epidemiological model covid
22247160
Epidemiological models with age structure, proportionate mixing, and cross immunity
Infection by one strain of influenza type A provides some protection (cross immunity) against infection by a related strain. It is important to determine how this influences the observed co circulation of comparatively minor variants of the H1N1 and H3N2 subtypes. To this end, we formulate discrete and continuous time models with two viral strains, cross immunity, age structure, and infectious disease dynamics. Simulation and analysis of models with cross immunity indicate that sustained oscillations cannot be maintained by age specific infection activity level rates when the mortality rate is constant; but are possible if mortalities are age specific, even if activity levels are independent of age. Sustained oscillations do not seem possible for a single strain model, even in the presence of age specific mortalities; and thus it is suggested that the interplay between cross immunity and age specific mortalities may underlie observed oscillations.
Journal of mathematical biology
Carlos Castillo-Chavez Herbert W Hethcote Viggo Andreasen Simon A Levin Wanfa Liu
214641000
Epidemiological study of novel coronavirus (COVID 19)
We report a statistical analysis of some highly infected countries by the novel coronavirus (COVID 19) The cumulative infected data were fitted with various growth models (e.g. Logistic equation, Weibull equation and Hill equation) and obtained the power index of top ten highly infected countries. The newly infected data were fitted with Gaussian distribution with the peak at ~40 days for the countries whose infection curves are seem to be saturated. The similarity in growth kinetics of infected people of different countries provides first hand guidelines to take proper precautions to minimize human damage.
Jagadish Kumar K P S S Hembram
214589845
An Epidemiological Study on COVID 19: A Rapidly Spreading Disease
Background The outbreak of the novel coronavirus disease in 2019 (COVID 19) caused a major public health crisis worldwide and challenged healthcare systems across the six continents. The high infectivity of the disease led many governments to adopt strict regulations and measures with the aim of containing its spread. The purpose of this study is to assess the incidence, severity, and territorial expansion of COVID 19. Methods Data from the World Health Organization was screened, and COVID 19 situation reports were extracted from January 21 up till March 14 (inclusive) Our data included the total number of cases, total number of new cases, total number of cured cases, and total number of related deaths. Percentage change of cases over the days of our study were calculated using the Joinpoint regression, with a significance level set at greater than 0.05. Results The total number of COVID 19 cases reached 156,622, with 5,845 subsequent deaths. China, Italy, and Iran have the highest number of cases worldwide. During the first 22 days, the incidence rate of COVID 19 increased significantly to reach 1.81 cases per million persons (p<0.001) That was followed by a significant decrease over the next 11 days (p<0.001) to reach 0.071 cases per million persons. A steady rise then followed, which saw a significant increase in incidence rate to 1.429 cases per million persons (p<0.001) Percentages of death and cured cases varied across the different countries; nevertheless, death percentages have generally been decreasing since the start of the crisis. Conclusion Adopting precautionary regulations such as social isolation, increasing sanitation, and employing strict quarantine measures have proved to be beneficial in containing the virus. Further research needs to be conducted to help discover therapeutic modalities and improve outcomes.
Cureus
Hussein H Khachfe Mohamad A Chahrour Julie Sammouri Hamza A Salhab Bassel Eldeen Makki Mohamad Y Fares
215718829
An epidemiological forecast model and software assessing interventions on COVID 19 epidemic in China
We develop a health informatics toolbox that enables public health workers to timely analyze and evaluate the time course dynamics of the novel coronavirus (COVID 19) infection using the public available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are emitted from the underlying infection dynamics governed by a Markov SIR infectious disease process. We extend the SIR model to incorporate various types of time varying quarantine protocols, including government level macro isolation policies and community level micro inspection measures. We develop a calibration procedure for under reported infected cases. This toolbox provides forecast, in both online and offline forms, of turning points of interest, including the time when daily infected proportion becomes smaller than the previous ones and the time when daily infected proportions becomes smaller than that of daily removed proportion, as well as the ending time of the epidemic. An R software is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.
medRxiv
Peter X K Song Lili Wang Yiwang Zhou Jie He Bin Zhu Fei Wang Lu Tang Marisa C Eisenberg
210957036
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019 nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019 nCoV pneumonia. Methods In this retrospective, single centre study, we included all confirmed cases of 2019 nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real time RT PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019 nCoV pneumonia, 49 (49% had a history of exposure to the Huanan seafood market. The average age of the patients was 55*5 years (SD 13*1) including 67 men and 32 women. 2019 nCoV was detected in all patients by real time RT PCR. 50 (51% patients had chronic diseases. Patients had clinical manifestations of fever (82 [83% patients) cough (81 [82% patients) shortness of breath (31 [31% patients) muscle ache (11 [11% patients) confusion (nine [9% patients) headache (eight [8% patients) sore throat (five [5% patients) rhinorrhoea (four [4% patients) chest pain (two [2% patients) diarrhoea (two [2% patients) and nausea and vomiting (one [1% patient) According to imaging examination, 74 (75% patients showed bilateral pneumonia, 14 (14% patients showed multiple mottling and ground glass opacity, and one (1% patient had pneumothorax. 17 (17% patients developed acute respiratory distress syndrome and, among them, 11 (11% patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019 nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019 nCoV infection. Funding National Key R&D Program of China.
The Lancet
Nanshan Chen Min Zhou Xuan Dong Jieming Qu Fengyun Gong Yang Han Yang Qiu Jingli Wang Ying Liu Yuan Wei Jia'an Xia Ting Yu Xinxin Zhang Liangsheng Zhang
212985615
Characterizing the transmission and identifying the control strategy for COVID 19 through epidemiological modeling
The outbreak of the novel coronavirus disease, COVID 19, originating from Wuhan, China in early December, has infected more than 70,000 people in China and other countries and has caused more than 2,000 deaths. As the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks. Through rigorous epidemiological analysis, we characterized the fast transmission of COVID 19 with a basic reproductive number 5.6 and proved a sole zoonotic source to originate in Wuhan. No changes in transmission have been noted across generations. By evaluating different control strategies through predictive modeling and Monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach. Government action to immediately enforce this quarantine is highly recommended.
medRxiv
Ke K Zhang Linglin Xie Lauren Lawless Huijuan Zhou Guannan Gao Chengbin Xue
211133882
[The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID 19) in China]
Objective: An outbreak of 2019 novel coronavirus diseases (COVID 19) in Wuhan, China has spread quickly nationwide. Here, we report results of a descriptive, exploratory analysis of all cases diagnosed as of February 11, 2020. Methods: All COVID 19 cases reported through February 11, 2020 were extracted from China's Infectious Disease Information System. Analyses included: 1) summary of patient characteristics; 2) examination of age distributions and sex ratios; 3) calculation of case fatality and mortality rates; 4) geo temporal analysis of viral spread; 5) epidemiological curve construction; and 6) subgroup analysis. Results: A total of 72 314 patient records 44 672 (61.8% confirmed cases, 16 186 (22.4% suspected cases, 10567 (14.6% clinical diagnosed cases (Hubei only) and 889 asymptomatic cases (1.2% contributed data for the analysis. Among confirmed cases, most were aged 30 79 years (86.6% diagnosed in Hubei (74.7% and considered mild (80.9% A total of 1 023 deaths occurred among confirmed cases for an overall case fatality rate of 2.3% The COVID 19 spread outward from Hubei sometime after December 2019 and by February 11, 2020, 1 386 counties across all 31 provinces were affected. The epidemic curve of onset of symptoms peaked in January 23 26, then began to decline leading up to February 11. A total of 1 716 health workers have become infected and 5 have died (0.3% Conclusions: The COVID 19 epidemic has spread very quickly. It only took 30 days to expand from Hubei to the rest of Mainland China. With many people returning from a long holiday, China needs to prepare for the possible rebound of the epidemic.
Zhonghua liu xing bing xue za zhi Zhonghua liuxingbingxue zazhi
Novel Coronavirus Pneumonia Emergency Response Epi
215718639
The epidemiological characteristics of 2019 novel coronavirus diseases (COVID 19) in Jingmen,Hubei,China
Summary Background: Some articles have reported the epidemiological and clinical characteristics of coronavirus disease (COVID 19) in Wuhan, but other cities have rarely been reported. This study explored the epidemiology of COVID 19 in Jingmen. Methods: All confirmed cases of COVID 19 in the First People's Hospital of Jingmen are included from January 12 to February 14,2020. Cases were analyzed for epidemiological data and were confirmed by real time PCR. Findings: Of the 213 cases (108 men and 105 women) 88 (41% had exposure to Wuhan. The median age was 48 years range,2 88 years;IQR,35 58.5) Thirty three severe patients with a median age of 66 years(range,33 82 years,IQR, 57 76) were treated in intensive care units; out of these patients, 66.7 (22) were men and 19 (57.5% had chronic diseases, including hypertension, diabetes, heart failure, stroke, and renal insufficiency. Under the controlled measures, the number of new patients gradually decreased and nearly disappeared after 20 days. Interpretation: All people are susceptible to the COVID 19, but older males and those with comorbid conditions are more likely to have severe symptoms. Even though COVID 19 is highly contagious, control measures have proven to be very effective.
medRxiv
Qijun Gao Yingfu Hu zhiguo dai Jing Wu Feng Xiao Jing Wang
214619190
Epidemiological data from the COVID 19 outbreak, real time case information
Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human to human transmission in China and elsewhere. To aid the analysis and tracking of the COVID 19 epidemic we collected and curated individual level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation) and travel history. The generation of detailed, real time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making. Measurement(s) coronavirus infectious disease Viral Epidemiology Technology Type(s) digital curation Factor Type(s) geolocation date travel history age sex Sample Characteristic Organism Homo sapiens SARS CoV 2 Betacoronavirus Machine accessible metadata file describing the reported data: https:/doi.org/10.6084/m9.figshare.11974344
Scientific Data
Bo Xu Bernardo Gutierrez Sumiko R Mekaru Kara C Sewalk Lauren Goodwin Alyssa Loskill Emily L Cohn Yulin Hswen Sarah C Hill Maria M Cobo Alexander E Zarebski Sabrina L Li Chieh-Hsi Wu Erin N Hulland Julia Deryn Morgan Li-Hung Wang Kate O'Brien Samuel V Scarpino John S Brownstein Oliver G Pybus David M Pigott Moritz U G Kraemer
215548473
Dynamic causal modelling of COVID 19.
This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g. social distancing) and differences among populations (e.g. herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state of the art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self organised mitigation process.
Wellcome open research
Karl J Friston Thomas Parr Peter Zeidman Adeel Razi Guillaume Flandin Jean Daunizeau Oliver James Hulme Alexander J Billig Vladimir Litvak Rosalyn J Moran Cathy J Price Christian Lambert