The Effect of Provincial Policies on the COVID-19 Mortality Rate: Risk Analysis Study in Hamadan Province

Background: Governments adopt different policies and strategies to control and reduce the mortality rate of COVID-19. In order to investigate the effect of the adopted policies on the reduction of mortality caused by this disease, the policies implemented by the Regional Headquarter for the Control of COVID-19 Epidemic in Hamedan Province were evaluated. Materials and Methods: The required information was obtained from the Vice-Chancellor of Health of Hamadan University of Medical Sciences and the minutes of the meetings of the Headquarter for the Control of COVID-19 Epidemic in Hamadan Governorate. All the information obtained dates to the period from April to August 2021. A Bayesian network model was used in GeNIe software version 2.2 for the analysis of the information. Results: In this study, seven models were used to evaluate the impact of the adopted strategies. The first model included social distancing, including travel restriction and limiting gatherings, and the mortality rate was estimated to reach 4.72% by implementing the model. The second model includes observing personal hygiene, wearing masks, and vaccination, and the mortality rate was estimated to reach 4.92% by its implementation. The third model encompassed both travel restrictions and business closures, and the mortality rate reached 6.41% after its implementation. Models 4, 5, and 6, which are a combination of the first, second, and third models, have estimated the mortality rate to reach 1.95%, 2.77%, and 2.26%, respectively. In addition, model 7, which combines the above conditions, made the mortality rate reach 2.35%. In the present study, model 6 was selected as the most suitable model with five policies and RMES=0.03005. Conclusion: According to the results obtained in this study, the simultaneous implementation of five policies, including travel restrictions, business closures, personal hygiene, wearing masks and vaccination, can greatly reduce the risk of mortality. © 2022, Bushehr University of Medical Sciences. All rights reserved.

COVID-19 impact on the agro-food and marine industries through a Bayesian stochastic volatility Fourier Series model

In this paper, we construct a hybrid model, consisted of a Bayesian Vector Autoregressive structure with Bayesian stochastic volatility (SVAR-SV), as well as, Fourier Series (FS). We test the model's performance in terms of forecasting ability, comparing it with simple Bayesian stochastic volatility (SV), and also with a classical econometric autoregressive model. By estimating the average prices of the major Food futures in the stock market, and the average prices of the biggest Marine companies' stocks, we test the effect of Covid-19 on these stocks, through the proposed hybrid model, and the impulse-response functions between the aforementioned. Through this approach, we test whether the Covid-19 pandemic hindered the performance of marine companies and affected the food prices, with those two affecting one another. Based on the findings, a shock is apparent from the Food futures to the Marine companies' stocks, and the hybrid model proposed is the best, in terms of forecasting ability.

Human factors analysis and risk assessment during the spread of the COVID-19 epidemic: a case study of the 2019 Wuhan outbreak in China

Since the discovery of novel coronavirus pneumonia (Covid-19) in Wuhan, China, in December 2019, it has spread to other Chinese provinces and continents in just one month, becoming a "public health emergency of international concern". The undesired behaviors of the public and patients during the Covid-19 epidemic cannot be ignored, but few scholars have studied them. In this study, we firstly adopted a qualitative analysis method based on a theoretical paradigm to to summarize the human factors in the spread of the COVID-19 epidemic, and defined the concept of "human factors of the epidemic". Then, we analyzed the distribution characteristics of "human factors of epidemic" at each stage by using statistical analysis, and constructed a human factors model of epidemic evolution. Finally, a multi-subject risk assessment model was constructed using a fuzzy Bayesian network analysis method to quantify the human factors risk in the spread of the COVID-19 epidemic. The results of the study are as follows. (1) The human factors of the COVID-19 epidemic mainly focused on five aspects, including cognitive bias, defective design, management bias, environmental defects, and intentional violations. (2) There were differences in the human factors at different stages of the spread of the COVID-19 epidemic. In the outbreak stage, human factors of the COVID-19 epidemic showed complex trends, with factors such as lack of knowledge and low awareness still prevailing on the one hand, and factors such as lack of capacity, overtly agree but covertly oppose, dereliction of duty, concealment and misreporting, lack of resources, protection defects, design defects, escape/fleeing, and public gathering on the other hand also being more prominent. (3) The risk of the spread of the COVID-19 epidemic due to undesired human factors in the subjects involved was high (p=0.641) under conventional intervention scenarios. Risk factors such as low awareness, poor decision making, lack of resources, lack of awareness, system deficiencies, public agglomeration, inadequate protection, misreporting, and dereliction of duty had relatively large sensitivity factors and were key human factors for the spread of the epidemic in Wuhan. Finally, targeted recommendations are proposed based on the evolutionary pattern and risk level of the human factors of the COVID-19 epidemic.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Bayesian investigation of SARS-CoV-2-related mortality in France

The SARS-CoV-2 epidemic in France has focused a lot of attention as it has had one of the largest death tolls in Europe. It provides an opportunity to examine the effect of the lockdown and of other events on the dynamics of the epidemic. In particular, it has been suggested that municipal elections held just before lockdown was ordered may have helped spread the virus. In this manuscript we use Bayesian models of the number of deaths through time to study the epidemic in 13 regions of France. We found that the models accurately predict the number of deaths 2 to 3 weeks in advance, and recover estimates that are in agreement with recent models that rely on a different structure and different input data. In particular, the lockdown reduced the viral reproduction number by 80%. However, using a mixture model, we found that the lockdown had had different effectiveness depending on the region, and that it had been slightly more effective in decreasing the reproduction number in denser regions. The mixture model predicts that 2.08 (95% CI: 1.85-2.47) million people had been infected by May 11, and that there were 2567 (95% CI: 1781-5182) new infections on May 10. We found no evidence that the reproduction numbers differ between week-ends and week days, and no evidence that the reproduction numbers increased on the election day. Finally, we evaluated counterfactual scenarios showing that ordering the lockdown 1 to 7 days sooner would have resulted in 19% to 76% fewer deaths, but that ordering it 1 to 7 days later would have resulted in 21% to 266% more deaths. Overall, the predictions of the model indicate that holding the elections on March 15 did not have a detectable impact on the total number of deaths, unless it motivated a delay in imposing the lockdown.

Bayesian estimation of epidemiological models: methods, causality, and policy trade-offs

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model captures the data dynamics very well, including the three waves of infections. We use the estimated (true) number of new cases and the time-varying effective reproduction number from the epidemiological model as information for structural vector autoregressions and local projections. We document how additional government-mandated mobility curtailments would have reduced deaths at zero cost or a very small cost in terms of output.

Key Points of Railway Police Work based on Bayesian Passenger Flow Forecast Method under the Normalization of COVID-19 Prevention and Control

China has entered the stage of normalized COVID-19 prevention and control, but the pressure of overseas COVID-19 input continues to increase, and the railway COVID-19 prevention and control police work is still facing a major test. This paper analyzes the characteristics of railway passenger flow under the influence of the COVID-19 situation, establishes a passenger flow forecasting method based on Bayesian theory, and puts forward the key points of railway police work by improving the police mode, strengthening daily police, optimizing smart policing, relying on mass prevention and mass treatment, It provides a theoretical reference for the railway public security organs to effectively control the public health risks and social security risks under the normalization of COVID-19 prevention and control, effectively maintain the railway operation order, and strive to ensure the safe travel of passengers. © 2021 Kriedt Enterprises Ltd. All right reserved.