scholarly journals A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

2021 ◽  
pp. 096228022110619
Author(s):  
Yuanke Qu ◽  
Chun Yin Lee ◽  
KF Lam
Keyword(s):  
Public Health ◽  
Real Time ◽  
Disease Severity ◽  
Null Hypothesis ◽  
Large Scale ◽  
Mainland China ◽  
Cumulative Number ◽  
Finite Sample ◽  
Test Statistic ◽  
Multiple Groups

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

scholarly journals The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

2021 ◽  
Author(s):  
Joshua A Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichida La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The U.S

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

2021 ◽  
Vol 118 (51) ◽  
pp. e2111454118 ◽  
Author(s):  
Joshua A. Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichada La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The Us

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals Characterization of emerging predominant HFMD pathogen, coxsackievirus A6 in mainland China

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
Y Song ◽  
Y Zhang ◽  
W B Xu
Keyword(s):  
Public Health ◽  
Health Problem ◽  
Large Scale ◽  
Mainland China ◽  
Public Health Problem ◽  
Clinical Samples ◽  
Future Research ◽  
Surveillance Network ◽  
The Past ◽  
Coxsackievirus A6

Abstract Hand, foot, and mouth disease (HFMD) had the highest yearly incidence, with over 10 million cases of HFMD annually reported in China. Enterovirus A71 (EV-A71) and coxsackievirus A16 (CV-A16) have been regarded as the leading pathogens of HFMD outbreaks worldwide and in China; however, in recent years, the leading pathogens have been changing, as large outbreaks of CV-A6-associated HFMD have been reported worldwide. Since 2013, repeated large-scale HFMD outbreaks caused by CV-A6 happened in mainland China, where, as a result, CV-A6 has surpassed EV-A71 and CV-A16 as the leading HFMD pathogen in most Chinese provinces. We sequenced the whole genomes of 158 CV-A6 clinical samples that were isolated between 2010 and 2018 from the HFMD Surveillance Network established in our laboratory. Our results showed that: seven recombination forms (RFs) of Chinese CV-A6 were detected; different CV-A6 RFs showed distinct virulence and transmissibility; VP1283T may play an important role in the virulence of Chinese CV-A6. HFMD epidemics in China have become a serious public health problem over the past decade. In this research, we have attempted to explore the causes of the high transmissibility of the emerging CV-A6 in mainland China on the basis of CV-A6 evolution based on 336 whole-genome sequences, and we have yielded some fruitful results for the future research and surveillance of HFMD in China. Key messages HFMD epidemics in China have become a serious public health problem over the past decade. CV-A6 has surpassed EV-A71 and CV-A16 as the leading HFMD pathogen in most Chinese provinces.

Enhancing COVID-19 Epidemics Forecasting Accuracy by Combining Real-time and Historical Data from Social Media, Online News Articles, and Search Queries (Preprint)

2021 ◽  
Author(s):  
Jingwei Li ◽  
Wei Huang ◽  
Choon Ling Sia ◽  
Zhuo Chen ◽  
Tailai Wu ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Historical Data ◽  
Mainland China ◽  
Hubei Province ◽  
Online News ◽  
Data Sources ◽  
Resources Allocation ◽  
Forecasting Accuracy ◽  
Proposed Model

BACKGROUND The SARS-COV-2 virus and its variants are posing extraordinary challenges for public health worldwide. More timely and accurate forecasting of COVID-19 epidemics is the key to maintaining timely interventions and policies and efficient resources allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs, but didn’t take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS We first used core terms and symptoms related keywords-based methods to extract COVID-19 related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating the real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used the lagged Pearson correlations for the COVID-19 forecasting timeliness analysis. RESULTS Our proposed model achieved the highest accuracy in all the five accuracy measures, compared with all the baseline models in both Hubei province and the rest of mainland China. In mainland China except Hubei, the COVID-19 epidemics forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t=–8.722, P<.001; model 2, t=–5.000, P<.001, model 3, t=–1.882, P =0.063, model 4, t=–4.644, P<.001; model 5, t=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical COVID-19 new confirmed case counts only (model 1, t=–1.732, P=0.086). Our results also showed that Internet-based sources could provide a 2-6 days earlier warning for COVID-19 outbreaks. CONCLUSIONS Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for COVID-19 epidemics and its variants, which may help improve public health agencies' interventions and resources allocation in mitigating and controlling new waves of COVID-19 or other epidemics.

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

2020 ◽  
Author(s):  
Chuanliang Han ◽  
Yimeng Liu ◽  
Saini Yang
Keyword(s):  
Public Health ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Data ◽  
Mainland China ◽  
Series Data ◽  
Cumulative Number ◽  
Descriptive Model ◽  
Infected Case ◽  
Novel Coronavirus

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.

CONSISTENT SPECIFICATION TESTING FOR CONDITIONAL SYMMETRY

1998 ◽  
Vol 14 (1) ◽  
pp. 139-149 ◽  
Author(s):  
John Xu Zheng
Keyword(s):  
Null Hypothesis ◽  
Kernel Method ◽  
Asymmetric Distribution ◽  
Local Alternatives ◽  
Finite Sample ◽  
Conditional Symmetry ◽  
Specification Test ◽  
Test Statistic ◽  
Specification Testing ◽  
Standard Normal

This paper presents a consistent specification test of conditional symmetry using a kernel method. The test statistic is shown to be asymptotically distributed as standard normal under the null hypothesis of conditional symmetry and consistent against any conditional asymmetric distribution. Power against local alternatives is also investigated. A Monte Carlo simulation is provided to evaluate the finite-sample performance of the test.

Detecting departures from meta-ellipticity for multivariate stationary time series

2021 ◽  
Vol 9 (1) ◽  
pp. 121-140
Author(s):  
Axel Bücher ◽  
Miriam Jaser ◽  
Aleksey Min
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Large Scale ◽  
Stationary Time Series ◽  
Serial Dependence ◽  
Finite Sample ◽  
Test Statistic ◽  
Stationary Time ◽  
Theoretical Results

Abstract A test for detecting departures from meta-ellipticity for multivariate stationary time series is proposed. The large sample behavior of the test statistic is shown to depend in a complicated way on the underlying copula as well as on the serial dependence. Valid asymptotic critical values are obtained by a bootstrap device based on subsampling. The finite-sample performance of the test is investigated in a large-scale simulation study, and the theoretical results are illustrated by a case study involving financial log returns.

scholarly journals Large-scale assessment of human mobility during COVID-19 outbreak

2020 ◽  
Author(s):  
Lucas Queiroz ◽  
Lucas Queiroz ◽  
José Luciano Melo ◽  
Gabriel Barboza ◽  
Alysson H. Urbanski ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Population Mobility ◽  
Social Distancing ◽  
Public Health Systems ◽  
Large Scale Assessment ◽  
Strong Control

Social distancing is an important measure to prevent the collapse of public health systems during the COVID-19 pandemic. While some countries have managed to impose a strong control for social distancing, countries like Brazil largely depend on the population’s cooperation. Therefore, it is very necessary to monitor human mobility to detect whether social distancing policies are being implemented and to adjust them in places where the population is not adhering to these. By using cell phone data of millions of people, we were able to assess the population mobility in Brazil’s biggest city, São Paulo. Our analysis revealed the reduction in the circulation of people in most neighborhoods after social distancing policies began. We also showed the dispersion of people by tracking the visits of people to the GRU airport and the visit locations of the same people after they left the airport. Over the course of a few days, it was possible to detect over 70,000 visits across Brazil, with distances greater than 2,000 km from the GRU airport. We hope that data when collected in real time can be useful to stem the progress of the COVID-19 epidemic, or at least to help “flatten the curve”.

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