Two-Age-Structured COVID-19 Epidemic Model: Estimation of Virulence Parameters to Interpret Effects of National and Regional Feedback Interventions and Vaccination

The COVID-19 epidemic has recently led in Italy to the implementation of different external strategies in order to limit the spread of the disease in response to its transmission rate: strict national lockdown rules, followed first by a weakening of the social distancing and contact reduction feedback interventions and finally the implementation of coordinated intermittent regional actions, up to the application, in this last context, of an age-stratified vaccine prioritization strategy. This paper originally aims at identifying, starting from the available age-structured real data at the national level during the specific aforementioned scenarios, external-scenario-dependent sets of virulence parameters for a two-age-structured COVID-19 epidemic compartmental model, in order to provide an interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19.

Development of a tool to prioritize the monitoring of COVID-19 patients by public health teams

Background: In the context of the COVID-19 pandemic, public health teams have struggled to conduct monitoring for confirmed or suspicious COVID-19 patients. However, monitoring these patients is critical to improving the chances of survival, and therefore, a prioritization strategy for these patients is warranted. This study developed a monitoring algorithm for COVID-19 patients for the Colombian Ministry of Health and Social Protection (MOH). Methods: This work included 1) a literature review, 2) consultations with MOH and National Institute of Health officials, and 3) data analysis of all positive COVID-19 cases and their outcomes. We used clinical and socioeconomic variables to develop a set of risk categories to identify severe cases of COVID-19. Results: This tool provided four different risk categories for COVID-19 patients. As soon as the time of diagnosis, this tool can identify 91% of all severe and fatal COVID-19 cases within the first two risk categories. Conclusion: This tool is a low-cost strategy to prioritize patients at higher risk of experiencing severe COVID-19. This tool was developed so public health teams can focus their scarce monitoring resources on individuals at higher mortality risk. This tool can be easily adapted to the context of other lower and middle-income countries. Policymakers would benefit from this low-cost strategy to reduce COVID-19 mortality, particularly during outbreaks.

Allocation of COVID-19 vaccination: When public prioritization preferences differ from official regulations

As vaccines against COVID-19 are scarce, many countries have developed vaccination prioritization strategies focusing on ethical and epidemiological considerations. However, public acceptance of such strategies should be monitored to ensure successful implementation. In an experiment with N = 1,379 German participants, we investigated whether the public’s vaccination allocation preferences matched the prioritization strategy approved by the German government. Results revealed different allocations. While the government had top-prioritized vulnerable people (being of high age or accommodated in nursing homes for the elderly), participants deprioritized these groups and preferred exclusive allocation of the first available vaccines to medical staff and personnel caring for the elderly. Interestingly, allocation preferences did not change when participants were told how many individuals were included in each group. As differences between allocation policies and public preferences can affect trust in the government and threaten the social contract between generations, we discuss possible strategies to align vaccination prioritizations.

(Very) polar organic compounds in the Danube river basin: a non-target screening workflow and prioritization strategy for extracting highly confident features

A hidden-target screening workflow to tentatively identify polar organic molecules yielded 67 candidate substances of which 11 are considered to be of high priority for the Danube river basin.

The Node Status as a Prioritization Strategy for Replica Balancing in a HDFS Cluster

Data replication is the main fault tolerance mechanism of HDFS, the Hadoop Distributed File System. Although replication is essential to ensure high availability and reliability, the replicas might not always be placed evenly among the nodes. The HDFS Balancer is an integrated solution of Apache Hadoop that performs replica balancing through the rearrangement of the data blocks stored in the file system. The Balancer, however, demands a high computational effort of the nodes during its operation. This work presents a customization for the HDFS Balancer that considers the status of the nodes as a strategy to minimize the overhead caused by the balancing operation in the cluster. To this end, metrics obtained at runtime are used as a way to prioritize the nodes during data redistribution, making it occurs primarily between nodes with low communication traffic. Also, the Balancer starts to operate aiming at a minimum balance level, reducing the number of data transfers required to even up the data stored in the cluster. The evaluation results showed that the proposed customization allows reducing the time and bandwidth needed to reach the system balance.

Analisis Strategi Pengembangan Usaha Bank Sampah Garuda Wastu Lestari

Research conducted at Waste Bank Garuda Wastu Lestari is located in the Peguyangan Village, Denpasar was done in order to analyze the environmental factors that affect the internal and external business development of waste bank and formulate alternative strategies that can be applied by Waste Bank Garuda Wastu Lestari. Determination of respondent committed intentionally with a specific purpose (pusposive sampling). Respondents in this study consisted of four respondents from the management company. Questionnaires conducted to analyze the internal and external environment. Determination of strategic alternatives conducted using SWOT Matrix and prioritization strategy that can be applied at this time adapted to the position of Waste Bank Garuda Wastu Lestari in the competition. SWOT analysis of the diagram looks that the company is in cell 1, in the cell Aggressive Strategy. While the outcome of the preparation through the SWOT Matrix are eleven alternative strategies that can be applied by Waste Bank Garuda Wastu Lestari. Priority strategy selection can be done by looking at the company's position in the current competition is with Strategy SO (Strength-Opportunity), utilizing the strength to seize the opportunities that exist. Thus the right business priorities do is: 1) to maintain and improve the quality of care produced; 2) maintain and enhance good relations with employees, customers, and the agency or agencies; 3) maintain and improve the promotion and dissemination activities; and 4) improve cooperation and networking of waste bank.

Analysis of Statements of Software Based on the Prioritization Strategy of Test Cases

Software testing is a major process in every software development cycle, which is a very expensive procedure. Throughout this phase software testers as well as development companies are eager to verify the software from various viewpoints for repeated testing. But detailed testing requires code execution along with all valid inputs in the form of test cases which is not possible because of resource limitations. The test cases are automatically generated in many of the software. One of the important concerns for any coder during the maintenance phase is the selection of successful test cases for code execution. This paper includes an algorithm that is designed to figure out the statement coverage of a code with proper inputs by executing the priority based approach which involves sorting the inputs in ascending order so as to have an appropriate method to test the code for maximum statement coverage.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

ABSTRACTPopulations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how observational uncertainty in abundance and connectivity, and imperfect knowledge of the damage function, affect return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.