Forecasting Based on Fuzzy Logic of the Level of Epidemiological Risk for the Mexican State of Tamaulipas

Nowadays, in Mexico there exists a traffic light monitoring system to regulate the use of public space according to the risk level of infection with SARS‒CoV‒2. The monitoring system is applied to each state in Mexico and consists of four levels of risk encoded with four colors: green, yellow, orange and red. In this chapter we propose a Fuzzy Time Series Model to forecast the next color to be assigned to the Mexican state of Tamaulipas based on historical data from the monitoring system. We conducted a computational experiment to measure the accuracy of the model. The model accuracy was measured by the well‒known Root Mean Square Error (RMSE) index. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

VECTOR AUTOREGRESSIVE INTEGRATING MOVING AVERAGE (VARIMA) MODEL OF COVID-19 PANDEMIC AND OIL PRICE

Purpose: A coronavirus associated with severe respiratory syndrome has created Coronavirus Disease 2019 (COVID-19), a highly contagious illness that affects the entire world population. On the other hand, COVID-19 is having a direct impact on human life because of its proliferation. So, the study's goal is to forecast and analyze the impact of the COVID-19 pandemic and the oil price utilizing multiple time series analysis methods (VARIMA model). Theoretical framework: Recent literature has reported that the multivariate time series is robust model for forecasting and analyzing dynamic relationship between series, while the univariate ARIMA model has been generalized to include vector variables, that is an extension of its capabilities. The VAR (p) model analyzes the interdependence between two or more series but does not take into account the impact of shocks at various time variable delays. Design/methodology/approach: This study uses VARMA (p, q) model which links a set of variables to their prior iterations as well as those of other variables and shocks to those same variables. Sample data concerning the COVID-19 pandemic and oil price was globally provided. It contains daily observations of them variables for the years 2020-2022. Findings: The best model is VARIMA (2,1,2), and the results shown that the oil price is not only influenced by itself but also influenced by the Covid-19 pandemic. Moreover, the standard error grows over time of the forecast. Research, Practical & Social implications: The best model is sound for short-term forecasting but unstable for long-term forecasting. Future researchers can integrate factors across areas. Include tourism demand and industry variables in modeling. Originality/value: Collecting COVID-19 pandemic data and oil price series in a modern model that is a multivariate time series model with a high predicted level of model accuracy between these variables in order to predict and analyze the effects between them series and estimate the interaction between these two series with the most recent data is the value of this study, and then offers merchants the chance to comprehend the forecasting of oil price throughout the covid-19 effects as well as the associated risks. © 2022 AOS-Estratagia and Inovacao. All rights reserved.

Investor sentiment in the tourism stock market

This study applies time-series analysis to observe investor sentiment in the tourism stock market. We infer that investor sentiment positively affects the capital flows to illustrate the behavioral finance in the tourism stock market. The vector autoregression and autoregressive-moving-average models of time-series analysis are adopted to analyze individual and overall capital flows of herding behavior. The empirical study collected quarterly data on 45 tourism-related stocks in China from 2018 to 2020. Results reaffirm that investor sentiment causes irrational investment and strong fluctuations of capital flows, including those during the Coronavirus 2019 pandemic. In practice, the overreaction of tourism-related stocks is discovered in the tourism market that requires long-term resilience. Theoretically, the rational capital asset pricing model needs adjustments with the sentiment factor based on behavioral finance theory. © 2022 Elsevier B.V.

Spatiotemporal impact of non-pharmaceutical interventions against COVID-19 on the incidence of infectious diarrhea in Xi'an, China.

Background: Non-pharmaceutical interventions (NPIs) against COVID-19 may prevent the spread of other infectious diseases. Our purpose was to assess the effects of NPIs against COVID-19 on infectious diarrhea in Xi'an, China. Methods: Based on the surveillance data of infectious diarrhea, and the different periods of emergence responses for COVID-19 in Xi'an from 2011 to 2021, we applied Bayesian structural time series model and interrupted time series model to evaluate the effects of NPIs against COVID-19 on the epidemiological characteristics and the causative pathogens of infectious diarrhea. Findings: A total of 102,051 cases of infectious diarrhea were reported in Xi'an from 2011 to 2021. The Bayesian structural time series model results demonstrated that the cases of infectious diarrhea during the emergency response period was 40.38% lower than predicted, corresponding to 3,211 fewer cases, during the COVID-19 epidemic period of 2020-2021. The reduction exhibited significant variations in the demography, temporal and geographical distribution. The decline in incidence was especially evident in children under 5-years-old, with decreases of 34.09% in 2020 and 33.99% in 2021, relative to the 2017-2019 average. Meanwhile, the incidence decreased more significantly in industrial areas. Interpretation: NPIs against COVID-19 were associated with short- and long-term reductions in the incidence of infectious diarrhea, and this effect exhibited significant variations in epidemiological characteristics.

Hierarchical mortality forecasting with EVT tails: An application to solvency capital requirement

In this paper, we propose a new framework to coherently produce probabilistic mortality forecasts by exploiting techniques in seasonal time-series models, extreme value theory (EVT), and hierarchical forecast reconciliation. In a hierarchical setting, coherent forecasts are those forecasts that add up in a manner consistent with the aggregation structure of the collection of time series. We are amongst the first to model and analyze U.S. monthly death data during the period from 1968–2019 to explore the seasonality and the age–gender dependence structure of mortality. Our results indicate that incorporating EVT and hierarchical forecast reconciliation greatly improves the overall forecast accuracy, which has important implications for life insurers in terms of rate making, reserve setting, and capital adequacy compliance. Using the solvency capital requirement (SCR) under Solvency II as an example, we show that the SCR calculated by our approach is considerably higher than those calculated by alternative models, suggesting that failing to account for extreme mortality risk and mortality dependence in the hierarchy can result in significantly underfunded problems for life insurers. We also find that our model can yield death forecasts that are largely consistent with actual observations in most of months in 2021 when death tolls surged due to COVID-19. This provides additional evidence of the effectiveness of our model for practical uses.

Reaction to the COVID-19 pandemic in Seoul with biostatistics.

This paper discusses our collaboration work with government officers in the health department of Seoul during the COVID-19 pandemic. First, we focus on short-term forecasting for the number of new confirmed cases and severe cases. Second, we focus on understanding how much of the current infections has been affected by external influx from neighborhood areas or internal transmission within the area. This understanding may be important because it is linked to the government policy determining non-pharmaceutical interventions. To obtain the decomposition of the effect, districts of Seoul should be considered simultaneously, and multivariate time series models are used. Third, we focus on predicting the number of new weekly confirmed cases for each district in Seoul. This detailed prediction may be important to the government policy on resource allocation. We consider an ensemble method to overcome poor prediction performance of simple models. This paper presents the methodological details and analysis results of the study.

Impact of the COVID-19 pandemic on the conduct of clinical trials: a quantitative analysis.

Background: Globally, healthcare has shouldered much of the socioeconomic brunt of the COVID-19 pandemic leading to numerous clinical trials suspended or discontinued. Objective: To estimate the COVID-19 impact on the number of clinical trials worldwide. Methods: Data deposited by 219 countries in the ClinicalTrials.gov database (2007-2020) were interrogated using targeted queries. A time series model was fitted to the data for studies ongoing, initiated, or ended between 2007 Quarter (Q) 1 and 2019 Q4 to predict the expected trials number in 2020 in the COVID-19 absence. The predicted values were compared with the actual 2020 data to quantify the pandemic impact. Results: The ongoing registered trials number grew from 2007 Q1 (33,739) to 2019 Q4 (80,319). By contrast, there were markedly fewer ongoing trials in all four quarters of 2020 compared with forecasted values (1.6%-2.8% decrease). When excluding COVID-19-related studies, this disparity grew further (3.4%-5.8% decrease), to a peak of almost 5,000 fewer ongoing trials than estimated for 2020 Q2. The initiated non-COVID-19 trials number was higher than predicted in 2020 Q4 (9.9%). Conclusions: This pandemic has impacted clinical trials. Provided that current trends persist, clinical trial activities may soon recover to at least pre-COVID-19 levels.

Investor sentiment in the tourism stock market

This study applies time-series analysis to observe investor sentiment in the tourism stock market. We infer that investor sentiment positively affects the capital flows to illustrate the behavioral finance in the tourism stock market. The vector autoregression and autoregressive-moving-average models of time-series analysis are adopted to analyze individual and overall capital flows of herding behavior. The empirical study collected quarterly data on 45 tourism-related stocks in China from 2018 to 2020. Results reaffirm that investor sentiment causes irrational investment and strong fluctuations of capital flows, including those during the Coronavirus 2019 pandemic. In practice, the overreaction of tourism-related stocks is discovered in the tourism market that requires long-term resilience. Theoretically, the rational capital asset pricing model needs adjustments with the sentiment factor based on behavioral finance theory.

Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis. Driving and walking data, as well as confinement policies were collected from February 2020 to February 2021 for twenty-five European countries and were implemented into Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) time-series models. Results reveal a significant number of models in order to estimate mobility during pandemic almost in every country of the study. School closing was found to be the most important exogenous factor for describing driving or walking, while “Stay at home” orders had not a significant effect on the evolution of people movements. In addition, countries which suffered the most due to the pandemic indicated a strong correlation with the restrictive measures. No time-series models were found to describe the countries which implemented weak confinement policies. © 2022 Marianthi Kallidoni, Christos Katrakazas, George Yannis.

Vaccination and Government Stringent Control as Effective Strategies in Preventing SARS-CoV-2 Infections: A Global Perspective.

With the rapid implementation of global vaccination against the coronavirus disease 2019 (COVID-19), the threat posed by the disease has been mitigated, yet it remains a major global public health concern. Few studies have estimated the effects of vaccination and government stringent control measures on the disease transmission from a global perspective. To address this, we collected 216 countries' data on COVID-19 daily reported cases, daily vaccinations, daily government stringency indexes (GSIs), and the human development index (HDI) from the dataset of the World Health Organization (WHO) and the Our World in Data COVID-19 (OWID). We utilized the interrupted time series (ITS) model to examine how the incidence was affected by the vaccination and GSI at continental and country levels from 22 January 2020 to 13 February 2022. We found that the effectiveness of vaccination was better in Europe, North America, and Africa than in Asia, South America, and Oceania. The long-term effects outperformed the short-term effects in most cases. Countries with a high HDI usually had a high vaccination coverage, resulting in better vaccination effects. Nonetheless, some countries with high vaccination coverage did not receive a relatively low incidence due to the weaker GSI. The results suggest that in addition to increasing population vaccination coverage, it is crucial to maintain a certain level of government stringent measures to prevent and control the disease. The strategy is particularly appropriate for countries with low vaccination coverage at present.

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

Covid-19 pandemic and resulting lockdown has created a wide impact on social life, including sudden rise in residential load demand. Utilities, for better load scheduling and economic operations, rely on different prediction models among which neural networks proved to be more appropriate. For such unforeseen situations, the non-availability of prior predictions elevated the utility challenges. Moreover, the stringency of lockdowns caused due to mutated COVID-19 virus, necessitates accurate lockdown load predictions. This paper proposes a Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM) model, trained to produce such predictions for two areas of residential sector. The model uses real-time residential load data from the year 2020, with and without weather parameters. The correlation factor (R) of proposed method 0.9683 outperformed the ARIMA's value 0.703. The model is evaluated with correlation factors of 0.9683 and 0.9235 without temp;0.90361 and 0.913662 with temperature for Apurupa and Jyothi colonies respectively located in Hyderabad, India. In addition, the error metrics namely, Mean absolute percentage error (MAPE) and Mean absolute error (MAE) are 2.0464 and 138.576 for Apurupa colony;0.015 and 201.648 for Jyothi colony respectively. However, the prediction error metrics increased slightly with temperature data. The proposed framework will assist utilities for effective load predictions during situations such as pandemic lockdown.

Gecko: A time-series model for COVID-19 hospital admission forecasting.

During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January-May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting.

Forecasting the spread of Covid-19 pandemic outbreak in India using ARIMA time series modelling

COVID-19 is the infectious disease caused by the most recently discovered corona virus. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019. This paper focuses on a Time Series Model to predict COVID-19 Outbreaks in India. Every day data of fresh COVID-19 confirmed cases act as an exogenous factor in this frame. Our data envelops the time period from 01st Sep, 2020 to 9th Dec, 2020. COVID-19 Corona virus disease has been recognized as a worldwide hazard, and most of the studies are being conducted using diverse mathematical techniques to forecast the probable evolution of this outbreak. These mathematical models based on various factors and analyses are subject to potential bias. Here, we put forward a natural Times Series (TS) model that could be very useful to predict the spread of COVID-19. Here, a popular method Auto Regressive Integrated Moving Average (ARIMA) TS model is performed on the real COVID-19 data set to predict the outbreak trend of the prevalence and incidence of COVID-19 in.India. The time series under study is a non-stationary. Results obtained in the study revealed that the ARIMA model has a strong potential for prediction. The model predicted maximum COVID-19 cases in India at around 14, 22,337 with an interval (12, 80,352 - 15, 69, 817) during 1st Sep to 9th Dec period cumulatively. As per the model, the number of new cases shall fluctuate drastically in India. The results will help governments to make necessary arrangements as per the estimated cases. This kind of analysis and implications of ARIMA models and fitting procedures are useful in forecasting COVID-19 Outbreaks in India. © 2022 Author(s).

Motorcycle Accident Casualties During the COVID-19 Epidemic in Taiwan

A descriptive time series study of casualties from motorcycle accidents in Taiwan between 2016 and 2020. The data on casualties were obtained from the road safety information system provided by the Ministry of Transportation and Communications. Between 2016 and 2020 the casualties increased from 2,571 to 3,191 (an increase of 241% in casualty rates during the period studied). High casualty rates in 2020 were observed in Taiwan. There was a significant increase in motorcycle accident casualty rates for the country as a whole during the studied period. © 2022 IEEE.

Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A death, infection, and recovery (DIR) model to forecast the COVID-19 spread.

BACKGROUND: The SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate. OBJECTIVE: In this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc. METHOD: The size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE). RESULTS: Experimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%. CONCLUSION: This percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.

Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models.

BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. RESULTS: The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of - 89 to 214 and 108-186, respectively. Deaths' forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1-17 and 4-20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of - 106 to 185 and 44-239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. CONCLUSION: The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

COVID - 19 Future forecasting using Time Series Model

The increase of COVID-19 has affected the entire nation and is therefore declared to be pandemic. Due to its vast disruption among the humanity, measures have been taken to forecast the future spread of COVID-19 trending disease in India using time series model. In this experiment, we have used prophet forecasting model that takes comparison between performance and accuracy of the active cases in India, that has been observed from Kaggle database. This model compares the second wave of COVID-19 data which is present in the last four months so as to measure the future forecasting. The result thus obtained predicts the future spread of active COVID-19 cases in our nation using the time series model. © 2021 IEEE.

An Ensemble Machine Learning Approach for Time Series Forecasting of COVID-19 Cases

Forecasting assists governments, epidemiologists, and policymakers make calculated decisions to mitigate the spread of the COVID-19 pandemic, thus saving lives. This paper presents an ensemble machine learning model by combining the distinctive strengths of autoregressive integrated moving averages (ARIMA) and stacked long short-term memory networks (S-LSTM) using extensive training procedures and model integration algorithms. We validated the model's generalization capabilities by analyzing time series data of four countries, such as the Philippines, United States, India, and Brazil spanning 467 days. The quantitative results show that our ensemble model outperforms stand-alone models of ARIMA and S-LSTM for a 15-day forecast accuracy of 93.50% (infected cases) and 87.97% (death cases). © 2021 IEEE.

A Bayesian Structural Time Series Model for Assessing Road Traffic Accidents during COVID-19 Period

This is a systematic study to understand the effect of the implementation and removal of restrictive measures on road accidents statistics, using newspaper-based database. The time frame of data is between 1st January 2020 to 31st December 2020. In this period, 26th of March was the first day of official lockdown and 30th of May was the day of lockdown withdrawal in Bangladesh. However, the first COVID-19 affected case was identified on 8th of March followed by the closure of educational institutions on 18th of March of the same year. In the selected time frame, the total number of accidents was recorded 3,069 with total injuries and fatalities being 593 and 3,570 respectively. The new confirmed cases, death and recovered cases of Coronavirus affected people were coll ected from Humanitarian Data Exchange, 2020 to understand their correlations with accident statistics. This study considered two interventions: starting of lockdown and reopening. Results suggest that the number of daily traffic accidents and related fatalities increased by about the same amount after the reopening as they had decreased due to the lockdown. This study also conducted the two-sample t-tests to find variations of mean in overall incident cases, casualties, and injuries on a daily basis before, during, and after the mandatory lockdown. It is found that there is a significant reduction in total road traffic accidents in the lockdown period than pre-lockdown period, followed by a slight increase in the post lockdown period. In case of fatalities, before and during the lockdown, a drastic reduction has been observed from 13.94 to 5.80 with p-value < 2.2e-16, followed by an increase. Finally, statistics of mean injuries have also experienced a steep drop during the lowdown period and rise after the lockdown. The effect size of the obtained means is also evaluated using Cohen's d test. © 2021, Avestia Publishing, Switzerland. All rights reserved.