Alert Level Research Articles

Evaluation of Resource and Environmental Carrying Capacity at Provincial Level in China Using a Pressure–Support–Adjustment Ternary System

Resources and environment are essential elements of social and economic development. Whether the current resources and environment can sufficiently support economic and social development is an important scientific issue. Existing studies have not reached a consensus on the definition, assessment, and forewarning methods of resource and environmental carrying capacity (RECC). Therefore, this study puts forward a ternary theoretical system of RECC from the perspective of pressure, support, and adjustment, constructs a novel evaluation indices system of RECC, introduces a ternary evaluation model, and proposes a hierarchical forewarning method of RECC with a threshold of 1. Furthermore, an empirical demonstration of 31 provinces in China is presented. The results show that: (1) the average RECC index of 31 areas revealed a downward trend from 2012 to 2021, indicating the gradually improving RECC; (2) the RECC indices of central areas were always high, which manifests the poor RECC; (3) the number of provinces with RECC at Alert Level III decreased from 18 to 7, and the number of provinces at Non-Alert Level increased from 6 to 8.

Evaluation and forewarning of the resource and environmental carrying capacity from the perspective of pressure-support-adjustment: a case study of Yichang city, China

Resources and the environment are essential for socioeconomic development. A scientific and reasonable evaluation and forewarning of the resource and environmental carrying capacity (RECC) is of great significance for regional sustainable development. Although great progress has made in existing research on RECC, there is still no consensus on the definition, evaluation and forewarning method for RECC. Therefore, this study introduced the novel concepts of pressure, support and adjustment, proposed a theoretical framework of RECC reflecting the interaction between the above three, constructed a new evaluation index system of RECC, evaluated RECC based on the ratio method, identified important influencing factors using obstacle model, and put forward the hierarchical forewarning method of RECC with the threshold of 1. Finally, a case demonstration in regard to Yichang city, with a prominent ecological and economic status as the location of China’s Three Gorges Project, was conducted. The results revealed that: 1) The average pressure, support, and adjustment exhibited overall upward trends. The pressure and adjustment increased from west to east, while the support of central districts was relatively lower. 2) The RECC of Yichang city and counties improved from 2016 to 2021, increasing from west to east, and the differences in the RECC of various counties were remarkable but gradually shrank. 3) The RECC of Yichang city remained at the Non-Alert Level. The number of counties with RECC values at Alert Level II decreased from two to zero, and the number of counties at the Non-Alert Level increased from eight to ten. Xiaoting and Zhijiang, with more RECC values at Alert Level II, should receive more attention. This study is expected to enrich the methodology of RECC evaluation and forewarning, and provide possible implications for economic development and environmental protection planning in Yichang and other cities of the same type.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning.

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Service Uptake Challenges Experienced by Pasifika Communities during COVID-19 Lockdowns in New Zealand

Background: New Zealand was one of the first countries to adopt an elimination strategy based on a four-tier Alert Level system that included strict lockdowns at levels three and four. The lockdowns meant shutting out external social networks and being confining to individual household ‘bubbles’ only. This presented challenges for minority Pasifika communities in New Zealand as their cultural values are based on social bonding, interconnectedness, community engagement and caring for each other. The aim of this research was to conduct a small-scale pilot study to test and refine the study design and identify the major challenges faced by Pasifika communities in relation to accessing health, social and mental services while in lockdown. Methods: This exploratory study was designed using an online anonymous questionnaire survey targeting people who identified as Pasifika. A total of eighty-seven respondents were included in our analyses following the questionnaire survey. Results: Five main barriers to accessing health, social and mental health services during lockdowns were identified; (1) unavailability of or limited services; (2) fear of contracting the virus; (3) perception of high costs associated with seeking medical assistance; (4) transportation difficulties; and (5) lack of time. Almost a quarter of the respondents reported losing their jobs, 80% indicated a decline in their household income and more than half experienced some form of psychosocial distress while in lockdown. Conclusions: The findings of this research highlight existing challenges faced by Pasifika communities in adequately accessing essential services. Future research can focus on the key barriers to access identified in this research to gain a deeper understanding of services and its interaction with Pasifika communities during lockdowns.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L−1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Assessing the relationship between cyanobacterial blooms and respiratory-related hospital visits: Green bay, Wisconsin 2017–2019

Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017–2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (ChlBS). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (ChlBS) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of ChlBS were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between ChlBS and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.

The impact of non-pharmaceutical interventions on the first COVID-19 epidemic wave in South Africa

ObjectiveIn this study, we investigated the impact of COVID-19 NPIs in South Africa to understand their effectiveness in the reduction of transmission of COVID-19 in the South African population. This study also investigated the COVID-19 testing, reporting, hospitalised cases, excess deaths and COVID-19 modelling in the first wave of the COVID-19 epidemic in South Africa.MethodsA semi-reactive stochastic COVID-19 model, the ARI COVID-19 SEIR model, was used to investigate the impact of NPIs in South Africa to understand their effectiveness in the reduction of COVID-19 transmission in the South African population. COVID-19 testing, reporting, hospitalised cases and excess deaths in the first COVID-19 epidemic wave in South Africa were investigated using regressional analysis and descriptive statistics.FindingsThe general trend in population movement in South African locations shows that the COVID-19 NPIs (National Lockdown Alert Levels 5,4,3,2) were approximately 30% more effective in reducing population movement concerning each increase by 1 Alert Level. The translated reduction in the effective SARS-CoV-2 daily contact number (β) was 6.12% to 36.1% concerning increasing Alert Levels. Due to the implemented NPIs, the effective SARS-CoV-2 daily contact number in the first COVID-19 epidemic wave in South Africa was reduced by 58.1–71.1% while the peak was delayed by 84 days. The estimated COVID-19 reproductive number was between 1.98 to 0.40. During South Africa’s first COVID-19 epidemic wave, the mean COVID-19 admission status in South African hospitals was 58.5%, 95% CI [58.1–59.0] in the general ward, 13.4%, 95% CI [13.1–13.7] in the intensive care unit, 13.3%, 95% CI [12.6–14.0] on oxygen, 6.37%, 95% CI [6.23–6.51] in high care, 6.29%, 95% CI [6.02–6.55] on ventilator and 2.13%, 95% CI [1.87–2.43] in isolation ward respectively. The estimated mean South African COVID-19 patient discharge rate was 11.9 days per patient. While the estimated mean of the South African COVID-19 patient case fatality rate (CFR) in hospital and outside the hospital was 2.06%, 95% CI [1.86–2.25] (deaths per admitted patients) and 2.30%, 95% CI [1.12–3.83](deaths per severe and critical cases) respectively. The relatively high coefficient of variance in COVID-19 model outputs observed in this study shows the uncertainty in the accuracy of the reviewed COVID-19 models in predicting the severity of COVID-19. However, the reviewed COVID-19 models were accurate in predicting the progression of the first COVID-19 epidemic wave in South Africa.ConclusionThe results from this study show that the COVID-19 NPI policies implemented by the Government of South Africa played a significant role in the reduction of COVID-19 active, hospitalised cases and deaths in South Africa’s first COVID-19 epidemic wave. The results also show the use of COVID-19 modelling to understand the COVID-19 pandemic and the impact of regressor variables in an epidemic.

The Fono's 'Alert Level 4' Story

During the 1970s and 1980s, Pacific people tended to seek medical care from Accident and Emergency centres only when they were in an acute condition. As a result, Pacific mortality rates were high and Pacific people were unnecessarily suffering with poorer health outcomes. By 1987, a group of Pacific community leaders in Auckland came together and formed The Fono (originally known as Pasifika Health Care), to provide a Pacific community-led health practice and improve access to high quality, culturally appropriate primary care services. By 2020, The Fono had nine sites with four medical clinics, three dental clinics, a vast range of public health and social services, and a trades training academy. Aotearoa New Zealand’s initial Covid-19 Alert Level 4 period was a time of intensive service delivery and significant innovation at The Fono. As a result, incubation projects were catapulted into life, transforming key aspects of the organisation. For The Fono, this transformation occurred on the following timeline: pre-Covid (time before Alert Level 4, before 26/03/2020); Covid (Alert Level 4 period, 26/03/2020–3/05/2020); post-Covid (the time after Alert level 4, 13/05/2020 onwards). This viewpoint outlines the projects that contributed to organisational change at The Fono with the first Covid wave in 2020.

Label-Free Cyanobacteria Quantification Using a Microflow Cytometry Platform for Early Warning Detection and Characterization of Hazardous Cyanobacteria Blooms.

The eutrophication of aquatic ecosystems caused by rapid human urbanization has led to an increased production of potentially hazardous bacterial populations, known as blooms. One of the most notorious forms of these aquatic blooms are cyanobacteria, which in sufficiently large quantities can pose a hazard to human health through ingestion or prolonged exposure. Currently, one of the greatest difficulties in regulating and monitoring these potential hazards is the early detection of cyanobacterial blooms, in real time. Therefore, this paper presents an integrated microflow cytometry platform for label-free phycocyanin fluorescence detection, which can be used for the rapid quantification of low-level cyanobacteria and provide early warning alerts for potential harmful cyanobacterial blooms. An automated cyanobacterial concentration and recovery system (ACCRS) was developed and optimized to reduce the assay volume, from 1000 mL to 1 mL, to act as a pre-concentrator and subsequently enhance the detection limit. The microflow cytometry platform utilizes an on-chip laser-facilitated detection to measure the in vivo fluorescence emitted from each individual cyanobacterial cell, as opposed to measuring overall fluorescence of the whole sample, potentially decreasing the detection limit. By applying transit time and amplitude thresholds, the proposed cyanobacteria detection method was verified by the traditional cell counting technique using a hemocytometer with an R2 value of 0.993. It was shown that the limit of quantification of this microflow cytometry platform can be as low as 5 cells/mL for Microcystis aeruginosa, 400-fold lower than the Alert Level 1 (2000 cells/mL) set by the World Health Organization (WHO). Furthermore, the decreased detection limit may facilitate the future characterization of cyanobacterial bloom formation to better provide authorities with ample time to take the appropriate actions to mitigate human risk from these potentially hazardous blooms.

Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns.This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done.The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.

The Water Monitoring System in Flood Alert Level Design Based on Internet of Things (IoT)

This study aimed to produce a flood alert system that can monitor water-levels in watersheds based on the Internet-of-Things (IoT), using the NodeMCU ESP8266 as a microcontroller and connecting internet connections. The main hardware used was a probe sensor as a water-level detector, an LED as a water level indicator, an LCD displaying the water level status, and a nodeMCU ESP8266 as a processor for the probe sensor detection results and sending information on the water-level status to the Telegram on Android. The status consisted of a safe level when the water level was at 1.5 m, an alert status was at 2 m, and a hazard status was at 3 m. When the water surface touched the probe sensor, nodeMCU ESP8266 read and processed the detection results. Then the status information was sent to Telegram and displayed on the LCD.

How to control the spatiotemporal spread of Omicron in the region with low vaccination rates.

Currently, finding ways to effectively control the spread of Omicron in regions with low vaccination rates is an urgent issue. In this study, we use a district-level model for predicting the COVID-19 symptom onset risk to explore and control the whole process of spread of Omicron in South Africa at a finer spatial scale. We found that in the early stage of the accelerated spread, Omicron spreads rapidly from the districts at the center of human mobility to other important districts of the human mobility network and its peripheral districts. In the subsequent diffusion-contraction stage, Omicron rapidly spreads to districts with low human mobility and then mainly contracts to districts with the highest human mobility. We found that increasing daily vaccination rates 10 times mainly reduced the symptom onset risk in remote areas with low human mobility. Implementing Alert Level 5 in the three districts at the epicenter, and Alert Level 1 in the remaining 49 districts, the spatial spread related to human mobility was effectively restricted, and the daily onset risk in districts with high human mobility also decreased by 20-80%.

Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand.

During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, Rt, during Aotearoa New Zealand’s August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading drivers of variation in transmission in this period and we describe how changes in these factors coincided with changes in Rt. Alert Level 4, New Zealand’s most stringent restriction setting which includes stay-at-home measures, was initially effective at reducing the median Rtto 0.6 (90% CrI 0.4, 0.8) on 29 August 2021. As New Zealand eased certain restrictions and switched from an elimination strategy to a suppression strategy, Rt subsequently increased to a median 1.3 (1.2, 1.4). Increasing vaccination coverage along with regional restrictions were eventually sufficient to reduce Rtbelow 1. The outbreak peaked at an estimated 198 (172, 229) new infected cases on 10 November, after which cases declined until January 2022. We continue to update Rtestimates in real time as new case data become available to inform New Zealand’s ongoing pandemic response.

Contribution of Frequent Snacking to the Energy, Sugar, and Sodium Intakes of Adolescents Aged 13- 18 During the COVID-19 Pandemic Under Alert Level 1 Restrictions

Contribution of Frequent Snacking to the Energy, Sugar, and Sodium Intakes of Adolescents Aged 13- 18 During the COVID-19 Pandemic Under Alert Level 1 Restrictions

Tourists’ behavior for volcanic disaster risk reduction: A case study of Mount Aso in Japan

Despite preceding research on the general public's knowledge and perceptions of natural hazards, there remains a lack of information specifically on volcanoes in Japan. This study seeks to determine whether people who had visited Mount Aso—one of the active volcanoes in Japan—had taken any self-protective measures. We asked people who visited the area around the crater of Mount Aso about their thoughts and actions related to volcanic disaster risk reduction. Internet-based questionnaires were disseminated nationally via the Rakuten Research, Inc. platform. The total sample size was 400. The logistic regression analysis shows that on-site information provided near the craters—such as knowledge of past damage, bulletin boards, and broadcasts—had a relatively greater impact on safety-seeking actions than prior checking of websites and the Volcanic Alert Level by tourists. Based on our findings, future evidence-based policies regarding volcanic disaster risk reduction should emphasize on-site efforts together with wide dissemination of publicly available information, including the Volcanic Alert Level.

On-site alert-level earthquake early warning using machine-learning-based prediction equations

SUMMARYTo rapidly and accurately provide alerts at target sites near the epicentre, we develop an on-site alert-level earthquake early warning (EEW) strategy involving P-wave signals and machine-learning-based prediction equations. These prediction equations are established for magnitude estimation and peak ground velocity (PGV) prediction accounting for multiple feature inputs and the support vector machine (SVM). These prediction equations are called SVM-M model for estimating magnitude and SVM-PGV model for predicting PGV, respectively. According to comparison between the predicted magnitude and PGV values with the predicted threshold values (M = 5.7 and PGV = 9.12 cm s–1, respectively), different alert level (0, 1, 2, 3) is issued at the different recording site when the predicted magnitude or PGV values exceed the given threshold values. Alert level 3 means that both the predicted magnitude and the predicted PGV exceed a given threshold, and there may be serious damage in this recording site. We apply the method to three destructive earthquake events (M ≥ 6.5) occurred in Japan, and our results indicate that with regard to the performance of SVM-PGV model for predicting PGV, at 3 s after P-wave arrival, the percentage of successful alarms (SAs) for these three events is higher than 95, 73 and 94 per cent, respectively, and the percentage of false alarms approaches 0. Additionally, with regard to the performance of SVM-M model for estimating magnitude, at 3 s after P-wave arrival, the percentage of SAs for these three events exceeds 95 per cent, and the percentage of missed alarms approaches 0. Moreover, almost all stations in the areas PGV ≥ 16 cm s–1 (IMM ≥ VII) near the epicentre issue alert level 3. The proposed method provides potential applications in EEW system.

The Benefits of Planting Edible Plants Amidst The Pandemic

Incarcerated at home or what we call in layman’s term as “house arrest” is what happened to all of us during the pandemic as it hits from 15th of March in the year 2020. People are forced to stay in their own dwellings to keep everybody safe and be away from any contact to anyone that might be carrying the silent presence of the virus covid-19. Since then, people was able to utilize and exert all efforts to do the things that they are not doing or was not even their choice of doing prior to covid-19 when all is carefree living their own lives. One of which is gardening that spreads throughout the world as contagious as anyone’s sweet smile. People began to consider its importance and benefits of planting your own crops in your own backyard using whatever you got around your perimeter. Aside from the obvious reason that people are obliged to stay at home until the Alert Level was down to the safest status of the virus. We are one of those who did indulged ourselves in planting our own vegetables and I would say that it was so fulfilling and the feeling of being like a co-creator of the Almighty for recreation of these beneficial living things.

Longitudinal Change in Authoritarian Factors as Explained by Political Beliefs and a Distrust of Science

During the COVID-19 pandemic there have been marked changes in individuals' belief systems (e.g., support for lockdowns) as a result of the threat of COVID-19. In the current study, we investigated whether these belief systems change as a function of changes in the threat of COVID-19. Specifically, we conducted a longitudinal study, with authoritarianism measured at the height of the COVID-19 pandemic in New Zealand and when the threat of COVID-19 was low (i.e., no known COVID-19 cases in the community). A total of 888 participants responded at both timepoints, completing measures of political orientation and distrust of science, in addition to the measure of authoritarianism. We had two hypotheses. First, that liberals would display a more marked reduction in authoritarian submission between Alert Level 4 and Alert Level 1 relative to conservatives. Second, that changes would be mediated by trust in science. Both hypotheses were supported, demonstrating that authoritarianism is sensitive to threat, even for those on the political left, and that trust in science helps to explain these changes. We suggest that fluctuations in authoritarianism may be different across the political spectrum due to underlying belief systems such as a distrust of science.

A qualitative study of living through the first New Zealand COVID-19 lockdown: Affordances, positive outcomes, and reflections

This study thematically analyses free text responses from telephone interviews with 141 participants from the Prospective Outcomes of Injury Study (POIS-10/POIS-10 Māori) to explore their “lived experiences” of the COVID-19 Alert Level 4 lockdown in Aotearoa/New Zealand (NZ). Interviews occurred in March-April 2020, at which time, NZ underwent some of the most stringent COVID-19 public health measures internationally. This study provides “real time” insights into participants’ experiences and views during this time in NZ.Many participants experienced losses, limitations, and restrictions due to the COVID-19 Alert Level 4 lockdown. Nevertheless, positive outcomes and reflections were reported alongside, and sometimes in conjunction with, the negative. Although the lockdown was limiting for many participants, some highlighted that this unique environment facilitated certain affordances including positive experiences with work, supporting others, and being supported, family dynamics changing for the better, more free time and positive reflections such as enhanced feelings of unity, closeness to others and a sense of gratefulness.The identification of affordances facilitated by the lockdown environment has implications for future pandemic response management, as these may be leveraged by policymakers, health care practitioners and individuals, potentially resulting in more favorable outcomes in future lockdowns.