Which preventive control measure initiated the “flattening of the curve”

BACKGROUND: When a country introduces different COVID-19 control measures over time, it is important to identify the specific measure that was effective and therefore responsible for “flattening the curve”. This information helps policymakers find the right decision and saves the economy by avoiding severe yet ineffective measures. OBJECTIVE: This paper aims to fill the literature gap by investigating two regions that introduced two or three consecutive measures during the second COVID-19 wave, namely Austria and Victoria. METHOD: We calculated the first derivative (acceleration) of the filtered daily case data and identified the date of the start and end of the acceleration’s major downturn (effective phase) relative to the date when the control measures were introduced (Austria: soft/hard lockdowns; Victoria: stages 3/4 lockdowns, mask order). RESULTS: In Austria, the effective phase started 5 days after the introduction of the soft lockdown and ended at the start of the hard lockdown. In Victoria, the effective phase started 19 days after the introduction of the stage 3 lockdown, 5 days after the introduction of the mask order, and ended 6 days after the start of the stage 4 lockdown. CONCLUSION: Considering that the effect of control measures is expected the earliest one serial interval after their introduction, the control measure responsible for “flattening the curve” was the soft lockdown in Austria and the mask mandate in Victoria. The severe lockdowns in both regions were ineffective

FINGER AND THUMB FORCES DURING BOWLING SHOTS

The purpose of this study was to measure the forces exerted to a bowling ball by thumb and fingers during two different shots. For this task, an instrumented bowling ball was designed and produced, which allowed for force measurement and display of vector diagrams. The highest force is applied by the thumb (up to 120N), followed by middle and the ring finger. The overall moment applied to the ball by thumb and fingers during twisting of the ball reaches 3 Nm

Mathematical modelling of the digital tendon pulleys

A mathematical model of the A2 pulley system will enable us to have a better understanding of the mechanics of the pulley-tendon system. The A2 pulley was modelled based on paralel pulley fibres attached to a phalanx with a tendon passing them. Mechanical properties of the pulleys such as stiffness, strength and friction were included in the model. A convergence test was done to ensure the accuracy of the test. The model managed to show the degree of flexion of the fingers affect the force distribution of the pulleys. High loads on flexed finger may lead to pulley ruptures. Further studies on the rupture mechanism showed that pullley ruptures are self propagating when a constant is applied and the rate of rupture increases as less intact fibres are present to support the load. In addition to human application, the model was applied to animals as well, and it proved the advantages of a curved phalanx as compared to a straight one. This is important in deciding the aboreality and terrestiality of primates and hominids. Further application includes explaining the tendon locking mechanism in bats, birds, and some climbing rodents. The relationship between friction coefficient of the pulley-tendon interface and the residual force at the proximal tendon was developed for the tendon locking mechanism.MASTER OF ENGINEERING (SCBE

Accuracy of Centre of Pressure Gait Measurements from Two Pressure-Sensitive Insoles

Footwear-based wearable applications are relevant to numerous fields and have great commercial and clinical potential. However, scientifically validated, reliable data on these devices is largely missing. Centre of pressure (COP) is an important and common factor for measuring balance and gait and hence the validity of such devices is essential for reading accurate data. This study aims to investigate COP accuracy of an existing system, Pedar (PE), and a newly designed Smart Insole (SI) using a force plate (FP). This was done by means of COP data noise (R2), and gradient of the fit function (k). For the SI, the maximum COPx and COPy data achieved R2 values of 0.7837 and 0.9368 and k values of 0.8867 and 0.8538 respectively when compared with the FP. Conversely, the Pedar achieved R2 values of 0.8409 and 0.9401 and k values of 1.0492 and 1.08 when compared with the FP respectively

The Difference in Wave Dynamics between SARS-CoV-2 Pre-Omicron and Omicron Variant Waves

Compared to previous SARS-CoV-2 variants, the Omicron variant exhibited different epidemiological features. The purpose of this study was to assess the wave dynamics of pre-Omicron and Omicron waves in terms of differences and similarities. We investigated the COVID-19 waves since the beginning of the pandemic up to 28 August 2022, 1000 waves in total, as to their effectiveness for flattening the curve, calculated from the first and second time derivative of the daily case data. The average number of Omicron waves per month (42.78) was greater than the one of pre-Omicron waves per month (25.62). Omicron waves steepen and flatten the curve significantly faster, more effectively and with sharper peaks. Omicron waves generated more daily case data than pre-Omicron waves; the pre-Omicron trend showed increasing numbers over time, whereas the Omicron trend showed decreasing numbers. In denser populated countries, pre-Omicron waves were managed more effectively, in contrast to Omicron waves which were managed less effectively (but more effectively in less densely populated countries). This study provides the evidence for a different behaviour of Omicron waves in terms of wave dynamics, and thereby confirms that the Omicron variant is not only genetically different but even more so in terms of epidemiological dynamics

Measurement Accuracy of the Body Weight with Smart Insoles

A participant wearing the Pedar-X performed 6 activities on level ground: Slow, medium and fast walk, medium and fast run, and limping. Static BW was measured prior each activity. The dynamic and static BWs were calculated from the mean of the sum of forces of both feet over time and compared to the force measured from the force-plate. As the base pressure during the swing phase was not zero, it was treated in 3 ways: including the base pressure; subtracting the mean base pressure from the swing phase; subtraction of the base pressure from the entire signal. The calculated BWs were normalised to the actual BW of the participant. From the results, the BWs calculated had 10% error when static and 6% error when walking. To zero or subtract the baseline pressures improved the BW measurement by 1.75% and 4% respectively. Running data could not be analysed at a sampling rate of 50 Hz

Accuracy of Centre of Pressure Gait Measurements from Two Pressure-Sensitive Insoles

Footwear-based wearable applications are relevant to numerous fields and have great commercial and clinical potential. However, scientifically validated, reliable data on these devices is largely missing. Centre of pressure (COP) is an important and common factor for measuring balance and gait and hence the validity of such devices is essential for reading accurate data. This study aims to investigate COP accuracy of an existing system, Pedar (PE), and a newly designed Smart Insole (SI) using a force plate (FP). This was done by means of COP data noise (R2), and gradient of the fit function (k). For the SI, the maximum COPx and COPy data achieved R2 values of 0.7837 and 0.9368 and k values of 0.8867 and 0.8538 respectively when compared with the FP. Conversely, the Pedar achieved R2 values of 0.8409 and 0.9401 and k values of 1.0492 and 1.08 when compared with the FP respectively

COVID-19 Pandemic: How Effective Are Preventive Control Measures and Is a Complete Lockdown Justified? A Comparison of Countries and States

For fighting the COVID-19 pandemic, countries used control measures of different severity, from “relaxed” to lockdown. Drastic lockdown measures are considered more effective but also have a negative impact on the economy. When comparing the financial value of lost lives to the losses of an economic disaster, the better option seems to be lockdown measures. We developed a new parameter, the effectiveness of control measures, calculated from the 2nd time derivative of daily case data, for 92 countries, states and provinces. We compared this parameter, and also the mortality during and after the effective phase, for countries with and without lockdowns measures by means of the Mann–Whitney test. We did not find any statistically significant difference in the effectiveness between countries with and without lockdowns (p > 0.76). There was also no significant difference in mortality during the effective phase (p > 0.1); however, a significant difference after the effective phase, with higher mortality for lockdown countries, was identified. The effectiveness correlated well with a parameter derived from the reproductive number (R2 = 0.9480). The average duration of the effective phase was 17.3 ± 10.5 days. The results indicated that lockdown measures are not necessarily superior to relaxed measures, which in turn are not necessarily a recipe for failure. Relaxed measures are, however, more economy-friendly

The Difference in Wave Dynamics between SARS-CoV-2 Pre-Omicron and Omicron Variant Waves

Compared to previous SARS-CoV-2 variants, the Omicron variant exhibited different epidemiological features. The purpose of this study was to assess the wave dynamics of pre-Omicron and Omicron waves in terms of differences and similarities. We investigated the COVID-19 waves since the beginning of the pandemic up to 28 August 2022, 1000 waves in total, as to their effectiveness for flattening the curve, calculated from the first and second time derivative of the daily case data. The average number of Omicron waves per month (42.78) was greater than the one of pre-Omicron waves per month (25.62). Omicron waves steepen and flatten the curve significantly faster, more effectively and with sharper peaks. Omicron waves generated more daily case data than pre-Omicron waves; the pre-Omicron trend showed increasing numbers over time, whereas the Omicron trend showed decreasing numbers. In denser populated countries, pre-Omicron waves were managed more effectively, in contrast to Omicron waves which were managed less effectively (but more effectively in less densely populated countries). This study provides the evidence for a different behaviour of Omicron waves in terms of wave dynamics, and thereby confirms that the Omicron variant is not only genetically different but even more so in terms of epidemiological dynamics

Retrospective Evaluation of the Effectiveness of COVID-19 Control Strategies Implemented by the Victorian Government in Melbourne—A Proposal for a Standardized Approach to Review and Reappraise Control Measures

In evaluating the effectiveness of COVID-19 control measures, we propose a standardized approach to assess the impact of COVID-19 management on flattening the curve by analyzing the case data of Victoria, Australia. Its capital, Melbourne, is considered the most lock-downed city in the world. We used the daily case data from Victoria and their first time derivative and compared the dates when the six lockdowns were imposed with the start and end of the effective period, i.e., the period between the maximum and minimum acceleration. Lockdowns 1, 2 (Level 4 restrictions), 3, and 4 were found to be implemented too late, as they were expected to come into effect at the end or after the effective phase, and they were therefore ineffective. It was determined that Lockdown 2 (Level 3 restrictions) did not initiate the effective phase, and it was therefore ineffective, too. Lockdown 5 was expected to take effect in the second half of the effective phase, but showed no changes in the acceleration curve, and it was therefore also ineffective. Lockdown 6, implemented well before the effective period, did not flatten the curve, and was thus also found to be ineffective. The mask mandate between Lockdown 2 (Level 3 and 4 restrictions) initiated the effective phase (likely along with Lockdown 2, Level 3 restrictions), and was therefore found to effectively flatten the curve. The temporal relationship between the assumed cause (control measure) and the observed effect (flattening of the curve) is thus a crucial parameter for assessing the effect of control measures