使用流行病學數據即時追踪 2022 年之猴痘爆發
圖：2022年猴痘疫情迅速擴大
自2022 年疫情爆發之首次的報告病例以來累計確診病例數（按確認日期），以及確認報告案例的累計國家數量。
猴痘病毒於 1970 年代首次在人類中記錄，許多國家都報告了爆發，大多數病例僅限於流行地區。2022 年 5 月上旬，英國、西班牙和歐洲其他地方報告了猴痘病例（圖, 附錄)。與過去更局部化且經常發生在資源匱乏社區的爆發相比，地理分散模式要大得多。爆發集群的規模每天都在增長，地理分佈在歐洲和北美。在最初報告的第一周內，24 個國家報告了疑似和確診的猴痘病毒病例，其中一些已知與英國、西班牙、加拿大和西歐有旅行聯繫。截至2022年6月5日，累計確診病例920例，疑似病例70例。在已知旅行史的 64 例確診病例中，32 例與來自歐洲的旅行有關，3 例來自西非，2 例來自加拿大，1 例來自澳大利亞。對於 26 例，旅行歷史地點仍然未知。
世衛組織於 2022 年 5 月 20 日召開專家和技術諮詢小組會議，調查爆發原因，並發布了有關監測、病例調查和接觸者追踪的最新指南。爆發的原因具有更廣泛的地理範圍和國家，國際公共衛生界以及研究界正在對其進行調查，有助於更深入地了解疫情動態。然而，天花疫苗接種計劃的停止、人類侵入森林地區以及國際流動性的增加似乎在猴痘病毒爆發的流行病學中發揮著重要作用。
為了支持全球響應工作，我們的團隊創建了一個開放訪問數據庫和可視化來追踪不同國家/地區的病例發生情況。此外，在可用的情況下，我們添加了有關年齡（匯總到年齡範圍，最小範圍為 5 歲）、性別、症狀發作和實驗室確認的日期、症狀、位置（匯總到州級）、旅行歷史、和 WHO. 定義的其他數據元。
數據來自經過驗證的來源，包括政府和公共衛生組織的報告以及衛生官方聲明的新聞媒體報導。隨著經過驗證的信息和官方聲明的發布，我們記錄了二級來源並更新了數據集中的數據元。為策展人制定了每週 7 天、每天 24 小時的待命時間表，以確保數據近乎即時地更新。在通過我們的 Global.health GitHub 存儲庫提供每個案例之前，至少有兩名策展人查看和討論每個案例，並且每天至少四次推送到可視化地圖。
在疫情爆發的早期階段，獲得有關病例特徵的可靠、綜合數據是一項挑戰，尤其是在全球範圍內。我們的工作試圖協調各國之間的信息並提供額外的數據，以支持對此次疫情的起源和傳播動態的流行病學了解。理想情況下，這些數據與病毒基因組數據配對，並直接與各國的流行病學行列表數據相結合。在我們的資料庫中，我們還與同事和世衛組織大流行和流行病情報中心合作，定義聯繫數據模式，使各國和研究人員能夠估計和重新估計關鍵流行病學參數，例如潛伏期和連續間隔，跨越不同設置。
如果疫情進一步擴大，需要即時數據來規劃有效的控制措施。這項工作建立在為流行病控制和大流行病準備而開發的基礎設施之上，並被用於 COVID-19 大流行病。需要全球努力確保在未來新興和再浮現的爆發期間支持類似的努力，以快速協調和發布詳細的病原體流行病學數據。這個例子將成為在全球範圍內建立更好的監控系統的學習途徑。
*Moritz U G Kraemer, Houriiyah Tegally, David M Pigott, Abhishek Dasgupta, James Sheldon, Eduan Wilkinson, Marinanicole Schultheiss, Aimee Han, Mark Oglia, Spencer Marks, Joshua Kanner, Katelynn O’Brien, Sudheer Dandamudi, Benjamin Rader, Kara Sewalk, Ana I Bento, Samuel V Scarpino, Tulio de Oliveira, Isaac I Bogoch, Rebecca Katz, *John S Brownstein moritz.kraemer@zoo.ox.ac.uk;約翰。 brownstein@childrens.harvard.edu
牛津大學生物系 (MUGK, AD)、大流行科學研究所 (MUGK) 和計算機科學系 (AD)，牛津 OX1 3SY，英國；南非斯泰倫博斯大學數據科學與計算思維學院流行病應對與創新中心（HT、EW、TdO）；美國華盛頓大學健康指標與評估研究所（DMP）；美國緬因州波特蘭東北大學魯克斯研究所（JS、SVS）；波士頓兒童醫院，哈佛醫學院，波士頓，麻薩諸塞州 02215，美國（MS、AH、MO、SM、JK、KO’B、SD、BR、KS、JSB）；印第安納大學公共衛生學院，美國印第安納州布盧明頓 (AIB)；大流行預防研究所，洛克菲勒基金會，紐約，美國（AIB，SVS）；聖達菲研究所，聖達菲，新墨西哥州，美國（SVS）；多倫多大學醫學系，多倫多，加拿大 (IIB)；喬治城大學，華盛頓特區，美國 (RK)
Tracking the 2022 monkeypox outbreak with epidemiological data in real-time
Figure: Rapid expansion of the 2022 monkeypox outbreak
Cumulative number of confirmed cases (by confirmation date) since the first reported case in the 2022 outbreak, and cumulative number of countries reporting confirmed cases.
Monkeypox virus was first documented in humans in the 1970s and outbreaks have been reported in many countries, with most cases restricted to endemic areas. In early May, 2022, monkeypox cases were reported in the UK, Spain, and elsewhere in Europe (figure, appendix). The pattern of geographical dispersal was much larger compared with past outbreaks that were more localised and occurred often in under-resourced communities. The size of the outbreak clusters is growing each day, as is the geographical spread across Europe and North America. Within the first week of the initial report, 24 countries reported suspected and confirmed cases of monkeypox virus, some of which had known travel links to the UK, Spain, Canada, and western Europe. As of June 5, 2022, there have been 920 confirmed and 70 suspected cases. Of 64 confirmed cases with known travel history, 32 were associated with travel from Europe, three from west Africa, two from Canada, and one from Australia. For 26 cases, travel history locations remain unknown.
WHO convened a meeting of experts and technical advisory groups on May 20, 2022, to investigate the causes of the outbreak and have released updated guidance on surveillance, case investigation, and contact tracing. The reason for the outbreak having a broader geographical reach is being investigated by the international and national public health community and the research community, contributing to a finer scale understanding of the outbreak dynamics. However, cessation of smallpox vaccination programmes, encroachment of humans into forested areas, and growing international mobility seem to be playing important roles in the epidemiology of monkeypox virus outbreaks.
To support global response efforts, our team created an open-access database and visualisation to track the occurrence of cases in different countries. In addition, where available, we added information on age (aggregated into age ranges, with a minimum range of 5 years), gender, dates of symptom onset and laboratory confirmation, symptoms, locations (aggregated to the state level), travel history, and additional metadata defined by WHO.
Data are compiled from verified sources, including reports from governments and public health organisations and news media reporting of health official statements. As verified information and official statements are published, we document secondary sources and update the metadata in the dataset. An on-call schedule for curators that runs 24 h a day, 7 days a week was established to ensure data are updated in near real-time. Each case is seen and discussed by at least two curators before being made available via our Global.health GitHub repository, and pushed to the map visualisation at least four times per day.
During the early stages of outbreaks, obtaining reliable, synthesised data on the characteristics of cases is a challenge, especially at a global scale. Our work attempts to harmonise information across countries and provide additional data to support the epidemiological understanding of the origins and transmission dynamics of this outbreak. Ideally, these data are paired with virus genomic data and integrated directly with countries’ epidemiological line-list data. In our repository, we are also working with colleagues and the WHO Hub for Pandemic and Epidemic Intelligence to define a contact data schema allowing countries and researchers to estimate and re-estimate key epidemiological parameters, such as the incubation period and serial interval, across different settings.
Real-time data are necessary to plan effective control measures should this outbreak grow further. The work builds on infrastructure developed for epidemic control and pandemic preparedness and was used for the COVID-19 pandemic. Global efforts are needed to ensure similar efforts to rapidly harmonise and publish detailed epidemiological data are supported during future outbreaks of emerging and re-emerging pathogens. This example will be a learning pathway to build better surveillance systems globally.
*Moritz U G Kraemer, Houriiyah Tegally, David M Pigott, Abhishek Dasgupta, James Sheldon, Eduan Wilkinson, Marinanicole Schultheiss, Aimee Han, Mark Oglia, Spencer Marks, Joshua Kanner, Katelynn O’Brien, Sudheer Dandamudi, Benjamin Rader, Kara Sewalk, Ana I Bento, Samuel V Scarpino, Tulio de Oliveira, Isaac I Bogoch, Rebecca Katz, *John S Brownstein moritz.kraemer@zoo.ox.ac.uk; john. brownstein@childrens.harvard.edu
Department of Biology (MUGK, AD), Pandemic Sciences Institute (MUGK), and Department of Computer Science (AD), University of Oxford, Oxford OX1 3SY, UK; Centre for Epidemic Response and Innovation, School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa (HT, EW, TdO); Institute for Health Metrics and Evaluation, University of Washington, DC, USA (DMP); The Roux Institute, Northeastern University, Portland, ME, USA (JS, SVS); Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA (MS, AH, MO, SM, JK, KO’B, SD, BR, KS, JSB); School of Public Health, Indiana University, Bloomington, IN, USA (AIB); Pandemic Prevention Institute, The Rockefeller Foundation, New York, NY, USA (AIB, SVS); Santa Fe Institute, Santa Fe, NM, USA (SVS); Department of Medicine, University of Toronto, Toronto, ON, Canada (IIB); Georgetown University, Washington, DC, USA (RK)