Impact of Population Mixing Between a Vaccinated Majority and Unvaccinated Minority on Disease Dynamics. Implications for SARS-CoV-2

Background: The speed of vaccine development has been a singular achievement during the SARS-CoV-2 pandemic. However, anti-vaccination movements and disinformation efforts have resulted in suboptimal uptake of available vaccines. Vaccine opponents often frame their opposition in terms of the rights of the unvaccinated. Our objective was to explore the impact of mixing of vaccinated and unvaccinated populations on risk among vaccinated individuals. Methods: We constructed a simple Susceptible-Infectious-Recovered (SIR) compartmental model of a respiratory infectious disease with two connected sub-populations: vaccinated individuals and unvaccinated individuals (Figure 1). We modeled the non-random mixing of these two groups using a matrix approach with a mixing constant varied to simulate a spectrum of patterns ranging from random mixing to complete assortativity. We evaluated the dynamics of an epidemic within each subgroup, and in the population as a whole, and also evaluated the contact-frequency-adjusted contribution of unvaccinated individuals to risk among the vaccinated. Results: As expected, the relative risk of infection was markedly higher among unvaccinated individuals than among vaccinated individuals. However, the contact-adjusted contribution of unvaccinated individuals to infection risk during the epidemic was disproportionate with unvaccinated individuals contributing to infection risk among the vaccinated at a rate up to 6.4 times higher than would have been expected based on contact numbers alone in the base case. As assortativity increased the final attack rate decreased among vaccinated individuals, but the contact-adjusted contribution to risk among vaccinated individuals derived from contact with unvaccinated individuals increased. Interpretation: While risk associated with avoiding vaccination during a virulent pandemic accrues chiefly to the unvaccinated, the choices of these individuals are likely to impact the health and safety of vaccinated individuals in a manner disproportionate to the fraction of unvaccinated individuals in the population.

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.

Taking account of asymptomatic infections: A modeling study of the COVID-19 outbreak on the Diamond Princess cruise ship

The COVID-19 outbreak on the Diamond Princess (DP) cruise ship has provided empirical data to study the transmission potential of COVID-19 with the presence of pre/asymptomatic cases. We studied the changes in R0 on DP from January 21 to February 19, 2020 based on chain binomial models under two scenarios: no quarantine assuming a random mixing condition, and quarantine of passengers in cabins—passengers may get infected either by an infectious case in a shared cabin or by pre/asymptomatic crew who continued to work. Estimates of R0 at the beginning of the epidemic were 3.27 (95% CI, 3.02–3.54) and 3.78 (95% CI, 3.49–4.09) respectively for serial intervals of 5 and 6 days; and when quarantine started, with the reported asymptomatic ratio 0.505, R0 rose to 4.18 (95%CI, 3.86–4.52) and 4.73 (95%CI, 4.37–5.12) respectively for passengers who might be exposed to the virus due to pre/asymptomatic crew. Results confirm that the higher the asymptomatic ratio is, the more infectious contacts would happen. We find evidence to support a US CDC report that “a high proportion of asymptomatic infections could partially explain the high attack rate among cruise ship passengers and crew.” Our study suggests that if the asymptomatic ratio is high, the conventional quarantine procedure may not be effective to stop the spread of virus.

Simulation of rainfall fields conditioned on rain gauge observations and radar estimates using random mixing

The accuracy of spatial precipitation estimates with the relatively high temporospatial resolution is of vital importance in various fields of research and practice. Yet the intricate variability and the intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radar and rain gauge are two complementary sources of precipitation information: the former is inaccurate in general but is a valid indicator for the spatial pattern of the rainfall field; the latter is relatively accurate but lack spatial coverage. Considering the pros and cons of the two sources of precipitation information, a number of radar-gauge merging techniques have been developed to obtain spatial precipitation estimates over the past years. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike the commonly used interpolation methods, the results from the conditional simulation is a range of possible estimates due to its Monte Carlo framework. Yet an obstacle that hampers the application of conditional simulation in spatial precipitation estimation is how to obtain the marginal distribution function of the rainfall field with accuracy. In this work, we propose a method to obtain the marginal distribution function of the rainfall field based on rain gauge observations and radar estimates. The conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The properties of the results from the proposed method are elaborated through the comparison with the results from other methods: ordinary kriging, kriging with external drift, and conditional merging. Finally, the sensitivity of the proposed method towards the two factors – density of rain gauges and random error in radar estimates – is analyzed.

Taking Account of Asymptomatic Infections – A Modeling Study on the COVID-19 Outbreak on the Diamond Princess Cruise Ship

BackgroundThe COVID-19 outbreak on the Diamond Princess (DP) cruise ship provided empirical data to study the transmission potential of COVID-19 under quarantine with the presence of asymptomatic cases.MethodsWe studied the changes in R0 on the DP from January 21 to February 19, 2020 based on chain-binomial models under two scenarios: no quarantine assuming a random mixing condition, and quarantine of passengers in cabins — passengers may get infected either by an infectious case in a shared cabin or by asymptomatic crew who continued to work.ResultsEstimates of R0 at the beginning of the epidemic were 3.27 (95% CI, 3.02-3.54) and 3.78 (95% CI, 3.49-4.09) respectively for serial intervals of 5 and 6 days; and when quarantine started, with the reported asymptomatic ratio 0.505, R0 rose to 4.18 (95%CI, 3.86-4.52) and 4.73 (95%CI, 4.37-5.12) respectively for passengers who might be exposed to the virus due to contacts with asymptomatic crew. The overall R0 for both crew and passengers was decreased to 2.55 (95%CI, 2.36-2.76) and 2.90 (95% CI, 2.67-3.13). Results show that the higher the asymptomatic ratio is, the more infectious contacts would happen.ConclusionsWe find evidence to support a US CDC report that “a high proportion of asymptomatic infections could partially explain the high attack rate among cruise ship passengers and crew.” Our study suggests that the effects of quarantine may be limited if the asymptomatic ratio is high, implying that a combination of preventive measures is needed to stop the spread of virus.

COMPARING COMPRESSIVE STRENGTHS OF LAYERED AND RANDOM PLACEMENT OF EXPANDED POLYSTYRENE WASTES IN QUARRY DUST BLOCKS

Despite intense research on building materials, the challenge of finding cheap and lightweight construction materials still persist for persons wishing to construct a house. A material that is getting attention of researchers and lightweight is Expanded Polystyrene (EPS). The aim of this study was to compare compressive strength and mass of blocks made when EPS were mixed randomly or in layered manner in cement-quarry dust mortar. The EPS wastes were placed randomly and in a single layer so as to give percentage volume of 0% (control), 10%, 20%, 30%, 40% and 50% EPS of the cube of 150 mm. The results showed that the average compressive strength of mortar was 18.67 ±1.33 N/mm2. The strength reduction proportionality factor for layered mixing was 0.76 to 1 and 0.29 to 1 for random mixing. This showed that reduction of strength was greater in random mixing than layered mixing. Increase of EPS above 30% randomly, resulted in lightweight blocks of between 1319 and 1669 Kg/m3, whereas increasing EPS in layered manner above 50% resulted in lightweight blocks of densities less than 1679 Kg/m3. This research showed that 40% EPS randomly mixing resulted in a light block which met the minimum strength criteria of 3.6 N/mm2.

Çukurova Bölgesinde Karşılaşılan Bal Arısı Ölümleri Üzerine Piretroit Grubu Pestisitlerin Etkisi

Apiculture is one of the agricultural activities carried out widely in our country and in the world. Our society attaches great importance to beekeeping and honey products, especially honey, so bee colony health is very important in the continuity of bee products. There are many factors that can be shown to cause colony loss in bees and one of them is poisoning caused by pesticides. Causes such as improper dosing of pesticides, day-to-day spraying, or potentiation or effect differentiation due to random mixing of drugs may cause collective bee deaths. In this study, 188 dead bee samples (each sample contains approximately 100 g dead bees) from different bee farms, which were collected from beekeepers and brought to Adana Veterinary Control Institute with the suspicion of pesticide poisoning due to intensive deaths, were examined. Qualitative examination was performed by gas chromatography (GC) device. In the examination of dead bee samples, tau-fluvalinate residue was found in 2 samples in 2015 and cypermethrin residue was found in 1 sample in 2016. In 2017 and 2018, no detectable substance was detected in terms of pyrethroid pesticides. In 188 samples which we examined in terms of pyrethroit group pesticides, 1 substance was found to be toxic for bees. This shows, at the samples examined, that pyrethroid pesticides are not involved in bee colony losses in Çukurova.