The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

Core Assumptions and the Competitive Advantage among Commercial Banks in Kenya

Purpose: This paper sought to examine the effect of verification of core assumptions on the competitive advantage among commercial banks in Kenya. Methodology: The target population for the study were directors or managers in-charge of planning or strategy in each of the forty banks in the country. Primary data was collected using a semi structured questionnaire. The questionnaire was tested for both validity and reliability and was found to meet the required threshold. . Data was analyzed using both descriptive and inferential statistics. Analysis was done with the assistance of SPSS computer packages. Findings: A response rate of 77.5% was achieved in the study and this was adequate for analysis. The study found that verification of core assumptions has a β =0.472 and a p-value of 0.000 which indicates that it has a significant effect on the ability of banks to sustain competitive advantage. The study therefore concluded that verification of core assumptions must be carried out continuously to track their validity on which the company’s strategies are grounded upon. Unique Contributions to Theory, Practice and Policy: The study therefore recommends that banks should raise the level of use of competitive intelligence in monitoring the competitive landscape to enable early verification of core assumptions. The study further recommends that banks should continuously monitor the various core assumptions that were considered during strategy formulation to verify their validity to enable the bank rapidly change the strategy, should the core assumption on which it was grounded on be found to be no longer valid.

64. Absolute Monocyte Count (AMC) as Early and Safe Marker for Discharge in Low-risk Pediatric Febrile Neutropenia with Cancer

Background Fever with neutropenia (FN) is common and the timing of antibiotic cessation in patients without an identified fever source is uncertain. Absolute neutrophile count (ANC) recovery has been used clinically to represent bone marrow recovery (BMR) but other options should be considered. We hypothesized that absolute monocyte count (AMC), and absolute phagocyte count (APC) are more sensitive, and an earlier safe marker of antibiotic cessation (AC) compared with ANC Methods A retrospective review was performed for FN episodes (FNEs) at UCM Comer Children’s Hospital between 2009 and 2016 in pediatric oncology patients. Eligible FNEs who were a febrile for 24 hours, had no bacterial source identified at time of AC, and did not receive chemotherapy 10 days following AC. Ten-day post-AC outcomes, length of stay and cost were assessed and compared among different BMR parameters (ANC vs AMC). Results A total of 928 FN episodes (FNEs) were identified. 391 eligible FNEs occurred in 235 patients. Three groups were compared based on ANC (cells/μL) at the time of AC : < 200 in 102 (26%), 200-500 in 111 (28%), and >500 /uL in 178 (46%) (Figure1) with an overall ten-day recurrent fever rate 7.4% (29/391) and readmission rate of 5.6% (22/391). No significant differences in recurrent fever rates were identified among 3 ANC groups (11.7%, 6.3% and 5.6% respectively, P=0.08) and readmission (10%,4.5%, 4%, respectively; P=0.07)(Table 1).In subset analysis of AMC for each ANC group, patients with AMC >100 at AC have favorable outcomes, regardless ANC threshold (P< 0.01) (Table 1). Median of length of stay of FN was 3 days shorter using AMC >100/uL for BMR compared with any threshold of ANC (P< 0.01) and decrease overall FN cost stay (P< 0.01) (Table 2). Similar analysis show APC >300/uL at time of AC has favourable outcomes and decrease LOS regardless ANC threshold (data not shown here). Conclusion Our results suggest that a AMC > 100 /uL regardless of ANC/uL, is a safe threshold value for empiric AC and discharge. This approach may shorten length of stay, reduce burden of cost of febrile neutropenia cost and potential long term antibiotics side effects. Disclosures Lara Danziger-Isakov, MD, MPH, Ansun (Individual(s) Involved: Self): Scientific Research Study Investigator; Astellas (Individual(s) Involved: Self): Scientific Research Study Investigator; Merck (Individual(s) Involved: Self): Consultant, Scientific Research Study Investigator; Pfizer (Individual(s) Involved: Self): Scientific Research Study Investigator; Shire (Individual(s) Involved: Self): Consultant, Scientific Research Study Investigator; Viracor: Grant/Research Support

Estimation of the Motor Threshold for Near-Rectangular Stimuli Using the Hodgkin–Huxley Model

The motor threshold measurement is a standard in preintervention probing in TMS experiments. We aim to predict the motor threshold for near-rectangular stimuli to efficiently determine the motor threshold size before any experiments take place. Estimating the behavior of large-scale networks requires dynamically accurate and efficient modeling. We utilized a Hodgkin–Huxley (HH) type model to evaluate motor threshold values and computationally validated its function with known true threshold data from 50 participants trials from state-of-the-art published datasets. For monophasic, bidirectional, and unidirectional rectangular stimuli in posterior-anterior or anterior-posterior directions as generated by the cTMS device, computational modeling of the HH model captured the experimentally measured population-averaged motor threshold values at high precision (maximum error ≤ 8%). The convergence of our biophysically based modeling study with experimental data in humans reveals that the effect of the stimulus shape is strongly correlated with the activation kinetics of the voltage-gated ion channels. The proposed method can reliably predict motor threshold size using the conductance-based neuronal models and could therefore be embedded in new generation neurostimulators. Advancements in neural modeling will make it possible to enhance treatment procedures by reducing the number of delivered magnetic stimuli to participants.

Cross-Layer Reliability, Energy Efficiency, and Performance Optimization of Near-Threshold Data Paths

Modern electronic devices are an indispensable part of our everyday life. A major enabler for such integration is the exponential increase of the computation capabilities as well as the drastic improvement in the energy efficiency over the last 50 years, commonly known as Moore’s law. In this regard, the demand for energy-efficient digital circuits, especially for application domains such as the Internet of Things (IoT), has faced an enormous growth. Since the power consumption of a circuit highly depends on the supply voltage, aggressive supply voltage scaling to the near-threshold voltage region, also known as Near-Threshold Computing (NTC), is an effective way of increasing the energy efficiency of a circuit by an order of magnitude. However, NTC comes with specific challenges with respect to performance and reliability, which mandates new sets of design techniques to fully harness its potential. While techniques merely focused at one abstraction level, in particular circuit-level design, can have limited benefits, cross-layer approaches result in far better optimizations. This paper presents instruction multi-cycling and functional unit partitioning methods to improve energy efficiency and resiliency of functional units. The proposed methods significantly improve the circuit timing, and at the same time considerably limit leakage energy, by employing a combination of cross-layer techniques based on circuit redesign and code replacement techniques. Simulation results show that the proposed methods improve performance and energy efficiency of an Arithmetic Logic Unit by 19% and 43%, respectively. Furthermore, the improved performance of the optimized circuits can be traded to improving the reliability.

Epidemic Potential of COVID-19 in Omsk Region during Anti-Epidemic Measures

Aim. To study the spread of COVID-19 among the population of the Omsk Region during 24 weeks of the epidemic on the background of anti-epidemic measures.Materials and methods. A descriptive epidemiological study was carried out based on publically available data и data from the Center for Hygiene and Epidemiology in the Omsk Region on the official registration and epidemiological investigation of detected COVID-19 cases in the Omsk Region for the period from March 27 to September 10, 2020. To assess the potential of COVID-19 to spread, the following indicators were calculated: exponential growth rate (r), basic reproduction number (R0), effective reproduction number (Rt), expected natural epidemic size and herd immunity threshold. Data processing was performed using MS Excel 2010. The cartogram was built using the QGIS 3.12-Bukuresti application in the EPSG: 3576 coordinate system.Results and discussion. For the period from March 27 to September 10, 2020, a total of 9779 cases of COVID-19 were registered in the Omsk Region, the cumulative incidence was 507,6 per 100000 (95 % CI 497,5÷517,6), the case-fatality rate for completed cases was 2.9 %, for identified cases – 2.4 %. The most active spread of COVID-19 was noted in Omsk and 4 out of 32 districts of the region (Moskalensky, Azov German National, Mariyanovsky, Novovarshavsky). During the ongoing anti-epidemic measures, the exponential growth rate of the cumulative number of COVID-19 cases was 4.5 % per day, R0 – 1.4–1.5, Rt – 1.10, herd immunity threshold – 28.6 %. The expected size of the epidemic in case of sustained anti-epidemic measures can reach 58.0 % of the recovered population. A decrease in the number of detected virus carriers, incomplete detection of COVID-19 among patients with community-acquired pneumonia introduced additional risks for the latent spread of infection and complications of the epidemic situation. Maintaining restrictive measures and increasing the proportion of the immune population (over 28.6 %) may significantly reduce the risks of increasing the spread of COVID-19 in the Omsk Region.

Suppression of COVID-19 outbreak in the municipality of Vo’, Italy

On the 21st of February 2020 a resident of the municipality of Vo’, a small town near Padua, died of pneumonia due to SARS-CoV-2 infection1. This was the first COVID-19 death detected in Italy since the emergence of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province2. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days3. We collected information on the demography, clinical presentation, hospitalization, contact network and presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo’ at two consecutive time points. On the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI) 2.1-3.3%). On the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI 0.8-1.8%). Notably, 43.2% (95% CI 32.2-54.7%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic. The mean serial interval was 6.9 days (95% CI 2.6-13.4). We found no statistically significant difference in the viral load (as measured by genome equivalents inferred from cycle threshold data) of symptomatic versus asymptomatic infections (p-values 0.6 and 0.2 for E and RdRp genes, respectively, Exact Wilcoxon-Mann-Whitney test). Contact tracing of the newly infected cases and transmission chain reconstruction revealed that most new infections in the second survey were infected in the community before the lockdown or from asymptomatic infections living in the same household. This study sheds new light on the frequency of asymptomatic SARS-CoV-2 infection and their infectivity (as measured by the viral load) and provides new insights into its transmission dynamics, the duration of viral load detectability and the efficacy of the implemented control measures.