scholarly journals Forecasting Scrub Typhus Cases in Eight High-Risk Counties in China: Evaluation of Time-Series Model Performance

2022 ◽  
Vol 9 ◽  
Author(s):  
Junyu He ◽  
Xianyu Wei ◽  
Wenwu Yin ◽  
Yong Wang ◽  
Quan Qian ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
High Risk ◽  
Scrub Typhus ◽  
Model Performance ◽  
Time Series Modeling ◽  
Exogenous Variables ◽  
Sarima Model ◽  
Risk Areas

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

scholarly journals Modeling Carbapenemase-producing Enterobacteriaceae episodes’ evolution in France over 2010-2020

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Roux ◽  
N Nekkab ◽  
P Astagneau ◽  
P Crépey
Keyword(s):  
Public Health ◽  
Time Series ◽  
Patient Safety ◽  
Healthcare Associated Infections ◽  
Time Series Modeling ◽  
National Healthcare ◽  
Response System ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Healthcare Associated

Abstract Background Incidence of Carbapenemase-Producing Enterobacteriaceae (CPE) episodes within hospitals is rising at an alarming rate and threaten health systems and patient safety worldwide. Their number is growing in France since 2009 associated with inter-regional dissemination and importation of international cases. This study aimed at describing the dynamics of CPE episodes in France over 2010-2016 and forecasting their evolution for 2017-2020. Methods Surveillance data of CPE episodes (imported and non-imported) from August 2010 to November 2016 were issued from the French national Healthcare-Associated Infections Early Warning and Response System. Impact of seasonality on the number of CPE episodes was analyzed using seasonal-to-irregular ratios. Seven models issued from time series analysis and three ensemble stacking models (average, convex and linear stacking) were used to describe and forecast CPE episodes. The model with the best forecasting’s quality was then trained on all available data (2010-2016) and used to predict CPE episodes over 2017-2020. Results Over 2010-2016, 3,559 CPE episodes were observed in France. Compared to the average yearly trend, we observed a 30% increase in the number of CPE episodes in September and October. On the opposite, a decrease of 20% was noticed in February compared to other months. We also noticed a 1-month lagged seasonality of non-imported episodes compared to imported ones. The number of non-imported episodes appeared to grow faster than imported ones starting from 2014. Average stacking gave the best forecasts and predicted an increase over 2017-2020 with a peak up to 345 CPE episodes (95% PI [124-1,158], 80% PI [171-742]) in September 2020. Conclusions The number of CPE episodes is predicted to rise in the next years in France because of non-imported episodes. These results could help public health authorities in the definition and evaluation of new containment strategies. Key messages Time series modeling predicts an increase in the number of CPE episodes in France in the next few years with a quicker rise of non-imported episodes. An increase of 30% in the number of CPE episodes was observed in September and October with a 1-month lagged seasonality impact of non-imported episodes compared to imported one.

scholarly journals Using an Open GIS Framework and Epidemiological Intelligence for Dengue Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Ta-Chien Chan ◽  
Bo-Cheng Lin ◽  
Chiao-Ling Kuo ◽  
Li-hsiang Chiang
Keyword(s):  
Public Health ◽  
High Risk ◽  
Dengue Fever ◽  
Surveillance System ◽  
Health Workers ◽  
Open Data ◽  
Outbreak Investigation ◽  
First Line ◽  
Public Health Workers ◽  
Risk Areas

Objective: In this paper we designed one cross-platform surveillance system to assist dengue fever surveillance, outbreak investigation and risk management of dengue fever.Introduction:In the 2015 dengue outbreak in Taiwan, 43,784 people were infected and 228 died, making it the nation’s largest outbreak ever. Facing the increasing threat of dengue, the integration of health information for prevention and control of outbreaks becomes very important. Based on past epidemics, the areas with higher incidence of dengue fever are located in southern Taiwan. Without a smart and integrated surveillance system, the information on case distribution, high risk areas, mosquito surveillance, flooding areas and so on is fragmented. The first-line public health workers need to check all this information through different systems manually. When outbreaks occurred, paper-based outbreak investigation forms had to be prepared and filled in by public health workers. Then, they needed to enter part of this information into Taiwan CDC’s system. Duplicated work occurred and cost lots of labor time during the epidemic period. Therefore, we choose one rural county, Pingtung County, with scarce financial resources, to set up a new dengue surveillance system.Methods: We designed a web-based cross-platform system based on an open geographical information system (GIS) framework including Openlayers, Javascript, PHP, MySQL and open data from government open data in Taiwan. There were seven epidemiological intelligence functions within the system including risk management, outbreak investigation, planning controlled areas, intelligent detection of high-risk areas, useful tools for decision making, historical epidemics, and system management. The website was developed by responsive web design which can let public health workers check information and fill in the investigation form by any devices.Results: The system was promptly set up in June 2016. With first-line public health workers’ efforts and the help of the surveillance system, there were no indigenous dengue fever cases after the system was implemented. There were sporadic imported cases from south-east Asia. The dengue surveillance system achieved three major improvements: integration of all decision support information; digitalization and automation of outbreak investigation; and planning the control areas. The results on outbreak investigation and mosquito surveillance can directly transfer to Taiwan CDC’s database by Web Application Programming Interface (API). It can avoid duplicated work for disease surveillance.Conclusions: Through introducing the new dengue surveillance system into local health departments, first-line public health workers can update all epidemic information at the same time. During epidemic periods, it can provide demographic, epidemiological, environmental, and entomological information for decision making. During non-epidemic periods, it can highlight the high risk areas for enhanced surveillance to reduce the risk of outbreaks.

scholarly journals Identifying Vulnerable Populations in Subtropical Brisbane, Australia: A Guide for Heatwave Preparedness and Health Promotion

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Margaret Loughnan ◽  
Nigel Tapper ◽  
Thu Phan
Keyword(s):  
Public Health ◽  
Health Promotion ◽  
Spatial Distribution ◽  
High Risk ◽  
Urban Areas ◽  
Spatiotemporal Analysis ◽  
Prevention Policy ◽  
Increased Risk ◽  
Risk Areas ◽  
Heat Events

Building healthy societies is a key step towards climate resilient communities. Ill health is related to increased risk during heat events and is disproportionally distributed within and between communities. To understand the differences in the spatial distribution of climate related health risks and how this will change in the future we have undertaken a spatiotemporal analysis of heatwave risks in urban populations in Brisbane, Australia. The aim of this was to advise emergency managers and public health authorities of high-risk areas during extreme heat events (EHEs). The spatial distribution of heat related morbidity identified areas of high healthcare service demand during EHEs. An index of risk was developed based on social and environmental determinants of vulnerability. Regression analysis was used to determine the key drivers of heat related morbidity from the index. A weighted map of population vulnerability was produced which identified the high risk areas and provided key information to target public health interventions and heat stress prevention policy. The predicted changes in high risk populations such as the proportion of elderly people living in urban areas were also mapped to support longer term adaptation and develop health care infrastructure and health promotion strategies.

scholarly journals A revolutionary step; Role of Qatar`s central hubs in managing the Covid-19 Pandemic

2021 ◽  
Vol 7 (2) ◽  
pp. 30-32
Author(s):  
Z Habib ◽  
◽  
Y Hafeez ◽  
Imen Mbarek ◽  
M Ul Haque ◽  
...  
Keyword(s):  
Public Health ◽  
High Risk ◽  
Virus Disease ◽  
Public Health Emergency ◽  
The State ◽  
Corona Virus ◽  
The World

WHO declared Corona Virus disease 2019 (COVID-19) as a public health emergency on the 30th of January 2020. Soon afterward, COVID-19 cases started to emerge from all parts of the world. The state of Qatar was extremely vigilant from the very outset. Special measures were introduced immediately to restrict the influx of people from high-risk countries such as China and Iran. The Ministry of public health (MOPH), Qatar started preparing for an impending pandemic in the meantime. The first cluster of COVID-19 positive cases was declared on March the 11th 2019. A total of 238 cases were declared positive on this day. It raised the alarm to roll over all those preparations on the ground into practice

scholarly journals Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiong He ◽  
Chunshan Zhou ◽  
Yuqu Wang ◽  
Xiaodie Yuan
Keyword(s):  
Public Health ◽  
High Risk ◽  
Prevention And Control ◽  
Health Hazard ◽  
Regional Distribution ◽  
Floating Population ◽  
Effective Control ◽  
The World ◽  
Risk Areas ◽  
And Control

COVID-19 is a highly infectious disease and public health hazard that has been wreaking havoc around the world; thus, assessing and simulating the risk of the current pandemic is crucial to its management and prevention. The severe situation of COVID-19 around the world cannot be ignored, and there are signs of a second outbreak; therefore, the accurate assessment and prediction of COVID-19 risks, as well as the prevention and control of COVID-19, will remain the top priority of major public health agencies for the foreseeable future. In this study, the risk of the epidemic in Guangzhou was first assessed through logistic regression (LR) on the basis of Tencent-migration data and urban point of interest (POI) data, and then the regional distribution of high- and low-risk epidemic outbreaks in Guangzhou in February 2021 was predicted. The main factors affecting the distribution of the epidemic were also analyzed by using geographical detectors. The results show that the number of cases mainly exhibited a declining and then increasing trend in 2020, and the high-risk areas were concentrated in areas with resident populations and floating populations. In addition, in February 2021, the “Spring Festival travel rush” in China was predicted to be the peak period of population movement. The epidemic risk value was also predicted to reach its highest level at external transportation stations, such as Baiyun Airport and Guangzhou South Railway Station. The accuracy verification showed that the prediction accuracy exceeded 99%. Finally, the interaction between the resident population and floating population could explain the risk of COVID-19 to the highest degree, which indicates that the effective control of population agglomeration and interaction is conducive to the prevention and control of COVID-19. This study identifies and predicts high-risk areas of the epidemic, which has important practical value for urban public health prevention and control and containment of the second outbreak of COVID-19.

scholarly journals COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

2020 ◽  
Vol 10 (11) ◽  
pp. 3880 ◽  
Author(s):  
Vasilis Papastefanopoulos ◽  
Pantelis Linardatos ◽  
Sotiris Kotsiantis
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Time Series ◽  
Health System ◽  
Total Population ◽  
Outbreak Detection ◽  
Time Series Modeling ◽  
Learning Time ◽  
Raising Awareness ◽  
The Impact

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

scholarly journals The Role of Sarcopenic Obesity in Cancer and Cardiovascular Disease: A Synthesis of the Evidence on Pathophysiological Aspects and Clinical Implications

2021 ◽  
Vol 22 (9) ◽  
pp. 4339
Author(s):  
Erika Aparecida Silveira ◽  
Rômulo Roosevelt da Silva Filho ◽  
Maria Claudia Bernardes Spexoto ◽  
Fahimeh Haghighatdoost ◽  
Nizal Sarrafzadegan ◽  
...  
Keyword(s):  
Public Health ◽  
Cardiovascular Disease ◽  
Older Adults ◽  
High Risk ◽  
Sarcopenic Obesity ◽  
Health Concern ◽  
Clinical Implications ◽  
Public Health Concern ◽  
Epidemiological Evidence

Obesity is globally a serious public health concern and is associated with a high risk of cardiovascular disease (CVD) and various types of cancers. It is important to evaluate various types of obesity, such as visceral and sarcopenic obesity. The evidence on the associated risk of CVD, cancer and sarcopenic obesity, including pathophysiological aspects, occurrence, clinical implications and survival, needs further investigation. Sarcopenic obesity is a relatively new term. It is a clinical condition that primarily affects older adults. There are several endocrine-hormonal, metabolic and lifestyle aspects involved in the occurrence of sarcopenic obesity that affect pathophysiological aspects that, in turn, contribute to CVD and neoplasms. However, there is no available evidence on the role of sarcopenic obesity in the occurrence of CVD and cancer and its pathophysiological interplay. Therefore, this review aims to describe the pathophysiological aspects and the clinical and epidemiological evidence on the role of sarcopenic obesity related to the occurrence and mortality risk of various types of cancer and cardiovascular disease. This literature review highlights the need for further research on sarcopenic obesity to demonstrate the interrelation of these various associations.

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