with the epidemic in high-risk areas, while gradually starting to relax the interventions in medium and low-risk areas.

We used the patient records to calculate the age distribution and gender of cases disaggregated by case severity. Further, we distinguished between locally acquired infections and those with travel history from Wuhan/Hubei. Cases with unknown travel history are considered as locally acquired infections. Additionally, we calculated distributions of time intervals from symptom onset to hospital admission and from symptom onset to reporting for those cases where all information was available.

The basic reproduction number R_{0} represents the mean number of secondary cases generated by a primary infector during the exponential growth phase of the epidemic, before interventions are applied and when the depletion of susceptible individuals is negligible [

To estimate R(t), we use the same methodology adopted by Zhang et al. [

The likelihood Λ of the observed time series cases from day 1 to T can be written as

We then use Metropolis-Hastings MCMC sampling to estimate the posterior distribution of R(t). The Markov chains were run for 100,000 iterations, considering a burn-in period of 10,000 steps, and assuming non-informative prior distributions of R(t) (flat distribution in the range (0–1000]). Convergence was checked by visual inspection by running multiple chains starting from different starting points. Finally, we use a 5-day moving average to visualize the trajectory of R(t).

To estimate R_{0}, we used the same equation adopted to estimate R(t). Here, however, we estimated a constant daily reproduction number R(t) = R_{0} over a time window early on in the outbreak and before the implementation of interventions [_{0} over the 1-week time window before the declaration of the outbreak, namely from January 18 to 24 and, when the outbreak was growing exponentially. In addition, as a sensitivity analysis, we estimated R_{0} over a more conservative 2-week time window (from January 11 to 24, 2020).

To estimate the number of cases averted by the policies implemented in Sichuan since the declaration of a public health emergency, we provided a set of counterfactual scenarios where we consider different starting dates of the interventions. To project the number of new COVID-19 cases (assuming a different starting date of the interventions), we use the renewal equation [

The proposed counterfactual scenarios consider a schematic representation of the dynamics of R(t) that we have estimated for Sichuan (see Section Results). In particular, to mimic the estimated dynamics of R(t), we assume R(t) = R_{0} before the implementation of the control strategies; then we consider R(t) to linearly decrease over a 1-week time window to its final constant value (R_{final}) estimated over the period from February 1 to the end of the epidemic.

Four counterfactual scenarios are considered, each one accounting for a different starting date of the interventions ranging from 1 week (January 31) to 4 weeks (February 21) after the actual declaration of the public health emergency from Sichuan health authorities on January 24. Each counterfactual scenario is based on 1,000 simulations, each one considering a value of R_{0} and a value of R_{final}, sampled from the two estimated posterior distributions. It is important to stress that over the projection periods, the depletion of the susceptible population is negligible as compared with actual size of the Sichuan population (about 83,000,000 individuals [

In addition to the main analysis described above, we consider two sensitivity analyses. In the first one, we consider a lower value of R_{0}, as estimated over a 2-week time period before the declaration of the public health emergency. In the second one, instead of a linear decrease from R_{0} to R_{final}, we consider an instantaneous switch between the two values occurring on the day when the public health emergency is declared. Such an instantaneous switch is different from what we estimated for Sichuan, but we decided to consider this additional scenario as there is no guarantee that, should the interventions be implemented at a later time, the dynamics of R(t) would have been the same as the observed ones.

We define the number of averted cases as the difference between the final number of cases projected by the model and the actual number of reported cases. Similarly, we defined the number of averted severe and critical cases by multiplying the projected number of cases by the probability of developing severe or critical condition as estimated from the analysis of the patient records.

As of the March 16, 2020 a total of 539 cases were confirmed in Sichuan, including four asymptomatic subjects, 115 mild cases, 331 symptomatic cases, 57 severe cases, and 32 critical cases that required ICU treatment (

Characteristics/Case | Total | Mild | Symptomatic | Severe | Critical |
---|---|---|---|---|---|

Male—no./total no. (%) | 285/539 (52.9%) | 56/115 (48.7%) | 177/331 (53.5%) | 32/57 (56.1%) | 17/32 (53.1%) |

Median age (range)—years | 45 (1–87) | 36 (1–75) | 45 (2–79) | 48 (28–84) | 64 (33–87) |

Age group—no./total no. (%) | |||||

0–19 years | 34/539 (6.3%) | 18/115 (15.7%) | 15/331 (4.5%) | 0/57 (0%) | 0/32 (0%) |

20–39 years | 190/539 (35.1%) | 46/115 (40.0%) | 122/331 (36.9%) | 16/57 (28.1%) | 4/32 (12.5%) |

40–64 years | 255/539 (47.3%) | 42/115 (36.5%) | 170/331 (51.4%) | 28/57 (49.1%) | 13/32 (40.6%) |

≥65 years | 60/539 (11.1%) | 9/115 (7.8%) | 23/331 (7.0%) | 13/57 (22.8%) | 15/32 (46.9%) |

Travel history to Wuhan/Hubei | 253/539 (46.9%) | 50/115 (43.5%) | 168/331 (50.8%) | 25/57 (43.9%) | 10/32 (31.2%) |

Notes. The total number of cases includes also 4 PCR positive asymptomatic individuals. Age and sex variables are available for all cases. Cases with unknown travel history are considered as locally acquired infections. Percentages might not total 100% because of rounding.

The epidemic spread undetected in Sichuan Province until January 21, when the first COVID-19 case was identified. In the following days, 44 cases were identified to have symptom onset before that date (

The median age of the overall cases was 45 years (1–87) (

Characteristics | Imported cases | Local cases |
---|---|---|

Median age (range)—years | 40.0 (2–81) | 48.0 (1–87) |

Age group—no./total no. (%) | ||

0–19 years | 17/253 (6.7%) | 12/200 (6.0%) |

20–39 years | 103/253 (40.7%) | 58/200 (29.0%) |

40–64 years | 120/253 (47.4%) | 99/200 (49.5%) |

≥65 years | 13/253 (5.1%) | 31/200 (15.5%) |

Case severity | ||

Mild | 50 (19.8%) | 46 (23.0%) |

Symptomatic | 168 (66.4%) | 119 (59.5%) |

Severe | 25 (9.9%) | 24 (12.0%) |

Critical | 10 (4.0%) | 10 (5.0%) |

Male—no./total no. (%) | 144/253 (56.9%) | 97/200 (48.5%) |

Notes. Age and sex variables are available for all cases. Cases with unknown travel history are considered as locally acquired infections. Percentages might not total 100% because of rounding.

Overall, the proportion of male cases is around 50% for all level of severity (

The mean time interval from symptom onset to hospital admission was estimated at 2.5 days (95% CI of the mean: 1.8–3.2, n = 153). By considering only cases reported before the declaration of the public health emergency, the mean was estimated at 3.2 days (95% CI of the mean: 1.2–5.3, n = 17) and 2.4 days (95% CI of the mean: 1.7–14.0, n = 136) thereafter; the variation between the two periods is not significant (two-sided t-test: p = 0.47). This mean time interval from symptom onset to reporting was estimated at 5.0 days (95% CI of the mean: 4.6–5.3, n = 535). Before the declaration of the public health emergency the mean time interval from symptom onset to reporting was estimated at 5.4 days (95% CI of the mean: 3.5–7.3, n = 27) and at 4.9 days (95% CI of the mean: 4.5–5.3, n = 508) thereafter; the variation between the two periods is not significant (two-sided t-test: p = 0.62).

Led by the first few imported cases from Wuhan/Hubei, we estimated the daily reproductive number to be well above the epidemic threshold at the beginning of the outbreak in Sichuan (_{0} was estimated at 2.4 (95% CI: 1.6–3.7) over the period from January 18 to January 24. This figure becomes 2.1 (95% CI: 1.6–2.7) if we consider the 2-week period of exponential growth from January 11 to January 24.

We estimated that the mean R(t) above the epidemic threshold for about 2.5 weeks from January 10 to 27. From the declaration of the public health emergency in Sichuan, the estimated R(t) continued to decline with a mean crossing the epidemic threshold on January 27, 3 days after the declaration. Since then, R(t) was estimated to fluctuate constantly below the epidemic threshold (

Were the public health emergency been declared one week later, we estimated that the epidemic would have lasted about one week longer. However, were the declaration been done four weeks later, we estimated a non-linear effect, with an epidemic lasting 49 days longer (95% CI: 31–68 days) and the last case reported on April 19 (95% CI: April 1-May 10) (

We provided a characterization of the COVID-19 epidemiology in Sichuan Province of China. The outbreak accounted for a total of 539 PCR positive subjects and was characterized by a combination of local transmission and case importations. We estimated that SARS-CoV-2 transmissibility was above the epidemic threshold for about 4 weeks and then quickly declined after the declaration of a public health emergency in the province and the implementation of strict control measures. We found clear positive effects of the interventions implemented in Sichuan, possibly in combination with an increased awareness of the population about the epidemic spread, which achieved the interruption of transmission leading to a dramatic reduction of the COVID-19 burden in Sichuan.

With a total of 539 confirmed cases, Sichuan was able to successfully contain the COVID-19 outbreak. The epidemic started due to the importation of cases from Wuhan/Hubei. Then, we found a clear relationship between the lockdown of Hubei province, with only a handful of cases were imported from Wuhan/Hubei since early February. We observe a disproportionate fraction of COVID-19 cases being male in imported cases. This result indicates potential differential exposure by sex occurring at the beginning of the epidemic (e.g., sex difference among travelers) and is in overall in line with [

In agreement with previous studies focusing on the spread of COVID-19 in China [

In the early phase of the outbreak, the daily reproduction number was essentially led by the first few imported cases from Wuhan/Hubei and we estimated the basic reproduction number to be in the ballpark of previous studies about COVID-19 spread in China [

We found that the implemented control strategies and population awareness have been highly effective in greatly limiting the burden of COVID-19. In particular, should the health authorities waited four weeks longer to declare the public health emergency, the epidemic would lasted more than six weeks longer and the number of cases would have been of the order of several thousands. It is important to stress that this figure would be much larger if we consider the number of infections instead of cases. In fact, asymptomatic individuals represent a sizable share of SARS-CoV-2 infected individuals [

It is important to stress that, to estimate the number of averted cases, we assumed that, before the detection of the outbreak, the epidemic would have continued its spread with the same basic reproduction number estimated during the initial exponential growth phase of the epidemic. It is however possible that, even if the public health emergency were not timely declared in Sichuan, the population could have adopted significantly different behaviors based on the knowledge that the epidemic was spreading in other areas of China. Moreover, the renewal equation approach is extremely simple and does not account for all the details of the mechanisms of SARS-CoV-2 transmission, such as the influx of imported cases or the underlying structure of the contact network of the population. In particular, we assumed that the distribution of cases by severity would have remained unchanged with respect to that estimated in the early phase of the outbreak. This may have not been the case had the age-distribution of cases changed over time or had healthcare system been overwhelmed. Moreover, we did not account for the depletion of susceptible individuals in the population. However, since we projected cases in a short time window (a few weeks), the depletion of susceptible individuals is negligible and we do not expect to observe dramatic changes in the age distribution of cases over such a short time frame.

In conclusion, our results show the success in control strategies and adaptive behavioral changes of the population were instrumental in interrupting the SARS-CoV-2 transmission in Sichuan Province and preserved the healthcare system from a possible disruptive failure due to overwhelming stress imposed by the large number of severe and critical COVID-19 cases. Nevertheless, it is important to remark that the COVID-19 pandemic is far from being controlled at the global scale as we are still far from herd immunity and large proportion of the population remains susceptible. Thus, the course of the pandemic will rely on the efficiency of control strategies and individual behavior in the foreseeable future.

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_{0} = 2.4 (95% CI: 1.6–3.7) and R_{final} = 0.47 (95% CI: 0.4–0.54); R(t) is assumed to follow a 1-week linear decrease from R_{0} to R_{final}. R_{final} was estimated over the period from February 1 (i.e., one week after the declaration of the emergency) to the end of the outbreak. Projections are obtained assuming four different values of the reporting rate, namely 1%, 10%, 50%, and 100%.

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_{0} = 2.1 (95% CI: 1.6–2.7) and R_{final} = 0.47 (95% CI: 0.4–0.54); R(t) is assumed to follow a 1-week linear decrease from R_{0} to R_{final}. R_{final} was estimated over the period from February 1 (i.e., one week after the declaration of the emergency) to the end of the outbreak. B Same as A, but for the date of the last case of the simulated epidemics. C Same as A, but for severe cases. D Same as B, but for critical cases.

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_{0} = 2.4 (95% CI: 1.6–3.7) and R_{final} = 0.53 (95% CI: 0.47–0.60); R(t) is assumed to instantaneously drop from R_{0} to R_{final}. R_{final} was estimated over the period from January 25 (i.e., the day after the declaration of the emergency) to the end of the outbreak. B Same as A, but for the date of the last case of the simulated epidemics. C Same as A, but for severe cases. D Same as B, but for critical cases.

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The authors thank the West China Biomedical Big Data Center providing the data and the access to the platform for simulations and Nicole Samay for her assistance in preparing the figures.