COVID-19 infection risk assessment and management at the Tokyo 2020 Olympic and Paralympic Games: A scoping review.

The Tokyo 2020 Olympic and Paralympic Games was one of the largest international mass-gathering events held after the beginning of coronavirus disease 2019 (COVID-19) pandemic. In this scoping review, we extracted papers discussing COVID-19 risk assessment or management at the Tokyo 2020 Games to determine the nature of studies that were conducted. Among the 75 papers obtained from two search engines (PubMed and ScienceDirect) and four papers collected from hand-searches, 30 papers were extracted. Only eight papers performed both COVID-19 prior risk assessment and quantitative evaluation of effectiveness measures, highlighting the importance of rapid, solution-focused risk assessment. Furthermore, this review revealed that the findings regarding the spread of COVID-19 infection to citizens in the host country were inconsistent depending on the assessment methods and that assessments of the spread of infection outside the host country were lacking.

Quantifying the effect of isolation and negative certification on COVID-19 transmission.

Isolation of close contact people and negative test certification are used to manage the spread of new coronavirus infections worldwide. These effectively prevent the spread of infection in advance, but they can lead to a decline in socio-economic activity. Thus, the present study quantified the extent to which isolation and negative test certification respectively reduce the risk of infection. To this end, a discrete-time SEIR model was used as the infectious disease model, and equations for calculating the conditional probability of non-infection status given negative test results on two different days were derived. Then the respective non-infection probabilities with two negative PCR test results, and with one negative PCR test result and one antigen test result, were quantified. By substituting initial parameters of the SEIR model into these probabilities, the present study revealed the following: (1) isolating close contact individuals can reduce by [Formula: see text] the risk of infection during the first 5 days, but five more days are needed to reduce the risk [Formula: see text] more, and seven more days to reduce the risk [Formula: see text] more; and (2) if an individual with a negative PCR test result has a negative antigen test result the next day, then his or her infection probability is between 0.6 and [Formula: see text]. Our results show that 5-day isolation has a proportionally greater effect on risk reduction, compared to longer isolation; and thus, if an isolation period of longer than 5 days is contemplated, both the risk reduction and the negative effects from such increased isolation should be considered. Regarding negative test certification, our results provide those in managerial positions, who must decide whether to accept the risk and hold mass-gathering events, with quantitative information that may be useful in their decision-making.

Sensitivity of rapid antigen tests for COVID-19 during the Omicron variant outbreak among players and staff members of the Japan Professional Football League and clubs: a retrospective observational study.

OBJECTIVES: Rapid antigen tests have been used to prevent the spread of the COVID-19; however, there have been concerns about their decreased sensitivity to the Omicron variant. In this study, we assessed the sensitivity and specificity of the rapid antigen test compared with the PCR test among the players and staff members of the Japan Professional Football League and clubs. Furthermore, we evaluated the relationship between the sensitivity and the duration from the onset of symptoms to testing or vaccine status. DESIGN: This was a retrospective observational study. METHODS: We used 656 results from both the rapid antigen and PCR tests for COVID-19 using samples collected on the same day from 12 January to 2 March 2022, during the Omicron variant outbreak in Japan. RESULTS: The sensitivity of the rapid antigen test compared with the PCR test was 0.63 (95% CI: 0.53 to 0.73) and the specificity was 0.998 (95% CI: 0.995 to 1.000). There were no significant associations between the sensitivity and the duration from the onset of symptoms to testing (including asymptomatic cases in the category) or vaccination status (p>0.05) with small effect sizes (Cramer's V or φ: ≤0.22). CONCLUSIONS: Even during the Omicron outbreak, the sensitivity of the rapid antigen tests did not depend on the duration from the onset of symptoms to testing.

Revisiting assessment factors for species sensitivity distributions as a function of sample size and variation in species sensitivity.

To use species sensitivity distributions (SSDs) for ecological risk assessment, there are various uncertainties, which require applying assessment factors (AFs) accordingly. This study aims to quantify the uncertainty of estimating statistical distributions. Given a management goal of protecting 95% of species, the concentration that affects 5% of the species (HC5) is estimated. Since the true concentration affecting 5% of the species (population HC5) is unknown, the estimated HC5 is divided by an AF to derive the predicted no-effect concentration (PNEC), which is set as the protection goal, to compensate for the deviation in the estimated HC5 from the population HC5. Although the deviation between these two HC5 values depends on the sample size and the variation in sensitivity (standard deviation of the distribution) among species, there has been little discussion of how to quantify the degree of uncertainty. By assuming that toxicity values are a random sample from a lognormal distribution, we mathematically analyzed the SSD to derive the magnitude of AF needed to achieve a given protection goal (as an example, the protection of 95% of species with a probability of 95%). We successfully derived an equation that explicitly relates the magnitude of AF to the sample size and the variation in species sensitivity, providing a new basis to statistically determine the magnitude of AF for ecological risk assessments.

COVID-19 testing systems and their effectiveness in small, semi-isolated groups for sports events.

In this study, we quantitatively assessed the effectiveness of systems for COVID-19 testing in small groups of sport teams that are semi-isolated from the general population by countermeasures against infection. Two types of group were assumed, and the dynamics of infection within each group was modeled by using a compartment model of infectious disease. One group (Group A) comprised domestic professional sports teams that play many games over a season while remaining within a relatively small region. Polymerase chain reaction (PCR) tests were routinely conducted once every 2 weeks, and the number of infected individuals that could not be quarantined after identification by testing or checking for symptoms was defined as the risk. The other group (Group B) comprised teams that travel across borders for mass-gathering events like the Olympic and Paralympic Games. The teams were isolated for 2 weeks at their destination; frequent testing and checking for symptoms was conducted, and any infected individuals were quarantined. The number of infected individuals participating in games after the isolation period was defined as the risk. In Group A, the number of infected individuals detected by routinely conducted PCR testing was lower than the number of infected individuals detected by checking for symptoms, indicating that routine testing every 2 weeks was not very effective. In Group B, daily PCR testing was the most effective, followed by daily antigen testing. Dual testing, in which individuals with a positive antigen test were given an additional PCR test, was the least effective with an effect equal to PCR testing every other day. These results indicate that repeated testing does not necessarily increase the detection of infected individuals.

Effects of test timing and isolation length to reduce the risk of COVID-19 infection associated with airplane travel, as determined by infectious disease dynamics modeling.

Effective measures to reduce the risk of coronavirus disease 2019 (COVID-19) infection in overseas travelers are urgently needed. However, the effectiveness of current testing and isolation protocols is not yet fully understood. Here, we examined how the timing of testing and the number of tests conducted affect the spread of COVID-19 infection associated with airplane travel. We used two mathematical models of infectious disease dynamics to examine how different test protocols changed the density of infected individuals traveling by airplane and entering another country. We found that the timing of testing markedly affected the spread of COVID-19 infection. A single test conducted on the day before departure was the most effective at reducing the density of infected individuals travelling; this effectiveness decreased with increasing time before departure. After arrival, immediate testing was found to overlook individuals infected on the airplane. With respect to preventing infected individuals from entering the destination country, isolation with a single test on day 7 or 8 after arrival was comparable with isolation only for 11 or 14 days, respectively, depending on the model used, indicating that isolation length can be shortened with appropriately timed testing.

Insights into the Methanogenic Population and Potential in Subsurface Marine Sediments Based on Coenzyme F430 as a Function-Specific Biomarker.

Coenzyme F430, the prosthetic group of methyl coenzyme M reductase (MCR), is a key compound in methane metabolism. We applied coenzyme F430 as a function-specific biomarker of methanogenesis to subsurface marine sediments collected below the sulfate reduction zone to investigate the distribution and activity of methanogens. In addition, we examined the kinetics of the epimerization of coenzyme F430, which is the first stage of the degradation process after cell death, at various temperatures (4, 15, 34, 60 °C) and pH (5, 7, 9) conditions, which cover in situ conditions of drilled sediments used in this study. The degradation experiments revealed that the kinetics of the epimerization well follow the thermodynamic laws, and the half-life of coenzyme F430 is decreasing from 304 days to 11 h with increasing the in situ temperature. It indicates that the native F430 detected in the sediments is derived from living methanogens, because the abiotic degradation of F430 is much faster than the sedimentation rate and will not be fossilized. Based on coenzyme F430 analysis and degradation experiments, the native form of F430 detected in subseafloor sediments off the Shimokita Peninsula originates from living methanogen cells, which is protected from degradation in cells but disappears soon after cell death. The biomass of methanogens calculated from in situ F430 concentration and F430 contents in cultivable methanogen species decreases by 2 orders of magnitude up to a sediment depth of 2.5 km, with a maximum value at ∼70 m below the seafloor (mbsf), while the proportion of methanogens to the total prokaryotic cell abundance increases with the depth, which is 1 to 2 orders of magnitude higher than expected previously. Our results indicate the presence of undetectable methanogens using conventional techniques.

Comparing impacts of metal contamination on macroinvertebrate and fish assemblages in a northern Japanese river.

Researchers have long assessed the ecological impacts of metals in running waters, but few such studies investigated multiple biological groups. Our goals in this study were to assess the ecological impacts of metal contamination on macroinvertebrates and fishes in a northern Japanese river receiving treated mine discharge and to evaluate whether there was any difference between the metrics based on macroinvertebrates and those based on fishes in assessing these impacts. Macroinvertebrate communities and fish populations were little affected at the downstream contaminated sites where concentrations of Cu, Zn, Pb, and Cd were 0.1-1.5 times higher than water-quality criteria established by the U.S. Environmental Protection Agency. We detected a significant reduction in a few macroinvertebrate metrics such as mayfly abundance and the abundance of heptageniid mayflies at the two most upstream contaminated sites with metal concentrations 0.8-3.7 times higher than the water-quality criteria. There were, however, no remarkable effects on the abundance or condition factor of the four dominant fishes, including masu salmon (Oncorhynchus masou). These results suggest that the richness and abundance of macroinvertebrates are more sensitive to metal contamination than abundance and condition factor of fishes in the studied river. Because the sensitivity to metal contamination can depend on the biological metrics used, and fish-based metrics in this study were limited, it would be valuable to accumulate empirical evidence for ecological indicators sensitive to metal contamination within and among biological groups to help in choosing which groups to survey for general environmental impact assessments in metal-contaminated rivers.

An all-in-one tool for multipurpose ecological risk assessment and management (MeRAM) of chemical substances in aquatic environment.

A quality-assured ecological risk assessment (ERA) requires enormous resources (time and labor) in collection/assessment of hazard data, as well as considerable expertise to interpret the risk. The ERA of chemicals is thereby considered difficult or impossible for those with little assessment experience and cumbersome or complicated for practitioners. To meet the concerns regarding ERA and accelerate the risk assessment and management of chemicals, we developed an all-in-one free tool for multi-purpose ecological risk assessment management (MeRAM) of chemical substances in aquatic environment called the AIST-MeRAM Ver. 2.0.0 (Copyright No: H28PRO-2007). It allows users from beginners to experts to conduct ERA without any preparation because all the necessary ecotoxicity test data and methodologies are available in the system. Approximately 270,000 ecotoxicity test data points for 3900 chemical substances together with the scientific methodologies from traditional simple hazard quotient (HQ) to more ecologically relevant complicated assessments such as species sensitivity distribution (SSD) and population-level assessment are embedded in the AIST-MeRAM. In addition, users can easily understand the Japanese regulatory RA and management of chemical substances due to a special function based on the Japanese Chemical Substance Control Law (CSCL). Here, we demonstrate a tiered ERA using the embedded sample data to evaluate and ensure the functions of AIST-MERAM. We show that the AIST-MeRAM can provide a comprehensive and accurate ERA, suggesting that it is a powerful IT solution for cumbersome ERA.

Does a sum of toxic units exceeding 1 imply adverse impacts on macroinvertebrate assemblages? A field study in a northern Japanese river receiving treated mine discharge.

In ecological risk assessment, sum-of-toxic-unit approaches based on measured water quality factors such as trace metals are used to infer ecological impacts in the environment. However, it is uncertain whether the use of such approaches yields accurate risk predictions. To address this issue, we investigated and compared (1) water quality, including trace metals, and (2) benthic macroinvertebrate communities in a northern Japanese river receiving treated discharge from an abandoned mine and in a nearby reference river. As a sum-of-toxic-unit approach, we employed a cumulative criterion unit (CCU), namely, the sum of the ratios of the dissolved concentrations of a metal (Cu, Zn, Cd, or Pb) divided by the US Environmental Protection Agency hardness-adjusted environmental water quality criterion for that metal. Compared with the reference sites, at the metal-contaminated sites, the richness, abundance, and structure of macroinvertebrate communities were little affected, with CCUs of 1.7 to 7.4, suggesting that CCU values exceeding 1 do not always indicate marked adverse impacts on these metrics. Further study is still required to derive a more compelling conclusion on the generally applicable relationships between CCUs and ecological impacts on river invertebrates. This would lead to better ecological risk assessments based on sum-of-toxic-unit approaches.

Quantifying the precision of ecological risk: Conventional assessment factor method vs. species sensitivity distribution method.

In ecological risk assessment, the Predicted No Effect Concentration (PNEC) of a substance is generally derived by one of two methods: either by applying an Assessment Factor (AF) or by using a Species Sensitivity Distribution (SSD). With the AF method, which is the conventional way, the PNEC is determined by dividing the lowest No Observed Effect Concentration (NOEC) by an AF of a certain fixed magnitude. With the SSD method, which is becoming increasingly used in the European Union and the United States, an HC5 value (Hazardous Concentration for 5% of species) is estimated from the NOEC and then divided by an AF to derive the PNEC. This study aimed to explore the most appropriate AF and the most effective application of each method. The performances of the SSD and AF methods were compared on the assumption that the better method is that in which more PNECs are lower than HC5. We concluded that the performance of these methods depends on sample size and variation in species sensitivity. As the sample size increases (i.e., if there are more toxicity data), the performance of each method increases. The performance of the AF method is better when variation in species sensitivity is small (i.e., all species tend to have a similar NOEC), but it declines as variation in sensitivity rises, implying that persisting with either of the methods may misrepresent the ecological risk. Our results suggest that the variation in sensitivity is an important factor affecting the ecological risk and more effort should be paid to understanding why the variation varies depending on chemical substances.

Model construction for estimating potential vulnerability of Japanese soils to cadmium pollution based on intact soil properties.

Prediction of heavy metal bioavailability in intact soil is important to manage soil pollution risks. We developed a regression model for representative Japanese soils to judge their potential vulnerability to cadmium (Cd) pollution. We added four rates of Cd to 17 sample soils to mimic artificial contamination. After aging the contaminated soils, we measured Cd's bioavailability using the diffusive gradients in thin-films (DGT) technique. We then evaluated the relationships between bioavailability of Cd ([CdDGT]) and intact soil properties by statistical analyses. Cation exchange capacity (CEC) and pH emerged as significant factors to explain the cadmium bioavailability in Japanese soils. Specifically, lower CEC and lower pH were associated with higher [CdDGT], which poses a higher risk for soil ecosystems. The correlation between pH and [CdDGT] had a high dependence on [CdAdd], whereas that for CEC did not. Regression analysis also showed that the interaction between intact soil pH and spiked concentration ([CdAdd]) had a significant contribution to [CdDGT]. The regression model developed was rationally supported by a biotic ligand model. This simplified but realistic model would be useful in estimating the vulnerability of representative Japanese soils and determining the risk for Japanese soils in relation to Cd contamination.

Much ado about interaction: A combination of linear processes yields non-linear toxic effects in chemical mixtures.

Predicting chemical mixture toxicity is an important issue for risk assessment. Loewe's concentration addition (CA) is a major model for predicting such toxicity. The CA is an additivity-based model, and if the results of toxicity test deviate from the CA prediction, it is considered that the toxic effect of the mixture is non-additive, and that "interaction" has played some role. In the present study, using as an example a biotic ligand model (BLM), which predicts metal toxicities, we theoretically investigated the toxic effect of mixture and found that the effects are almost always non-additive if the effects are evaluated by total metal concentrations, and the non-additivity is not derived by interactions among metals but by a combination of processes of metal kinetics. Once non-additive effects are observed in chemical mixture, it is often expected that there should be some complex toxic mechanisms or some toxic interaction. Our results suggest that the expectation may not be always true. Since at least two processes are entrained in the metal toxicity (metal speciation and binding of metals to biotic ligand in BLM framework), there is a possibility that the non-additivity is generated by the combination of processes and interaction is nothing to do with it. Our results imply that toxic effects of metal mixture can be predicted more easily than we generally expected.

Criteria for deviation from predictions by the concentration addition model.

Loewe's additivity (concentration addition) is a well-known model for predicting the toxic effects of chemical mixtures under the additivity assumption of toxicity. However, from the perspective of chemical risk assessment and/or management, it is important to identify chemicals whose toxicities are additive when present concurrently, that is, it should be established whether there are chemical mixtures to which the concentration addition predictive model can be applied. The objective of the present study was to develop criteria for judging test results that deviated from the predictions by the concentration addition chemical mixture model. These criteria were based on the confidence interval of the concentration addition model's prediction and on estimation of errors of the predicted concentration-effect curves by toxicity tests after exposure to single chemicals. A log-logit model with 2 parameters was assumed for the concentration-effect curve of each individual chemical. These parameters were determined by the maximum-likelihood method, and the criteria were defined using the variances and the covariance of the parameters. In addition, the criteria were applied to a toxicity test of a binary mixture of p-n-nonylphenol and p-n-octylphenol using the Japanese killifish, medaka (Oryzias latipes). Consequently, the concentration addition model using confidence interval was capable of predicting the test results at any level, and no reason for rejecting the concentration addition was found. Environ Toxicol Chem 2016;35:1806-1814. © 2015 SETAC.

Explanation of non-additive effects in mixtures of similar mode of action chemicals.

Many models have been developed to predict the combined effect of drugs and chemicals. Most models are classified into two additive models: independent action (IA) and concentration addition (CA). It is generally considered if the modes of action of chemicals are similar then the combined effect obeys CA; however, many empirical studies report nonlinear effects deviating from the predictions by CA. Such deviations are termed synergism and antagonism. Synergism, which leads to a stronger toxicity, requires more careful management, and hence it is important to understand how and which combinations of chemicals lead to synergism. In this paper, three types of chemical reactions are mathematically modeled and the cause of the nonlinear effects among chemicals with similar modes of action was investigated. Our results show that combined effects obey CA only when the modes of action are exactly the same. Contrary to existing knowledge, combined effects are generally nonlinear even if the modes of action of the chemicals are similar. Our results further show that the nonlinear effects vanish out when the chemical concentrations are low, suggesting that the current management procedure of assuming CA is rarely inappropriate because environmental concentrations of chemicals are generally low.

Theoretical lessons for increasing algal biofuel: Evolution of oil accumulation to avert carbon starvation in microalgae.

Microalgae-derived oil is considered as a feasible alternative to fossil-derived oil. To produce more algal biomass, both algal population size and oil accumulation in algae must be maximized. Most of the previous studies have concentrated on only one of these issues, and relatively little attention has been devoted to considering the tradeoff between them. In this paper, we first theoretically investigated evolutionary reasons for oil accumulation and then by coupling population and evolutionary dynamics, we searched for conditions that may provide better yields. Using our model, we assume that algae allocate assimilated carbon to growth, maintenance, and carbon accumulation as biofuel and that the amount of essential materials (carbon and nitrate) are strongly linked in fixed proportions. Such stoichiometrically explicit models showed that (i) algae with more oil show slower population growth; therefore, the use of such algae results in lower total yields of biofuel and (ii) oil accumulation in algae is caused by carbon and not nitrate starvation. The latter can be interpreted as a strategy for avoiding the risk of increased death rate by carbon starvation. Our model also showed that both strong carbon starvation and moderately limited nitrate will promote total biofuel production. Our results highlight considering the life-history traits for a higher total yields of biofuel, which leads to insight into both establishing a prolonged culture and collection of desired strains from a natural environment.

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches.

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Testing an application of a biotic ligand model to predict acute toxicity of metal mixtures to rainbow trout.

The authors tested the applicability of a previously developed biotic ligand model (BLM) to predict acute toxicity of single metals and metal mixtures (cadmium, lead, and zinc) to rainbow trout fry (Oncorhynchus mykiss) from a single available dataset. The BLM used in the present study hypothesizes that metals inhibit an essential cation (calcium) and organisms die as a result of its deficiency, leading to an assumption that the proportion of metal-binding ligand (f) is responsible for the toxic effects of metals on the survival of rainbow trout. The f value is a function of free-ion concentrations of metals computed by a chemical speciation model, and the function has affinity constants as model parameters. First, the survival effects of single metals were statistically modeled separately (i.e., f-survival relationship) by using the generalized linear mixed model with binomial distribution. The modeled responses of survival rates to f overlapped reasonably irrespective of metals tested, supporting the theoretical prediction from the BLM that f-survival relationships are comparable regardless of metal species. The authors thus developed the generalized linear mixed model based on all data pooled across the single-metal tests. The best-fitted model well predicted the survival responses observed in mixture tests (r = 0.97), providing support for the applicability of the BLM to predict effects of metal mixtures.

Setting the most robust effluent level under severe uncertainty: application of information-gap decision theory to chemical management.

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals.