Using Proper Mean Generation Intervals in Modeling of COVID-19
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STI Screening Timetable
Patient Education Information from University Health Center’s STI Screening Clinic Page 1 of 1 STI Screening Timetable How long until STI (sexually transmitted infection) screening tests turn positive? How long until STI symptoms might show up? The time between infection and a positive test, or between infection and symptoms, is variable and depends on many factors, including the behavior of the infectious agent, how and where the body is infected, and the state of a person’s immune system and personal health. Many STIs don’t have any symptoms. The incubation period times listed in the chart below are averages only. If you have further questions or concerns, you can schedule an appointment with a clinician at 541-346-2770. STI screening test Window period (time from exposure until Incubation period (time between exposure and screening test turns positive) when symptoms appear) Chlamydia (urine specimen or swab of 1 week most of the time Often no symptoms vagina, rectum, throat) 2 weeks catches almost all 1-3 weeks on average Gonorrhea (urine specimen on swab of 1 week most of the time Often no symptoms, especially vaginal vagina, rectum, throat) 2 weeks catches almost all infections usually within 2-8 days but can be up to 2 weeks Syphilis (blood test, RPR) 1 month catches most Often symptoms too mild to notice 3 months catches almost all 10-90 days average 21 days HIV (oral cheek swab) 1 month catches most Sometimes mild body aches and fever within 1-2 3 months catches almost all weeks then can be months to years HIV (blood test, antigen/antibody -
Estimating the Serial Interval of the Novel Coronavirus Disease (COVID
medRxiv preprint doi: https://doi.org/10.1101/2020.02.21.20026559; this version posted February 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 Estimating the serial interval of the novel coronavirus disease 2 (COVID-19): A statistical analysis using the public data in Hong Kong 3 from January 16 to February 15, 2020 4 Shi Zhao1,2,*, Daozhou Gao3, Zian Zhuang4, Marc KC Chong1,2, Yongli Cai5, Jinjun Ran6, Peihua Cao7, Kai 5 Wang8, Yijun Lou4, Weiming Wang5,*, Lin Yang9, Daihai He4,*, and Maggie H Wang1,2 6 7 1 JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China 8 2 Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China 9 3 Department of Mathematics, Shanghai Normal University, Shanghai, China 10 4 Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China 11 5 School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China 12 6 School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China 13 7 Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China 14 8 Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, China 15 9 School of Nursing, Hong Kong Polytechnic University, Hong Kong, China 16 * Correspondence -
Incubation Period and Other Epidemiological
Journal of Clinical Medicine Article Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data 1, 1, 1 1 Natalie M. Linton y , Tetsuro Kobayashi y, Yichi Yang , Katsuma Hayashi , Andrei R. Akhmetzhanov 1 , Sung-mok Jung 1 , Baoyin Yuan 1, Ryo Kinoshita 1 and Hiroshi Nishiura 1,2,* 1 Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan; [email protected] (N.M.L.); [email protected] (T.K.); [email protected] (Y.Y.); katsuma5miff[email protected] (K.H.); [email protected] (A.R.A.); [email protected] (S.-m.J.); [email protected] (B.Y.); [email protected] (R.K.) 2 Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama 332-0012, Japan * Correspondence: [email protected]; Tel.: +81-11-706-5066 These authors contributed equally to this work. y Received: 25 January 2020; Accepted: 10 February 2020; Published: 17 February 2020 Abstract: The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. -
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 Uncertainties Underlying Herd Immunity Against COVID-19
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 8 July 2020 doi:10.20944/preprints202007.0159.v1 Communication The uncertainties underlying herd immunity against COVID-19 Farid Rahimi1*and Amin Talebi Bezmin Abadi2* 1 Research School of Biology, The Australian National University, Canberra, Australia; [email protected] 2 Department of Bacteriology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran; Amin [email protected] * Correspondence: [email protected]; Tel.: +61-2-6125-2851 (F.R.); [email protected]; Tel.: +98-8288-4883 (A.T.B.A.) Abstract: Herd immunity happens when a relatively large proportion of a population becomes infected by an agent, subsequently recovers, and attains immunity against the same agent. That proportion thus indirectly protects the naïve population by preventing the spread of the infection. Herd immunity has been suggested to interrupt and control the COVID-19 pandemic. However, relying on establishing herd immunity can be catastrophic considering the virulence and lethality of SARS-CoV-2. Meanwhile our understanding of the pathogenesis, case-fatality rate, transmission routes, and antiviral therapy for COVID-19 remains limited now. Interrupting or slowing the COVID-19 transmission seems more opportune than vaccination, antiviral therapy, or herd immunity, all of which will take some time to yield. Thus, social distancing, face-masking, and hygiene are the most appropriate immediate countermeasures. Because the social fabrics, economic implications, and local demands of various nations are unique, early relaxation of restrictions may seem hasty particularly when fatality rates are high, or when the healthcare systems could be inadequate or become inundated. -
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. -
The Serial Interval of COVID-19 from Publicly Reported Confirmed Cases
medRxiv preprint doi: https://doi.org/10.1101/2020.02.19.20025452; this version posted March 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Title: The serial interval of COVID-19 from publicly reported confirmed cases Running Head: The serial interval of COVID-19 1,+ 2+ 3,4+ 5, 6 Authors: Zhanwei Du , Xiaoke Xu , Ye Wu , Lin Wang , Benjamin J. Cowling , and Lauren Ancel Meyers1,7* Affiliations: 1. The University of Texas at Austin, Austin, Texas 78712, The United States of America 2. Dalian Minzu University, Dalian 116600, China 3. Computational Communication Research Center, Beijing Normal University, Zhuhai, 519087, China 4. School of Journalism and Communication, Beijing Normal University, Beijing, 100875, China 5. Institut Pasteur, 28 rue du Dr Roux, Paris 75015, France 6. The University of Hong Kong, Hong Kong SAR, China 7. Santa Fe Institute, Santa Fe, New Mexico, The United States of America Corresponding author: Lauren Ancel Meyers Corresponding author email: [email protected] + These first authors contributed equally to this article Short Abstract (50 words) We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. -
Superspreading of Airborne Pathogens in a Heterogeneous World Julius B
www.nature.com/scientificreports OPEN Superspreading of airborne pathogens in a heterogeneous world Julius B. Kirkegaard*, Joachim Mathiesen & Kim Sneppen Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We fnd that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation eforts applied to our model. We fnd that the efect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity. Te statistics of an on-going epidemic depend on a number of factors. Most directly: How easily is it transmit- ted? And how long are individuals afected and infectious? Scientifc papers and news paper articles alike tend to summarize the intensity of epidemics in a single number, R0 . Tis basic reproduction number is a measure of the average number of individuals an infected patient will successfully transmit the disease to. -
Herd Immunity Is an Important—And Often Misunderstood—Public Health Phenomenon
CORE CONCEPTS Herd immunity is an important—and often misunderstood—public health phenomenon CORE CONCEPTS Amy McDermott, Science Writer In December, Anthony Fauci predicted that 70 to 85% Since the earliest months of the COVID-19 pan- of the United States population may need to be demic, “herd immunity” has become shorthand for vaccinated to achieve “herd immunity” against SARS- the day when enough people are immune to the virus CoV-2 (1). Yet he was very careful to qualify his com- that social distancing can end and healthcare systems ments. “We need to have some humility here,” he said are no longer overwhelmed. People have been clam- ’ in a New York Times interview, “We really don’t know oring to know when we ll get there. But even as Fauci — what the real number is.” (2) Fauci, director of the and other officials continue to try to calculate and communicate—when it might happen, they have to National Institute of Allergy and Infectious Diseases, contend with both public misunderstanding of the admitted that the number could be as high as 90%. term and scientific disagreement over what it means. But even a year into the pandemic, there were too The public health concept of herd immunity has a many uncertainties to offer a definite threshold. And more nuanced definition than its popular usage, which with vaccine hesitancy widespread, he worried that is one reason why predicting when we’ll achieve it re- setting the bar at such a high number would cause mains difficult. In theory, estimating the threshold to the public to despair of ever reaching it. -
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. -
Malaria and COVID-19: Common and Different Findings
Tropical Medicine and Infectious Disease Viewpoint Malaria and COVID-19: Common and Different Findings Francesco Di Gennaro 1 , Claudia Marotta 1,*, Pietro Locantore 2, Damiano Pizzol 3 and Giovanni Putoto 1 1 Operational Research Unit, Doctors with Africa CUAMM, 35121 Padova, Italy; [email protected] (F.D.G.); [email protected] (G.P.) 2 Institute of Endocrinology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; [email protected] 3 Italian Agency for Development Cooperation, Khartoum 79371, Sudan; [email protected] * Correspondence: [email protected] or [email protected] Received: 31 July 2020; Accepted: 3 September 2020; Published: 6 September 2020 Abstract: Malaria and COVID-19 may have similar aspects and seem to have a strong potential for mutual influence. They have already caused millions of deaths, and the regions where malaria is endemic are at risk of further suffering from the consequences of COVID-19 due to mutual side effects, such as less access to treatment for patients with malaria due to the fear of access to healthcare centers leading to diagnostic delays and worse outcomes. Moreover, the similar and generic symptoms make it harder to achieve an immediate diagnosis. Healthcare systems and professionals will face a great challenge in the case of a COVID-19 and malaria syndemic. Here, we present an overview of common and different findings for both diseases with possible mutual influences of one on the other, especially in countries with limited resources. Keywords: malaria; SARS-CoV-2; COVID-19; preparedness; Africa; emergency; pandemic 1. Background On 11 March 2020, the WHO declared the outbreak of SARS-CoV-2 to be a pandemic infection. -
Prediction of the Incubation Period for COVID-19 and Future Virus Disease Outbreaks Ayal B
Gussow et al. BMC Biology (2020) 18:186 https://doi.org/10.1186/s12915-020-00919-9 RESEARCH ARTICLE Open Access Prediction of the incubation period for COVID-19 and future virus disease outbreaks Ayal B. Gussow†, Noam Auslander*†, Yuri I. Wolf and Eugene V. Koonin* Abstract Background: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well- being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood. Results: We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks. Conclusions: The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.