Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis

Xu, Hang; Yuan, Min; Ma, Liya; Li, Meng; Zhang, Yi; Liu, Wenwen; Gan, Hong; Tao, Fangbiao

doi:10.1101/2020.04.13.20060228

“…In a study that was carried out on 69 patients who were diagnosed with COVID-19 in China to evaluate the relation between P-LCR and severity of disease, there was a negative correlation between P-LCR severe COVID-19 patients (19). This result is contrary to our result that found no correlation between disease severity and the value of P-LCR in hospitalized individuals with COVID-19.…”

Section: Discussioncontrasting

confidence: 99%

Evaluation of the relation between disease severity with platelet distribution width, platelet large cell ratio, mean platelet volume and red cell distribution width in severe and critically ill hospitalized patients with coronavirus disease 2019

Hajian

¹

,

Sarbazi-Golezari

²

,

Karbasi

³

et al. 2022

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Introduction: Hematological parameters are raised for classification and determining of prognosis in coronavirus disease 2019 (COVID-19). Objectives: The aim of this study is to determine the relation between severity of the illness and serum level of red cell distribution width (RDW), mean platelet volume (MPV), platelet distribution width (PDW) and platelet large cell ratio (P-LCR) in patients with COVID-19 focusing on severe and critically ill patients. Patients and Methods: In this cross-sectional investigation, we examined the correlation between severity of disease and value of RDW, PDW, P-LCR and MPV in hospitalized patients with COVID-19 in Velayat hospital, located in Qazvin province in Iran from November 2020 to March 2021. According to the clinical characteristics, the patients were divided into two group of severe and critically ill. Patients’ data including age, gender, and the value of RDW, PDW, P-LCR and MPV were extracted from the health information system of our hospital. Then, these parameters were analyzed and compared among the groups. Results: The data of 59 patients were evaluated. The mean age of patients was 62.07 years and 59.3% of these patients were female. Around 64.4 percent of the patients were critically ill. The median the value of RDW in the critically ill group was significantly higher than the severe group (P = 0.009). The value of RDW had a significant positive correlation with the severity of the disease (correlation coefficient: 0.341, P = 0.008). Conclusion: Our data showed that RDW might be helpful to differentiate the critically ill patient with COVID-19 from severe one.

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“…Statistical models often eschew deterministic population dynamics and fit the observed data as a function of time and possibly other covariates in a regression (or equivalent) framework. Log-linear [73], generalized Richards [74], ARIMA [75,76], exponential [77], Gaussian CDF [78], and logistic [79][80][81] models, which all accommodate the generally sigmoidal shape of the cumulative infection count that is often observed in epidemics, as well as various other models [82][83][84][85] including machine learning algorithms [86][87][88] have been proposed for COVID-19. Murray et al and Woody et al take similar approaches for modeling COVID-19 deaths using the error function (ERF) [89,90].…”

Section: Introductionmentioning

confidence: 99%

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

Watson

¹

,

Xiong

²

,

Zhang

³

et al. 2021

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

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“…68 Statistical models often eschew deterministic population dynamics and fit the 69 observed data as a function of time and possibly other covariates in a regression (or 70 equivalent) framework. Log-linear [74], generalized Richards [75], ARIMA [76,77], 71 exponential [78], Gaussian CDF [79], and logistic [80][81][82] models, which all 72 accommodate the generally sigmoidal shape of the cumulative infection count that is 73 often observed in epidemics, as well as various other models [83][84][85][86] including machine 74 learning algorithms [87][88][89] have been proposed for COVID-19. Murray et al and 75 Woody et al take similar approaches for modeling COVID-19 deaths using the error 76 function (ERF) [90,91].…”

mentioning

confidence: 99%

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Watson

¹

,

Xiong

²

,

Zhang

³

et al. 2020

Preprint

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random : medRxiv preprint forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

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Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis

Cited by 3 publications

References 42 publications

Evaluation of the relation between disease severity with platelet distribution width, platelet large cell ratio, mean platelet volume and red cell distribution width in severe and critically ill hospitalized patients with coronavirus disease 2019

Evaluation of the relation between disease severity with platelet distribution width, platelet large cell ratio, mean platelet volume and red cell distribution width in severe and critically ill hospitalized patients with coronavirus disease 2019

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

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