Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens' perceptions and behavioral responses to stringent policies. This paper traces the crosscountry associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.
We compare the importance of market factors against that of coronavirus disease-19 (COVID-19) dynamics and policy responses in explaining Eurozone sovereign spreads. First, we estimate a multifactor model for changes in credit default swap (CDS) spreads over 2014 to June 2019. Then, we apply a synthetic control-type procedure to extrapolate model-implied changes in CDS. The factor model does very well over the rest of 2019 but breaks down during the pandemic, especially during March 2020. We find that the March 2020 divergence is well accounted for by COVID-specific risks and associated policies, mortality outcomes, and policy announcements, rather than traditional determinants. Daily CDS widening ceased almost immediately after the European Central Bank announced the Pandemic Emergency Purchase Programme, but the divergence between actual and model-implied changes persisted. This points to COVID-19 Dominance—widening spreads during the pandemic has led to unconventional monetary policies that primarily aim to mitigate short-run fears, temporarily pushing away concerns over fiscal risk.
This case study compares the importance of prevailing market factors against that of COVID-19 dynamics and policy responses in explaining the evolution of Eurozone (EZ) sovereign spreads during the first half of 2020. Focusing on daily Eurozone CDS spreads, we adopt a multi-stage econometric approach. First, we estimate a multi-factor model for changes in EZ CDS spreads over the pre-COVID-19 period of January 2014 through June 2019. Then, we apply a synthetic control-type procedure to extrapolate model-implied changes in the CDS from July 2019 through June 2020. We find that the factor model does very well in tracing the realized sovereign spreads over the rest of 2019, but breaks down during the pandemic-diverging substantially in March 2020. In the second stage, focusing specifically on the 2020 period, we find that the March 2020 divergence is well accounted for by COVID-specific risks and associated policies. In particular, mortality outcomes and policy announcements, rather than traditional determinants like fiscal space and systematic risk, drove CDS adjustment over this period. Daily CDS spread widening ceased almost immediately after the ECB announced the PEPP, but the divergence between actual and model-implied changes persisted. This divergence can be traced back to the fact that fiscally secure EZ Core countries saw spreads widen further than implied-comparable to the widening of more fragile countries-as several of the Core countries were hit hard by COVID-19. Taken all together, this points to COVID-19 Dominance: The widening spreads during the pandemic induced by COVID-specific risks and fiscal responses has led to unconventional monetary policies that primarily aim to mitigate the short-run fear of the worst economic outcomes, temporarily pushing away concerns over fiscal risk.
Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.
Today, a large portion of the human population around the globe has no access to freshwater for drinking, cooking, and other domestic applications. Water resources in numerous countries are becoming scarce due to over urbanization, rapid industrial growth, and current global warming. Water is often stored in the aboveground or underground tanks. In developing countries, these tanks are maintained manually, and in some cases, water is wasted due to human negligence. In addition, water could also leak out from tanks and supply pipes due to the decayed infrastructure. To address these issues, researchers worldwide turned to the Internet-of-Things (IoT) technology to efficiently monitor water levels, detect leakage, and auto refill tanks whenever needed. Notably, this technology can also supply real-time feedback to end-users and other experts through a webpage or a smartphone. Literature reveals a plethora of review articles on smart water monitoring, including water quality, supply pipes leakage, and water waste recycling. However, none of the reviews focus on the IoT-based solution to monitor water level, detect water leakage, and auto control water pumps, especially at the induvial level that form a vast proportion of water consumers worldwide. To fill this gap in the literature, this study presents a review of IoT-controlled water storage tanks (IoT-WST). Some important contributions of our work include surveying contemporary work on IoT-WST, elaborating current techniques and technologies in IoT-WST, targeting proper hardware, and selecting a secure IoT cloud server.
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