Oil price and the Russian stock market volatility

Thesis(Master) --KDI School:Master of Public Policy,2012OutstandingmasterpublishedAnton Pak

Does high public trust amplify compliance with stringent COVID-19 government health guidelines? A multi-country analysis using data from 102,627 individuals

Purpose To examine how public trust mediates the people’s adherence to levels of stringent government health policies and to establish if these effects vary across the political regimes. Methods This study utilizes data from two large-scale surveys: the global behaviors and perceptions at the onset of COVID-19 pandemic and the Oxford COVID-19 Government Response Tracker (OxCGRT). Linear regression models were used to estimate the effects of public trust and strictness of restriction measures on people’s compliance level. The model accounted for individual and daily variations in country-level stringency of preventative measures. Differences in the dynamics between public trust, the stringent level of government health guidelines and policy compliance were also examined among countries based on political regimes. Results We find strong evidence of the increase in compliance due to the imposition of stricter government restrictions. The examination of heterogeneous effects suggests that high public trust in government and the perception of its truthfulness double the impact of policy restrictions on public compliance. Among political regimes, higher levels of public trust significantly increase the predicted compliance as stringency level rises in authoritarian and democratic countries. Conclusion This study highlights the importance of public trust in government and its institutions during public health emergencies such as the COVID-19 pandemic. Our results are relevant and help understand why governments need to address the risks of non-compliance among low trusting individuals to achieve the success of the containment policies

Statistical Approaches to Infectious Diseases Modelling in Developing Countries: A Case of COVID-19

Essential skills required for both statistical consulting and collaboration are mostly informal and are rarely taught in the training institutions in developing countries. These critical skills constitute a significant missing gap and a major hindrance to the growth and development of capacity in statistics and data science practice in developing countries. The advent of LISA 2020 initiative is bridging this gap with a fast-growing network of “stat labs” spread across higher education institutions in Africa, India, Brazil and other parts of the world. This chapter will highlight how LISA 2020 Stat Labs (and other potential labs outside LISA 2020) engage in building capacity to improve informal statistical skills through training and collaborations. In addition, the chapter will review the activities and programs of the stat labs and the contributions being made to bring data science to bear on real-world problems. The chapter plans to draw out lessons that are unique and common to the different stat labs in the network

Are we better-off? The benefits and costs of Australian COVID-19 lockdown

When compared with other countries, Australia has fared much better in COVID-19 outcomes, having experienced low COVID-19 cases, hospitalisations, and deaths. Although it is difficult to know with certainty what and to what degree led to these advantageous outcomes, many attributed this success to the early implementation of strict border closure limiting cross-border transmission and being an Island nation (1–3). Australia has been proceeding with the elimination strategy aiming to contain and crush emerging outbreaks quickly through a suite of public health interventions, with lockdowns playing a central role. However, as vaccination rates continue to rise in Australia, we opine that the lockdowns and other stringent non-pharmaceutical interventions should be phasedown as the cost to the individuals, community, and the economy is likely to outweigh the benefits of these restrictions. At the beginning of the pandemic, most countries followed and defended the implementation of lockdowns, with the early calculations suggesting that benefits far outweigh the costs (3–5). Some empirical studies also observed heterogeneity in the effectiveness of lockdowns and advocated for a careful consideration of demographic, economic, and societal factors before implementing stay-at-home orders, especially in developing countries in which many people rely on day-to-day economic resources (6, 7). However, using more recent data, others provided a different assessment arguing that lockdowns cause more harm than good even in developing countries—with the benefit-cost ratio being significantly overestimated (8, 9). Considering the burden of prolonged lockdown that Sydney and Melbourne have been experiencing and taking into account the increasing vaccination rates across the country, our governments need to carefully consider when and how to lift lockdown and other restrictions, as there is no doubt the cost of getting this wrong is very high. Following a critical review by Allen (10), we discuss the issues associated with the evaluation of lockdown costs and benefits and provide an opinion on lockdowns doing potentially more harm than good as Australia achieves high vaccination rates. This may be useful in timely discussions among the public, media, public health officials, and decision-makers

Hospitalisations related to lower respiratory tract infections in Northern Queensland

Abstract Objective: To investigate the admission characteristics and hospital outcomes for patients admitted with lower respiratory tract infections (LRTI) in Northern Queensland. Methods: We perform a retrospective analysis of the data covering an 11‐year period, 2006–2016. Length of hospital stay (LOS) is modelled by negative binomial regression and heterogeneous effects are checked using interaction terms. Results: A total of 11,726 patients were admitted due to LRTI; 2,430 (20.9%) were of Indigenous descent. We found higher hospitalisations due to LRTI for Indigenous than non‐Indigenous patients, with a disproportionate increase in hospitalisations occurring during winter. The LOS for Indigenous patients was higher by 2.5 days [95%CI: ‐0.15; 5.05] than for non‐Indigenous patients. The average marginal effect of 17.5 [95%CI: 15.3; 29.7] implies that the LOS for a patient, who was admitted to ICU, was higher by 17.5 days. Conclusions: We highlighted the increased burden of LRTIs experienced by Indigenous populations, with this information potentially being useful for enhancing community‐level policy making. Implications for public health: Future guidelines can use these results to make recommendations for preventative measures in Indigenous communities. Improvements in engagement and partnership with Indigenous communities and consumers can help increase healthcare uptake and reduce the burden of respiratory diseases

Editorial: Environmental stressors, multi-hazards and their impact on health

[Extract] nvironmental stressors, such as air pollution, noise pollution, and chemical exposure, can adversely affect human health by increasing the risk of chronic diseases and mortality. The sixth assessment report of the United Nations’ Intergovernmental Panel on Climate Change reported that the global temperature is projected to reach or exceed 1.5◦C of warming over the next 20 years, exacerbating exposure to environmental stressors. Air pollution alone is estimated to cause 4.2 million deaths annually, and most of the world’s population (99%) is exposed to air quality levels that exceed the WHO Air Quality Guidelines

Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease

Bovine respiratory disease (BRD) is a major cause of illness and death in cattle; however, its global extent and distribution remain unclear. As climate change continues to impact the environment, it is important to understand the environmental factors contributing to BRD’s emergence and re-emergence. In this study, we used machine-learning models and remotely sensed climate data at 2.5 min (21 km2) resolution environmental layers to estimate the risk of BRD and predict its potential future distribution. We analysed 13,431 BRD cases from 1727 cities worldwide between 2005 and 2021 using two machine-learning models, maximum entropy (MaxEnt) and Boosted Regression Trees (BRT), to predict the risk and geographical distribution of the risk of BRD globally with varying model parameters. Different re-sampling regimes were used to visualise and measure various sources of uncertainty and prediction performance. The best-fitting model was assessed based on the area under the receiver operator curve (AUC-ROC), positive predictive power and Cohen’s Kappa. We found that BRT had better predictive power compared with MaxEnt. Our findings showed that favourable habitats for BRD occurrence were associated with the mean annual temperature, precipitation of the coldest quarter, mean diurnal range and minimum temperature of the coldest month. Similarly, we showed that the risk of BRD is not limited to the currently known suitable regions of Europe and west and central Africa but extends to other areas, such as Russia, China and Australia. This study highlights the need for global surveillance and early detection systems to prevent the spread of disease across borders. The findings also underscore the importance of bio-security surveillance and livestock sector interventions, such as policy-making and farmer education, to address the impact of climate change on animal diseases and prevent emergencies and the spread of BRD to new areas

Travel-Related Monkeypox Outbreaks in the Era of COVID-19 Pandemic: Are We Prepared?

Several neglected infectious pathogens, such as the monkeypox virus (MPXV), have re-emerged in the last few decades, becoming a global health burden. Despite the incipient vaccine against MPXV infection, the global incidence of travel-related outbreaks continues to rise. About 472 confirmed cases have been reported in 27 countries as of 31 May 2022, the largest recorded number of cases outside Africa since the disease was discovered in the early 1970s

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study

Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. Methods Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. Results We found significant negative association between disease arrival time and number of cases imported from Italy (r = −0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. Conclusion We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities