Recording of the ship’s motion in operation on irregular waves and its processing

The safety of the ship state during the voyage is ensured, among other things, by monitoring the state of the ship’s stability. A device for measuring the parameters of the ship’s motion and software for discrete motion recording into a computer have been developed in Siberian State University of Water Transport. With the use of the device, a full-scale experiment to record the motion of the motor ship “Grumant” during an operational voyage has been carried out. For this, the half-periods of rolling and their amplitudes are obtained. Amplitude-half-period distribution of rolling has been built. According to the maximum probability, the half-period of its own roll has been found from it, which makes it possible to assess the stability state of the vessel during the voyage. The spectrum of motion and rolling dispersion has been obtained. If it is possible to obtain objective information about the actual wave spectrum, this opens the way to obtaining the amplitude-frequency characteristic of the motion based on the Wiener- Khinchin theorem. This task is set as a priority for the development of research in this area with the prospect of optimizing vessel navigation in difficult hydrometeorological conditions.

Modeling the effect of age on quantiles of the incubation period distribution of COVID-19

Background The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. Methods We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. Results Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. Conclusions Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.

Estimation of the serial interval and proportion of pre-symptomatic transmission events of COVID− 19 in Ireland using contact tracing data

Background The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. Results After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. Conclusions Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

Estimating the proportion of SARS-CoV-2 infections reported through diagnostic testing

The number of positive diagnostic tests for SARS-CoV-2 is a critical metric that is commonly used to assess epidemic severity and the efficacy of current levels of control. However, a proportion of individuals infected with SARS-CoV-2 may never receive a diagnostic test, while many of those who are tested may receive a false negative result. Consequently, cases reported through testing of symptomatic individuals represent only a fraction of the total number of infections, and this proportion is expected to vary depending on changes in natural factors and variability in test-seeking behaviour. Here we combine a number of data sources from England to estimate the proportion of infections that have resulted in a positive diagnosis. Using published estimates of the incubation period distribution and time-dependent test sensitivity, we estimate SARS-CoV-2 incidence from daily reported diagnostic test data. By calibrating this estimate against surveillance data we find that approximately 25% of infections were consistently reported through diagnostic testing before November 2020. This percentage increased through the final months of 2020, predominantly in regions with a large presence of the the UK variant of concern (VOC), before falling rapidly in the last two weeks of January 2021. These changes are not explained by variation in rates of lateral flow device or PCR testing, but are consistent with there being an increased probability for the VOC that infection will result in an eventual positive diagnosis.

Examining geographical disparities in the incubation period of the COVID-19 infected cases in Shenzhen and Hefei, China

Background Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions. Methods This retrospective study mainly applied big data analytics and methodology, using the publicly accessible clinical report for patients (n = 543) confirmed as infected in Shenzhen and Hefei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological method. Statistical and econometric methods were employed to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. Results The median incubation period of the COVID-19 for all the 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Hefei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10 °C to 15 °C, may decrease viral virulence and result in more extended incubation periods. Conclusion Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation. Methodologies based on big data released by local public health authorities are applicable for identifying incubation period and relevant epidemiological research.

Efficient multi-period distribution mechanism for the innovation investment system under uncertainty

In this paper, we study the problem of a venture investor who distributes the budget between several innovation projects under conditions of uncertainty. A common method for solving this problem is through bilateral negotiations with the external evaluation of projects. However, the effectiveness almost entirely depends on the evaluation quality, but external evaluation seldom reduces the knowledge asymmetry for innovation projects. We propose an iterative revelation mechanism for this problem when the investor sequentially offers possible allocations of the limited budget in the form of threshold dividing questions. The binary choices of innovators serve as a signal of internal estimates of the project implementation costs. Under perfect information, such a mechanism, regardless of the method for determining budget allocations, always produces an effective allocation in subgame-perfect Nash equilibrium. Under uncertainty, the method of offering distribution options matters – the optimal solution is found under the English auction class of mechanisms. In an efficient iterative allocation mechanism for innovation investment, the investor proposes a new allocation of the budget each round until an efficient allocation is achieved. The proposed mechanism does not necessarily need to identify the exact minimum budgets for each innovator. Another advantage of the proposed mechanism is the ability to use different processes for organizing rounds.

Modeling the Effect of Age on Quantiles of the Incubation Period Distribution of COVID-19

Background: Since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019, the novel coronavirus pneumonia has placed a serious impact on many aspects of the world. Note that the incubation period distribution plays important roles in prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 on the age of infected cases, and estimate its corresponding quantiles from information of 2172 confirmed cases from 29 provinces outside Hubei in China.Methods: We collected data including the infection dates, onset dates, and ages of the confirmed cases from the websites of the centres of disease control, or the daily public reports through February 16th, 2020. A maximum likelihood method was developed to account for the biased sampling issue of the data as the epidemic was still ongoing at the time of collecting data. Results: Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 varies over ages. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others. We estimated that the 0.95-th quantile related to people in the age group 23∼55 is less than 15 days. Conclusions: Observing that the conditional quantiles vary over ages, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration, rather than a uniform 14-days quarantine, in practice. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼ 22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.