Corpus overview


MeSH Disease

Human Phenotype


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    A Compartmental Epidemic Model Incorporating Probable Cases to Model COVID-19 Outbreak in Regions with Limited Testing Capacity

    Authors: Agus Hasan; Yuki Nasution

    doi:10.1101/2020.07.30.20165282 Date: 2020-08-02 Source: medRxiv

    We propose a new compartmental epidemic model taking into account people who has symptoms with no confirmatory laboratory testing (probable cases). We prove well-posedness of the model and provide an explicit expression for the basic reproduction number TRANS R0 TRANS. We use the model together with an extended Kalman filter (EKF) to estimate the time-varying effective reproduction number TRANS Rt of COVID-19 in West Java province, Indonesia, where laboratory testing capacity is limited. Based on our estimation, the value of Rt is higher when the probable cases are taken into account. This correction can be used by decision and policy makers when considering re-opening policy and evaluation of measures.

    Data-driven modeling and forecasting of COVID-19 outbreak for public policy making

    Authors: Agus Hasan; Endah Putri; Hadi Susanto; Nuning Nuraini

    doi:10.1101/2020.07.30.20165555 Date: 2020-08-02 Source: medRxiv

    This paper presents a data-driven approach for COVID-19 outbreak modeling and forecasting, which can be used by public policy and decision makers to control the outbreak through Non-Pharmaceutical Interventions (NPI). First, we apply an extended Kalman filter (EKF) to a discrete-time stochastic augmented compartmental model to estimate the time-varying effective reproduction number TRANS Rt. We use daily confirmed cases TRANS, active cases, recovered cases, deceased cases, Case-Fatality-Rate (CFR), and infectious time as inputs for the model. Furthermore, we define a Transmission TRANS Index (TI) as a ratio between the instantaneous and the maximum value of the effective reproduction number TRANS. The value of TI shows the disease MESHD transmission TRANS in a contact between a susceptible and an infectious individual due to current measures such as physical distancing and lock-down relative to a normal condition. Based on the value of TI, we forecast different scenarios to see the effect of relaxing and tightening public measures. Case studies in three countries are provided to show the practicability of our approach.

    Covid-19 mortality rates in Northamptonshire UK: initial sub-regional comparisons and provisional SEIR model of disease MESHD disease spread TRANS spread

    Authors: Nick Petford; Jackie Campbell

    doi:10.1101/2020.07.30.20165399 Date: 2020-08-02 Source: medRxiv

    We analysed mortality rates in a non-metropolitan UK subregion (Northamptonshire) to understand SARS-CoV-2 disease MESHD fatalities at sub 1000000 population levels. A numerical (SEIR) model was then developed to predict the spread of Covid-19 in Northamptonshire. A combined approach using statistically-weighted data to fit the start of the epidemic to the mortality record. Parameter estimates were then derived for the transmission TRANS rate and basic reproduction number TRANS. Age TRANS standardised mortality rates are highest in Northampton (urban) and lowest in semi-rural districts. Northamptonshire has a statistically higher Covid-19 mortality rate than for the East Midlands and England as a whole. Model outputs suggest the number of infected individuals exceed official estimates, meaning less than 40 percent of the population may require immunisation. Combining published (sub-regional) mortality rate data with deterministic models on disease MESHD disease spread TRANS spread has the potential to help public health practitioners develop bespoke mitigations, guided by local population demographics.

    Mathematical modeling of the transmission TRANS of SARS-CoV-2 '' Evaluating the impact of isolation in Sao Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of covid-19

    Authors: Hyun Mo Yang; Luis Pedro Lombardi Jr.; Fabio Fernandes Morato Castro; Ariana Campos Yang

    doi:10.1101/2020.07.30.20165191 Date: 2020-08-01 Source: medRxiv

    Coronavirus disease MESHD 2019 (covid-19), with the fatality rate in elder (60 years old or more) being much higher than young (60 years old or less) patients, was declared a pandemic by the World Health Organization on March 11, 2020. Taking into account this age TRANS-dependent fatality rate, a mathematical model considering young and elder subpopulations was formulated based on the natural history of covid-19 to study the transmission TRANS of the SARS-CoV-2. This model can be applied to study the epidemiological scenario resulting from the adoption of isolation or lockdown in many countries to control the rapid propagation of covid-19. We chose as examples the isolation adopted in Sao Paulo State (Brazil) in the early phase but not at the beginning of the epidemic, and the lockdown implemented in Spain when the number of severe covid-19 cases was increasing rapidly. Based on the data collected from Sa o Paulo State and Spain, the model parameters were evaluated and we obtained higher estimation for the basic reproduction number TRANS R0 TRANS (9.24 for Sao Paulo State, and 8 for Spain) compared to the currently accepted estimation of R0 TRANS around 3. The model allowed to explain the flattening of the epidemic curves by isolation in Sao Paulo State and lockdown in Spain when associated with the protective measures (face mask and social distancing) adopted by the population. However, a simplified mathematical model providing lower estimation for R0 TRANS did not explain the flattening of the epidemic curves. The implementation of the isolation in Sa o Paulo State before the rapidly increasing phase of the epidemic enlarged the period of the first wave of the epidemic and delayed its peak, which are the desirable results of isolation to avoid the overloading in the health care system.

    Optimal periodic closure for minimizing risk in emerging disease MESHD outbreaks

    Authors: Jason Hindes; Simone Bianco; Ira B. Schwartz

    id:2007.16151v1 Date: 2020-07-31 Source: arXiv

    Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases MESHD such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases MESHD, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number TRANS and incubation periods TRANS of a disease MESHD, as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases MESHD with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variability.

    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 MESHD) 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 MESHD 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 MESHD 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.

    The basic reproduction number TRANS of SARS-CoV-2: a scoping review of available evidence

    Authors: Ann Barber; John M Griffin; Miriam Casey; Aine Collins; Elizabeth A Lane; Quirine Ten Bosch; Mart De Jong; David Mc Evoy; Andrew W Byrne; Conor G McAloon; Francis Butler; Kevin Hunt; Simon J More

    doi:10.1101/2020.07.28.20163535 Date: 2020-07-30 Source: medRxiv

    Background: The transmissibility TRANS of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection MESHD. The basic reproduction number TRANS, R0 TRANS, provides a quantitative measure of the transmission TRANS potential of a pathogen. Objective: Conduct a scoping review of the available literature providing estimates of R0 TRANS for SARS-CoV-2, provide an overview of the drivers of variation in R0 TRANS estimates and the considerations taken in the calculation of the parameter. Design: Scoping review of available literature between the 01 December 2019 and 07 May 2020. Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteria: Studies were selected for review if (i) the estimation of R0 TRANS represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (lockdown), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0 TRANS. Results: A total of 20 R0 TRANS estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell HP within a wider range of 1.94-6.94 than statistical models which fell HP between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period TRANS which ranged from 5.8-20 days and the serial interval TRANS which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval TRANS equates to a higher R0 TRANS. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. Conclusions: The variation in reported estimates of R0 TRANS reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 TRANS is a fundamental parameter in the study of infectious disease MESHD dynamics however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

    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.

    The Role of Weather Conditions in COVID-19 Transmission TRANS: A Study of a Global Panel of 1236 Regions

    Authors: Chen Zhang; Hua Liao; Eric Strol; Hui Li; Ru Li; Steen Solvang Jensen; Ying Zhang

    doi:10.1101/2020.07.29.20164152 Date: 2020-07-30 Source: medRxiv

    Weather condition may impact COVID-19 transmission TRANS. The effects of temperature and humidity on COVID-19 transmission TRANS are not clear due to the difficulties in separating impacts of social distancing. We collected COVID-19 data and social-economic features of 1236 regions in the world (1112 regions at the provincial level and 124 countries with small land area). Moreover, a large-scale satellite data was combined with these data with a regression analysis model to explore effects of temperature and relative humidity on COVID-19 spreading, as well as the possible transmission risk TRANS due to temperature change driven by seasonal cycles. The result showed every degree Celsius increase in average temperature appears to cause a 2.88% decrease in the fraction of new daily cases 6 days later and a 0.62 percent point decrease in the reproductive number TRANS ( R0 TRANS). Every percentage point increase in relative humidity is found to lead to a 0.19% decrease in the fraction of new daily cases and a 0.02 percent point decrease in R0 TRANS 6 days later. Further, the effect of temperature and humidity is near to linear based on our samples. Government intervention (e.g. lockdown policies) and lower population movement contributed to the decrease the new daily case ratio. The conclusions withstand several robustness checks, such as observation scales and maximum/minimum temperature. The conclusion indicates air temperature and relative humidity are shown to be negatively correlated with COVID-19 transmission TRANS throughout the world. Given the diversity in both climate and social-economic conditions, the risk of transmission TRANS varies globally and possibly amplifies existing global health inequalities. Weather conditions are not the decisive factor in COVID-19 transmission TRANS, in that government intervention as well as public awareness, could contribute to the mitigation of the spreading of the virus.

    Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis

    Authors: Kenji Yamanishi; Linchuan Xu; Ryo Yuki; Shintaro Fukushima; Chuan-hao Lin

    id:2007.15179v1 Date: 2020-07-30 Source: arXiv

    We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about $64\%$ of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number TRANS $ R0 TRANS$ and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint. The software for the experiments is available at An online detection system is available at

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MeSH Disease
Human Phenotype

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