You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#sec0005}
============

On 31st December 2019, a pneumonia case of unknown etiology from Wuhan, Hubei Province, China was reported to the World Health Organization (WHO), which was later confirmed as a new coronavirus disease (COVID-19) with infection of a novel coronavirus named as "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) ([@bib0040]). As of 8th April 2020, more than 200 countries worldwide have reported over 1.35 million confirmed cases and 79,000 deaths ([@bib0045]). A total of 83,157 cases and 3,342 deaths were reported in China, and approximately 81% of (67,803) cases and 96% (3,215) of deaths were from the Hubei Province ([@bib0015]).

To contain the spread of COVID-19, the Chinese government initiated an unprecedented metropolitan-wide quarantine on 13 major cities in Hubei province since 23rd January 2020, which significantly reduced the movement of people by terminating most forms of transportation ([@bib0030], [@bib0005]). The quarantine has substantially reduced social contacts in the public space but increased contacts in households. The quarantine lasted for eleven weeks, and Wuhan, the last city of Hubei province in quarantine, was eventually resumed its economic activities and public transportation on 8th April 2020. As for now, a major intervention in the first epicenter of the COVID-19 epidemic has formally concluded. During this quarantine, a WHO report ([@bib0050]) has shown that most (78--85%) of the transmissions in China had occurred in a household setting. The completed cycle of quarantine-resumption of Hubei province provides a unique opportunity to assess its intervention impact and the pattern of SARS-COV-2 transmission during the quarantine period. The preceding experiences in Hubei, China, will provide important evidence and scientific insights to other worldwide countries that are currently under quarantine.

Based on a dynamic compartmental model, this study aims to evaluate the impact of the metropolitan-wide quarantine on the trend and transmission route of the SARS-CoV-2 epidemic during the period of 23rd January--8th April 2020.

Methods {#sec0010}
=======

We collected data on the number of cumulative confirmed cases and deaths from 15th January 2020 to 8th April 2020 from the [@bib0015] (Table S1). We developed a compartmental model (Figure S1) to describe the transmission of COVID-19 in Hubei Province. Different from most existing models ([@bib0035]), we modelled two modes of transmission, including social contacts in public space (e.g. public transportations, supermarkets, offices, etc.) and households. We assumed that the average number of daily contacts was reduced by 80% (71--94%) in the public space ([@bib0020]) but increased by two times in households ([@bib0010]) during the quarantine. Detailed model structure, parameters, and calibration are shown in the Appendix.

Results {#sec0015}
=======

In the presence of the quarantine, we estimated that 100,610 (95%CI: 82,326--118,900) infections may have occurred and among which 68,975 (56,621--81,330) would be diagnosed, and 3,252 (2,667--3,837) would have died, which are closely consistent with the observations. This indicated that only 68.58% (65.58--71.58%) of infections could be diagnosed, whereas 31.42% (28.42--34.42%) of infections were spontaneously recovered and undocumented. Among these infections, we estimated that the number of infections occurred in the public space and households were 30,351 (24,931--35,771) and 70,262 (57,391--83,133), accounting for 30.17% (29.96--30.38%) and 69.83% (69.62--70.04%) of all infections, respectively ([Figures 1](#fig0005){ref-type="fig"} and S4). If the quarantine had never been in place, the COVID-19 epidemic would result in 491,320 (331,470--651,180) infections and 15,907 (10,950--20,865) deaths. Among these infections, infections in the public space and households would reach 238,110 (162,390--313,830) and 253,210 (169,060--337,360), accounting for 48.49% (48.06--48.92%) and 51.51% (51.08--51.94%) of all new infections, respectively. This suggests that the quarantine would prevent 79.27% (75.10--83.45%) of deaths, 87.08% (84.68--89.49%) and 71.84% (66.39--77.29%) of infections in public space and households, respectively. Despite the reduction, household transmission accounts for a higher proportion (69.83%) of all transmission compared with 51.51% in the absence of quarantine.Figure 1(a--b) Model calibration and prediction based on reported confirmed COVID-19 cases and deaths in Hubei province, China; (c) numbers of new infections in the public space and households with and without metropolitan-wide quarantine.Figure 1

Discussion {#sec0020}
==========

We estimated that the metropolitan-wide quarantine strategy implemented in Hubei province might have reduced more than 70% of epidemic compared with the absence of the quarantine. This is consistent with previous evaluations based on various modelling approaches ([@bib0035]).

Regardless of quarantine, household transmission appears to the dominant route of transmission, although quarantine substantially increases the contribution of household transmission to the epidemic. Household contacts are often without preventive measures and prone to superspreading. It is estimated an average of 5.3 cases may subsequently be infected if there is a single seeding case in the household ([@bib0025]). Prevention of household transmission is difficult, especially when infected individuals are in the latent period or habor an asymptomatic infection. Regular cleaning with disinfectants and active screening of household members may help to prevent the infection and facilitate early diagnosis. Providing effective protection to uninfected household members remains challenging until a vaccine becomes available.

This study has several limitations. First, we model the effects of metropolitan-wide quarantine with a reduction in the effective contact rate. We cannot distinguish whether the effects were due to media reports and voluntary isolation. Second, we assumed 100% adherence to the social distancing policy during the quarantine, which may not be the case in reality. The effect of policy on negative externalities such as fear and public disorder may undermine the value of the intervention. Third, we assumed the quarantine has the same impact on all 13 cities underwent quarantine. Fourth, we did not consider the effect of asymptomatic infection, which may underestimate the number of infected cases. Fifth, we did not consider nosocomial infections where more than 3000 medical staffs were infected during the early stage of the epidemic. This transmission route can be considered as a part of infections in public space and does not affect the main conclusions. Despite these limitations, this study fills an important research gap by assessing the impact of the metropolitan-wide quarantine on the trend and transmission route of the SARS-CoV-2 epidemic.

We concluded that the metropolitan-wide quarantine in Hubei has greatly reduced the number of new infections in the public space and, consequently, in households.

Funding source {#sec0025}
==============

This work was supported by the 10.13039/501100001809National Natural Science Foundation of China (grant numbers: 8191101420 (LZ), 11801435 (MS), 11631012 (YX), 81673275 (ZP), 91846302 (ZP)); Outstanding Young Scholars Funding (Grant number: 3111500001 (LZ)); Xi'an Jiaotong University Basic Research and Profession Grant (Grant number: xtr022019003 (LZ); xzy032020032 (LZ)) and Xi'an Jiaotong University Young Talent Support Grant (Grant number: YX6J004 (LZ)); 10.13039/501100002858China Postdoctoral Science Foundation (grant number 2018M631134); the 10.13039/501100012226Fundamental Research Funds for the Central Universities (grant number xjh012019055, xzy032020026); Natural Science Basic Research Program of Shaanxi Province (Grant number: 2019JQ-187); Xi\'an Special Science and Technology Projects on Prevention and Treatment of Novel Coronavirus Penumonia Emergency (Grant number: 20200005YX005); the National S&T Major Project Foundation of China (2018ZX10715002-004, 2018ZX10713001-001) and the 10.13039/501100012246Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). YG was supported by Career Development Fellowships of the Australian National Health and Medical Research Council (numbers APP1107107 and APP1163693). L. Rong was supported by 10.13039/100000001National Science Foundation (DMS-1758290).

Ethical approval {#sec0030}
================

Waived, since all data utilized are publicly available.

Authors' contributions {#sec0035}
======================

M.S. and L.Z. conceived and designed the study. M.S. analyzed the data, carried out the analysis and performed numerical simulations. M.S. wrote the first draft of the manuscript. All the authors contributed to writing the paper and agreed with manuscript results and conclusions.

Conflict of interest {#sec0040}
====================

All authors declare that they have no competing interests.

[^1]: Authors contribute equally.