DOCSLIB.ORG

Incubation Periods Impact the Spatial Predictability of Cholera and Ebola Outbreaks in Sierra Leone

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Incubation Periods Impact the Spatial Predictability of Cholera and Ebola Outbreaks in Sierra Leone Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone Rebecca Kahna,1, Corey M. Peaka,1, Juan Fernández-Graciaa,b, Alexandra Hillc, Amara Jambaid, Louisa Gandae, Marcia C. Castrof, and Caroline O. Buckeea,2 aCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115; bInstitute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears - Consell Superior d’Investigacions Científiques, E-07122 Palma de Mallorca, Spain; cDisease Control in Humanitarian Emergencies, World Health Organization, CH-1211 Geneva 27, Switzerland; dDisease Control and Prevention, Sierra Leone Ministry of Health and Sanitation, Freetown, Sierra Leone FPGG+89; eCountry Office, World Health Organization, Freetown, Sierra Leone FPGG+89; and fDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115 Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved January 22, 2020 (received for review July 29, 2019) Forecasting the spatiotemporal spread of infectious diseases during total number of secondary infections by an infectious individual in an outbreak is an important component of epidemic response. a completely susceptible population (i.e., R0) (13, 14). Indeed, the However, it remains challenging both methodologically and with basis of contact tracing protocols during an outbreak reflects the respect to data requirements, as disease spread is influenced by need to identify and contain individuals during the incubation numerous factors, including the pathogen’s underlying transmission period, and the relative effectiveness of interventions such as parameters and epidemiological dynamics, social networks and pop- symptom monitoring or quarantine significantly depends on the ulation connectivity, and environmental conditions. Here, using data relationship between infectiousness and symptoms (14). Additional from Sierra Leone, we analyze the spatiotemporal dynamics of re- related metrics, the generation interval (i.e., the time between in- cent cholera and Ebola outbreaks and compare and contrast the fection of an infector−infectee pair) and the serial interval (i.e., the spread of these two pathogens in the same population. We develop time between symptom onset of an infector−infectee pair), as well a simulation model of the spatial spread of an epidemic in order to as their variances, can further impact the growth rate and total examine the impact of a pathogen’s incubation period on the dy- number of infections during an epidemic (15). The incubation namics of spread and the predictability of outbreaks. We find that period is also likely to play a particularly important role in de- differences in the incubation period alone can determine the limits of termining the spatial spread of an epidemic, because one’stypical POPULATION BIOLOGY predictability for diseases with different natural history, both empir- travel may continue prior to symptom onset, whereas travel be- ically and in our simulations. Our results show that diseases with havior may change or stop altogether during illness (16), particu- longer incubation periods, such as Ebola, where infected individuals larly when symptoms are severe or immobilizing; even if symptoms can travel farther before becoming infectious, result in more long- are mild, if one knows they are infected, behavior may also change, distance sparking events and less predictable disease trajectories, as impacting transmission. compared to the more predictable wave-like spread of diseases with Back-to-back epidemics of cholera (2012−2013) and Ebola shorter incubation periods, such as cholera. (2014−2015) in Sierra Leone present a unique opportunity to compare the spatial dynamics of two epidemics in the same cholera | Ebola | epidemics | modeling | predictability Significance pidemics of emerging infectious diseases such as Ebola and EZika underscore the need to improve global capacity for Understanding how infectious diseases spread is critical for surveillance and response (1–3). Forecasting the spatiotemporal preventing and containing outbreaks. While advances have spread of infectious diseases during an outbreak can enable re- been made in forecasting epidemics, much is still unknown. sponders to stay ahead of an epidemic. However, it remains Here we show that the incubation period, the time between challenging both methodologically and with respect to data re- exposure to a pathogen and onset of symptoms, is an impor- quirements (4, 5), as disease spread is influenced by multiple tant factor in predicting spatiotemporal spread of disease and factors, including the pathogen’s underlying transmission pa- provides one explanation for the different trajectories of re- rameters and epidemiological dynamics, social networks and cent Ebola and cholera outbreaks in Sierra Leone. We find that population connectivity, and environmental conditions (6–9). outbreaks of pathogens with longer incubation periods, such Previous forecasting efforts have had varying levels of success in as Ebola, tend to have less predictable spread, whereas path- predicting the total number of cases and spatiotemporal spread of ogens with shorter incubation periods, such as cholera, spread outbreaks like Ebola, and few have actually been used in real time in a more predictable, wavelike pattern. These findings have in the midst of an epidemic (7). Efforts to understand the likely implications for the scale and timing of reactive interventions, performance of forecasts have shown that heterogeneity in contact such as vaccination campaigns. structure and number of secondary infections can pose challenges, but reasonable predictions can be made in some cases, depending Author contributions: R.K., C.M.P., J.F.-G., A.H., A.J., L.G., M.C.C., and C.O.B. designed on disease-specific parameters (6). However, the epidemiological research; R.K., C.M.P., and J.F.-G. performed research; R.K., C.M.P., J.F.-G., and M.C.C. attributes that determine predictability remain uncertain in real- analyzed data; and R.K., C.M.P., J.F.-G., L.G., and C.O.B. wrote the paper. world settings (10–12). The authors declare no competing interest. The time from when individuals are infected to when they be- This article is a PNAS Direct Submission. come infectious (the latent period) and to when they become Published under the PNAS license. symptomatic (the incubation period), and the relationship between Data deposition: Code and data are available on Github, https://github.com/rek160/Sierra- the two, have been shown to play a large role in the epidemic Leone-Cholera-Ebola. potential of diseases (9, 13, 14). In particular, transmission that 1R.K. and C.M.P. contributed equally to this work. occurs during the incubation period before an individual develops 2To whom correspondence may be addressed. Email: [email protected]. symptoms can contribute to rapid disease spread. When the latent This article contains supporting information online at https://www.pnas.org/lookup/suppl/ period is shorter than the incubation period for an infectious in- doi:10.1073/pnas.1913052117/-/DCSupplemental. dividual, presymptomatic transmission can be a strong driver of the First published February 13, 2020. www.pnas.org/cgi/doi/10.1073/pnas.1913052117 PNAS | March 3, 2020 | vol. 117 | no. 9 | 5067–5073 Downloaded by guest on September 26, 2021 predictable disease trajectories, as compared to the more pre- dictable wave-like spread of diseases with shorter incubation periods, such as cholera. Results We first summarize the cholera and Ebola epidemics in terms of their dynamics in time and space. More cases were reported during the cholera epidemic (22,691) than during the Ebola epidemic (11,903); however, far fewer cholera cases were fatal (324 vs. 3,956). Both epidemics lasted for similar periods of time, with cholera (January 7, 2012 to May 14, 2013) occurring 2 y prior to Ebola (May 18, 2014 to September 12, 2015). Data for both outbreaks were reported at the chiefdom level, the third- level administrative units. The times between the onset of an outbreak and when half or all of its cases were reported were longer when outbreaks were aggregated by district (second-level administrative units, comprised of chiefdoms) instead of chief- dom (Fig. 1), which has implications for the optimal scale for surveillance and response measures. The median time for a chiefdom cholera outbreak to report half of its case total was 3.9 wk, and median outbreak duration was 11.3 wk. The median time for Fig. 1. The proportion of cholera and Ebola cases reported over time dif- district outbreaks to report half of their cholera cases was 7.9 wk, fered between district and chiefdom levels. The times between the onset of and the median outbreak duration was 43.7 wk. Analysis of an outbreak and when half or all of its cases were reported were longer Ebola revealed similar trends, with chiefdoms reporting half of when outbreaks were aggregated by district instead of chiefdom, which has their cases at a median of 13.9 wk and median outbreak duration implications for the optimal scale for surveillance and response measures. of 43.3 wk, and districts reporting half of their cases at a median The median time for a chiefdom cholera outbreak to report half of its case total was 3.9 wk, and a median of 7.9 wk for district cholera outbreaks. For of 23.5 wk and median outbreak duration of 64.1 wk. Ebola, chiefdoms reported half of their cases at a median of 13.9 wk, and Both the cholera and Ebola epidemics were widespread, each districts reported at a median of 23.5 wk. reaching more than 75% of the country’s chiefdoms. However, population caused by pathogens with notable similarities in both the drivers of outbreaks and the interventions used to curtail them, including oral rehydration (17, 18). Both are transmitted through contact with contaminated diarrhea or vomitus (plus other bodily fluids for Ebola), and the basic reproductive number (R0) for both diseases is thought to be between 1 and 3 (19, 20).
Load more

Recommended publications
  • Wavelet Entropy Energy Measure (WEEM): a Multiscale Measure to Grade a Geophysical System's Predictability
    EGU21-703, updated on 28 Sep 2021 https://doi.org/10.5194/egusphere-egu21-703 EGU General Assembly 2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability Ravi Kumar Guntu and Ankit Agarwal Indian Institute of Technology Roorkee, Hydrology, Roorkee, India ([email protected]) Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • Biostatistics for Oral Healthcare
    Biostatistics for Oral Healthcare Jay S. Kim, Ph.D. Loma Linda University School of Dentistry Loma Linda, California 92350 Ronald J. Dailey, Ph.D. Loma Linda University School of Dentistry Loma Linda, California 92350 Biostatistics for Oral Healthcare Biostatistics for Oral Healthcare Jay S. Kim, Ph.D. Loma Linda University School of Dentistry Loma Linda, California 92350 Ronald J. Dailey, Ph.D. Loma Linda University School of Dentistry Loma Linda, California 92350 JayS.Kim, PhD, is Professor of Biostatistics at Loma Linda University, CA. A specialist in this area, he has been teaching biostatistics since 1997 to students in public health, medical school, and dental school. Currently his primary responsibility is teaching biostatistics courses to hygiene students, predoctoral dental students, and dental residents. He also collaborates with the faculty members on a variety of research projects. Ronald J. Dailey is the Associate Dean for Academic Affairs at Loma Linda and an active member of American Dental Educational Association. C 2008 by Blackwell Munksgaard, a Blackwell Publishing Company Editorial Offices: Blackwell Publishing Professional, 2121 State Avenue, Ames, Iowa 50014-8300, USA Tel: +1 515 292 0140 9600 Garsington Road, Oxford OX4 2DQ Tel: 01865 776868 Blackwell Publishing Asia Pty Ltd, 550 Swanston Street, Carlton South, Victoria 3053, Australia Tel: +61 (0)3 9347 0300 Blackwell Wissenschafts Verlag, Kurf¨urstendamm57, 10707 Berlin, Germany Tel: +49 (0)30 32 79 060 The right of the Author to be identified as the Author of this Work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.
    [Show full text]
  • 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.
    [Show full text]
  • Big Data for Reliability Engineering: Threat and Opportunity
    Reliability, February 2016 Big Data for Reliability Engineering: Threat and Opportunity Vitali Volovoi Independent Consultant [email protected] more recently, analytics). It shares with the rest of the fields Abstract - The confluence of several technologies promises under this umbrella the need to abstract away most stormy waters ahead for reliability engineering. News reports domain-specific information, and to use tools that are mainly are full of buzzwords relevant to the future of the field—Big domain-independent1. As a result, it increasingly shares the Data, the Internet of Things, predictive and prescriptive lingua franca of modern systems engineering—probability and analytics—the sexier sisters of reliability engineering, both statistics that are required to balance the otherwise orderly and exciting and threatening. Can we reliability engineers join the deterministic engineering world. party and suddenly become popular (and better paid), or are And yet, reliability engineering does not wear the fancy we at risk of being superseded and driven into obsolescence? clothes of its sisters. There is nothing privileged about it. It is This article argues that“big-picture” thinking, which is at the rarely studied in engineering schools, and it is definitely not core of the concept of the System of Systems, is key for a studied in business schools! Instead, it is perceived as a bright future for reliability engineering. necessary evil (especially if the reliability issues in question are safety-related). The community of reliability engineers Keywords - System of Systems, complex systems, Big Data, consists of engineers from other fields who were mainly Internet of Things, industrial internet, predictive analytics, trained on the job (instead of receiving formal degrees in the prescriptive analytics field).
    [Show full text]
  • Volatility Estimation of Stock Prices Using Garch Method
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by International Institute for Science, Technology and Education (IISTE): E-Journals European Journal of Business and Management www.iiste.org ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) Vol.7, No.19, 2015 Volatility Estimation of Stock Prices using Garch Method Koima, J.K, Mwita, P.N Nassiuma, D.K Kabarak University Abstract Economic decisions are modeled based on perceived distribution of the random variables in the future, assessment and measurement of the variance which has a significant impact on the future profit or losses of particular portfolio. The ability to accurately measure and predict the stock market volatility has a wide spread implications. Volatility plays a very significant role in many financial decisions. The main purpose of this study is to examine the nature and the characteristics of stock market volatility of Kenyan stock markets and its stylized facts using GARCH models. Symmetric volatility model namly GARCH model was used to estimate volatility of stock returns. GARCH (1, 1) explains volatility of Kenyan stock markets and its stylized facts including volatility clustering, fat tails and mean reverting more satisfactorily.The results indicates the evidence of time varying stock return volatility over the sampled period of time. In conclusion it follows that in a financial crisis; the negative returns shocks have higher volatility than positive returns shocks. Keywords: GARCH, Stylized facts, Volatility clustering INTRODUCTION Volatility forecasting in financial market is very significant particularly in Investment, financial risk management and monetory policy making Poon and Granger (2003).Because of the link between volatility and risk,volatility can form a basis for efficient price discovery.Volatility implying predictability is very important phenomenon for traders and medium term - investors.
    [Show full text]
  • 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.
    [Show full text]
  • A Python Library for Neuroimaging Based Machine Learning
    Brain Predictability toolbox: a Python library for neuroimaging based machine learning 1 1 2 1 1 1 Hahn, S. ,​ Yuan, D.K. ,​ Thompson, W.K. ,​ Owens, M. ,​ Allgaier, N .​ and Garavan, H ​ ​ ​ ​ ​ ​ 1. Departments of Psychiatry and Complex Systems, University of Vermont, Burlington, VT 05401, USA 2. Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA 92093, USA Abstract Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric, behavioral, and physiological variables) and neuroimaging specific derived data (e.g., brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging based ML questions, in particular, those queried from large human datasets. Availability and Implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at ​ https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be ​ downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app. ​ Contact Please contact Sage Hahn at [email protected] ​ Main Text 1 Introduction Large datasets in all domains are becoming increasingly prevalent as data from smaller existing studies are pooled and larger studies are funded. This increase in available data offers an unprecedented opportunity for researchers interested in applying machine learning (ML) based methodologies, especially those working in domains such as neuroimaging where data collection is quite expensive.
    [Show full text]
  • 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).
    [Show full text]
  • BIOSTATS Documentation
    BIOSTATS Documentation BIOSTATS is a collection of R functions written to aid in the statistical analysis of ecological data sets using both univariate and multivariate procedures. All of these functions make use of existing R functions and many are simply convenience wrappers for functions contained in other popular libraries, such as VEGAN and LABDSV for multivariate statistics, however others are custom functions written using functions in the base or stats libraries. Author: Kevin McGarigal, Professor Department of Environmental Conservation University of Massachusetts, Amherst Date Last Updated: 9 November 2016 Functions: all.subsets.gam.. 3 all.subsets.glm. 6 box.plots. 12 by.names. 14 ci.lines. 16 class.monte. 17 clus.composite.. 19 clus.stats. 21 cohen.kappa. 23 col.summary. 25 commission.mc. 27 concordance. 29 contrast.matrix. 33 cov.test. 35 data.dist. 37 data.stand. 41 data.trans. 44 dist.plots. 48 distributions. 50 drop.var. 53 edf.plots.. 55 ecdf.plots. 57 foa.plots.. 59 hclus.cophenetic. 62 hclus.scree. 64 hclus.table. 66 hist.plots. 68 intrasetcor. 70 Biostats Library 2 kappa.mc. 72 lda.structure.. 74 mantel2. 76 mantel.part. 78 mrpp2. 82 mv.outliers.. 84 nhclus.scree. 87 nmds.monte. 89 nmds.scree.. 91 norm.test. 93 ordi.monte.. 95 ordi.overlay. 97 ordi.part.. 99 ordi.scree. 105 pca.communality. 107 pca.eigenval. 109 pca.eigenvec. 111 pca.structure. 113 plot.anosim. 115 plot.mantel. 117 plot.mrpp.. 119 plot.ordi.part. 121 qqnorm.plots.. 123 ran.split. ..
    [Show full text]