Corpus overview


MeSH Disease

Human Phenotype



There are no seroprevalence terms in the subcorpus

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    Network model and analysis of the spread of Covid-19 with social distancing

    Authors: Parul Maheshwari; RĂ©ka Albert

    id:2006.09189v1 Date: 2020-06-16 Source: arXiv

    The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human-human interactions that could be mediators of disease MESHD disease spread TRANS spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions HP social interactions TRANS. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease MESHD parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease MESHD in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers TRANS during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission TRANS rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection MESHD in each of these scenarios.

    Analysis of the outbreak of COVID-19 in Japan on the basis of an SIQR model

    Authors: Takashi Odagaki

    doi:10.1101/2020.06.02.20117341 Date: 2020-06-05 Source: medRxiv

    The SIR model is modified, which may be called an SIQR model, so as to be appropriate for COVID-19 which has the following characteristics: [1] a long incubation period TRANS, [2] transmission TRANS of the virus by asymptomatic TRANS patients and [3] quarantine of patients identified through PCR testing. It is assumed that the society consists of four compartments; susceptibles (S), infecteds at large (simply called infecteds) (I), quarantined patients (Q) and recovered individuals (R), and the time evolution of the pandemic is described by a set of ordinary differential equations. It is shown that the quarantine rate can be determined from the time dependence of the daily confirmed new cases, from which the number of the infecteds at large can be estimated. The number of daily confirmed new cases is shown to be proportional to the number of infecteds a characteristic time earlier, and the infection MESHD rate and quarantine rate are determined for the period from mid-February to mid-April in Japan, and transmission TRANS characteristics of the initial stages of the outbreak in Japan are analyzed. The effectiveness of different measures is discussed for controlling the outbreak and it is shown that identifying patients through PCR testing and isolating them in a quarantine is more effective than lockdown measures aimed at inhibiting social interactions HP social interactions TRANS of the general population. An effective reproduction number TRANS for infecteds at large is introduced which is appropriate to epidemics controlled by quarantine measures.

    Social heterogeneity and the COVID-19 lockdown in a multi-group SEIR model

    Authors: Jean Dolbeault; Gabriel TURINICI

    doi:10.1101/2020.05.15.20103010 Date: 2020-05-20 Source: medRxiv

    The goal of the lockdown is to mitigate and if possible prevent the spread of an epidemic. It consists in reducing social interactions HP social interactions TRANS. This is taken into account by the introduction of a factor of reduction of social interactions HP social interactions TRANS q, and by decreasing the transmission TRANS coefficient of the disease MESHD accordingly. Evaluating q is a difficult question and one can ask if it makes sense to compute an average coefficient q for a given population, in order to make predictions on the basic reproduction rate R0 TRANS, the dynamics of the epidemic or the fraction of the population that will have been infected by the end of the epidemic. On a very simple example, we show that the computation of R0 TRANS in a heterogeneous population is not reduced to the computation of an average q but rather to the direct computation of an average coefficient R0 TRANS. Even more interesting is the fact that, in a range of data compatible with the Covid-19 outbreak, the size of the epidemic is deeply modified by social heterogeneity, as is the height of the epidemic peak, while the date at which it is reached mainly depends on the average R0 TRANS coefficient.

    Forecasting the scale of the COVID-19 epidemic in Kenya

    Authors: Samuel P C Brand; Rabia Aziza; Ivy K Kombe; Charles N Agoti; Joe Hilton; Kat S Rock; Andrea Parisi; D James Nokes; Matt Keeling; Edwine Barasa

    doi:10.1101/2020.04.09.20059865 Date: 2020-04-14 Source: medRxiv

    Background The first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age TRANS distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya. Methods We developed a modelling framework to simulate SARS-CoV-2 transmission TRANS in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission TRANS both within, and between, different Kenyan regions and age groups TRANS. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group TRANS social interaction HP social interaction TRANS rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number TRANS and the age TRANS-specific rate of developing COVID-19 symptoms after infection MESHD with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age TRANS distribution of cases observed in the Chinese outbreak. Results We find that if person-to-person transmission TRANS becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission TRANS scenario our reproductive number TRANS estimates for Kenya range from 1.78 (95% CI 1.44 - 2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic TRANS infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission TRANS, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

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MeSH Disease
Human Phenotype

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