Forecast of the U.S. Copper Demand: a Framework Based on Scenario Analysis and Stock Dynamics

In a world of finite metallic minerals, demand forecasting is crucial for managing the stocks and flows of these critical resources. Previous studies have projected copper supply and demand at the global level and the regional level of EU and China. However, no comprehensive study exists for the U.S., which has displayed unique copper consumption and dematerialization trends. In this study, we adapted the stock dynamics approach to forecast the U.S. copper in-use stock (IUS), consumption, and end-of-life (EOL) flows from 2016 to 2070 under various U.S.-specific scenarios. Assuming different socio-technological development trajectories, our model results are consistent with a stabilization range of 215-260 kg/person for the IUS. This is projected along with steady growth in the annual copper consumption and EOL copper generation driven mainly by the growing U.S. population. This stabilization trend of per capita IUS indicates that future copper consumption will largely recuperate IUS losses, allowing 34-39% of future demand to be met potentially by recycling 43% of domestic EOL copper. Despite the recent trends of "dematerialization", adaptive policies still need to be designed for enhancing the EOL recovery, especially in light of a potential transitioning to a "green technology" future with increased electrification dictating higher copper demand.

Modeling the Number of Confirmed Cases and Deaths from the COVID-19 Pandemic in the UK and Forecasting from April 15 to May 30, 2020

OBJECTIVE

The UK is one of the epicenters of coronavirus disease (COVID-19) in the world. As of April 14, there have been 93 873 confirmed patients of COVID-19 in the UK and 12 107 deaths with confirmed infection. On April 14, it was reported that COVID-19 was the cause of more than half of the deaths in London.

METHODS

The present paper addresses the modeling and forecasting of the outbreak of COVID-19 in the UK. This modeling must be accomplished through a 2-part time series model to study the number of confirmed cases and deaths. The period we aimed at a forecast was 46 days from April 15 to May 30, 2020. All the computations and simulations were conducted on Matlab R2015b, and the average curves and confidence intervals were calculated based on 100 simulations of the fitted models.

RESULTS

According to the obtained model, we expect that the cumulative number of confirmed cases will reach 282 000 with an 80% confidence interval (242 000 to 316 500) on May 30, from 93 873 on April 14. In addition, it is expected that, over this period, the number of daily new confirmed cases will fall to the interval 1330 to 6450 with the probability of 0.80 by the point estimation around 3100. Regarding death, our model establishes that the real case fatality rate of the pandemic in the UK approaches 11% (80% confidence interval: 8%-15%). Accordingly, we forecast that the total death in the UK will rise to 35 000 (28 000-50 000 with the probability of 80%).

CONCLUSIONS

The drawback of this study is the shortage of observations. Also, to conduct a more exact study, it is possible to take the number of the tests into account as an explanatory variable besides time.

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.

A niche for null models in adaptive resource management

As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision-making steps in ARM. Many applications of ARM use multiple model-based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed "ecological null models," which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence (Nt +1 = Nt ), provide more robust near-term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision-makers keep pace with a rapidly changing world.

The Value of Meteorological Data in Optimizing the Pattern of Physical Load-A Forecast Model of Rowing Pacing Strategy

Mastering the information of arena environment is the premise for athletes to optimize their patterns of physical load. Therefore, improving the forecast accuracy of the arena conditions is an urgent task in competitive sports. This paper excavates the meteorological features that have great influence on outdoor events such as rowing and their influence on the pacing strategy. We selected the meteorological data of Tokyo from 1979 to 2020 to forecast the meteorology during the Tokyo 2021 Olympic Games, analyzed the athletes' pacing choice under different temperatures, humidity and sports levels, and then recommend the best pacing strategy for rowing teams of China. The model proposed in this paper complements the absence of meteorological features in the arena environment assessment and provides an algorithm basis for improving the forecast performance of pacing strategies in outdoor sports.

Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections

Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971-2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971-2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends

Introduction: We assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemic's dynamics at local and national levels and for improving forecasting models.

Methods: SARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium.

Results: Over 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak, and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes.

Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic's trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.

Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.

Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.

Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

A financial and global demand analysis to inform decisions for funding and clinical development of GBS vaccines for pregnant women

Background

Despite group B Streptococcus (GBS) being a leading cause of maternal and infant morbidity and mortality, no vaccine is currently available. To inform vaccine developers, countries, and funders, we analyzed the key factors likely to influence the demand for a GBS vaccine and the long-term financial sustainability for a vaccine developer.

Methods

Using population-based forecasting, we estimated the demand for a GBS vaccine; using a discounted cash flow model we estimated the financial viability for a vaccine developer.

Results

Demand for this vaccine can be significant if countries adopt policy recommendations for use, in particular, the largest ones, most of which have a burden that justifies use of the vaccine, and if financing for the vaccine is made available either by countries or by funding mechanisms such as Gavi, the Vaccine Alliance.

Conclusions

This analysis suggests the potential for financial and commercial viability for a vaccine developer pursuing the commercialization of a GBS vaccine. Risks exists in relation to the clinical trial design and costs, the level of competition, countries’ ability to pay, the administration schedule, and the availability of policies that encourage use of the vaccine. To reduce those risks and ensure equitable access to a GBS vaccine, the role of donors or financers can prove very important, as can a coordinated operational research agenda that aims at clarifying those areas of uncertainty.

Association between RT-PCR Ct values and COVID-19 new daily cases: a multicenter cross-sectional study

Proactive prediction of the epidemiologic dynamics of viral diseases and outbreaks of the type of COVID-19 has remained a difficult pursuit for scientists, public health researchers, and policymakers. It is unclear whether RT-PCR Cycle Threshold (Ct) values of COVID-19 - or any other virus - as indicator of viral load, could represent a possible predictor for underlying epidemiologic changes on a population level. The study objective is thus to investigate whether population-wide changes in SARS-CoV-2 RT-PCR Ct values over time are associated with the daily fraction of positive COVID-19 tests. In addition, this study analyses the factors that could influence RT-PCR Ct values. A retrospective cross-sectional study was conducted on 63,879 patients from May 4, 2020 to September 30, 2020, in all COVID-19 facilities in the Kingdom of Bahrain. Data collected included number of tests and newly diagnosed cases, as well as Ct values, age, sex nationality, and symptomatic status. Ct values were found to be negatively and very weakly correlated with the fraction of daily positive tests in the population r = -0.06 (CI 95%: -0.06; -0.05; p=0.001). The R-squared for the regression model (adjusting for age and number of daily tests) showed an accuracy of 45.3%. Ct Values showed an association with nationality (p=0.012). After the stratification, the association between Ct values and the fraction of daily positive cases was only maintained for the female sex and Bahraini-nationality. Symptomatic presentation was significantly associated with lower Ct values (higher viral loads). Ct values do not show any correlation with age (p=0.333) or sex (p=0.522). We report one of the first and largest studies to investigate the epidemiologic associations of Ct values with COVID-19. Although changes in Ct values showed a moderate association with daily cases, our results indicate that it may not be as predictive within a simple model. More population studies and models from global cohorts are necessary.

Leveraging Strategic Foresight to Advance Worker Safety, Health, and Well-Being

Attending to the ever-expanding list of factors impacting work, the workplace, and the workforce will require innovative methods and approaches for occupational safety and health (OSH) research and practice. This paper explores strategic foresight as a tool that can enhance OSH capacity to anticipate, and even shape, the future as it pertains to work. Equal parts science and art, strategic foresight includes the development and analysis of plausible alternative futures as inputs to strategic plans and actions. Here, we review several published foresight approaches and examples of work-related futures scenarios. We also present a working foresight framework tailored for OSH and offer recommendations for next steps to incorporate strategic foresight into research and practice in order to advance worker safety, health, and well-being.

Cardiorespiratory Fitness in Occupational Groups-Trends over 20 Years and Future Forecasts

BACKGROUND

Reports have indicated a negative trend in cardiorespiratory fitness (CRF) in the general population. However, trends in relation to different occupational groups are missing. Therefore, the aim of our study was to examine the trends in CRF during the last 20 years, and to provide a prognosis of future trends in CRF, in different occupational groups of Swedish workers.

METHODS

Data from 516,122 health profile assessments performed between 2001 to 2020 were included. CRF was assessed as maximal oxygen consumption and was estimated from a submaximal cycling test. Analyses include CRF as a weighted average, standardized proportions with low CRF (<32 mL/min/kg), adjusted annual change in CRF, and forecasting of future trends in CRF.

RESULTS

There was a decrease in CRF over the study period, with the largest decrease in both absolute and relative CRF seen for individuals working in administrative and customer service (-10.1% and -9.4%) and mechanical manufacturing (-6.5% and -7.8%) occupations. The greatest annual decrease was seen in transport occupations (-1.62 mL/min/kg, 95% CI -0.190 to -0.134). Men and younger individuals had in generally a more pronounced decrease in CRF. The proportion with a low CRF increased, with the greatest increase noted for blue-collar and low-skilled occupations (range: +19% to +27% relative change). The forecast analyses predicted a continuing downward trend of CRF.

CONCLUSION

CRF has declined in most occupational groups in Sweden over the last two decades, with a more pronounced decline in blue-collar and low-skilled occupational groups.

Analysis and forecasting of confirmed, death, and recovered cases of COVID-19 infections in Nigeria: Implications for university administrators

The coronavirus (COVID-19) disease outbreak was a public health emergency of international concern which eventually evolved into a pandemic. Nigeria was locked down in March, 2020 as the country battled to contain the spread of the disease. By August 2020, phase-by-phase easing of the lockdown was commenced and university students will soon return for academic activities. This study undertakes some epidemiological analysis of the Nigerian COVID-19 data to help the government and university administrators make informed decisions on the safety of personnel and students.The COVID-19 data on confirmed cases, deaths, and recovered were obtained from the website of the Nigerian Centre for Disease Control (NCDC) from April 2, 2020 to August 24, 2020. The infection rate, prevalence, ratio, cause-specific death rate, and case recovery rate were used to evaluate the epidemiological characteristics of the pandemic in Nigeria. Exponential smoothing was adopted in modeling the time series data and forecasting the pandemic in Nigeria up to January 31, 2021.The results indicated that the pandemic had infection rate of at most 3 infections per 1 million per day from April to August 2020. The death rate was 5 persons per 1 million during the period of study while recovery rate was 747 persons per 1000 infections. Analysis of forecast data showed steady but gradual decrease in the daily infection rate and death rate and substantial increase in the recovery rate, 975 recoveries per 1000 infections.In general, the epidemiological attributes of the pandemic from the original data and the forecast data indicated optimism in the decrease in the rate of infection and death in the future. Moreover, the infection rate, prevalence and death rate in January 2021 coincided with the predictions based on the analysis. Therefore, the Nigerian government is encouraged to allow universities in the country to reopen while university administrators set up the necessary protocols for strict adherence to safety measures.

Investor Sentiment and Stock Returns During the COVID-19 Pandemic

In this paper, we regard the Baidu index as an indicator of investors' attention to China's epidemic stocks. We believe that when seeking information to guide investment decisions, investor sentiment is usually affected by the information provided by the Baidu search engine, which may cause stock prices to fluctuate. Therefore, we constructed a GARCH extended model including the Baidu index to predict the return of epidemic stocks and compared it with the benchmark model. The empirical research in this paper finds that the forecast model including the Baidu index is significantly better than the benchmark model. This has important reference value both for investors in predicting stock trends and for the government's formulation of policies to prevent excessive stock market volatility.

Pan-African evolution of within- and between-country COVID-19 dynamics

We created a strategy for understanding the evolution of the COVID-19 pandemic throughout the African continent. Because high-quality mobility data are challenging to obtain across Africa, the approach provides the ability to distinguish cases arising from within a country or from its neighbors. The results further show how testing capacity and social and health policy contribute to the dynamics of cases, and generate short-term prediction of the evolution of the pandemic on a country-by-country basis. This framework improves the ability to interpret and act upon real-time complex COVID-19 data from the African continent. These findings emphasize that regional efforts to coordinate country-specific strategies in transmission suppression should be a continental priority to control the COVID-19 pandemic in Africa.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

Forecasting Trachoma Control and Identifying Transmission-Hotspots

Background

Tremendous progress towards elimination of trachoma as a public health problem has been made. However, there are areas where the clinical indicator of disease, trachomatous inflammation—follicular (TF), remains prevalent. We quantify the progress that has been made, and forecast how TF prevalence will evolve with current interventions. We also determine the probability that a district is a transmission-hotspot based on its TF prevalence (ie, reproduction number greater than one).

Methods

Data on trachoma prevalence come from the GET2020 global repository organized by the World Health Organization and the International Trachoma Initiative. Forecasts of TF prevalence and the percent of districts with local control is achieved by regressing the coefficients of a fitted exponential distribution for the year-by-year distribution of TF prevalence. The probability of a district being a transmission-hotspot is extrapolated from the residuals of the regression.

Results

Forecasts suggest that with current interventions, 96.5% of surveyed districts will have TF prevalence among children aged 1–9 years <5% by 2030 (95% CI: 86.6%–100.0%). Districts with TF prevalence < 20% appear unlikely to be transmission-hotspots. However, a district having TF prevalence of over 28% in 2016–2019 corresponds to at least 50% probability of being a transmission-hotspot.

Conclusions

Sustainable control of trachoma appears achievable. However there are transmission-hotspots that are not responding to annual mass drug administration of azithromycin and require enhanced treatment in order to reach local control.

A forecast of maternal deaths with and without vaccination of pregnant women against COVID-19 in Mexico

With 100% COVID‐19 vaccination among pregnant women during May and June of 2021, the overall predicted number of maternal deaths for 2021 in Mexico would significantly decrease.

Alternative Formulations of Trap Lures for Operational Detection, Population Monitoring, and Outbreak Forecasting of Southern Pine Beetle in the United States

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Curculionidae: Scolytinae) is a major destructive pest of Pinus L. In the southeastern United States, numbers of this species and a major predator, Thanasimus dubius (Fabricius) (Coleoptera: Cleridae), captured during an annual springtime trapping survey are used to make forecasts of the likelihood and severity of an outbreak during the following summer. We investigated responses by both species to six lure formulations to evaluate their suitability for the survey and allow integration of historical data sets produced with differing lure compositions. Trapping trials were performed at four locations across three states (Louisiana, Mississippi, and Alabama) during spring, and at these and one additional location (North Carolina) in fall 2016. All lures included the pheromone component frontalin. Southern pine beetle preferred lures that additionally included the pheromone component endo-brevicomin and turpentine as a source of host odors (rather than a 7:3 mixture of monoterpenes alpha- and beta-pinene). Thanasimus dubius displayed little discrimination among lure compositions. Lure preferences by southern pine beetle did not differ significantly among locations in spring but were influenced by season. Gas chromatography (GC)-electroantennographic detection analyses with southern pine beetle and GC-mass spectrometry identified numerous known and potential semiochemicals that distinguished volatiles released by the tested host odor devices. The lure combination that included endo-brevicomin and alpha/beta-pinene is recommended for the trapping survey because of its high sensitivity for southern pine beetle and potential for greater data integrity resulting from its reproducible composition.

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

BACKGROUND

Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.

OBJECTIVE

Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.

METHODS

We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.

RESULTS

Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.

CONCLUSIONS

Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.

Prognosticating the Spread of Covid-19 Pandemic Based on Optimal Arima Estimators

COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Carbon emission post-coronavirus: Continual decline or rebound?

Studies have shown that the COVID-19 pandemic has led to a significant drop in carbon emissions in 2020, however, it is an open question whether carbon emissions continue to decline after the COVID-19 pandemic. To forecast the changes in carbon emissions after the pandemic, this study analyzed the long-term relationship between the extreme events and carbon emissions since 1960, and short-term drivers of the changes in carbon emissions before and after the 2008 financial crisis. Extreme events cannot change the upward trend of carbon emission in the long run. Specifically, the extreme events (1973 oil crisis, the American Reserve Loan Association crisis, the disintegration of the former Soviet Union, the Asian financial crisis and the 2008 financial crisis) led to a decline in carbon emissions temporarily, however, a retaliatory rebound of carbon emission were occurred after the extreme events. The long-term relationship between extreme events and carbon emission indicate that this unfolding extreme event (COVID-19 pandemic) cannot change the trend the carbon emission, and carbon emission will be rebound after the pandemic. In addition, the decomposition results showed the main contributor to the retaliatory rebound of carbon emissions after the 2008 financial crisis was the decline in energy efficiency. The decline in energy efficiency was caused by the economic recovery plan post 2008 financial crisis, which stimulated the economy and employment at a cost of energy efficiency and environmental protection. The current economic recovery plans to deal with COVID-19 pandemic also prioritizes economic development and job creation, while ignoring energy efficiency. Therefore, the post-pandemic carbon emissions will repeat the carbon emissions after the 2008 financial crisis, i.e., there will a retaliatory rebound. To avoid the retaliatory rebound, improving energy efficiency should be included in these economic recovery plan to cope with COVID-19 pandemic.