{ "PMCID": "PMC7266579", "pubtype": [ "Journal Article", "research-article" ], "data_availability_statement": [ "We collected publicly available data from 436 Chinese cities, inclusive of prefectures and municipalities, that reported on cases of COVID-19 (number of confirmed cases, deaths, and recovered cases) from 16th January to 15th March 2020, at which time the epidemic in China had mostly resolved. Our primary data source was Dingxiangyuan (DXY) (https://ncov.dxy.cn/), an online platform built by Chinese medical community members, which integrated COVID-19 case information from both local media and government reports. We included a total of 45 cities with more than 100 confirmed cases in our analysis. In contrast, 391 cities reported less than 100 cases over the two months; the outbreaks in these cities were presumably controlled by the nationwide emergency public health response. These cities were not included in our analysis. The analysis was conducted in 31 provinces of mainland China, excluding the two Chinese special administrative regions (Macau and Hong Kong) and Taiwan. This is because these regions were under a different epidemic surveillance system, and the outbreaks had undergone a significant growth after 15th March. We also excluded the city of Jining, Shandong province, because most of its reported cases were due to an outbreak in a prison. For included cities, we obtained the population size of each of these cities from the statistical yearbooks of these cities or their respective provinces." ], "data_availability_url": [ "https://ncov.dxy.cn/" ], "has_data_availability_statement": true, "crossref": { "deposited": { "date-parts": [ [ 2025, 10, 22 ] ], "date-time": "2025-10-22T07:52:39Z", "timestamp": 1761119559000 } }, "DOI": "10.1016/j.ijid.2020.05.122", "published_year": 2020, "published_date": "2020-06-02", "issn": [ "1201-9712" ], "subject": [], "subtitle": [], "volume": "97", "publisher": "Elsevier BV", "funder": [ { "name": "The work was supported by the Bill & Melinda Gates Foundation" }, { "DOI": "10.13039/501100001809", "name": "National Natural Science Foundation of China", "award": [ "81950410639" ], "award-info": [ { "award-number": [ "81950410639" ] } ], "id": [ { "id": "10.13039/501100001809", "id-type": "DOI", "asserted-by": "publisher" } ] }, { "name": "Outstanding Young Scholars Funding", "award": [ "3111500001" ], "award-info": [ { "award-number": [ "3111500001" ] } ] }, { "DOI": "10.13039/501100002412", "name": "Xi’an Jiaotong University", "award": [ "xtr022019003" ], "award-info": [ { "award-number": [ "xtr022019003" ] } ], "id": [ { "id": "10.13039/501100002412", "id-type": "DOI", "asserted-by": "crossref" } ] }, { "DOI": "10.13039/501100002412", "name": "Xi’an Jiaotong University", "award": [ "xzy032020032" ], "award-info": [ { "award-number": [ "xzy032020032" ] } ], "id": [ { "id": "10.13039/501100002412", "id-type": "DOI", "asserted-by": "crossref" } ] }, { "name": "Xi’an Jiaotong University Young Talent", "award": [ "YX6J004" ], "award-info": [ { "award-number": [ "YX6J004" ] } ] }, { "DOI": "10.13039/501100001809", "name": "National Natural Science Foundation of China", "award": [ "11801435 (MS)" ], "award-info": [ { "award-number": [ "11801435 (MS)" ] } ], "id": [ { "id": "10.13039/501100001809", "id-type": "DOI", "asserted-by": "publisher" } ] }, { "DOI": "10.13039/501100002858", "name": "China Postdoctoral Science Foundation", "award": [ "2018M631134" ], "award-info": [ { "award-number": [ "2018M631134" ] } ], "id": [ { "id": "10.13039/501100002858", "id-type": "DOI", "asserted-by": "publisher" } ] }, { "DOI": "10.13039/501100012226", "name": "Fundamental Research Funds for the Central Universities", "award": [ "xjh012019055" ], "award-info": [ { "award-number": [ "xjh012019055" ] } ], "id": [ { "id": "10.13039/501100012226", "id-type": "DOI", "asserted-by": "publisher" } ] }, { "DOI": "10.13039/501100012226", "name": "Fundamental Research Funds for the Central Universities", "award": [ "xzy032020026" ], "award-info": [ { "award-number": [ "xzy032020026" ] } ], "id": [ { "id": "10.13039/501100012226", "id-type": "DOI", "asserted-by": "publisher" } ] }, { "name": "Natural Science Basic Research Program of Shaanxi Province", "award": [ "2019JQ-187" ], "award-info": [ { "award-number": [ "2019JQ-187" ] } ] }, { "name": "Xi’an Special Science and Technology Projects on Prevention and Treatment of Novel Coronavirus Pneumonia Emergency", "award": [ "20200005YX005" ], "award-info": [ { "award-number": [ "20200005YX005" ] } ] } ], "assertion": [ { "value": "Elsevier", "name": "publisher", "label": "This article is maintained by" }, { "value": "Early characteristics of the COVID-19 outbreak predict the subsequent epidemic scope", "name": "articletitle", "label": "Article Title" }, { "value": "International Journal of Infectious Diseases", "name": "journaltitle", "label": "Journal Title" }, { "value": "https://doi.org/10.1016/j.ijid.2020.05.122", "name": "articlelink", "label": "CrossRef DOI link to publisher maintained version" }, { "value": "article", "name": "content_type", "label": "Content Type" }, { "value": "© 2020 The Author(s). 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"https://openalex.org/W3010308535", "https://openalex.org/W3012320055", "https://openalex.org/W3013188135", "https://openalex.org/W3013862524", "https://openalex.org/W3015106362", "https://openalex.org/W3015792206", "https://openalex.org/W3027117983", "https://openalex.org/W6775489320" ], "related_works": [ "https://openalex.org/W2373635223", "https://openalex.org/W2412355096", "https://openalex.org/W1990012352", "https://openalex.org/W2431766951", "https://openalex.org/W4385969441", "https://openalex.org/W2120215430", "https://openalex.org/W3168893505", "https://openalex.org/W2352680682", "https://openalex.org/W2158081092", "https://openalex.org/W2436836973" ], "counts_by_year": [ { "year": 2025, "cited_by_count": 1 }, { "year": 2022, "cited_by_count": 4 }, { "year": 2021, "cited_by_count": 7 }, { "year": 2020, "cited_by_count": 5 } ], "updated_date": "2026-05-06T08:25:59.206177", "created_date": "2025-10-10T00:00:00", "abstract": "OBJECTIVES: The mostly-resolved first wave of the COVID-19 epidemic in China provided a unique opportunity to investigate how the initial characteristics of the COVID-19 outbreak predict its subsequent magnitude. METHODS: We collected publicly available COVID-19 epidemiological data from 436 Chinese cities from 16th January-15th March 2020. Based on 45 cities that reported >100 confirmed cases, we examined the correlation between early-stage epidemic characteristics and subsequent epidemic magnitude. RESULTS: We identified a transition point from a slow- to a fast-growing phase for COVID-19 at 5.5 (95% CI, 4.6-6.4) days after the first report, and 30 confirmed cases marked a critical threshold for this transition. The average time for the number of confirmed cases to increase from 30 to 100 (time from 30-to-100) was 6.6 (5.3-7.9) days, and the average case-fatality rate in the first 100 confirmed cases (CFR-100) was 0.8% (0.2-1.4%). The subsequent epidemic size per million population was significantly associated with both of these indicators. We predicted a ranking of epidemic size in the cities based on these two indicators and found it highly correlated with the actual classification of epidemic size. 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