(No) country for old men? Intergenerational welfare distribution across welfare state regimes

PurposeThe article examines the interplay between welfare state regimes and the distribution of welfare between generations.Design/methodology/approachUsing data from 2017 for 24 European countries on six standard of living dimensions, the authors investigate the intergenerational welfare distribution in a two-stage procedure: (1) the authors compare the intergenerational welfare distribution across welfare state regimes using their existing typologies and find a moderate nexus. Therefore, (2) the authors employ clustering procedure to look for a new classification that would better reflect the cross-country variation in the intergenerational welfare division.FindingsThe authors find a complex relationship between the welfare state model and welfare distribution across generations and identify the policy patterns that shape it. Continental and liberal regimes are quite similar in these terms and favour the elderly generation. Social-democratic and CEE regimes seem to be a bit more balanced. COVID-19 pandemic will probably increase the intergenerational imbalance in terms of welfare distribution in favour of the elderly.Originality/valueIn contrast to the majority of previous studies, which employ inputs (social expenditures) or outputs (benefits, incomes), the authors use intergenerational balance indicators reflecting living conditions of a given generation as compared to the reference point defined as an average situation of all generations.

Race, poverty and unemployment as quantitative predictors of voter turnout in St. Louis amidst COVID-19 and a racial justice movement

PurposeThe 2020 election season brought with it a global public health pandemic and a reenergized racial justice movement. Given the social context of the intertwined pandemics of COVID-19 and racialized violence, do the traditional predictors of voter turnout – race, poverty rates and unemployment rates – remain significant?Design/methodology/approachUsing county-level, publicly available data from twelve Midwest states with similar demographic and cultural characteristics, voter turnout in St. Louis City and St. Louis County were predicted using race, poverty rates and unemployment rates.FindingsFindings demonstrate that despite high concentration of poverty rates and above average percentages of Black residents, voter turnout was significantly higher than predicted. Additionally, findings contradict previous studies that found higher unemployment rates resulted in higher voter participation rates.Originality/valueThis study suggests that the threat of COVID-19 and fear of an increase in police violence may have introduced physical risk as a new theoretical component to rational choice theory for the general election in 2020.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

The Impact of COVID-19 on Financial Markets and the Real Economy

Unlike the 2007–2009 economic meltdown, the COVID-19 pandemic was not caused by problematic market situation or reckless financial policy;it was, in fact, completely unpredicted (Hsu and Tang 2022). [...]it contrasted from other earlier dramatic events caused by economic and financial circumstances, including the Asian financial crisis in 1997–1998 or the European debt crisis in 2010–2013 (Dong et al. 2022). [...]the similarity of these downturns is that they commenced in one nation or area and spread rapidly to other markets, prompting considerable disruption in the worldwide financial system (Zhang et al. 2022). [...]COVID-19 has been regarded as an "exogenous shock” or potentially a "black swan”, as it was such a rare occurrence that has major repercussions for stock markets without any reasonable anticipation (Costola et al. 2023). According to Yu and Xiao (2023), the pessimistic news from COVID-19 government restriction policies generated more instability in stock markets than the optimistic news. [...]Conlon and McGee (2020) raised suspicions on Bitcoin's potential to provide protection from volatility in conventional markets. [...]the publications featured in this Special Issue expanded our comprehension surrounding the effect of the COVID-19 pandemic on financial markets and the real economy, and they proposed appealing future research avenues.

Implementing Bisection Method on Forex Trading Database for Early Diagnosis of Inflection Point

Many people are trading in the forex market during the COVID-19 pandemic with the hope of earning money, but they are experiencing shortages due to the lack of information and technology-based tools for existing daily data. Sometimes traders only use moving averages in trading data, even though this information needs to be processed again to get the right inflection point. The objective of this research is to find inflection points based on Forex trading database. Another algorithm can also be used to determine the inflection point between two points on a moving average. This can be supported by the Bisection method used because it can guarantee that convergence will occur. The results show that the points resulting from the bisection calculation on the moving average provide a fairly accurate decision support for the location where the inflection point is located. From 10,000 data there is a standard deviation of 0.71 points which is very small compared to an average of 20 pips (points used as the difference in price values in forex). The use of the bisection method provides an accuracy of the results in seeing the inflection point of 87%. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Analysis of the relationship between bitcoin, oil prices, and the Dow Jones Industrial Average using a wavelet-based approach

This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine. © COPYRIGHT SPIE.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales;and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. © 2023 by the authors.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Asymmetric effects of economic policy uncertainty on Bitcoin's hedging power

PurposeEven though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.Design/methodology/approachThe authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.FindingsFirst, the authors found a strong evidence of Bitcoin's mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.Originality/valueThe study significantly extends the empirical literature of Bitcoin's portfolio diversification by taking EPU into consideration. To the best of authors' knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin's hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.

Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language. © 2022 IEEE.

Application of Time Series Analysis Methods to Assess the impact of COVID-19 on Carbon Dioxide Emission Reduction in Connecticut

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

The revise is given as follows: The rapid emergence of the super-spreader COVID-19 with severe economic calamities with devastating social impact worldwide created the demand for effective research on the spread dynamics of the disease to combat and create surveillance systems on a global scale. In this study, a novel hybrid Deterministic Autoregressive Fractional Integral Moving Average (ARFIMA) model is presented to forecast the bimodal COVID-19 transmission dynamics. The heterogeneity of multimodal behavior of the COVID-19 pandemic in Pakistan is modeled by a hybrid paradigm, in which a deterministic pattern is combined with the ARFIMA model to absorb the inherent chaotic pattern of the pandemic spread. The fractional fluctuation of the real epidemic system is effectively taken as a paradigm by stochastic type improved the deterministic model and ARFIMA process. Special transformations are also introduced to enhance the convergent rate of the bimodal paradigm in deterministic modeling. The outcome of the improved deterministic model is combined with the ARFIMA model is evaluated on the spread pattern of pandemic data in Pakistan for the next 30 days. The performance-indices of the hybrid-model based on Relative-Errors and RMSE statistics confirmed the effectiveness of the proposed paradigm for long-term epidemic modeling compared to other classical and machine learning algorithms. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Financial Efficiency Analysis the Malmquist TFP Method: An Application on Star Alliance Member Airlines

Bu çalışmanın amacı, en büyük uluslararası stratejik havayolu işbirliği olarak kabul edilen Star Alliance grubuna üye olan 15 havayolu işletmesinin 2016-2019 dönemine ait finansal etkinlik ve verimlilik analizinin Malmquist Total Factor Productivity yöntemi ile incelenmesidir. Bunun yanı sıra Star Alliance grubuna üye olan havayolu işletmelerinin Covid-19 salgını öncesi son dönem finansal etkinlik ve verimliliklerinin değerlendirilmesidir. Analiz sonucunda söz konusu havayolu işletmelerinin 2016-2017 döneminde TPEC ve TFP değerlerinin arttığı, TEC değerlerinin ise azaldığı tespit edilmiştir. 2017-2018 döneminde ise tam tersi bir durumun yaşandığı görülmüştür. 2018-2019 döneminde ilgili havayolu işletmelerinin tümünün TEC, TPEC ve TFP değerlerinde azalışların olduğu tespit edilmiştir. Havayolu işletmelerinin ilgili dönemde ortalama TEC, TPEC ve TFP değerlerinin tüm dönem boyunca azaldığı görülmüştür. Air Canada ve Turkish Airlines işletmelerinin tüm dönem boyunca etkinlik ve verimlilik değerlerini arttırdığı tespit edilirken Air New Zealand'ın TFP değerlerinin ve Asiana Airlines'ın ise TPEC değerlerinin tüm dönem boyunca azaldığı tespit edilmiştir.Alternate abstract: The purpose of this study is to analyze the financial efficiency of 15 airlines that are members of the Star Alliance which is considered the largest international strategic airline network for the period 2016-2019 using the Malmquist Total Factor Efficiency method. In addition, other purposes include the comparison of the change in technical efficiency (TE), technological change (TD) and total factor productivity (TFP) values of airlines that are members of the Star Alliance As a result of the analysis, it was found that the average technological change and total factor productivity values of the airlines in question increased in the period 2016-2017, and the average technical efficiency values decreased. On the other hand, in the period 2017-2018, the opposite situation was observed. In the period 2018-2019, technical efficiency, technological change and total factor productivity values decreased. It was found that the technical efficiency values of the Air Canada and Turkish Airlines increased during the entire period, while the average technical efficiency, technological change and total factor productivity values of the Air New Zealand, Asiana Airlines, Avianca, Lufthansa and Thai Airways decreased.

On the relation between the crude oil market and pandemic Covid-19

PurposeThe crude oil market has experienced an unprecedented overreaction in the first half of the pandemic year 2020. This study aims to show the performance of the global crude oil market amid Covid-19 and spillover relations with other asset classes.Design/methodology/approachThe authors employ various pandemic outbreak indicators to show the overreaction of the crude oil market due to Covid-19 infection. The analysis also presents market connectedness and spillover relations between the crude oil market and other asset classes.FindingsOne of the essential findings the authors report is that the crude oil market remains more responsive to pandemic fake news. The shock of the global pandemic panic index and pandemic sentiment index appears to be more promising. It has also been noticed that the energy trader's sentiment (OVX and OIV) was measured at a too high level within the Covid-19 outbreak. Volatility spillover analysis shows that crude oil and other market are closely connected, and the total connectedness index directs on average 35% contribution from spillover. During the initial growth of the infection, other macroeconomic and political events remained to favor the market. The second phase amidst the pandemic outbreak harms the global crude oil market. The authors find that infectious diseases increase investor panic and anxiety.Practical implicationsThe crude oil investors' sentiment index OVX indicates fear and panic due to infectious diseases and lack of hedge funds to protect energy investments. The unparalleled overreaction of the investors gauged in OVX indicates market participants have paid an excessive put option (protection) premium over the contagious outbreak of the infectious disease.Originality/valueThe empirical model and result reported amid Covid-19 are novel in terms of employing a news-based index of the pandemic, which are based on the content analysis and text search using natural processing language with the aid of computer algorithms.

Predicting the upshot of covid-19 on crude-oil prices in nigeria using mlparima model

Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy. © 2022 IEEE.

Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study

The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student's academic performance. In this research, we aim to identify the factors that impact the student's academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lightweight IoT-Based System for COVID-19 Patient Health Monitoring and Prediction

With the present Coronavirus disease (COVID-19) pandemic, Internet of Things (IoT)-based health monitoring devices are precious to COVID-19 patients. We present a real-time IoT-based health monitoring system that monitors patients' heart rate and oxygen saturation, the most significant measures necessary for critical care. Specifically, the proposed IoT-based system is built with Arduino Uno-based hardware and a web application for retrieving the patients' health information. In addition, we implement the Autoregressive Integrated Moving Average (ARIMA) method in the back-end server to predict future patient measurements based on current and past measurements. Compared to commercially available devices, the system's results are adequately accurate, with an acceptable RMSE for predicted value. © 2022 IEEE.

Connectedness of green investments and uncertainties: new evidence from emerging markets

Purpose>This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.Design/methodology/approach>This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons;providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.Findings>Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.Originality/value>This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries' green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects;which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

The first 2 years of COVID-19 in Italy: Incidence, lethality, and health policies.

Background: The novel coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was first recognized in China in December 2019. This paper aims to provide a detailed overview of the first 2 years of the pandemic in Italy. Design and methods: Using the negative binomial distribution, the daily incidence of infections was estimated through the virus's lethality and the moving-averaged deaths. The lethality of the original strain (estimated through national sero-surveys) was adjusted daily for age of infections, hazard ratios of virus variants, and the cumulative distribution of vaccinated individuals. Results: From February 24, 2020, to February 28, 2022, there were 20,833,018 (20,728,924-20,937,375) cases distributed over five waves. The overall lethality rate was 0.73%, but daily it ranged from 2.78% (in the first wave) to 0.15% (in the last wave). The first two waves had the highest number of daily deaths (about 710) and the last wave showed the highest peak of daily infections (220,487). Restriction measures of population mobility strongly slowed the viral spread. During the 2nd year of the pandemic, vaccines prevented 10,000,000 infections and 115,000 deaths. Conclusion: Almost 40% of COVID-19 infections have gone undetected and they were mostly concentrated in the first year of the pandemic. From the second year, a massive test campaign made it possible to detect more asymptomatic cases, especially among the youngest. Mobility restriction measures were an effective suppression strategy while distance learning and smart working were effective mitigation strategies. Despite the variants of concern, vaccines strongly reduced the pandemic impact on the healthcare system avoiding strong restriction measures.

Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic

This study describes an activity based traffic indicator system to provide information for COVID-19 pandemic management. The activity based traffic indicator system does this by utilizing a social probability model based on the birthday paradox to determine the exposure risk, the probability of meeting someone infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases (7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September 2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for the indicator. While there is no correlation for the 7-DMA of DGR, PoMSI is strongly correlated (0.671 to 0.996) with the cumulative confirmed cases and it can be said that as the cases continuously rise, the probability of meeting someone COVID positive will also be higher. This shows that indicator not only shows the current exposure risk of certain activities but it also has a predictive nature since it correlates to cumulative confirmed cases of next week and can be used to anticipate the values of confirmed cumulative cases. This information can then be used for pandemic management. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.