Intrinsic growth rules of patients infected, dead and recovered with 2019 novel coronavirus in mainland China

AbstractAn outbreak of a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) was first diagnosed in Wuhan, China, in December 2019 and then spread rapidly to other regions. We collected the time series data of the cumulative number of confirmed infected, dead, and cured cases from the health commissions in 31 provinces in mainland China. A descriptive model in a logistic form was formulated to infer the intrinsic epidemic rules of COVID-19, which illustrates robustness spatially and temporally. Our model is robust (R2>0.95) to depict the intrinsic growth rule for the cumulative number of confirmed infected, dead, and cured cases in 31 provinces in mainland China. Furthermore, we compared the intrinsic epidemic rules of COVID-19 in Hubei with that of severe acute respiratory syndrome (SARS) in Beijing, which was obtained from the Ministry of Public Health of China in 2003. We found that the infected case is the earliest to be saturated and has the lowest semi-saturation period compared with deaths and cured cases for both COVID-19 and SARS. All the three types of SARS cases are later to saturate and have longer semi-saturation period than that of COVID-19. Despite the virus caused SARS (SARS-CoV) and the virus caused COVID-19 (SARS-CoV-2) are homologous, the duration of the outbreak would be shorter for COVID-19.

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Modelling the dynamics of a public health care system: evidence from time-series data

Applied Economics ◽

10.1080/00036846.2011.568407 ◽

2011 ◽

Vol 44 (23) ◽

pp. 2955-2968 ◽

Cited By ~ 1

Author(s):

Fabrizio Iacone ◽

Steve Martin ◽

Luigi Siciliani ◽

Peter C. Smith

Keyword(s):

Public Health ◽

Health Care ◽

Time Series ◽

Health Care System ◽

Time Series Data ◽

Public Health Care ◽

Series Data ◽

Public Health Care System ◽

Care System

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Tracking and Classifying Global COVID-19 Cases by using 1D Deep Convolution Neural Networks

10.1101/2020.06.09.20126565 ◽

2020 ◽

Author(s):

Mark Amo-Boateng

Keyword(s):

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Sequence Data ◽

Series Data ◽

The Novel ◽

Response Curves ◽

Convolution Neural Networks ◽

Link Type ◽

Novel Coronavirus

ABSTRACTThe novel coronavirus disease (COVID-19) and pandemic has taken the world by surprise and simultaneously challenged the health infrastructure of every country. Governments have resorted to draconian measures to contain the spread of the disease despite its devastating effect on their economies and education. Tracking the novel coronavirus 2019 disease remains vital as it influences the executive decisions needed to tighten or ease restrictions meant to curb the pandemic. One-Dimensional (1D) Convolution Neural Networks (CNN) have been used classify and predict several time-series and sequence data. Here 1D-CNN is applied to the time-series data of confirmed COVID-19 cases for all reporting countries and territories. The model performance was 90.5% accurate. The model was used to develop an automated AI tracker web app (AI Country Monitor) and is hosted on https://aicountrymonitor.org. This article also presents a novel concept of pandemic response curves based on cumulative confirmed cases that can be use to classify the stage of a country or reporting territory. It is our firm believe that this Artificial Intelligence COVID-19 tracker can be extended to other domains such as the monitoring/tracking of Sustainable Development Goals (SDGs) in addition to monitoring and tracking pandemics.

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Analysis of the attention to COVID-19 epidemic based on visibility graph network

Modern Physics Letters B ◽

10.1142/s0217984921503164 ◽

2021 ◽

pp. 2150316

Author(s):

Qingxiang Feng ◽

Haipeng Wei ◽

Jun Hu ◽

Wenzhe Xu ◽

Fan Li ◽

...

Keyword(s):

Public Health ◽

Time Series ◽

Time Series Data ◽

Search Behavior ◽

Series Data ◽

Global Public Health ◽

Health Crisis ◽

Time Space ◽

Epidemic Period ◽

Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

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Modelling the Cumulative Number of COVID-19 Cases

Advances in Environmental and Engineering Research ◽

10.21926/aeer.2102014 ◽

2021 ◽

Vol 02 (02) ◽

pp. 1-1

Author(s):

Mieczysław Szyszkowicz ◽

Keyword(s):

Time Series ◽

Time Series Data ◽

Distribution Functions ◽

Cumulative Distribution ◽

Series Data ◽

Cumulative Number ◽

Daily Time Series ◽

Non Linear ◽

The Usa ◽

Daily Time

Each country has its own characteristics of COVID-19 infection trajectory and epidemic waves. Differences in government-implemented restrictions and social regulations result in variability of the virus transmissions and spread dynamics. This in turn results in various shapes of the growth function used to represent and describe the propagation of infection. Statistical methods are applied to fit non-linear functions to represent daily time-series data of the cumulative numbers of COVID-19 cases. The aim of this work is to fit various statistical models to the cumulative number of COVID-19 cases. Also to overview various types of the existed numerical methodologies. The data (since December 31, 2019) are available for almost each country in the world. As the examples, we used daily time-series data of the cumulative number of COVID-19 cases in Poland, Italy, Canada, and the USA. Non-linear approximations are applied to represent these time series data. The fitted functions allow us to investigate the dynamics of the pandemic. The constructed approximations are compositions of a few nonlinear functions, which describe the growth process of the COVID-19 infection trajectories. Two Gompertz functions and cumulative distribution functions (cdf) were estimated for the data of Poland and Italy (using the cdf for the normal distribution) and for the data of Canada and the USA (using the cdf for the gamma distribution). An analytical (parametric) functions representation of the number of COVID-19 cumulative cases is a useful tool to study the propagation of epidemics.

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Transformed time series analysis of first-wave COVID-19: universal similarities found in the Group of Twenty (G20) Countries

10.1101/2020.06.11.20128991 ◽

2020 ◽

Cited By ~ 2

Author(s):

Albert S Kim

Keyword(s):

Public Health ◽

Time Series ◽

Health Policy ◽

Public Health Policy ◽

Policy Making ◽

Series Data ◽

Data Sets ◽

Cumulative Number ◽

Transformation Algorithm ◽

Reduced Time

As of April 30, 2020, the number of cumulative confirmed coronavirus disease 2019 (COVID-19) cases exceeded 3 million worldwide and 1 million in the US with an estimated fatality rate of more than 7 percent. Because the patterns of the occurrence of new confirmed cases and deaths over time are complex and seemingly country-specific, estimating the long-term pandemic spread is challenging. I developed a simple transformation algorithm to investigate the characteristics of the case and death time series per nation, and described the universal similarities observed in the transformed time series of 19 nations in the Group of Twenty (G20). To investigate the universal similarities among the cumulative profiles of confirmed cases and deaths of 19 individual nations in the G20, a transformation algorithm of the time series data sets was developed with open-source software programs. The algorithm was used to extract and analyze statistical information from daily updated COVID-19 pandemic data sets from the European Centre for Disease Prevention and Control (ECDC). Two new parameters for each nation were suggested as factors for time-shifting and time-scaling to define reduced time, which was used to quantify the degree of universal similarities among nations. After the cumulative confirmed case and death profiles of a nation were transformed by using reduced time, most of the 19 nations, with few exceptions, had transformed profiles that closely converged to those of Italy after the onset of cases and deaths. The initial profiles of the cumulative confirmed cases per nation universally showed 3 – 4 week latency periods, during which the total number of cases remained at approximately ten. The latency period of the cumulative number of deaths was approximately half the latency number of cumulative cases, and subsequent uncontrollable increases in human deaths seemed unavoidable because the coronavirus had already widely spread. Immediate governmental actions, including responsive public-health policy-making and enforcement, are observed to be critical to minimize (and possibly stop) further infections and subsequent deaths. In the pandemic spread of infectious viral diseases, such as COVID-19 studied in this work, different nations show dissimilar and seemingly uncorrelated time series profiles of infected cases and deaths. After these statistical phenomena were viewed as identical events occurring at a distinct rate in each country, the reported algorithm of the data transformation using the reduced time revealed a nation-independent, universal profile (especially initial periods of the pandemic spread) from which a nation-specific, predictive estimation could be made and used to assist in immediate public-health policy-making.

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Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach

PLoS ONE ◽

10.1371/journal.pone.0248597 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248597

Author(s):

Guo-hua Ye ◽

Mirxat Alim ◽

Peng Guan ◽

De-sheng Huang ◽

Bao-sen Zhou ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Mean Squared Error ◽

Arima Model ◽

Mainland China ◽

Hemorrhagic Fever ◽

Series Data ◽

Percentage Error ◽

Forecasting Performance ◽

The Future

Objective Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. Method We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). Result There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). Conclusion The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.

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New statistical model for misreported data with application to current public health challenges

Scientific Reports ◽

10.1038/s41598-021-02620-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

David Moriña ◽

Amanda Fernández-Fontelo ◽

Alejandra Cabaña ◽

Pedro Puig

Keyword(s):

Public Health ◽

Time Series ◽

Human Papillomavirus ◽

Continuous Time ◽

Time Series Data ◽

Real Data ◽

Current Data ◽

Series Data ◽

Proposed Model ◽

Public Health Challenges

AbstractThe main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

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Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

10.1101/2021.03.14.21253554 ◽

2021 ◽

Author(s):

Meshrif Alruily ◽

Mohamed Ezz ◽

Ayman Mohamed Mostafa ◽

Nacim Yanes ◽

Mostafa Abbas ◽

...

Keyword(s):

Public Health ◽

Time Series ◽

Computational Models ◽

Time Series Data ◽

Short Term Memory ◽

Emerging Infectious Diseases ◽

The United States ◽

Series Data ◽

Percentage Error ◽

Google Search

ABSTRACTAccurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.

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Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review

BMJ Open ◽

10.1136/bmjopen-2018-024096 ◽

2019 ◽

Vol 9 (1) ◽

pp. e024096 ◽

Cited By ~ 6

Author(s):

Simon L Turner ◽

Amalia Karahalios ◽

Andrew B Forbes ◽

Monica Taljaard ◽

Jeremy M Grimshaw ◽

...

Keyword(s):

Public Health ◽

Time Series ◽

Statistical Methods ◽

Time Series Data ◽

Interrupted Time Series ◽

Health Interventions ◽

Series Data ◽

Public Health Interventions ◽

Design Characteristics ◽

The Impact

IntroductionAn interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation.Methods and analysisWe will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data.Ethics and disseminationEthics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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