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      Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data

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          Abstract

          When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated “backward” reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number ( R t ) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.

          Author summary

          When surveillance systems cannot be repurposed quickly enough for novel infectious agents, missing information becomes a major challenge in monitoring the outbreak in real time. We propose a statistical approach that uses available data to construct the epidemic curves, which describe the number of individuals infected over time. We apply our 3-step approach to estimate these real-time epidemic curves during the early SARS-CoV-2 outbreak in Spain. We demonstrate that our approach, combined with the understanding of its limitations, can (a) provide useful information earlier and more reliably than conventional surveillance approaches, and (b) aid in the decision-making process towards outbreak mitigation in real-time.

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          Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

          Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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            Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

            Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
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              Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

              The newly emergent human virus SARS-CoV-2 is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact-tracing needed to stop the epidemic. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                31 March 2022
                March 2022
                : 18
                : 3
                : e1009964
                Affiliations
                [1 ] Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of america
                [2 ] Machine Intelligence Lab, Boston Children’s Hospital, Boston, Massachusetts, United States
                [3 ] Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of america
                [4 ] Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
                [5 ] Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
                [6 ] Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
                [7 ] Directorate-General for Public Health, Madrid General Health Authority, Madrid, Spain
                [8 ] Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
                [9 ] Consorcio de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINF), Madrid, Spain
                [10 ] Department of Pediatrics, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of america
                [11 ] CAUSALab, Department of Epidemiology and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of america
                University of Washington, UNITED STATES
                Author notes

                I have read the journal’s policy and some of the co-authors of this manuscript have the following competing interests: ML discloses honoraria/consulting from Merck, Affinivax, Sanofi-Pasteur, Bristol Myers-Squibb, and Antigen Discovery; research funding (institutional) from Pfizer, and an unpaid scientific advice to Janssen, Astra-Zeneca, One Day Sooner, and Covaxx (United Biomedical). MS discloses having received institutional research support from Johnson and Johnson.The rest of co-authors declare no competing interest.

                ‡ These authors are joint senior authors on this work.

                Author information
                https://orcid.org/0000-0002-8096-2001
                https://orcid.org/0000-0003-1026-5734
                https://orcid.org/0000-0002-1998-1844
                https://orcid.org/0000-0001-7388-1767
                https://orcid.org/0000-0001-9427-2581
                https://orcid.org/0000-0003-1620-7113
                https://orcid.org/0000-0001-6516-7176
                https://orcid.org/0000-0002-4481-1302
                https://orcid.org/0000-0001-5931-3966
                https://orcid.org/0000-0001-5662-6772
                https://orcid.org/0000-0002-1704-2245
                https://orcid.org/0000-0003-0082-1397
                https://orcid.org/0000-0003-1504-9213
                https://orcid.org/0000-0002-3052-3430
                https://orcid.org/0000-0002-4206-418X
                Article
                PCOMPBIOL-D-20-02097
                10.1371/journal.pcbi.1009964
                9004750
                35358171
                fa3223b8-26ec-4857-a3cc-a3e27c8fcc5a
                © 2022 De Salazar et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 November 2020
                : 24 February 2022
                Page count
                Figures: 3, Tables: 0, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008054, Fundación Ramón Areces;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: U54GM088558
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000052, NIH Office of the Director;
                Award ID: DP5-OD028145
                Award Recipient :
                Funded by: Morris-Singer
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000030, centers for disease control and prevention;
                Award ID: U01IP001121
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01GM130668-02
                Award Recipient :
                PMD was supported by the fellowship Ramón Areces Foundation. JAH was funded by the National Institute of General Medical Sciences, Award U54GM088558, and the National Institutes of Health Director’s Early Independence, Award DP5-OD028145. ML was supported by the Morris-Singer Fund and by a subcontract from the Carnegie Mellon University under an award from the US Centers for Disease Control and Prevention, Award U01IP001121). MS was supported by the National Institute Of General Medical Sciences, Award R01GM130668-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Custom metadata
                vor-update-to-uncorrected-proof
                2022-04-12
                The data that support the findings of the study was obtained from the Spanish System for Surveillance at the National Center of Epidemiology (RENAVE) through the Web platform SiViEs (System for Surveillance in Spain). The Spanish Ministry of Health has the policy not to provide publicly available line-listed data for confidentiality standards. Here, the authors provide code and anonymized line-list data (for the intermediate period of analysis) that allows reproduction of the results in the manuscript at: https://github.com/pdesalazar/Nowcasting_covid19_Spain. A version of the dataset used in the analysis but aggregated daily is publicly available at https://cnecovid.isciii.es/covid19/.
                COVID-19

                Quantitative & Systems biology
                Quantitative & Systems biology

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