scholarly journals COVID-19 Forecasting: A Statistical Approach

2021 ◽  
pp. 85-96
Author(s):  
Arti Saxena ◽  
Falak Bhardwaj ◽  
Vijay Kumar
Keyword(s):  
Statistical Approach ◽  
Moving Average ◽  
Severe Disease ◽  
Medical Science ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
The Past ◽  
Health Goal ◽  
Public Health Goal ◽  
Very High

Background: SARS-coronavirus-2 is a new virus infecting people and causing COVID-19 disease. The disease is causing a worldwide pandemic. Although some people never develop any signs or symptoms of disease when they are infected, other people are at very high risk for severe disease and death. Objective: If we’re able to intervene to prevent even some transmission, we can dramatically reduce the number of cases. And this is the public health goal for controlling COVID-19. Methods: This article initializes an approach for comparatively accurate values prediction of new cases and deaths for a particular day in order to be considered for preventive measures. The three statistical analysis methods considered for forecasting are Fbprophet, Moving average and the Autoregressive Integrated Moving Average algorithm. Results: The results obtained are in-line with the past and present trend of COVID-19 data collected from WHO website. Conclusion: The output is satisfactory for further consideration. Bangladesh Journal of Medical Science Vol.20(5) 2021 p.85-96

What the past can say about the present and future of fire

2020 ◽  
Vol 96 ◽  
pp. 66-87
Author(s):  
Jennifer R. Marlon
Keyword(s):  
Climate Change ◽  
Terrestrial Ecosystems ◽  
Combustion Products ◽  
Temperature Changes ◽  
The Public ◽  
The Past ◽  
Warming Climate ◽  
Highly Sensitive ◽  
In Fire ◽  
Very High

AbstractWildfires are an integral part of most terrestrial ecosystems. Paleofire records composed of charcoal, soot, and other combustion products deposited in lake and marine sediments, soils, and ice provide a record of the varying importance of fire over time on every continent. This study reviews paleofire research to identify lessons about the nature of fire on Earth and how its past variability is relevant to modern environmental challenges. Four lessons are identified. First, fire is highly sensitive to climate change, and specifically to temperature changes. As long as there is abundant, dry fuel, we can expect that in a warming climate, fires will continue to grow unusually large, severe, and uncontrollable in fire-prone environments. Second, a better understanding of “slow” (interannual to multidecadal) socioecological processes is essential for predicting future wildfire and carbon emissions. Third, current patterns of burning, which are very low in some areas and very high in others—are often unprecedented in the context of the Holocene. Taken together, these insights point to a fourth lesson—that current changes in wildfire dynamics provide an opportunity for paleoecologists to engage the public and help them understand the potential consequences of anthropogenic climate change.

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Leading Role ◽  
Joinpoint Regression Analysis

Abstract BackgroundChina has always been one of the countries with the most serious tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.MethodsThe data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the Spatial autocorrelation analysis was performed to detect. The heterogeneous transmission of TB was detected by the multivariate time series model. ResultsWe included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. ConclusionIn a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The spatial heterogeneity of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

INTER-RELATED RESPONSIBILITIES FOR MILK PRODUCTION AND QUALITY CONTROL1

1969 ◽  
Vol 32 (9) ◽  
pp. 350-353
Author(s):  
Abraham E. Abrahamson
Keyword(s):  
Public Health ◽  
Milk Production ◽  
Control Program ◽  
Regulatory Agencies ◽  
The Public ◽  
The Past ◽  
Maintenance Program ◽  
Regulatory Bodies ◽  
High Level ◽  
Very High

The capacity to work cooperatively, industry with the various agencies, concerned with milk production and quality control has been demonstrated. Cooperation among the agencies having responsibility in milk control, in a period of looming budget crisis, is more imperative than ever. While all the problem bearing on the public health aspects of milk control have not been solved, there do not appear to be any serious threats beyond the problem to provide maintenance efforts to assure continuance of the gains made. For the maintenance program it seems a very high level of cooperation among regulatory agencies is necessary and continued efforts of industry to work with regulatory bodies must be encouraged. Solving of new problems may have to be under-taken with out added resources, therefore making it necessary to develop better techniques to tackle new tasks without losing control in the older and more traditional areas. Inter-related efforts which were carefully developed in the past will be needed to supplement as well as complement to prevent deficits from affecting the whole coordinated milk control Program.

Pemodelan Prakiraan Kas Pemerintah

2017 ◽  
Vol 2 (3) ◽  
pp. 1-11
Author(s):  
NFN Iskandar
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Government Policies ◽  
Forecasting Model ◽  
Cash Management ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Greek Crisis ◽  
Artificial Neural Network Ann

ABSTRACT Government cash management refers to the strategies for managing government money to fulfil governments’ obligations effectively. Failure to manage cash effectively risks undermining the implementation of government policies. The Greek crisis in 2010 is an example of a government failing to manage resources effectively. Despite the importance of effective government cash management, the literature on effective cash forecasting, as one of effective government cash management’s pillars, in the public sector is scarce. This paper addresses this shortcoming by developing a government cash forecasting model with an accuracy that meets acceptable levels of materiality for the cash manager. Using Indonesian government expenditures data in a case study, we utilise Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to build cash forecasting models. The results provide evidence that the ANN method is superior then the ARIMA model to build a government cash forecasting model. ABSTRAK Pengelolaan Kas Pemerintah mengacu pada serangkaian strategi yang dilakukan oleh pemerintah dalam mengelola uang pemerintah secara efektif dalam rangka memenuhi kewajiban pemerintah. Kegagalan dalam mengelola uang pemerintah secara efektif beresiko mengganggu pelaksanaan kebijakan pemerintah. Krisis yang dialami Yunani di tahun 2010 merupakan salah satu contoh dampak yang dapat ditimbulkan dari tidak berhasilnya suatu pemerintahan mengelola sumber daya keuangan yang mereka milik secara efektif. Terlepas dari pentingnya mengelola kas pemerintah secara efektif, literatur tentang bagaimana menyusun prakiraan kas yang efektif – sebagai salah satu pilar Pengelolaan Kas Pemerintah – bagi sektor publik masih langka. Penelitian ini bertujuan untuk mengisi kesenjangan dalam literatur dengan memperkenalkan salah satu cara menyusun model prakiraan kas pemerintah dengan tingkat akurasi yang memenuhi harapan Pengelola Kas pemerintah. Dengan menggunakan data historis harian pengeluaran pemerintah Indonesia sebagai sebuah studi kasus, penelitian ini menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Jaringan Syaraf Tiruan (JST) untuk menyusun model prakiraan kas. Penelitian ini menunjukkan bahwa penggunaan metode Jaringan Syaraf Tiruan (JST) dapat menjadi alternatif dalam menyusun model prakiraan kas pemerintah dengan tingkat akurasi model prakiraan kas yang lebih tinggi dibandingkan menggunakan ARIMA model.

scholarly journals FORECASTING THE CROP YIELD OF A COCONUT ESTATE

CORD ◽  
1989 ◽  
Vol 5 (02) ◽  
pp. 34
Author(s):  
T. S. G. Peiris
Keyword(s):  
Moving Average ◽  
Day Length ◽  
Physical Factors ◽  
Present Value ◽  
Short Term ◽  
Yield Data ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Variance Stabilization ◽  
Lower Limits

Seasonal Autoregressive Integrated Moving Average (ARIMA) process of (0,1,2) x (0,1,1) x 6 that best fits a set of crop‑wise coconut yield data, in Bandirippuwa, Lunuwila is identified with­out using variance stabilization transformation. In this process the present value of the series may be described as a linear function of the past observation of the series and past disturbances. The physical factors such as rainfall, temperature, day length etc. are not required for this method, however the past crop figures in the estate is needed. While such model is useful for short term fore­casting, it also gives the upper and lower limits of the forecasts at a given probability. These intervals would provide the quantified information on the degree of duration of the forecasts.

scholarly journals A new causative agent of epidemic cerebrospinal meningitis. As reported by the Bureau of the Public Health Service

2021 ◽  
Vol 32 (1) ◽  
pp. 91-91
Author(s):  
Z. B.
Keyword(s):  
Public Health ◽  
United States ◽  
Health Service ◽  
Causative Agent ◽  
Public Health Service ◽  
The United States ◽  
The Public ◽  
The Past ◽  
Very High

According to the Bureau of the Public Health Service (Washington), over the past five years, the number of diseases in the United States has been epidemic. cerebrospin. meningitis was very high (numbers not indicated), exceeding the number of diseases in the period since the beginning of the worlds, war.

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Leading Role ◽  
Joinpoint Regression Analysis

Abstract Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. Conclusion In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

scholarly journals Visitor Management in World Heritage Destinations before and after Covid-19, Angkor

2020 ◽  
Vol 12 (23) ◽  
pp. 9929
Author(s):  
Antonio Alvarez-Sousa ◽  
Jose Luis Paniza Prados
Keyword(s):  
Public Health ◽  
Qualitative Methodology ◽  
World Heritage ◽  
Risk Society ◽  
The Public ◽  
Visitor Management ◽  
New Public Health ◽  
Health Goal ◽  
Before And After ◽  
Public Health Goal

The purpose of this research was to analyze the visitor-management tactics and strategies in World Heritage destinations. The Temples of Angkor (Cambodia) were selected as case studies. The analysis was carried out in two phases—before and after COVID-19. A qualitative methodology was used. Participant observation was employed for the pre-COVID-19 strategies, and recommendations of scholars and bodies responsible for tourism were the basis for the strategies proposed for the post-COVID-19 scenario. Grounded theory and the Atlas.ti qualitative analysis software were used. The results showed that the public health goal, together with its related strategies and tactics, should be added to the classic sustainability goals and the hard and soft strategies (physical, regulatory, and educational). It was also noted that new actors came into play—those responsible for public health. In conclusion, this new public health goal and its tactics will condition classic factors such as carrying capacity, and can conflict with goals such as the economic and social goals. The sustainability paradigm is maintained, but with the addition of risk society and the public health goal playing a key role.

scholarly journals Rational Curiosity and Information-Seeking in the COVID-19 Pandemic

2020 ◽  
Author(s):  
Yaniv Abir ◽  
Caroline Marvin ◽  
Camilla van Geen ◽  
Maya Leshkowitz ◽  
Ran Hassin ◽  
...  
Keyword(s):  
Information Seeking ◽  
Fundamental Aspect ◽  
Motivational State ◽  
Optimal Learning ◽  
The Past ◽  
Health Goal ◽  
Personal Utility ◽  
Normative Economic ◽  
Public Health Goal ◽  
Utility Of Learning

Curiosity is a powerful determinant of behavior. The past decade has seen a surge of scientific research on curiosity, an endeavor recently imbibed with urgency by the WHO, which set managing information-seeking as a public health goal during pandemics. And yet, a fundamental aspect of curiosity has remained unresolved: its relationship to utility. Is curiosity a drive towards information simply for the sake of obtaining that information, or is it a rational drive towards optimal learning? We leveraged people’s curiosity about COVID-19 to study information-seeking and learning in a large sample (n=5376) during the spring of 2020. Our findings reveal that curiosity is goal-rational in that it maximizes the personal utility of learning. Personal utility, unlike normative economic utility, is contingent on a person’s motivational state. On the basis of these findings, we explain information-seeking during the pandemic with a rational theoretical framework for curiosity.

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