Superspreading of Airborne Pathogens in a Heterogeneous World Julius B
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Pattern of Early Human-To-Human Transmission of Wuhan 2019 Novel Coronavirus (2019-Ncov), December 2019 to January 2020
Rapid communication Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020 Julien Riou1 , Christian L. Althaus1 1. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland Correspondence: Julien Riou ([email protected]) Citation style for this article: Riou Julien , Althaus Christian L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Euro Surveill. 2020;25(4):pii=2000058. https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000058 Article submitted on 24 Jan 2020 / accepted on 30 Jan 2020 / published on 30 Jan 2020 Since December 2019, China has been experiencing Epidemic parameters a large outbreak of a novel coronavirus (2019-nCoV) Two key properties will determine further spread which can cause respiratory disease and severe pneu- of 2019-nCoV. Firstly, the basic reproduction num- monia. We estimated the basic reproduction num- ber R0 describes the average number of secondary ber R0 of 2019-nCoV to be around 2.2 (90% high density cases generated by an infectious index case in a fully interval: 1.4–3.8), indicating the potential for sus- susceptible population, as was the case during the tained human-to-human transmission. Transmission early phase of the outbreak. If R0 is above the critical characteristics appear to be of similar magnitude to threshold of 1, continuous human-to-human transmis- severe acute respiratory syndrome-related coronavi- sion with sustained transmission chains will occur. rus (SARS-CoV) and pandemic influenza, indicating a Secondly, the individual variation in the number of risk of global spread. -
Period of Presymptomatic Transmission
PERIOD OF PRESYMPTOMATIC TRANSMISSION RAG 17/09/2020 QUESTION Transmission of SARS-CoV-2 before onset of symptoms in the index is known, and this is supported by data on viral shedding. Based on the assumption that the viral load in the upper respiratory tract is highest one day before and the days immediately after onset of symptoms, current procedures for contact tracing (high and low risk contacts), go back two days before start of symptoms in the index (or sampling date in asymptomatic persons) (1), (2), (3). This is in line with the ECDC and WHO guidelines to consider all potential contacts of a case starting 48h before symptom onset (4) (5). The question was asked whether this period should be extended. BACKGROUND Viral load According to ECDC and WHO, viral RNA can be detected from one to three days before the onset of symptoms (6,7). The highest viral loads, as measured by RT-PCR, are observed around the day of symptom onset, followed by a gradual decline over time (1,3,8–10) . Period of transmission A much-cited study by He and colleagues and published in Nature used publicly available data from 77 transmission pairs to model infectiousness, using the reported serial interval (the period between symptom onset in infector-infectee) and combining this with the median incubation period. They conclude that infectiousness peaks around symptom onset. The initial article stated that the infectious period started at 2.3 days before symptom onset. However, a Swiss team spotted an error in their code and the authors issued a correction, stating the infectious period can start from as early as 12.3 days before symptom onset (11). -
The Sturgis Motorcycle Rally and COVID-19
DISCUSSION PAPER SERIES IZA DP No. 13670 The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19 Dhaval Dave Andrew I. Friedson Drew McNichols Joseph J. Sabia SEPTEMBER 2020 DISCUSSION PAPER SERIES IZA DP No. 13670 The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19 Dhaval Dave Joseph J. Sabia Bentley University, IZA, NBER and CHEPS CHEPS, San Diego State University and IZA Andrew I. Friedson University of Colorado Denver and CHEPS Drew McNichols CHEPS, University of San Diego-California and San Diego State University SEPTEMBER 2020 Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. ISSN: 2365-9793 IZA – Institute of Labor Economics Schaumburg-Lippe-Straße 5–9 Phone: +49-228-3894-0 53113 Bonn, Germany Email: [email protected] www.iza.org IZA DP No. -
Measles: Chapter 7.1 Chapter 7: Measles Paul A
VPD Surveillance Manual 7 Measles: Chapter 7.1 Chapter 7: Measles Paul A. Gastanaduy, MD, MPH; Susan B. Redd; Nakia S. Clemmons, MPH; Adria D. Lee, MSPH; Carole J. Hickman, PhD; Paul A. Rota, PhD; Manisha Patel, MD, MS I. Disease Description Measles is an acute viral illness caused by a virus in the family paramyxovirus, genus Morbillivirus. Measles is characterized by a prodrome of fever (as high as 105°F) and malaise, cough, coryza, and conjunctivitis, followed by a maculopapular rash.1 The rash spreads from head to trunk to lower extremities. Measles is usually a mild or moderately severe illness. However, measles can result in complications such as pneumonia, encephalitis, and death. Approximately one case of encephalitis2 and two to three deaths may occur for every 1,000 reported measles cases.3 One rare long-term sequelae of measles virus infection is subacute sclerosing panencephalitis (SSPE), a fatal disease of the central nervous system that generally develops 7–10 years after infection. Among persons who contracted measles during the resurgence in the United States (U.S.) in 1989–1991, the risk of SSPE was estimated to be 7–11 cases/100,000 cases of measles.4 The risk of developing SSPE may be higher when measles occurs prior to the second year of life.4 The average incubation period for measles is 11–12 days,5 and the average interval between exposure and rash onset is 14 days, with a range of 7–21 days.1, 6 Persons with measles are usually considered infectious from four days before until four days after onset of rash with the rash onset being considered as day zero. -
Mechanistic Transmission Modeling of COVID-19 on the Diamond Princess Cruise Ship Demonstrates the Importance of Aerosol Transmission
Mechanistic transmission modeling of COVID-19 on the Diamond Princess cruise ship demonstrates the importance of aerosol transmission Parham Azimia,1, Zahra Keshavarza, Jose Guillermo Cedeno Laurenta, Brent Stephensb, and Joseph G. Allena,1 aEnvironmental Health Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115; and bDepartment of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616 Edited by Andrea Rinaldo, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, and approved January 7, 2021 (received for review July 22, 2020) Several lines of existing evidence support the possibility of spreads (3). CDC has also acknowledged that airborne trans- airborne transmission of coronavirus disease 2019 (COVID-19). mission by smaller droplets traveling more than 1.8 m away from However, quantitative information on the relative importance of infected individual(s) can sometimes occur (4). transmission pathways of severe acute respiratory syndrome coro- Since the beginning of the pandemic, numerous researchers navirus 2 (SARS-CoV-2) remains limited. To evaluate the relative (5–15) and professional societies [e.g., American Society of Heat- importance of multiple transmission routes for SARS-CoV-2, we ing, Refrigerating and Air-Conditioning Engineers (16)] have raised developed a modeling framework and leveraged detailed informa- concerns that transmission of SARS-CoV-2 can occur from both tion available from the Diamond Princess cruise ship outbreak that symptomatic and asymptomatic (or presymptomatic) individuals to occurred in early 2020. We modeled 21,600 scenarios to generate a others beyond close-range contact through a combination of larger matrix of solutions across a full range of assumptions for eight respiratory droplets that are carried further than 1 to 2 m via air- unknown or uncertain epidemic and mechanistic transmission fac- flow patterns and smaller inhalable aerosols that can remain sus- R2 > tors. -
Overdispersion in COVID-19 Increases the Effectiveness of Limiting Nonrepetitive Contacts for Transmission Control
Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control Kim Sneppena,1, Bjarke Frost Nielsena, Robert J. Taylorb, and Lone Simonsenb aNiels Bohr Institute, University of Copenhagen, 2100 København Ø, Denmark; and bDepartment of Science and Environment, Roskilde University, 4000 Roskilde, Denmark Edited by Simon Asher Levin, Princeton University, Princeton, NJ, and approved March 8, 2021 (received for review August 5, 2020) Increasing evidence indicates that superspreading plays a domi- generated by an individual is distributed around the mean, with nant role in COVID-19 transmission. Recent estimates suggest that lower values of k corresponding to a broader distribution. the dispersion parameter k for severe acute respiratory syndrome Multiple studies have found that k for SARS-CoV-2 is on the coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corre- order of 0.1, corresponding to ∼10% of infected people causing sponds to about 10% of cases being the source of 80% of infec- 80% of new infections (12–15) This also implies that the majority tions. To investigate how overdispersion might affect the outcome of infected individuals cause less than one secondary infection of various mitigation strategies, we developed an agent-based and thus, cannot sustain the epidemic on their own should the model with a social network that allows transmission through con- superspreading events somehow be prevented. tact in three sectors: “close” (a small, unchanging group of mutual Consistent with this, the household attack rate is low, as shown “ ” contacts as might be found in a household), regular (a larger, un- by several studies. -
Evidence That Coronavirus Superspreading Is Fat-Tailed BRIEF REPORT Felix Wonga,B,C and James J
Evidence that coronavirus superspreading is fat-tailed BRIEF REPORT Felix Wonga,b,c and James J. Collinsa,b,c,d,1 aInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139; bDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139; cInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142; and dWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115 Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved September 28, 2020 (received for review September 1, 2020) Superspreaders, infected individuals who result in an outsized CoV and SARS-CoV-2 exhibited an exponential tail. We number of secondary cases, are believed to underlie a significant searched the scientific literature for global accounts of SSEs, in fraction of total SARS-CoV-2 transmission. Here, we combine em- which single cases resulted in numbers of secondary cases pirical observations of SARS-CoV and SARS-CoV-2 transmission and greater than R0, estimated to be ∼3 to 6 for both coronaviruses extreme value statistics to show that the distribution of secondary (1, 7). To broadly sample the right tail, we focused on SSEs cases is consistent with being fat-tailed, implying that large super- resulting in >6 secondary cases, and as data on SSEs are sparse, spreading events are extremal, yet probable, occurrences. We inte- perhaps due in part to a lack of data sharing, we pooled data for grate these results with interaction-based network models of disease SARS-CoV and SARS-CoV-2. Moreover, to avoid higher- transmission and show that superspreading, when it is fat-tailed, order transmission obfuscating the cases generated directly by leads to pronounced transmission by increasing dispersion. -
Using Proper Mean Generation Intervals in Modeling of COVID-19
ORIGINAL RESEARCH published: 05 July 2021 doi: 10.3389/fpubh.2021.691262 Using Proper Mean Generation Intervals in Modeling of COVID-19 Xiujuan Tang 1, Salihu S. Musa 2,3, Shi Zhao 4,5, Shujiang Mei 1 and Daihai He 2* 1 Shenzhen Center for Disease Control and Prevention, Shenzhen, China, 2 Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China, 3 Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria, 4 The Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China, 5 Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China In susceptible–exposed–infectious–recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of Edited by: infection attack rate (AR) and control efficacy. We argue that it is important to use Reza Lashgari, suitable epidemiological parameter values for proper estimation/prediction. Furthermore, Institute for Research in Fundamental we propose an epidemic model to assess the transmission dynamics of COVID-19 Sciences, Iran for Belgium, Israel, and the United Arab Emirates (UAE). -
Mumps Public Information
Louisiana Office of Public Health Infectious Disease Epidemiology Section Phone: 1-800-256-2748 www.infectiousdisease.dhh.louisiana.gov Mumps What is mumps? How is mumps diagnosed? Mumps is a disease that is caused by the mumps virus. It spreads Mumps is diagnosed by a combination of symptoms and physical easily through coughing and sneezing. Mumps can cause fever, signs and laboratory confirmation of the virus, as not all cases headache, body aches, fatigue and inflammation of the salivary develop characteristic parotitis, and not all cases of parotitis are (spit) glands, which can lead to swelling of the cheeks and jaws. caused by mumps. Who gets mumps? What is the treatment for mumps? Mumps is a common childhood disease, but adults can also get There is no “cure” for mumps, only supportive treatment (bed mumps. While vaccination reduces the chances of getting ill rest, fluids and fever reduction). Most cases will recover on their considerably, even those fully immunized can get the disease. own. How do people get mumps? If someone becomes very ill, he/she should seek medical Mumps is spread from person to person. When an infected attention. The ill person should call the doctor in advance so that person talks, coughs or sneezes, the virus is released into the air he/she doesn’t have to sit in the waiting room for a long time and and enters another person’s body through the nose, mouth or possibly infect other patients. throat. People can also become sick if they eat food or use utensils, cups or other objects that have come into contact with How can mumps be prevented? the mucus or saliva (spit) from an infected person. -
(COVID-19) Infections from a Family Gathering in Hong Kong
This is an online COVID-19 pre-publication manuscript which has been submitted to WPSAR. Pre-publication manuscripts are preliminary reports and have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. A superspreading event involving a cluster of 14 coronavirus disease 2019 (COVID-19) infections from a family gathering in Hong Kong LAM Ho Yeung, LAM Tsz Sum, WONG Chi Hong, LAM Wing Hang, LEUNG Chi Mei Emily, LAM Chau Kuen Yonnie, LAU Tin Wai Winnie, HO Chi Hin Billy, WONG Ka Hing, CHUANG Shuk Kwan Abstract Objectives An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China in December 2019, with subsequent spread around the world. Hong Kong, a Special Administrative Region of China, recorded its first confirmed case on 23 January 2020. In this report, we describe a family cluster of 12 confirmed cases with 2 additional confirmed cases from secondary transmission. Method We reported the epidemiological, clinical and laboratory findings of the family cluster and the public health measures instituted. Results All 12 confirmed COVID-19 cases were among the 19 attendees of a 3-hour Chinese New Year family dinner consisting of hotpot and barbecue. Environmental sampling of the gathering venue was negative. 2 additional confirmed cases who were the co-workers of 2 confirmed cases were later identified, indicating secondary transmission. Contact tracing, quarantine and environmental disinfection were instituted to contain further spread. -
Transmission of SARS-Cov-2: Implications for Infection Prevention Precautions
Transmission of SARS-CoV-2: implications for infection prevention precautions Scientific brief 9 July 2020 This document is an update to the scientific brief published on 29 March 2020 entitled “Modes of transmission of virus causing COVID-19: implications for infection prevention and control (IPC) precaution recommendations” and includes new scientific evidence available on transmission of SARS-CoV-2, the virus that causes COVID-19. Overview This scientific brief provides an overview of the modes of transmission of SARS-CoV-2, what is known about when infected people transmit the virus, and the implications for infection prevention and control precautions within and outside health facilities. This scientific brief is not a systematic review. Rather, it reflects the consolidation of rapid reviews of publications in peer-reviewed journals and of non-peer-reviewed manuscripts on pre-print servers, undertaken by WHO and partners. Preprint findings should be interpreted with caution in the absence of peer review. This brief is also informed by several discussions via teleconferences with the WHO Health Emergencies Programme ad hoc Experts Advisory Panel for IPC Preparedness, Readiness and Response to COVID-19, the WHO ad hoc COVID-19 IPC Guidance Development Group (COVID-19 IPC GDG), and by review of external experts with relevant technical backgrounds. The overarching aim of the global Strategic Preparedness and Response Plan for COVID-19(1) is to control COVID-19 by suppressing transmission of the virus and preventing associated illness and death. Current evidence suggests that SARS-CoV-2, the virus that causes COVID-19, is predominantly spread from person-to-person. -
Cellular Superspreaders: an Epidemiological Perspective on HIV Infection Inside the Body
Opinion Cellular Superspreaders: An Epidemiological Perspective on HIV Infection inside the Body Kristina Talbert-Slagle1,2*, Katherine E. Atkins1, Koon-Kiu Yan3, Ekta Khurana3, Mark Gerstein3, Elizabeth H. Bradley1,2, David Berg2,4, Alison P. Galvani1, Jeffrey P. Townsend1 1 Yale School of Public Health, New Haven, Connecticut, United States of America, 2 Global Health Leadership Institute, Yale University, New Haven, Connecticut, United States of America, 3 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America, 4 Yale University School of Medicine, New Haven, Connecticut, United States of America Introduction CD4+ T cells may play a similarly critical population, there exists a distribution of role in the establishment and spread of individual reproductive numbers, of which Worldwide, more than 250 people HIV in the genital mucosa after sexual R0 is the mean [14]. In populations of become infected with HIV every hour exposure. homogeneous individuals, the distribution [1], yet an individual’s chance of becom- of individual reproductive numbers will be ing infected after a single sexual exposure, Basic and Individual clustered around the population average the predominant mode of HIV transmis- value of R , and thus, this average value Reproductive Number 0 sion, is often lower than one in 100 [2]. will more accurately predict the likelihood When sexually transmitted HIV-1 infec- To quantify the spread of infectious of transmission from each infected to each tion does occur, it is usually initiated by a disease, epidemiologists use the basic susceptible individual. If R0.1, then an single virus, called the founder strain, reproductive number, R , which describes outbreak is likely to become an epidemic, despite the presence of thousands of 0 the average number of secondary infec- and if R0,1, then an outbreak will not genetically diverse viral strains in the tions that arise from one infected individ- spread beyond a few initially infected transmitting partner [3].