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81: Evidence Supports a Causal Model for Vitamin D in COVID-19 Outcomes
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Posted 06 May 2020

Evidence Supports a Causal Model for Vitamin D in COVID-19 Outcomes
14,274 downloads medRxiv epidemiology

Gareth Davies, Attila R Garami, Joanna C Byers

Background: The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 seemed to affect locations in the northern hemisphere most severely appearing to overlap with the pattern of seasonal vitamin D deficiency. Integrating available knowledge, we hypothesised that vitamin D status could play a causal role in COVID-19 outcomes. Objectives: We set out to analyse the relationship between COVID-19 severity and latitude, and construct a causal inference framework to validate this hypothesis. Methods: We analysed global daily reports of fatalities and recoveries from 239 locations from 22nd Jan 2020 to 9th April 2020. We quantified local COVID-19 outbreak severity to clearly distinguish the latitude relationship and identify any outliers breaking this pattern, and analysed the timeline of spread. We then used a causal inference framework to distinguish correlation from cause using observational data with a hypothetico-deductive method of proof. We constructed two contrasting directed acyclic graph (DAG) models, one causal and one acausal with respect to vitamin D and COVID-19 severity, allowing us to make 19 verifiable and falsifiable predictions for each. Results: Our analysis confirmed a striking correlation between COVID-19 severity and latitude, and ruled out the temporal spread of infection as an explanation. We compared observed severity for 239 locations with our contrasting model. In the causal model, 16 predictions matched observed data and 3 predictions were untestable; in the acausal model, 14 predictions strongly contradicted observed data, 2 appeared to contradict data, and 3 were untestable. Discussion: We show in advance of RCTs that observed data strongly match predictions made by the causal model but contradict those of the acausal model. We present historic evidence that vitamin D supplementation prevented past respiratory virus pandemics. We discuss how molecular mechanisms of vitamin D action can prevent respiratory viral infections and protect against ARDS. We highlight vitamin D's direct effect on the renin-angiotensin-system (RAS), which in concert with additional effects, can modify host responses thus preventing a cytokine storm and SARS-CoV-2-induced pathological changes. Emerging clinical research confirms striking correlations between hypovitaminosis D and COVID-19 severity, in full alignment with our study. Conclusions: Our novel causal inference analysis of global data verifies that vitamin D status plays a key role in COVID-19 outcomes. The data set size, supporting historical, biomolecular, and emerging clinical research evidence altogether suggest that a very high level of confidence is justified. Vitamin D prophylaxis potentially offers a widely available, low-risk, highly-scalable, and cost-effective pandemic management strategy including the mitigation of local outbreaks and a second wave. Timely implementation of vitamin D supplementation programmes worldwide is critical with initial priority given to those who are at the highest risk, including the elderly, immobile, homebound, BAME and healthcare professionals. Population-wide vitamin D sufficiency could also prevent seasonal respiratory epidemics, decrease our dependence on pharmaceutical solutions, reduce hospitalisations, and thus greatly lower healthcare costs while significantly increasing quality of life.

82: Estimate of the development of the epidemic reproduction number Rt from Coronavirus SARS-CoV-2 case data and implications for political measures based on prognostics
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Posted 07 Apr 2020

Estimate of the development of the epidemic reproduction number Rt from Coronavirus SARS-CoV-2 case data and implications for political measures based on prognostics
13,962 downloads medRxiv epidemiology

Sahamoddin Khailaie, Tanmay Mitra, Arnab Bandyopadhyay, Marta Schips, Pietro Mascheroni, Patrizio Vanella, Berit Lange, Sebastian Binder, Michael Meyer-Hermann

The novel Coronavirus SARS-CoV-2 (CoV) has induced a world-wide pandemic and subsequent non-pharmaceutical interventions (NPI) in order to control the spreading of the virus. NPIs are considered to be critical in order to at least delay the peak number of infected individuals and to prevent the health care system becoming overwhelmed by the number of patients to treat in hospitals or in intensive care units (ICUs). However, there is also increasing concern that the NPIs in place would increase mortality because of other diseases, increase the frequency of suicide and increase the risk of an economic recession with unforeseeable implications. It is therefore instrumental to evaluate the necessity of NPIs and to monitor the progress of containment of the virus spreading. We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states. Based on an extended infection-epidemic model, parameterized with data from the Robert Koch-Institute and, alternatively, with parameters stemming from a fit to the initial phase of CoV spreading in different regions of Italy, we consistently found that the reproduction number was turned down to a range near 1 in all federal states. We used the latest reproduction number as a starting point for the simulation of epidemic progression and varied the reproduction number, mimicking either release or strengthening of NPIs. Germany is currently, April 3rd, 2020, at the border line of a reproduction number between the scenarios of major immunisation of the population or eradication of the virus. We strongly recommend to keep all NPIs in place and suggest to even strengthen the measures in order to accelerate reaching the state of full control, thus, also limiting collateral damage of the NPIs in time.

83: Fractal kinetics of COVID-19 pandemic
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Posted 20 Feb 2020

Fractal kinetics of COVID-19 pandemic
13,888 downloads medRxiv epidemiology

Anna L. Ziff, Robert M. Ziff

We give an update to the original paper posted on 2/17/20 - now (as of 3/1/20) the China deaths are rapidly decreasing, and we find an exponential decline to the power law similar to the that predicted by the network model of Vazquez [2006]. At the same time, we see non-China deaths increasing rapidly, and similar to the early behavior of the China statistics. Thus, we see three stages of the spread of the disease in terms of number of deaths: exponential growth, power-law behavior, and then exponential decline in the daily rate. (Original abstract) The novel coronavirus (COVID-19) continues to grow rapidly in China and is spreading in other parts of the world. The classic epidemiological approach in studying this growth is to quantify a reproduction number and infection time, and this is the approach followed by many studies on the epidemiology of this disease. However, this assumption leads to exponential growth, and while the growth rate is high, it is not following exponential behavior. One approach that is being used is to simply keep adjusting the reproduction number to match the dynamics. Other approaches use rate equations such as the SEIR and logistical models. Here we show that the current growth closely follows power-law kinetics, indicative of an underlying fractal or small-world network of connections between susceptible and infected individuals. Positive deviations from this growth law might indicate either a failure of the current containment efforts while negative deviations might indicate the beginnings of the end of the pandemic. We cannot predict the ultimate extent of the pandemic but can get an estimate of the growth of the disease.

84: Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes
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Posted 03 Sep 2020

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes
13,850 downloads medRxiv epidemiology

Beatrice Nash, Anthony Badea, Ankita Reddy, Miguel Bosch, Nol Salcedo, Adam R. Gomez, Alice Versiani, Gislaine Celestino Dutra, Thayza Maria Izabel Lopes dos Santos, Bruno H. G. A. Milhim, Marilia M Moraes, Guilherme Rodrigues Fernandes Campos, Flávia Quieroz, Andreia Francesli Negri Reis, Mauricio L Nogueira, Elena N. Naumova, Irene Bosch, Bobby Brooke Herrera

High frequency screening of populations has been proposed as a strategy in facilitating control of the COVID-19 pandemic. We use computational modeling, coupled with clinical data from rapid antigen tests, to predict the impact of frequent viral antigen rapid testing on COVID-19 spread and outcomes. Using patient nasal or nasopharyngeal swab specimens, we demonstrate that the sensitivity/specificity of two rapid antigen tests compared to quantitative real-time polymerase chain reaction (qRT-PCR) are 82.0%/100% and 84.7%/85.7%, respectively; moreover, sensitivity correlates directly with viral load. Based on COVID-19 data from three regions in the United States and Sao Jose do Rio Preto, Brazil, we show that high frequency, strategic population-wide rapid testing, even at varied accuracy levels, diminishes COVID-19 infections, hospitalizations, and deaths at a fraction of the cost of nucleic acid detection via qRT-PCR. We propose large-scale antigen-based surveillance as a viable strategy to control SARS-CoV-2 spread and to enable societal re-opening.

85: Mortality among Adults Ages 25-44 in the United States During the COVID-19 Pandemic.
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Posted 25 Oct 2020

Mortality among Adults Ages 25-44 in the United States During the COVID-19 Pandemic.
13,666 downloads medRxiv epidemiology

Jeremy Faust, Harlan Krumholz, Katherine L. Dickerson, Zhenqiu Lin, Cleavon Gilman, Rochelle P Walensky

IntroductionCoronavirus disease-19 (COVID-19) has caused a marked increase in all-cause deaths in the United States, mostly among adults aged 65 and older. Because younger adults have far lower infection fatality rates, less attention has been focused on the mortality burden of COVID-19 in this demographic. MethodsWe performed an observational cohort study using public data from the National Center for Health Statistics at the United States Centers for Disease Control and Prevention, and CDC Wonder. We analyzed all-cause mortality among adults ages 25-44 during the COVID-19 pandemic in the United States. Further, we compared COVID-19-related deaths in this age group during the pandemic period to all drug overdose deaths and opioid-specific overdose deaths in each of the ten Health and Human Services (HHS) regions during the corresponding period of 2018, the most recent year for which data are available. ResultsAs of September 6, 2020, 74,027 all-cause deaths occurred among persons ages 25-44 years during the period from March 1st to July 31st, 2020, 14,155 more than during the same period of 2019, a 23% relative increase (incident rate ratio 1.23; 95% CI 1.21-1.24), with a peak of 30% occurring in May (IRR 1.30; 95% CI 1.27-1.33). In HHS Region 2 (New York, New Jersey), HHS Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma, Texas), and HHS Region 9 (Arizona, California, Hawaii, Nevada), COVID-19 deaths exceeded 2018 unintentional opioid overdose deaths during at least one month. Combined, 2,450 COVID-19 deaths were recorded in these three regions during the pandemic period, compared to 2,445 opioid deaths during the same period of 2018. MeaningWe find that COVID-19 has likely become the leading cause of death--surpassing unintentional overdoses--among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks. NoteThe data presented here have since been updated. As a result, an additional 1,902 all-cause deaths occurring among US adults ages 25-44 during the period of interest are not accounted for in this manuscript.

86: Covid-19 health care demand and mortality in Sweden in response to non-pharmaceutical (NPIs) mitigation and suppression scenarios
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Posted 23 Mar 2020

Covid-19 health care demand and mortality in Sweden in response to non-pharmaceutical (NPIs) mitigation and suppression scenarios
13,657 downloads medRxiv epidemiology

Joacim Rocklov

BackgroundWhile the COVID-19 outbreak in China now appears contained, Europe has become the epicenter, with both Italy and Spain reporting more deaths than China. Here we analyse the potential consequences of different response strategies to COVID-19 within Sweden, the resulting demand for care, critical care, deaths and their associated direct health care related costs. MethodsWe use an age stratified health-care demand extended SEIR compartmental model fitted to the municipality level for all municipalities in Sweden, and a radiation model describing inter-municipality mobility. ResultsOur models fit well with the observed deaths in Sweden up to 25th of March. The critical care demand is estimated to peak just above 16,000 patients per day by early May in the unmitigated scenario, while isolation of elderly and intermediate social distancing can reduce it to around 5000-9000 per day peaking in June. These peaks exceed the normal critical care capacity in Sweden at 526 beds by an order of magnitude. We find, however, that by employing strong social distancing and isolation of families with confirmed cases, as guided by testing, the outbreak can be suppressed to levels below the normal critical care capacity. We estimate death rates in COVID-19 are closely related to the different response strategies. ConclusionThe impact of different combinations of non-pharmaceutical interventions, especially the extent of social distancing and isolation, reduce deaths and lower health care costs in Sweden. In most mitigation scenarios, demand on ICU beds would rapidly exceed total ICU capacity, thus calling for immediate expansion of ICU beds. These findings have relevance for Swedish policy and response to the COVID-19 pandemic.

87: SARS-CoV-2 infection fatality risk in a nationwide seroepidemiological study
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Posted 07 Aug 2020

SARS-CoV-2 infection fatality risk in a nationwide seroepidemiological study
13,332 downloads medRxiv epidemiology

Roberto Pastor-Barriuso, Beatriz Perez-Gomez, Miguel A Hernan, Mayte Perez-Olmeda, Raquel Yotti, Jesus Oteo-Iglesias, Jose Luis Sanmartin, Inmaculada Leon-Gomez, Aurora Fernandez-Garcia, Pablo Fernandez-Navarro, Israel Cruz, Mariano Martin, Concepcion Delgado-Sanz, Nerea Fernandez de Larrea, Jose Leon Paniagua, Juan Fernando Munoz-Montalvo, Faustino Blanco, Amparo Larrauri, Marina Pollan

Objective: To estimate the range of the age- and sex-specific infection fatality risk (IFR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on confirmed coronavirus disease 2019 (COVID-19) deaths and excess all-cause deaths. Design: Nationwide population-based seroepidemiological study combined with two national surveillance systems. Setting and participants: Non-institutionalized Spanish population of all ages. Main outcome measures: The range of IFR was calculated as the observed number of COVID-19 deaths and excess deaths divided by the estimated number of SARS-CoV-2 infections in the non-institutionalized Spanish population. Laboratory-confirmed COVID-19 deaths were obtained from the National Epidemiological Surveillance Network (RENAVE) and excess all-cause deaths from the Monitoring Mortality System (MoMo) up to July 15, 2020. SARS-CoV-2 infections were derived from the estimated seroprevalence by a chemiluminiscent microparticle immunoassay for IgG antibodies in 61,092 participants in the ENE-COVID nationwide serosurvey between April 27 and June 22, 2020. Results: The overall IFR (95% confidence interval) was 0.8% (0.8% to 0.9%) for confirmed COVID-19 deaths and 1.1% (1.0% to 1.2%) for excess deaths. The IFR ranged between 1.1% (1.0% to 1.2%) and 1.4% (1.3% to 1.5%) in men and between 0.6% (0.5% to 0.6%) and 0.8% (0.7% to 0.8%) in women. The IFR increased sharply after age 50, ranging between 11.6% (8.1% to 16.5%) and 16.4% (11.4% to 23.2%) in men [≥]80 years and between 4.6% (3.4% to 6.3%) and 6.5% (4.7% to 8.8%) in women [≥]80 years. Conclusion: The sharp increase in SARS-CoV-2 IFR after age 50 was more marked in men than in women. Fatality from COVID-19 is substantially greater than that reported for other common respiratory diseases such as seasonal influenza.

88: Efficacy Estimates for Various COVID-19 Vaccines: What we Know from the Literature and Reports
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Posted 21 May 2021

Efficacy Estimates for Various COVID-19 Vaccines: What we Know from the Literature and Reports
13,152 downloads medRxiv epidemiology

Julia Shapiro, Natalie E Dean, Zachary J. Madewell, Yang Yang, M. Elizabeth Halloran, Ira M Longini

In this report, we provide summary estimates, from publications and reports, of vaccine efficacy (VE) for the COVID-19 vaccines that are being rolled out on a global scale. We find that, on average, the efficacy against any disease with infection is 85% (95% CI: 71 - 93%) after a full course of vaccination. The VE against severe disease, hospitalization or death averages close to 100%. The average VE against infection, regardless of symptoms, is 84% (95% CI: 70 - 91%). We also find that the average VE against transmission to others for Infected vaccinated people is 54% (95% CI: 38 - 66%). Finally, we prove summary estimates of the VE against any disease with infection for some of the variants of concern (VOC). The average VE for the VOC B.1.1.7, B.1.1.28 (P1) and B.1.351 are 86% (95% CI: 65 - 84%), 61% (95% CI: 43 - 73%) and 56% (95% CI: 29 - 73%), respectively.

89: Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany
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Posted 24 Mar 2020

Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany
12,814 downloads medRxiv epidemiology

P. Magal, G. Webb

We model the COVID-19 coronavirus epidemic in South Korea, Italy, France, and Germany. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

90: Modeling the spread of Covid-19 under active management
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Posted 23 Nov 2020

Modeling the spread of Covid-19 under active management
12,743 downloads medRxiv epidemiology

Ivan Cherednik

Classical approaches to modeling the spread of epidemics are based on two assumptions: the exponential growth of the total number of infections and the saturation due to the herd immunity. With Covid-19, the growth is essentially power-type, especially during the middle stages, and the saturation is currently mostly due to the protective measures. Focusing on these features and the role of epidemic management, we obtain differential equations for the total number of detected cases of Covid-19, which describe the actual curves in many countries almost with the accuracy of physics laws. The two-phase solution we propose works very well almost for the whole periods of the spread practically in all countries we analyzed that reached the saturation during the first waves. Bessel functions play the key role in our approach. Due to a very small number of parameters, namely, the initial transmission rate and the intensity of the hard and soft measures, we obtain a convincing explanation of the surprising uniformity of the curves of the total numbers of detected infections in many different areas. This theory can serve as a tool for forecasting the epidemic spread and evaluating the efficiency of the protective measures, which is very much needed for epidemics. As its practical application, the computer programs aimed at providing projections for late stages of Covid-19 proved to be remarkably stable in many countries, including Western Europe, the USA and some in Asia. We provide a projection for the saturation of the 3rd wave in the USA: the corresponding number of total, detected or not, cases can presumably reach then the herd immunity levels (G-strains). This can be used to analyze the efficiency of the vaccinations.

91: Interactions between SARS-CoV-2 and Influenza and the impact of coinfection on disease severity: A test negative design
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Posted 18 Sep 2020

Interactions between SARS-CoV-2 and Influenza and the impact of coinfection on disease severity: A test negative design
12,716 downloads medRxiv epidemiology

Julia Stowe, Elise Tessier, Hongxin Zhao, Rebecca Guy, Berit Muller-Pebody, Maria Zambon, Nick Andrews, Mary E Ramsay, Jamie Lopez Bernal

Background: The potential impact of COVID-19 alongside influenza on morbidity, mortality and health service capacity is a major concern as the Northern Hemisphere winter approaches. This study investigates the interaction between influenza and COVID-19 during the latter part of the 2019-20 influenza season in England. Methods: Individuals tested for influenza and SARS-CoV-2 were extracted from national surveillance systems between 20/01/2020 and 25/04/2020. To estimate influenza infection on the risk of SARS-CoV-2 infection, univariable and multivariable analyses on the odds of SARS-CoV-2 in those who tested positive for influenza compared to those who tested negative for influenza. To assess whether a coinfection was associated with severe SARS-CoV-2 outcome, univariable and multivariable analyses on the odds of death adjusted for age, sex, ethnicity, comorbidity and coinfection status. Findings: The risk of testing positive for SARS-CoV-2 was 68% lower among influenza positive cases, suggesting possible pathogenic competition between the two viruses. Patients with a coinfection had a risk of death of 5.92 (95% CI, 3.21-10.91) times greater than among those with neither influenza nor SARS-CoV-2 suggesting possible synergistic effects in coinfected individuals. The odds of ventilator use or death and ICU admission or death was greatest among coinfection patients showing strong evidence of an interaction effect compared to SARS-CoV-2/influenza acting independently. Interpretation: Cocirculation of these viruses could have a significant impact on morbidity, mortality and health service demand. Testing for influenza alongside SARS-CoV-2 and maximising influenza vaccine uptake should be prioritised to mitigate these risks.

92: Adaptive cyclic exit strategies from lockdown to suppress COVID-19 and allow economic activity
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Posted 07 Apr 2020

Adaptive cyclic exit strategies from lockdown to suppress COVID-19 and allow economic activity
12,614 downloads medRxiv epidemiology

Omer Karin, Yinon M. Bar-On, Tomer Milo, Itay Katzir, Avi Mayo, Yael Korem, Boaz Dudovich, Eran Yashiv, Amos J. Zehavi, Nadav Davidovich, Ron Milo, Uri Alon

Many countries have applied lockdown that helped suppress COVID-19, but with devastating economic consequences. Here we propose exit strategies from lockdown that provide sustainable, albeit reduced, economic activity. We use mathematical models to show that a cyclic schedule of 4-day work and 10-day lockdown, or similar variants, can, in certain conditions, suppress the epidemic while providing part-time employment. The cycle reduces the reproduction number R by a combination of reduced exposure time and an anti-phasing effect in which those infected during work days reach peak infectiousness during lockdown days. The number of work days can be adapted in response to observations. Throughout, full epidemiological measures need to continue including hygiene, physical distancing, compartmentalization and extensive testing and contact tracing. We do not call for immediate adoption of this policy, but rather to consider it as a conceptual framework, which, when combined with other interventions to control the epidemic, can offer the beginnings of predictability to many economic sectors.

93: Characteristics and predictors of hospitalization and death in the first 9,519 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: A nationwide cohort
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Posted 26 May 2020

Characteristics and predictors of hospitalization and death in the first 9,519 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: A nationwide cohort
12,542 downloads medRxiv epidemiology

Mette Reilev, Kasper Bruun Kristensen, Anton Pottegaard, Lars Christian Lund, Jesper Hallas, Martin Thomsen Ernst, Christian Fynbo Christiansen, Henrik Toft Soerensen, Nanna Borup Johansen, Nikolai Constantin Brun, Marianne Voldstedlund, Henrik Stoevring, Marianne Kragh Thomsen, Steffen Christensen, Sophie Gubbels, Tyra Krause, Kaare Moelbak, Reimar Wernich Thomsen

Objective To provide population-level knowledge on individuals at high risk of severe and fatal coronavirus disease 2019 (COVID-19) in order to inform targeted protection strategies in the general population and appropriate triage of hospital contacts. Design, Setting, and Participants Nationwide population-based cohort of all 228.677 consecutive Danish individuals tested (positive or negative) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from the identification of the first COVID-19 case on February 27th, 2020 until April 30th, 2020. Main Outcomes and Measures We examined characteristics and predictors of inpatient hospitalization versus community-management, and death versus survival, adjusted for age-, sex- and number of comorbidities. Results We identified 9,519 SARS-CoV-2 PCR-positive cases of whom 78% were community-managed, 22% were hospitalized (3.2% at an intensive care unit) and 5.5% had died within 30 days. Median age varied from 45 years (interquartile range (IQR) 31-57) among community-managed cases to 82 years (IQR 75-89) among those who died. Age was a strong predictor of fatal disease (odds ratio (OR) 14 for 70-79-year old, OR 26 for 80-89-year old, and OR 82 for cases older than 90 years, when compared to 50-59-year old and adjusted for sex and number of comorbidities). Similarly, the number of comorbidities was strongly associated with fatal disease (OR 5.2, for cases with [≥]4 comorbidities versus no comorbidities), and 82% of fatal cases had at least 2 comorbidities. A wide range of major chronic diseases were associated with hospitalization with ORs ranging from 1.3-1.4 (e.g. stroke, ischemic heart disease) to 2.2-2.7 (e.g. heart failure, hospital-diagnosed kidney disease, chronic liver disease). Similarly, chronic diseases were associated with mortality with ORs ranging from 1.2-1.3 (e.g. ischemic heart disease, hypertension) to 2.4-2.7 (e.g. major psychiatric disorder, organ transplantation). In the absence of comorbidities, mortality was relatively low (5% or less) in persons aged up to 80 years. Conclusions and Relevance In this first nationwide population-based study, increasing age and number of comorbidities were strongly associated with hospitalization requirement and death in COVID-19. In the absence of comorbidities, the mortality was, however, lowest until the age of 80 years. These results may help in accurate identification, triage and protection of high-risk groups in general populations, i.e. when reopening societies.

94: Estimation of risk factors for COVID-19 mortality - preliminary results
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Posted 25 Feb 2020

Estimation of risk factors for COVID-19 mortality - preliminary results
12,539 downloads medRxiv epidemiology

F Caramelo, N Ferreira, B Oliveiros

Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China CDC has reported results of a descriptive exploratory analysis of all cases diagnosed until the 11th February 2020, presenting the epidemiologic curves and geo-temporal spread of COVID-19 along with case fatality rate according to some baseline characteristics, such as age, gender and several well-established high prevalence comorbidities. Despite this, we intend to increase even further the predictive value of that manuscript by presenting the odds ratio for mortality due to COVID-19 adjusted for the presence of those comorbidities and baseline characteristics such as age and gender. Besides, we present a way to determine the risk of each particular patient, given his characteristics. We found that age is the variable that presents higher risk of COVID-19 mortality, where 60 or older patients have an OR = 18.8161 (CI95%[7.1997; 41.5517]). Regarding comorbidities, cardiovascular disease appears to be the riskiest (OR= 12.8328 CI95%[10.2736; 15.8643], along with chronic respiratory disease (OR=7.7925 CI95%[5.5446; 10.4319]). Males are more likely to die from COVID-19 (OR=1.8518 (CI95%[1.5996; 2.1270]). Some limitations such as the lack of information about the correct prevalence of gender per age or about comorbidities per age and gender or the assumption of independence between risk factors are expected to have a small impact on results. A final point of paramount importance is that the equation presented here can be used to determine the probability of dying from COVID-19 for a particular patient, given its age interval, gender and comorbidities associated.

95: Estimate the incubation period of coronavirus 2019 (COVID-19)
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Posted 29 Feb 2020

Estimate the incubation period of coronavirus 2019 (COVID-19)
12,520 downloads medRxiv epidemiology

Ke Men, Xia Wang, Li Yihao, Guangwei Zhang, Jingjing Hu, Yanyan Gao, Henry Han

MotivationWuhan pneumonia is an acute infectious disease caused by the 2019 novel coronavirus (COVID-19). It is being treated as a Class A infectious disease though it was classified as Class B according to the Infectious Disease Prevention Act of China. Accurate estimation of the incubation period of the coronavirus is essential to the prevention and control. However, it remains unclear about its exact incubation period though it is believed that symptoms of COVID-19 can appear in as few as 2 days or as long as 14 or even more after exposure. The accurate incubation period calculation requires original chain-of-infection data that may not be fully available in the Wuhan regions. In this study, we aim to accurately calculate the incubation period of COVID-19 by taking advantage of the chain-of-infection data, which is well-documented and epidemiologically informative, outside the Wuhan regions. MethodsWe acquired and collected officially reported COVID-19 data from 10 regions in China except for Hubei province. To achieve the accurate calculation of the incubation period, we only involved the officially confirmed cases with a clear history of exposure and time of onset. We excluded those without relevant epidemiological descriptions, working or living in Wuhan for a long time, or hard to determine the possible exposure time. We proposed a Monte Caro simulation approach to estimate the incubation of COVID-19 as well as employed nonparametric ways. We also employed manifold learning and related statistical analysis to decipher the incubation relationships between different age/gender groups. ResultThe incubation period of COVID-19 did not follow general incubation distributions such as lognormal, Weibull, and Gamma distributions. We estimated that the mean and median of its incubation were 5.84 and 5.0 days via bootstrap and proposed Monte Carlo simulations. We found that the incubation periods of the groups with age>=40 years and age<40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter. It further suggested that different quarantine time should be applied to the groups for their different incubation periods. Our machine learning analysis also showed that the two groups were linearly separable. incubation of COVID-19 along with previous statistical analysis. Our results further indicated that the incubation difference between males and females did not demonstrate a statistical significance.

96: Sharing a household with children and risk of COVID-19: a study of over 300,000 adults living in healthcare worker households in Scotland
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Posted 22 Sep 2020

Sharing a household with children and risk of COVID-19: a study of over 300,000 adults living in healthcare worker households in Scotland
12,461 downloads medRxiv epidemiology

Rachael Wood, Emma C Thomson, Robert Galbraith, Ciara Gribben, David Caldwell, Jennifer Bishop, Martin Reid, Anoop Shah, Kate Templeton, David Goldberg, Chris Robertson, Sharon Hutchinson, Helen M Colhoun, Paul M McKeigue, David McAllister

Background Children are relatively protected from novel coronavirus infection (COVID-19). The reasons for this protection are not well understood but differences in the immune response to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) have been implicated. If such differences are due to differential exposure to non-SARS-CoV-2 infectious agents, adults who are close contacts of children may partly share in this protection. Such a protective effect would have important implications for the lives of children, not least in terms of schooling. Methods Using a Scotland-wide record-linkage based occupational cohort comprising healthcare workers and members of their households, we examined whether sharing a household with young children (aged 0 to 11) attenuated the risk of hospitalisation with COVID-19, and/or testing positive for COVID-19 infection of any severity (any case of Covid-19). All healthcare workers directly employed by the National health Service (NHS) in Scotland, or contracted to provide general practice services, were included. Outcome and covariate data were obtained via linkage to Scotland-wide microbiology, drug prescribing, hospitalisation and death data. Results 241,266 adults did not share a household with young children; 41,198, 23,783 and 3,850 shared a household with 1, 2 and 3 or more young children respectively. The risk of hospitalisation with COVID-19 was lower in those with one child and lower still in those with two or more children, adjusting for age the hazard ratio (HR) was 0.83 per child (95% CI 0.70-0.99). On additionally adjusting for sex, socioeconomic deprivation, occupation, professional role, staff/non-staff status, the number of adults and adolescents in each household, and comorbidity, the HR was 0.89 per child (95% CI 0.74-1.06). An association of the same magnitude, but more precisely estimated, was obtained for any case of COVID-19 (fully adjusted model, HR per child 0.89; 95% CI 0.84-0.95). Conclusion Increased household exposure to young children was associated with an attenuated risk of testing positive for SARS-CoV-2 and appeared to also be associated with an attenuated risk of COVID-19 disease severe enough to require hospitalisation.

97: The effect of travel restrictions on the spread of the 2019 novel coronavirus (2019-nCoV) outbreak
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Posted 11 Feb 2020

The effect of travel restrictions on the spread of the 2019 novel coronavirus (2019-nCoV) outbreak
12,436 downloads medRxiv epidemiology

Matteo Chinazzi, Jessica T Davis, Marco Ajelli, Corrado Gioannini, Maria Litvinova, Stefano Merler, Ana Pastore y Piontti, Luca Rossi, Kaiyuan Sun, Cécile Viboud, Xinyue Xiong, Hongjie Yu, M. Elizabeth Halloran, Ira M. Longini, Alessandro Vespignani

Motivated by the rapid spread of a novel coronavirus (2019-nCoV) in Mainland China, we use a global metapopulation disease transmission model to project the impact of both domestic and international travel limitations on the national and international spread of the epidemic. The model is calibrated on the evidence of internationally imported cases before the implementation of the travel quarantine of Wuhan. By assuming a generation time of 7.5 days, the reproduction number is estimated to be 2.4 [90% CI 2.2-2.6]. The median estimate for number of cases before the travel ban implementation on January 23, 2020 is 58,956 [90% CI 40,759 - 87,471] in Wuhan and 3,491 [90% CI 1,924 - 7,360] in other locations in Mainland China. The model shows that as of January 23, most Chinese cities had already received a considerable number of infected cases, and the travel quarantine delays the overall epidemic progression by only 3 to 5 days. The travel quarantine has a more marked effect at the international scale, where we estimate the number of case importations to be reduced by 80% until the end of February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

98: Diagnosis of Acute Respiratory Syndrome Coronavirus 2 Infection by Detection of Nucleocapsid Protein
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Posted 10 Mar 2020

Diagnosis of Acute Respiratory Syndrome Coronavirus 2 Infection by Detection of Nucleocapsid Protein
12,345 downloads medRxiv epidemiology

Bo Diao, Kun Wen, Jian Chen, Yueping Liu, Zilin Yuan, Chao Han, Jiahui Chen, Yuxian Pan, Li Chen, Yunjie Dan, Jing Wang, Yongwen Chen, Guohong Deng, Hongwei Zhou, Yuzhang Wu

BACKGROUNDNucleic acid test and antibody assay have been employed in the diagnosis for SARS-CoV-2 infection, but the use of viral antigen for diagnosis has not been successfully developed. Theoretically, viral antigen is the specific marker of the virus and precedes antibody appearance within the infected population. There is a clear need of detection of viral antigen for rapid and early diagnosis. METHODSWe included a cohort of 239 participants with suspected SARS-CoV-2 infection from 7 centers for the study. We measured nucleocapsid protein in nasopharyngeal swab samples in parallel with the nucleic acid test. Nucleic acid test was taken as the reference standard, and statistical evaluation was taken in blind. We detected nucleocapsid protein in 20 urine samples in another center, employing nasopharyngeal swab nucleic acid test as reference standard. RESULTSWe developed a fluorescence immunochromatographic assay for detecting nucleocapsid protein of SARS-CoV-2 in nasopharyngeal swab sample and urine within 10 minutes. 100% of nucleocapsid protein positive and negative participants accord with nucleic acid test for same samples. Further, earliest participant after 3 days of fever can be identified by the method. In an additional preliminary study, we detected nucleocapsid protein in urine in 73.6% of diagnosed COVID-19 patients. CONCLUSIONSThose findings indicate that nucleocapsid protein assay is an accurate, rapid, early and simple method for diagnosis of COVID-19. Appearance of nucleocapsid protein in urine coincides our finding of the SARS-CoV-2 invading kidney and might be of diagnostic value.

99: Estimation of the final size of the coronavirus epidemic by the logistic model
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Posted 18 Feb 2020

Estimation of the final size of the coronavirus epidemic by the logistic model
12,277 downloads medRxiv epidemiology

Milan Batista

In this short paper, the logistic growth model and classic susceptible-infected-recovered dynamic model are used to estimate the final size of the coronavirus epidemic.

100: Collider bias undermines our understanding of COVID-19 disease risk and severity
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Posted 08 May 2020

Collider bias undermines our understanding of COVID-19 disease risk and severity
12,270 downloads medRxiv epidemiology

Gareth Griffith, Tim T. Morris, Matt Tudball, Annie Herbert, Giulia Mancano, Lindsey Pike, Gemma C Sharp, Tom Palmer, George Davey Smith, Kate Tilling, Luisa Zuccolo, Neil M Davies, Gibran Hemani

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

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