### Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
displaying 1 - 10 records in total 47
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### COVID-19 outbreak and control in Kenya- Insights from a mathematical model

Authors: Rachel Waema Mbogo; Titus Okellow Orwa

doi:10.21203/rs.3.rs-77507/v1 Date: 2020-09-14 Source: ResearchSquare

The coronavirus disease MESHD 2019 ( COVID -19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID -19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As of May 25,2020 It had led to over 100,000 confirmed cases TRANS in Africa with over 3000 deaths. The trend poses a huge threat to global public health. Understanding the early transmission TRANS dynamics of the infection MESHD and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission TRANS to occur in new areas. We employed a SEIHCRD mathematical transmission TRANS model with reported Kenyan data on cases of COVID -19 to estimate how transmission TRANS varies over time. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak. The next generation matrix approach was adopted to calculate the basic reproduction number TRANS ( $R_0 TRANS$ ) from the model to assess the factors driving the infection . The results from the model analysis shows that non-pharmaceutical interventions over a relatively long period is needed to effectively get rid of the COVID -19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.

### A "Tail" of Two Cities: Fatality-based Modeling of COVID-19 Evolution in New York City and Cook County, IL

Authors: Joshua Frieman

doi:10.1101/2020.08.10.20170506 Date: 2020-08-12 Source: medRxiv

I describe SIR modeling of the COVID-19 pandemic in two U.S. urban environments, New York City (NYC) and Cook County, IL, from onset through the month of June, 2020. Since testing was not widespread early in the pandemic in the U.S., I do not use data on confirmed cases TRANS and rely solely on public fatality data to estimate model parameters. Fits to the first 20 days of data determine a degenerate combination of the basic reproduction number TRANS, R0 TRANS, and the mean time to removal from the infectious population, 1/{gamma} with {gamma}( R0 TRANS - 1) = 0.25(0.21) inverse days for NYC (Cook County). Equivalently, the initial doubling time was td = 2.8(3.4) days for NYC (Cook). The early fatality data suggest that both locations had infections MESHD in early February. I model the mitigation measures implemented in mid-March in both locations (distancing, quarantine, isolation, etc) via a time-dependent reproduction number TRANS Rt that declines MESHD monotonically from R0 TRANS to a smaller asymptotic TRANS value, with a parameterized functional form. The timing (mid-March) and duration (several days) of the transitions in Rt appear well determined by the data. However, the fatality data determine only a degenerate combination of the parameters R0 TRANS, the percentage reduction in social contact due to mitigation measures, X, and the infection fatality rate (IFR), f . With flat priors, based on simulations the NYC model parameters have 95.45% credible intervals of R0 TRANS = 3.0 - 5.4, X = 80 - 99.9% and f = 2 - 6%, with 5 - 13% of the population asymptotically infected. A strong external prior indicating a lower value of f or of 1/{gamma} would imply lower values of R0 TRANS and X and higher percentage infection of the population. For Cook County, the evolution was qualitatively different: after mitigation measures were implemented, the daily fatality counts reached a plateau for about a month before tailing off. This is consistent with an SIR model that exhibits "critical slowing-down", in which Rt plateaus at a value just above unity. For Cook County, the 95.45% credible intervals for the model parameters are much broader and shifted downward, R0 TRANS = 1.4 - 4.7, X = 26 - 54%, and f = 0.1 - 0.6% with 15 - 88% of the population asymptotically infected. Despite the apparently lower efficacy of its social contact reduction measures, Cook County has had significantly fewer fatalities per population than NYC, D{infty}/N = 100 vs. 270 per 100,000. In the model, this is attributed to the lower inferred IFR for Cook; an external prior pointing to similar values of the IFR for the two locations would instead chalk up the difference in D/N to differences in the relative growth rate of the disease. I derive a model-dependent threshold, Xcrit, for "safe" re-opening, that is, for easing of contact reduction that would not trigger a second wave; for NYC, the models predict that increasing social contact by more than 20% from post-mitigation levels will lead to renewed spread, while for Cook County the threshold value is very uncertain, given the parameter degeneracies. The timing of 2nd-wave growth will depend on the amplitude of contact increase relative to Xcrit and on the asymptotic TRANS growth rate, and the impact in terms of fatalities will depend on the parameter f .

### Dynamic Public Health Interventions Consistent With the Development of COVID-19 Epidemic: The Targeted Prevention and Control Guidelines in Mainland, China

Authors: Xinlei Miao; Zhiyuan Wu; Chen Qiao; Mengmeng Liu; Zhiwei Li; Yijie Wang; Zongkai Xu; Xiuhua Guo; Qun Meng

doi:10.21203/rs.3.rs-55959/v1 Date: 2020-08-08 Source: ResearchSquare

Background: This study aims to describe the dynamic characteristics of COVID-19 transmission TRANS and the public health interventions in three phases in mainland, China.Methods: The number of daily reported new confirmed cases TRANS, severe cases and asymptomatic TRANS infected MESHD cases from Jan 10 to Jul 10 was analyzed. We calculated the effective reproduction number TRANS (Rt) to reflect the dynamic characteristics of epidemic transmission TRANS and intervention effect. According to the overall guidelines for prevention and control, we divided the past six months into three phases and summarized the features of main public health interventions in each phase.Results: The daily confirmed cases TRANS and severe cases of COVID-19 mainly concentrated in the first phase and the maximum Rt reached 10.75 (95%CI: 10.26-11.24). With the society-wide efforts and joint prevention and control strategy, Rt began to decline below 1.0 from Feb 19. In the second phase, the occurrence of imported infected cases caused small fluctuations. The preventive strategy, preventing both imported cases and local spread of epidemic, was mainly taken. In the third phase, the government adopted policies to prevent imported cases and domestic re-infections, responding to the regular epidemic prevention demands. Conclusion: Social isolation, wearing masks, digital management based on community and area hierarchical control were effective public health interventions in consistent with the development of COVID-19 epidemic. The targeted dynamic interventions in different phases could provide reference for other countries and regions to deal with COVID-19.

### Genomic epidemiology reveals transmission TRANS patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand

Authors: Jemma L Geoghegan; Xiaoyun Ren; Matthew Storey; James Hadfield; Lauren Jelley; Sarah Jefferies; Jill Sherwood; Shevaun Paine; Sue Huang; Jordan Douglas; Fabio K L Mendes; Andrew Sporle; Michael G Baker; David R Murdoch; Nigel French; Colin R Simpson; David Welch; Alexei J Drummond; Edward C Holmes; Sebastian Duchene; Joep de Ligt

doi:10.1101/2020.08.05.20168930 Date: 2020-08-06 Source: medRxiv

New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nation-wide lockdown of all non-essential services to curb the spread of COVID-19. New Zealand has now effectively eliminated the virus, with low numbers of new cases limited to new arrivals in managed quarantine facilities at the border. Here, we generated 649 SARS-CoV-2 genome sequences from infected MESHD patients in New Zealand with samples collected between 26 February and 22 May 2020, representing 56% of all confirmed cases TRANS in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. The proportion of D614G variants in the virus spike protein increased over time due to an increase in their importation frequency, rather than selection within New Zealand. These data also helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number TRANS, Re, of New Zealand's largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in a transmission TRANS lineage of more than one additional case. Most of the cases that resulted in a transmission TRANS lineage originated from North America, rather than from Asia where the virus first emerged or from the nearest geographical neighbour, Australia. Genomic data also helped link more infections MESHD to a major transmission TRANS cluster than through epidemiological data alone, providing probable sources of infections for cases in which the source was unclear. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.

### Analysis of COVID-19 and comorbidity co-infection MESHD Model with Optimal Control

Authors: Dr. Andrew Omame; Nometa Ikenna

doi:10.1101/2020.08.04.20168013 Date: 2020-08-04 Source: medRxiv

The new coronavirus disease MESHD 2019 (COVID-19) infection MESHD is a double challenge for people infected MESHD with comorbidities such as cardiovascular and cerebrovascular diseases MESHD and diabetes MESHD. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection MESHD in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re-infection. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection MESHD by comorbid susceptibles as well as the rate of re-infection by those who have recovered from a previous COVID-19 infection MESHD. Sensitivity SERO analysis of the model when the population of individuals co-infected MESHD with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co-infection MESHD model are the effective contact rate for COVID-19 transmission TRANS, $\beta\sst{cv}$, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, $\chi\sst{cm}$, the comorbidity development rate, $\theta\sst{cm}$, the detection rate for singly infected and co-infected MESHD individuals, $\eta_1$ and $\eta_2$, as well as the recovery rate from COVID-19 for co-infected MESHD individuals, $\varphi\sst{i2}$. Simulations of the model reveal that the cumulative confirmed cases TRANS (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases TRANS (including those co-infected MESHD with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re-infection rates for COVID-19 is 0.1 per day. It may be worse than this if the re-infection rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection MESHD by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected MESHD individuals to 0.7 per day, then the reproduction number TRANS can be brought very low below one, and COVID-19 infection eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection MESHD by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

### Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan

Authors: Motoaki Utamura; Makoto Koizumi; Seiichi Kirikami

doi:10.1101/2020.07.31.20165829 Date: 2020-07-31 Source: medRxiv

Background: Coronavirus Disease MESHD 2019 (COVID19) currently poses a global public health threat. Although no exception, Tokyo, Japan was affected at first by only a small epidemic. Medical collapse nevertheless nearly happened because no predictive method existed for counting patients. A standard SIR epidemiological model and its derivatives predict susceptible, infectious, and removed (recovered/deaths) cases but ignore isolation of confirmed cases TRANS. Predicting COVID19 trends with hospitalized and infectious people in field separately is important to prepare beds and develop quarantine strategies. Methods: Time-series COVID19 data from February 28 to May 23, 2020 in Tokyo were adopted for this study. A novel epidemiological model based on delay differential equation was proposed. The model can evaluate patients in hospitals and infectious cases in the field. Various data such as daily new cases, cumulative infections MESHD, patients in hospital, and PCR test positivity ratios were used to examine the model. This approach derived an alternative formulation equivalent to the standard SIR model. Its results were compared quantitatively with those of the present isolation model. Results: The basic reproductive number TRANS, inferred as 2.30, is a dimensionless parameter composed of modeling parameters. Effects of intervention to mitigate the epidemic spread were assessed a posteriori. An exit policy of how and when to release a statement of emergency was also assessed using the model. Furthermore, results suggest that the rapid isolation of infectious cases has a large potential to effectively mitigate the spread of infection MESHD and restores social and economic activities safely. Conclusions: A novel mathematical model was proposed and examined using COVID19 data for Tokyo. Results show that shortening the period from infection to hospitalization is effective against outbreak without rigorous public health intervention and control. Faster and precise case cluster detection and wider and quicker introduction of testing measures are strongly recommended.

### Time is of the essence: containment of the SARS-CoV-2 epidemic in Switzerland from February to May 2020

Authors: Christian L Althaus; Daniel Probst; Anthony Hauser; Julien L Riou

doi:10.1101/2020.07.21.20158014 Date: 2020-07-25 Source: medRxiv

AIM: In late February and early March 2020, Switzerland experienced rapid growth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections MESHD with 30,243 confirmed cases TRANS and 1,860 deaths as of 10 May 2020. The sequential introduction of non-pharmaceutical interventions (NPIs) resulted in successful containment of the epidemic. A better understanding of how the timing of implementing NPIs influences the dynamics and outcome of SARS-CoV-2 epidemics will be crucial for the management of a potential resurgence in Switzerland. METHODS: We developed a dynamic transmission TRANS model that describes infection MESHD, hospitalization, recovery and death MESHD due to SARS-CoV-2 in Switzerland. Using a maximum likelihood framework, we fitted the model to aggregated daily numbers of hospitalized patients, ICU occupancy MESHD and death MESHD from 25 February to 10 May 2020. We estimated critical parameters of SARS-CoV-2 transmission TRANS in Switzerland and explored counterfactual scenarios of an earlier and later implementation of NPIs. RESULTS: We estimated the basic reproduction number TRANS R0 TRANS = 2.61 (95% compatibility interval, CI: 2.51-2.71) during the early exponential phase of the SARS-CoV-2 epidemic MESHD in Switzerland. After the implementation of NPIs, the effective reproduction number TRANS approached Re = 0.64 (95% CI: 0.61-0.66). Based on the observed doubling times of the epidemic before and after the implementation of NPIs, we estimated that one week of early exponential spread required 3.1 weeks (95% CI: 2.8-3.3 weeks) of 'lockdown' to reduce the number of infections to the same level. Introducing the same sequence of NPIs one week earlier or later would have resulted in substantially lower (399, 95% prediction interval, PI: 347-458) and higher (8,683, 95% PI: 8,038-9,453) numbers of deaths, respectively. CONCLUSIONS: The introduction of NPIs in March 2020 prevented thousands of SARS-CoV-2-related deaths in Switzerland. Early implementation of NPIs during SARS-CoV-2 outbreaks can reduce the number of deaths MESHD and the necessary duration of strict control measures considerably.

### A comprehensive analysis of R0 TRANS with different lockdown phase during covid-19 in India

Authors: Mayank Chhabra; Tushant Agrawal

doi:10.1101/2020.07.10.20150631 Date: 2020-07-11 Source: medRxiv

Background: World Health organization declared Covid-19 as an outbreak, hence preventive measure like lockdown should be taken to control the spread of infection MESHD. This study offers an exhaustive analysis of the reproductive number TRANS ( R0 TRANS) in India with major intervention for COVID-19 outbreaks and analysed the lockdown effects on the Covid-19. Methodology: Covid-19 data extracted from Ministry of Health and Family Welfare, Government of India. Then, a novel method implemented in the incidence and Optimum function in desolve package to the data of cumulative daily new confirmed cases TRANS for robustly estimating the reproduction number TRANS in the R software. Result: Analysis has been seen that the lockdown was really quite as effective, India has already shown a major steady decline. The growth rate has fluctuated about 20 percent with trend line projections in various lockdown. A comparative analysis gives an idea of decline in value of R0 TRANS from 1.73 to 1.08. Annotation plot showing the predicted R0 TRANS values based on previous lockdown in month of June and July. Conclusion: Without lockdown, the growth might not have been contained in India and may have gone into the exponential zone. We show that, the lockdown in India was fairly successful. The effect partial lifting of the lockdown (unlock) is also seen in the results, in terms of increment in R0 TRANS values. Hence this study provides a platform for policy makers and government authorities for implementing the strategies to prevent the spread of infection MESHD.

### Estimating the time-varying reproduction number TRANS of COVID-19 with a state-space method

Authors: Shinsuke Koyama; Taiki Horie; Shigeru Shinomoto

doi:10.1101/2020.07.09.20150219 Date: 2020-07-11 Source: medRxiv

After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their severe confinement measures in the face of critical damage to socioeconomic structures. At this point, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease TRANS; however, tracing TRANS back individual transmission TRANS of infections whose incubation periods TRANS are long and highly variable seems to be difficult. Nevertheless, it may be possible to estimate the changes that may have occurred in the past, if we can suitably fit a proper model to daily event-occurrences. We have devised a state-space method for fitting the Hawkes process to a given dataset of daily confirmed cases TRANS. This method detects changes occurring in the spread of the contagion in each country. Furthermore, this method can assess the impact of social events in terms of the temporally varying reproduction number TRANS representing the average number of cases directly caused by a single infected case. This information might serve as a reference for the behavioral guidelines that should be adopted according to the varying risk of infection TRANS risk of infection TRANS infection MESHD.

### Reopening universities during the COVID-19 pandemic: A testing strategy to minimize active cases and delay outbreaks

Authors: Lior Rennert; Corey Andrew Kalbaugh; Lu Shi; Christopher McMahan

doi:10.1101/2020.07.06.20147272 Date: 2020-07-07 Source: medRxiv

Background: University campuses present an ideal environment for viral spread and are therefore at extreme risk of serving as a hotbed for a COVID-19 outbreak. While active surveillance throughout the semester such as widespread testing, contact tracing TRANS, and case isolation, may assist in detecting and preventing early outbreaks, these strategies will not be sufficient should a larger outbreak occur. It is therefore necessary to limit the initial number of active cases at the start of the semester. We examine the impact of pre-semester NAT testing on disease spread TRANS in a university setting. Methods: We implement simple dynamic transmission TRANS models of SARS-CoV-2 infection MESHD to explore the effects of pre-semester testing strategies on the number of active infections MESHD and occupied isolation beds throughout the semester. We assume an infectious period TRANS of 3 days and vary R0 TRANS to represent the effectiveness of disease mitigation strategies throughout the semester. We assume the prevalence SERO of active cases at the beginning of the semester is 5%. The sensitivity SERO of the NAT test is set at 90%. Results: If no pre-semester screening is mandated, the peak number of active infections occurs in under 10 days and the size of the peak is substantial, ranging from 5,000 active infections when effective mitigation strategies ( R0 TRANS = 1.25) are implemented to over 15,000 active infections for less effective strategies ( R0 TRANS = 3). When one NAT test is mandated within one week of campus arrival, effective ( R0 TRANS = 1.25) and less effective ( R0 TRANS = 3) mitigation strategies delay the onset of the peak to 40 days and 17 days, respectively, and result in peak size ranging from 1,000 to over 15,000 active infections. When two NAT tests are mandated, effective ( R0 TRANS = 1.25) and less effective ( R0 TRANS = 3) mitigation strategies delay the onset of the peak through the end of fall HP semester and 20 days, respectively, and result in peak size ranging from less than 1,000 to over 15,000 active infections. If maximum occupancy of isolation beds is set to 2% of the student population, then isolation beds would only be available for a range of 1 in 2 confirmed cases TRANS ( R0 TRANS = 1.25) to 1 in 40 confirmed cases TRANS ( R0 TRANS = 3) before maximum occupancy is reached. Conclusion: Even with highly effective mitigation strategies throughout the semester, inadequate pre-semester testing will lead to early and large surges of the disease and result in universities quickly reaching their isolation bed capacity. We therefore recommend NAT testing within one week of campus return. While this strategy is sufficient for delaying the timing of the outbreak, pre-semester testing would need to be implemented in conjunction with effective mitigation strategies to reduce the outbreak size.

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 All None MeSH Disease Human Phenotype Transmission Seroprevalence

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