### Overview

MeSH Disease

Disease (38)

Infections (38)

Death (22)

Human Phenotype

Falls (4)

Cough (2)

Pneumonia (1)

Shock (1)

Fever (1)

Transmission

Seroprevalence
displaying 1 - 10 records in total 81
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### COVID-19: Time-Dependent Effective Reproduction Number TRANS and Sub-notification Effect Estimation Modeling

Authors: Eduardo Atem De Carvalho; Rogerio Atem De Carvalho

doi:10.1101/2020.07.28.20164087 Date: 2020-07-30 Source: medRxiv

Background: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection MESHD and death MESHD cycles, in order to be able to make better decisions. In particular, a series of reproduction number TRANS estimation models have been presented, with different practical results. Objective: This article aims to present an effective and efficient model for estimating the Reproduction Number TRANS and to discuss the impacts of sub-notification on these calculations. Methods: The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number TRANS, is derived from experimental data. The models are applied to real data and their performance SERO is presented. Results: Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance SERO of the methods here introduced. Conclusions: We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent Reproduction Numbers TRANS (Rt), as well as we demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

### Analyzing the dominant SARS-CoV-2 transmission TRANS modes towards an ab-initio SEIR model

Authors: Swetaprovo Chaudhuri; Saptarshi Basu; Abhishek Saha

id:2007.13596v1 Date: 2020-07-27 Source: arXiv

In this work, different transmission TRANS modes of the SARS-CoV-2 virus and their role in determining the evolution of the Covid-19 pandemic are analyzed. Probability of infection MESHD caused by inhaling infectious droplets (initial, ejection diameters between 0.5-750$\mu m$) and probability of infection MESHD by the corresponding desiccated nuclei that mostly encapsulate the virions post droplet evaporation, are calculated. At typical, air-conditioned yet quiescent, large indoor space, for the average viral loading, and at early times, cough MESHD cough HP droplets of initial diameter between $10 \mu m$ and $50 \mu m$ have the highest infection MESHD probability. However, by the time they are to be inhaled, the diameters are most likely $5-6$ times smaller with respect to their initial diameters. While the initially near unity infection MESHD probability due to droplets (airborne/ballistic) rapidly decays within the first $25$s, the small yet persistent infection MESHD probability of airborne desiccated nuclei decays appreciably only by $1000$s. Combined with molecular collision theory adapted to calculate frequency of contact TRANS frequency of contact SERO between the susceptible population and the droplet/nuclei cloud, infection MESHD probabilities are used to define infection MESHD rate constants, ab-initio, leading to a SEIR model. Assuming the virus sustains equally well within the dried droplet nuclei as in the droplets, the floating nuclei leads to a stronger contribution to the corresponding rate constants with respect to the droplets, in the above-mentioned conditions. Combining both pathways, the basic reproduction number TRANS $\mathcal{R}_0$ caused by cough MESHD cough HP droplets and nuclei are calculated. Viral load, minimum infectious dose, sensitivity SERO of the virus half-life to the phase of its vector, extent of dilution of the respiratory jet/puff by the entraining air are the important factors that determine specific physical modes of transmission TRANS and the pandemic evolution.

### A logistic model of CoV-2 propagation

Authors: Robert F Weiss

doi:10.1101/2020.07.20.20157826 Date: 2020-07-26 Source: medRxiv

We describe an elemental logistic model for the propagation of CoV-2 in a community and illustrate the sensitivity SERO of the model to key parameters such as R0 TRANS, the initial rate of infections MESHD per infected person, and A0 , the fraction of infected people developing neutralizing antibodies SERO. We demonstrate the importance of the duration of immunity in the population, the development of waves of new cases of infection MESHD, and the effect of premature opening of local economies.

### Clinical Impact, Costs, and Cost-Effectiveness of Expanded SARS-CoV-2 Testing in Massachusetts

Authors: Anne M Neilan; Elena Losina; Audrey C. Bangs; Clare Flanagan; Christopher Panella; G. Ege Eskibozkurt; Amir M. Mohareb; Emily P. Hyle; Justine A. Scott; Milton C. Weinstein; Mark J. Siedner; Krishna P Reddy; Guy Harling; Kenneth A. Freedberg; Fatma M. Shebl; Pooyan Kazemian; Andrea L. Ciaranello

doi:10.1101/2020.07.23.20160820 Date: 2020-07-24 Source: medRxiv

Background We projected the clinical and economic impact of alternative testing strategies on COVID-19 incidence and mortality in Massachusetts using a microsimulation model. Methods We compared five testing strategies: 1) PCR-severe-only: PCR testing only patients with severe/critical symptoms; 2) Self-screen: PCR-severe-only plus self-assessment of COVID-19-consistent symptoms with self-isolation if positive; 3) PCR-any-symptom: PCR for any COVID-19-consistent symptoms with self-isolation if positive; 4) PCR-all: PCR-any-symptom and one-time PCR for the entire population; and, 5) PCR-all-repeat: PCR-all with monthly re-testing. We examined effective reproduction numbers TRANS (Re, 0.9-2.0) at which policy conclusions would change. We used published data on disease progression MESHD and mortality, transmission TRANS, PCR sensitivity SERO/specificity (70/100%) and costs. Model-projected outcomes included infections MESHD, deaths MESHD, tests performed, hospital-days, and costs over 180-days, as well as incremental cost-effectiveness ratios (ICERs, \$/quality-adjusted life-year [QALY]). Results In all scenarios, PCR-all-repeat would lead to the best clinical outcomes and PCR-severe-only would lead to the worst; at Re 0.9, PCR-all-repeat vs. PCR-severe-only resulted in a 63% reduction in infections MESHD and a 44% reduction in deaths MESHD, but required >65-fold more tests/day with 4-fold higher costs. PCR-all-repeat had an ICER

### Mathematical Modeling and Optimal Control Analysis of COVID-19 in Ethiopia

Authors: Haileyesus Tessema Alemneh; Getachew Teshome Telahun

doi:10.1101/2020.07.23.20160473 Date: 2020-07-24 Source: medRxiv

In this paper we developed a deterministic mathematical model of the pandemic COVID-19 transmission TRANS in Ethiopia, which allows transmission TRANS by exposed humans. We proposed an SEIR model using system of ordinary differential equations. First the major qualitative analysis, like the disease MESHD free equilibruim point, endemic equilibruim point, basic reproduction number TRANS, stability analysis of equilibrium points and sensitivity SERO analysis was rigorously analysed. Second, we introduced time dependent controls to the basic model and extended to an optimal control model of the disease MESHD. We then analysed using Pontryagins Maximum Principle to derive necessary conditions for the optimal control of the pandemic. The numerical simulation indicated that, an integrated strategy effective in controling the epidemic and the gvernment must apply all control strategies in combating COVID-19 at short period of time.

### Impact of climatic, demographic and disease MESHD control factors on the transmission TRANS dynamics of COVID-19 in large cities worldwide

Authors: Soeren Metelmann; Karan Pattni; Liam Brierley; Lisa Cavalerie; Cyril Caminade; Marcus SC Blagrove; Joanne Turner; Kieran J Sharkey; Matthew Baylis

doi:10.1101/2020.07.17.20155226 Date: 2020-07-17 Source: medRxiv

We are now over seven months into a pandemic of COVID-19 caused by the SARS-CoV-2 virus and global incidence continues to rise. In some regions such as the temperate northern hemisphere there are fears of "second waves" of infections MESHD over the coming months, while in other, vulnerable regions such as Africa and South America, concerns remain that cases may still rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission TRANS has the same sensitivity SERO to climate and seasonality observed for other common respiratory viruses such as seasonal influenza. Here we investigate any empirical evidence of seasonality using a robust estimation framework. For 304 large cities across the world, we estimated the basic reproduction number TRANS ( R0 TRANS) using logistic growth curves fitted to cumulative case data. We then assessed evidence for association with climatic variables through mixed-effects and ordinary least squares (OLS) regression while adjusting for city-level variation in demographic and disease MESHD control factors. We find evidence of association between temperature and R0 TRANS during the early phase of the epidemic in China only. During subsequent pandemic spread outside China, we instead find evidence of seasonal change in R0 TRANS, with greater R0 TRANS within cities experiencing shorter daylight hours (direct effect coefficient = -0.247, p = 0.006), after separating out effects of calendar day. The effect of daylight hours may be driven by levels of UV radiation, which is known to have detrimental effects on coronaviruses, including SARS-CoV-2. In the global analysis excluding China, climatic variables had weaker explanatory power compared to demographic or disease MESHD control factors. Overall, we find a weak but detectable signal of climate variables on the transmission TRANS of COVID-19. As seasonal changes occur later in 2020, it is feasible that the transmission TRANS dynamics of COVID-19 may shift in a detectable manner. However, rates of transmission TRANS and health burden of the pandemic in the coming months will be ultimately determined by population factors and disease MESHD control policies.

### COVID-19 effective reproductive ratio determination: An application, and analysis of issues and influential factors

Authors: Luis Alfredo Bautista Balb&aacutes; Mario Gil Conesa; Gil Rodr&iacuteguez Caravaca; Blanca Bautista Balbas

doi:10.1101/2020.07.15.20154039 Date: 2020-07-16 Source: medRxiv

An essential indicator of COVID-19 transmission TRANS is the effective reproduction number TRANS (Rt), the number of cases which an infected individual is expected to infect at a particular moment of time; curves of the evolution of Rt over time ( transmission TRANS curves) reflect the impact of preventive measures and whether an epidemic is controlled. We have created a Shiny/R web application with user-selectable features: open data sources with daily COVID-19 incidences from all countries and many regions, customizable preprocessing options (smoothing, proportional increment, backwards distribution of negative corrections, etc), different MonteCarlo-Markov-Chain estimates of the generation time or serial interval TRANS distributions and state-of-the-art Rt estimation frameworks (EpiEstim, R0 TRANS). We have analyzed the impact of these factors in the obtained transmission TRANS curves. We also have obtained curves at the national and sub-national level and analyzed the impact of epidemic control strategies, superspreading events, socioeconomic factors and outbreaks. We conclude that country wealth and, to a lesser extent, mitigation strategies, were associated with poorer epidemic control. Dataset quality was an important factor, and sometimes dictated the necessity of time series smoothing. We couldn't find conclusive evidence regarding the impact of alleged superspreading events. In the reopening phase, outbreaks had an impact on transmission TRANS curves. This application could be used interactively as a tool both to obtain transmission TRANS estimates and to perform interactive sensitivity SERO analysis.

### A Novel Mathematical Model (SEIRQ) of the COVID-19 Epidemic: Assessing the Epidemiological Rates of Diseases MESHD Diseases Spread TRANS Spread in Saudi Arabia

Authors: Hamdy Youssef; Najat Alghamdi; Magdi A. Ezzat; Alaa A. El-Bary; Ahmed M. Shawky

id:202007.0253/v1 Date: 2020-07-12 Source: Preprints.org

This article aims to construct a new epidemic mathematical model for the outbreak of the novel coronavirus COVID-19. The SEIRQ pandemic model provides a new approach for evaluations and management of the COVID-19 epidemic. For mathematical modeling and dynamic analyses, this paper uses real data surrounding the spread of COVID-19 in Saudi Arabia. The dynamics of the SEIRQ model are presented with the reproduction number TRANS and with extensive stability analysis. We discuss the domain of the solution and equilibrium situation based on the SEIRQ model by using a Jacobian method of linearization. The condition of equilibrium and its uniqueness has been proven, and the stability analysis of disease MESHD-free equilibrium has been introduced. A sensitivity SERO analysis of the reproduction number TRANS against its internal parameters has been achieved. The global stability of the equilibrium of the new model has been proven by using the Lyapunov stability theorem. A numerical verification and predictions of the SEIRQ model have been provided by comparing the results based on the SEIRQ model with real data on the spread of COVID-19 in Saudi Arabia. The outcome of this work reveals that the SEIRQ model is a successful model for analyzing the spread of epidemics, such as COVID-19. At the end of this work, we introduce an ideal protocol that can help the Saudi population quickly stop the spread of COVID-19.

### Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

Authors: Adeyeri O.E.; Oyekan K.S.A.; Ige S.O.; Akinbobola A.; Okogbue E.C.

doi:10.21203/rs.3.rs-41193/v1 Date: 2020-07-11 Source: ResearchSquare

The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death MESHD cases in Nigeria using six estimation methods i.e. the attack rate TRANS, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity SERO analysis with respect to the mean generation time is used to quantify the associated reproduction number TRANS uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number TRANS estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number TRANS estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number TRANS estimate is different from the default reproduction number TRANS estimate.  Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death MESHD and cumulative confirmed cases TRANS are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.

### Repeat SARS-CoV-2 Testing Models for Residential College Populations

Authors: Joseph T. Chang; Forrest W. Crawford; Edward H. Kaplan

doi:10.1101/2020.07.09.20149351 Date: 2020-07-10 Source: medRxiv

Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections MESHD rapidly to block transmission TRANS that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity SERO that can depend on the time since infection MESHD, imported infections MESHD from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission TRANS of SARS-CoV-2 by age TRANS of infection MESHD, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers TRANS above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number TRANS remains below 1.75 (2.3) if transmission TRANS happens earlier (later) with time from infection MESHD, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).

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 All MeSH Disease Human Phenotype Transmission Seroprevalence

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