ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates

2021 ◽  
pp. 1-24 ◽  
Author(s):  
Mohanad A. Deif ◽  
Ahmed A. A. Solyman ◽  
Rania E. Hammam
Keyword(s):  
Genetic Algorithm ◽  
Moving Average ◽  
Arima Model ◽  
Hybrid Genetic Algorithm ◽  
Mortality Rates ◽  
Model Parameters ◽  
Efficient Manner ◽  
Contagious Diseases ◽  
Health Authorities ◽  
Public Health Authorities

This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model.

scholarly journals Forecasting incidence of infectious diarrhea using random forest in Jiangsu rovince, China

2020 ◽  
Author(s):  
Xinyu Fang ◽  
Wendong Liu ◽  
Jing Ai ◽  
Mike He ◽  
Ying Wu ◽  
...  
Keyword(s):  
Random Forest ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Practical Implementation ◽  
Infectious Diarrhea ◽  
Health Authorities ◽  
Public Health Authorities

Abstract Background: Infectious diarrhea can lead to a considerable global disease burden. Thus, the accurate prediction of an infectious diarrhea epidemic is crucial for public health authorities. This study was aimed at developing an optimal random forest (RF) model, considering meteorological factors used to predict an incidence of infectious diarrhea in Jiangsu Province, China. Methods: An RF model was developed and compared with classical autoregressive integrated moving average (ARIMA)/X models. Morbidity and meteorological data from 2012 to 2016 were used to construct the models and the data from 2017 were used for testing. Results: The RF model considered atmospheric pressure, precipitation, relative humidity, and their lagged terms, as well as 1–4 week lag morbidity and time variable as the predictors. Meanwhile, a univariate model ARIMA(1,0,1)(1,0,0) 52 (AIC=−575.92, BIC=−558.14) and a multivariable model ARIMAX(1,0,1)(1,0,0) 52 with 0-1 week lag precipitation (AIC=−578.58, BIC=−578.13) were developed as benchmarks . The RF model outperformed the ARIMA/X models with a mean absolute percentage error (MAPE) of approximately 20% . The performance of the ARIMAX model was comparable to that of the ARIMA model with a MAPE reaching approximately 30%. Conclusions: The RF model fitted the dynamic nature of an infectious diarrhea epidemic well and delivered an ideal prediction accuracy . It comprehensively combined the synchronous and lagged effects of meteorological factors; it also integrated the autocorrelation and seasonality of the morbidity. The RF model can be used to predict the epidemic level and has a high potential for practical implementation.

scholarly journals Forecasting incidence of infectious diarrhea using random forest in Jiangsu rovince, China

2020 ◽  
Author(s):  
Xinyu Fang ◽  
Wendong Liu ◽  
Jing Ai ◽  
Ying Wu ◽  
Yingying Shi ◽  
...  
Keyword(s):  
Random Forest ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Infectious Diarrhea ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Good Potential

Abstract Background: Infectious diarrhea can lead to considerable disease burden around the world. Thus, the accurate prediction of infectious diarrhea epidemic is crucial for public health authorities. This study aimed to develop an optimal random forest (RF) model considering meteorological factors to predict morbidity of infectious diarrhea in Jiangsu Province, China. Methods: A RF model was constructed and compared with the classical autoregressive integrated moving average (ARIMA)/X models. Morbidity and meteorological data from 2012−2016 were used for model construction and the rest data in 2017 were used for testing. Results: The RF model considered atmosphere pressure, precipitation, relative humidity and their lagged terms, 1-4 weeks’ lag morbidity and the time variable as predictors. Meanwhile, a univariate model ARIMA(1,0,1)(1,0,0) 52 (AIC=−575.92, BIC=−558.14) and a multivariable model ARIMAX(1,0,1)(1,0,0) 52 with 0-1 week’s lag precipitation (AIC=−578.58, BIC=−578.13) were developed as benchmark models . The RF model outperformed the ARIMA/X models with a mean absolute percentage error (MAPE) of approximately 20% . The performance of the ARIMAX model was similar to that of the ARIMA model with MAPE approximately as high as 30%. Conclusions: The RF model well fitted the dynamic of the infectious diarrhea epidemic and achieved ideal prediction accuracy. It comprehensively combined meteorological factors and their hysteresis effects. It also integrated the autocorrelation and seasonality of morbidity. The RF model could be used to predict the epidemic level, and has good potential of practical application.

scholarly journals Indoor Environmental Quality in Dwellings and Lifestyle Behaviors during the COVID-19 Pandemic: Russian Perspective

2021 ◽  
Vol 18 (11) ◽  
pp. 5975
Author(s):  
Vladimir Reshetnikov ◽  
Oleg Mitrokhin ◽  
Elena Belova ◽  
Victor Mikhailovsky ◽  
Maria Mikerova ◽  
...  
Keyword(s):  
Public Health ◽  
Medical Students ◽  
Environmental Quality ◽  
Indoor Environmental Quality ◽  
Lifestyle Behaviors ◽  
Contagious Diseases ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Isolation Index ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) outbreak is a public health emergency of international concern, and as a response, public health authorities started enforcing preventive measures like self-isolation and social distancing. The enforcement of isolation has consequences that may affect the lifestyle-related behavior of the general population. Quarantine encompasses a range of strategies that can be used to detain, isolate, or conditionally release individuals or populations infected or exposed to contagious diseases and should be tailored to circumstances. Interestingly, medical students may represent an example of how the COVID-19 pandemic can form new habits and change lifestyle behaviors. We conducted a web-based survey to assess changes in lifestyle-related behavior of self-isolated medical students during the COVID-19 pandemic. Then we analyzed the sanitary-hygienic regulations of the Russian Federation to determine the requirements for healthy buildings. Results showed that during the pandemic, the enforcement of isolation affects medical students’ lifestyle-related behavior and accompanies an increase in non-communicable diseases (NCDs). Indoor environmental quality (IEQ) and healthy buildings are cutting-edge factors in preventing COVID-19 and NCDs. The Russian sanitary-hygienic regulations support improving this factor with suitable requirements for ventilation, sewage, waste management, and disinfection. Herein, assessing isolation is possible through the hygienic self-isolation index.

scholarly journals Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

2021 ◽  
Author(s):  
Douglas E. Morrison ◽  
Roch Nianogo ◽  
Vladimir Manuel ◽  
Onyebuchi A. Arah ◽  
Nathaniel Anderson ◽  
...  
Keyword(s):  
School Attendance ◽  
Secondary Infection ◽  
Mitigation Strategies ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Safety Education ◽  
Vaccination Rates ◽  
Health Authorities ◽  
Infection Dynamics ◽  
Public Health Authorities

AbstractObjectiveTo support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsUnder default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model’s parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

scholarly journals Asthma Mortality Inequalities in Brazil: Tolerating the Unbearable

2012 ◽  
Vol 2012 ◽  
pp. 1-2 ◽  
Author(s):  
Carolina de Souza-Machado ◽  
Adelmir Souza-Machado ◽  
Alvaro A. Cruz
Keyword(s):  
Respiratory Diseases ◽  
Mortality Rates ◽  
High Morbidity ◽  
The Public ◽  
Health Authorities ◽  
National Mortality ◽  
Public Health Authorities ◽  
Linear Regressions ◽  
Asthma Mortality ◽  
Annual Mortality

Asthma is responsible for a high morbidity, resulting in hospitalizations, recurrent asphyxiation, and eventually death. In Brazil, where asthma is the third cause of hospitalizations for clinical illnesses and the fourth cause of death from respiratory diseases, some 20% of the population present wheezing. We evaluated the asthma mortality rates in the period between 1998 and 2009, using linear regressions, using the National Mortality Database (Ministry of Health of Brazil). The annual mortality rate (per 100,000 inhabitants) ranged from 1.68 in 1998 to 1.32 in 2009 (mean : 1.49). Brazil presents a slight tendency of reduction in asthma mortality. Asthma mortality rates trends declined in the most developed regions of the country:  Midwest, South, and Southeast, but it increased in the underprivileged regions: North (not statistically significant) and Northeast. This terrible sort of inequality requires urgent reaction from the public health authorities.

scholarly journals Trends in the nontuberculous mycobacterial disease mortality rate in Japan: A nationwide observational study, 1997-2016

2020 ◽  
Author(s):  
Ko Harada ◽  
Hideharu Hagiya ◽  
Tomoko Funahashi ◽  
Toshihiro Koyama ◽  
Mitsunobu R Kano ◽  
...  
Keyword(s):  
Mortality Rate ◽  
The Elderly ◽  
Mortality Rates ◽  
Vital Statistics ◽  
Female Population ◽  
Health Authorities ◽  
Disease Mortality ◽  
Crude Mortality ◽  
Public Health Authorities ◽  
Nationwide Observational Study

Abstract Background The incidence of nontuberculous mycobacterial (NTM) infections has been increasing worldwide, becoming a significant healthcare burden especially among elderly people. This study aimed to evaluate the trends in NTM-associated mortality in Japan. Methods This study used vital statistics data and data on all NTM-associated deaths (N=18,814) among individuals aged ≥40 years in Japan from 1997 to 2016. We calculated the crude and age-adjusted mortality rates by age and sex and used joinpoint regression to analyze trends and estimate the average annual percentage change (AAPC). We compared crude NTM- and tuberculosis (TB)-associated mortality rates by sex. Results The overall crude annual mortality rate increased from 0.63/100,000/year in 1997 to 1.93/100,000/year in 2016 and was the highest among individuals aged 80–84 years. The AAPC of the crude mortality rates among males of all ages and females aged 40–59 years were stable but increased among females aged 60–79 years (3.5%, 95% confidence interval [CI]: 2.8–4.3%) and ≥80 years (4.3%, 95% CI: 3.7–4.9%). Among males, the age-adjusted mortality rates did not show a significant trend, while among females, the rates increased over the study period (AAPC: 4.6%, 95% CI: 2.7–6.6%). In females, the crude NTM-associated mortality rate exceeded the TB mortality rate in 2014, 2015, and 2016. Conclusions  NTM mortality increased in Japan between 1997 and 2016, especially among the elderly female population. Given the increasing NTM-associated mortality and susceptible aging population, public health authorities in Japan should pay greater attention to NTM infections.

scholarly journals A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Za'er Abo-Hammour ◽  
Othman Alsmadi ◽  
Shaher Momani ◽  
Omar Abu Arqub
Keyword(s):  
Genetic Algorithm ◽  
Dynamical Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Input Output ◽  
Data Sequence ◽  
Output Data ◽  
Linear Dynamical Systems ◽  
Genetic Algorithm Approach

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

scholarly journals Modeling and Forecast of COVID-19 Vaccinations Population Size in China: A Non-Clinical Study

2021 ◽  
Author(s):  
Wenbin Du ◽  
Fengrui Hua ◽  
Shengyuan Xu ◽  
You Wu
Keyword(s):  
Public Health ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Model ◽  
Medical Community ◽  
Autoregressive Integrated Moving Average ◽  
Health Authorities ◽  
Worldwide Population ◽  
Sample Data ◽  
Optimum Model

Abstract BACKGROUNDSince its outbreak in December 2019, severe acute respiratory syndrome coronavirus-2, the virus responsible for the COVID-19 pandemic, has considerably affected the worldwide population. Health authorities and the medical community identify vaccines as an effective tool for managing public health.METHODSIn this study, the autoregressive integrated moving average (ARIMA) model built-in Python was adopted to establish the COVID-19 vaccination forecast model. In this study, the sample data were selected from the Our World in Data website. COVID-19 vaccinations administered daily in China from December 16, 2020 to March 21, 2021 were analyzed to establish an autoregressive integrated moving average (ARIMA) model.RESULTSThe built-in ARIMA module function of Python was used, and the optimum model was ARIMA (3, 2, 3) according to the established time series analysis. The analysis showed that the predicted COVID-19 vaccination uptake supplemented well with the actual values with a small relative error.CONCLUSIONSThis indicated that the ARIMA(3, 2, 3) model could be used to forecast the number of COVID-19 vaccinations in China.

scholarly journals Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model

2020 ◽  
Author(s):  
Lakshmi Rani Kundu ◽  
Most. Zannatul Ferdous ◽  
Ummay Soumayia Islam ◽  
Marjia Sultana
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Global Public Health ◽  
Prevention Measures ◽  
Expected Number ◽  
Upward Trend ◽  
Autoregressive Integrated Moving Average ◽  
Health Authorities ◽  
The Government ◽  
Daily Total

AbstractBackgroundCOVID-19 is one of the most serious global public health threats creating an alarming situation. Therefore, there is an urgent need for investigating and predicting COVID-19 incidence to control its spread more effectively. This study aim to forecast the expected number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 in Bangladesh for next 30 days.MethodsThe number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 from 8 March 2020 to 16 October, 2020 was collected to fit an Autoregressive Integrated Moving Average (ARIMA) model to forecast the spread of COVID-19 in Bangladesh from 17th October 2020 to 15th November 2020. All statistical analyses were conducted using R-3.6.3 software with a significant level of p< 0.05.ResultsThe ARIMA (0,2,1) and ARIMA (0,1,1) model was adopted for forecasting the number of daily total confirmed cases, total deaths and total confirmed new cases, new deaths of COVID-19, respectively. The results showed that an upward trend for the total confirmed cases and total deaths, while total confirmed new cases and total new death, will become stable in the next 30 days if prevention measures are strictly followed to limit the spread of COVID-19.ConclusionsThe forecasting results of COVID-19 will not be dreadful for upcoming month in Bangladesh. However, the government and health authorities should take new approaches and keep strong monitoring of the existing strategies to control the further spread of this pandemic.

scholarly journals Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model

2011 ◽  
Vol 14 (3) ◽  
pp. 784-799 ◽  
Author(s):  
Wen-Chuan Wang ◽  
Chun-Tian Cheng ◽  
Kwok-Wing Chau ◽  
Dong-Mei Xu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Evolutionary Process ◽  
Model Parameters ◽  
Optimal Model ◽  
Xinanjiang Model ◽  
Chaos Genetic Algorithm ◽  
Parameter Values ◽  
Stochastic Properties ◽  
Search Capability

Conceptual rainfall–runoff (CRR) model calibration is a global optimization problem with the main objective to find a set of optimal model parameter values that attain a best fit between observed and simulated flow. In this paper, a novel hybrid genetic algorithm (GA), which combines chaos and simulated annealing (SA) method, is proposed to exploit their advantages in a collaborative manner. It takes advantage of the ergodic and stochastic properties of chaotic variables, the global search capability of GA and the local optimal search capability of SA method. First, the single criterion of the mode calibration is employed to compare the performance of the evolutionary process of iteration with GA and chaos genetic algorithm (CGA). Then, the novel method together with fuzzy optimal model (FOM) is investigated for solving the multi-objective Xinanjiang model parameters calibration. Thirty-six historical floods with one-hour routing period for 5 years (2000–2004) in Shuangpai reservoir are employed to calibrate the model parameters whilst 12 floods in two recent years (2005–2006) are utilized to verify these parameters. The performance of the presented algorithm is compared with GA and CGA. The results show that the proposed hybrid algorithm performs better than GA and CGA.

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