Due to the impact of COVID-19, publicly available risk-based tools are becoming increasingly popular. However, subject experts develop most of these tools without consulting end users. Thus, this study aimed to explore user's perceptions, vision, and guidance for microbial risk assessment tool development through focus groups. This tool was intended to assist school health staff in decision-making regarding school respiratory viral outbreaks. Partnering with a school district in the Tucson metropolitan area, we conducted three focus groups with school health staff to gather feedback on a risk tool prototype. We discussed the staff's vision for the tool, their feedback on tool capabilities and design, and how they could leverage tool output for informing decisions, advocating with administration, or educating parents, students, or staff. Focus groups were conducted at the district health office, and the transcripts were analyzed by two researchers using inductively informed themes. Thematic analysis revealed that a comprehensive microbial risk assessment tool must have the potential to manage large amounts of data, scope for incorporation of existing data management systems, have real-time data processing, and produce context-specific recommendations for advocacy. Risk tools can expand personalized risk assessment and management strategies. Directly engaging users will advance microbial risk assessment impact and implementation. In the context of schools, a collaborative, comprehensive, digital and real time microbial risk assessment tool is a timely demand by the school health staff to manage microbial risks.

Host-associated fecal indicator measurements can be coupled with quantitative microbial risk assessment to develop risk-based thresholds for recreational use of potential sewage-contaminated waters. These assessments require information on the relative concentrations of indicators and pathogens in discharged sewage, typically based on data collected from wastewater treatment plant influent samples. However, most untreated sewage releases occur from within the collection system itself (i.e. compromised sewer laterals, compromised gravity and force mains, sanitary sewer overflows), where these relationships may differ. This study therefore analyzed the concentrations of a selected reference pathogen (norovirus) and fecal indicator (HF183) in sewage samples from upper and lower segments of gravity sewage collection systems, wastewater pumpstations, and the influent and effluent of treatment plants, to characterize variability in their relative concentrations. Norovirus detection rates were lower and more variable in upper collection system samples due to the smaller population represented; whereas, HF183 was routinely detected at all sites with higher concentrations in the collection system compared to treatment plant influent, resulting in variable comparative relationships across sample locations (types). Mean HF183:NoV ratios ranged from 1.0 × 10 for sewer lateral samples to 7 × 10° for force main samples. Results were used to develop risk-based thresholds for HF183 based on estimated recreational exposure to norovirus following a release from each potential sewage source, with higher thresholds for treatment facility influent compared to forced mains, or effluent. Consequently, this approach can allow for the rapid application of potential risk-based thresholds for recreational water quality applications based on different types of sewage discharge events.

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL. These results are around 2.0 log units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.

SARS-CoV-2 has during the last 3 years mutated several dozen times. Most mutations in the newly formed variants have been chemically and thermodynamically characterized. New variants have been declared as variants under monitoring. The European Centre for Disease Prevention and Control has suggested the hypothesis that the new BN.1, CH.1.1 and XBC variants could have properties similar to those of VOC. Thermodynamic properties of new variants have been reported in this manuscript for the first time. Gibbs energy of biosynthesis, as the driving force for viral multiplication, is less negative for the new variants than for the earlier variants. This indicates that the virus has evolved towards decrease in pathogenicity, which leads to less severe forms of COVID-19.

From the perspectives of molecular biology, genetics and biothermodynamics, SARS-CoV-2 is the among the best characterized viruses. Research on SARS-CoV-2 has shed a new light onto driving forces and molecular mechanisms of viral evolution. This paper reports results on empirical formulas, biosynthesis reactions and thermodynamic properties of biosynthesis (multiplication) for the Zeta P.2, Eta B.1.525, Theta P.3, Kappa B.1.617.1, Iota B.1.526, Lambda C.37 and Mu B.1.621 variants of SARS-CoV-2. Thermodynamic analysis has shown that the physical driving forces for evolution of SARS-CoV-2 are Gibbs energy of biosynthesis and Gibbs energy of binding. The driving forces have led SARS-CoV-2 through the evolution process from the original Hu-1 to the newest variants in accordance with the expectations of the evolution theory.

RNA viruses exhibit a great tendency to mutate. Mutations occur in the parts of the genome that encode the spike glycoprotein and less often in the rest of the genome. This is why Gibbs energy of binding changes more than that of biosynthesis. Starting from 2019, the wild type that was labeled Hu-1 has during the last 3 years evolved to produce several dozen new variants, as a consequence of mutations. Mutations cause changes in empirical formulas of new virus strains, which lead to change in thermodynamic properties of biosynthesis and binding. These changes cause changes in the rate of reactions of binding of virus antigen to the host cell receptor and the rate of virus multiplication in the host cell. Changes in thermodynamic and kinetic parameters lead to changes in biological parameters of infectivity and pathogenicity. Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has been evolving towards increase in infectivity and maintaining constant pathogenicity, or for some variants a slight decrease in pathogenicity. In the case of Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants pathogenicity is identical as in the Omicron BA.2.75 variant. On the other hand, infectivity of the Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants is greater than those of previous variants. This will most likely result in the phenomenon of asymmetric coinfection, that is circulation of several variants in the population, some being dominant.

SARS-CoV-2 resembles the ancient mythical creature Hydra. Just like with the Hydra, when one head is cut, it is followed by appearance of two more heads, suppression of one SARS-CoV-2 variant causes appearance of newer variants. Unlike Hydra that grows identical heads, newer SARS-CoV-2 variants are usually more infective, which can be observed as time evolution of the virus at hand, which occurs through acquisition of mutations during time. The appearance of new variants is followed by appearance of new COVID-19 pandemic waves. With the appearance of new pandemic waves and determining of sequences, in the scientific community and general public the question is always raised of whether the new variant will be more virulent and more pathogenic. The two variants characterized in this paper, BA.5.2 and BF.7, have caused a pandemic wave during the late 2022. This paper gives full chemical and thermodynamic characterization of the BA.5.2 and BF.7 variants of SARS-CoV-2. Having in mind that Gibbs energy of binding and biosynthesis represent the driving forces for the viral life cycle, based on the calculated thermodynamic properties we can conclude that the newer variants are more infective than earlier ones, but that their pathogenicity has not changed.

Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2.

This paper reports, for the first time, standard Gibbs energies of binding of the BA.1, BA.2, BA.3, BA.2.13, BA.2.12.1 and BA.4 Omicron variants of SARS-CoV-2, to the Human ACE2 receptor. Variants BA.1 through BA.3 exhibit a trend of decreasing standard Gibbs energy of binding and hence increased infectivity. The BA.4 variant exhibits a less negative standard Gibbs energy of binding, but also more efficient evasion of the immune response. Therefore, it was concluded that all the analyzed strains evolve in accordance with expectations of the theory of evolution, albeit using different strategies.

From march 2020 to march 2022 covid-19 has shown a consistent pattern of increasing infections during the Winter and low infection numbers during the Summer. Understanding the effects of seasonal variation on covid-19 spread is crucial for future epidemic modelling and management. In this study, seasonal variation in the transmission rate of covid-19, was estimated based on an epidemic population model of covid-19 in Denmark, which included changes in national restrictions and introduction of the -variant covid-19 strain, in the period March 2020 - March 2021. Seasonal variation was implemented as a logistic temperature dependent scaling of the transmission rate, and parameters for the logistic relationship was estimated through rejection-based approximate bayesian computation (ABC). The likelihoods used in the ABC were based on national hospital admission data and seroprevalence data stratified into nine and two age groups, respectively. The seasonally induced reduction in the transmission rate of covid-19 in Denmark was estimated to be , (95% CI [ ; ]), when moving from peak Winter to peak Summer. The reducing effect of seasonality on transmission rate per C in daily average temperature were shown to vary based on temperature, and were estimated to be pr. 1  C around C; pr. 1  C around C; and pr. 1  C around a daily average temperature of 11  C.

Ebola virus is among the most dangerous, contagious and deadly etiological causes of viral diseases. However, Ebola virus has never extensively spread in human population and never have led to a pandemic. Why? The mechanistic biophysical model revealing the biothermodynamic background of virus-host interaction) could help us to understand pathogenesis of Ebola virus disease (earlier known as the Ebola hemorrhagic fever). In this paper for the first time the empirical formula, thermodynamic properties of biosynthesis (including the driving force of virus multiplication in the susceptible host), binding constant and thermodynamic properties of binding are reported. Thermodynamic data for Ebola virus were compared with data for SARS-CoV-2 to explain why SARS-CoV-2 has caused a pandemic, while Ebola remains on local epidemic level. The empirical formula of the Ebola virus was found to be CHONPS. Standard Gibbs energy of biosynthesis of the Ebola virus nucleocapsid is -151.59 kJ/C-mol.

Monkeypox (MPX) is a zoonotic infectious disease caused by (MPXV), an enveloped DNA virus belonging to the family and the genus. Since early May 2022, a growing number of human cases of Monkeypox have been reported in non-endemic countries, with no history of contact with animals imported from endemic and enzootic areas, or travel to an area where the virus usually circulated before May 2022. This qualitative risk assessment aimed to investigate the probability that MPXV transmission occurs through food during its handling and consumption. The risk assessment used "top-down" (based on epidemiological data) and "bottom-up" (following the agent through the food chain to assess the risk of foodborne transmission to human) approaches, which were combined. The "top-down" approach first concluded that bushmeat was the only food suspected as a source of contamination in recorded cases of MPXV, by contact or ingestion. The "bottom-up" approach then evaluated the chain of events required for a human to become ill after handling or consuming food. This approach involves several conditions: (i) the food must be contaminated with MPXV (naturally, by an infected handler or after contact with a contaminated surface); (ii) the food must contain viable virus when it reaches the handler or consumer; (iii) the person must be exposed to the virus and; (iv) the person must be infected after exposure. Throughout the risk assessment, some data gaps were identified and highlighted. The conclusions of the top-down and bottom-up approaches are consistent and suggest that the risk of transmission of MPXV through food is hypothetical and that such an occurrence was never reported. In case of contamination, cooking ( 12 min at 70°C) could be considered effective in inactivating in foods. Recommendations for risk management are proposed. To our knowledge, this is the first risk assessment performed on foodborne transmission of MPXV.

In this paper, for the first time, empirical formulas have been reported of the Delta and Omicron strains of SARS-CoV-2. The empirical formula of the Delta strain entire virion was found to be CHONPS, while its nucleocapsid has the formula CHONPS. The empirical formula of the Omicron strain entire virion was found to be CHONPS, while its nucleocapsid has the formula CHONPS. Based on the empirical formulas, standard thermodynamic properties of formation and growth have been calculated and reported for the Delta and Omicron strains. Moreover, standard thermodynamic properties of binding have been reported for Wild type (Hu-1), Alpha, Beta, Gamma, Delta and Omicron strains. For all the strains, binding phenomenological coefficients and antigen-receptor (SGP-ACE2) binding rates have been determined and compared, which are proportional to infectivity. The results show that the binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain. The Omicron strain is characterized by a greater infectivity, based on the epidemiological data available in the literature. The increased infectivity was explained in this paper using Gibbs energy of binding. However, no indications exist for decreased pathogenicity of the Omicron strain. Pathogenicity is proportional to the virus multiplication rate, while Gibbs energies of multiplication are very similar for the Delta and Omicron strains. Thus, multiplication rate and pathogenicity are similar for the Delta and Omicron strains. The lower number of severe cases caused by the Omicron strain can be explained by increased number of immunized people. Immunization does not influence the possibility of occurrence of infection, but influences the rate of immune response, which is much more efficient in immunized people. This leads to prevention of more severe Omicron infection cases.

During the COVID-19 pandemic, many statistical and epidemiological studies have been published, trying to predict the future development of the SARS-CoV-2 pandemic. However, it would be beneficial to have a specific, mechanistic biophysical model, based on the driving forces of processes performed during virus-host interactions and fundamental laws of nature, allowing prediction of future evolution of SARS-CoV-2 and other viruses. In this paper, an attempt was made to predict the development of the pandemic, based on biothermodynamic parameters: Gibbs energy of binding and Gibbs energy of growth. Based on analysis of biothermodynamic parameters of various variants of SARS-CoV-2, SARS-CoV and MERS-CoV that appeared during evolution, an attempt was made to predict the future directions of evolution of SARS-CoV-2 and potential occurrence of new strains that could lead to new pandemic waves. Possible new mutations that could appear in the future could lead to changes in chemical composition, biothermodynamic properties (driving forces of new virus strains) and biological properties of SARS CoV-2 that represent a risk for humanity.

Thermodynamic equilibrium models predict the infectivity of novel and emerging viruses using molecular data including the binding affinity of the virus to the host cell (as represented by the association constant K) and the probability, p, of the virus replicating after entry to the cell. Here those models are adapted based on the principles of ligand binding to macromolecules to assess the effect on virus infectivity of inhibitor molecules which target specific proteins of the virus. Three types of inhibitor are considered using the thermodynamic equilibrium model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the human lung with parameters for the strength and nature of the interaction between the target virus protein and the inhibitor molecule. The first is competitive inhibition of the SARS-CoV-2 spike glycoprotein (SGP) trimer binding to its human angiotensin converting enzyme 2 (ACE2) receptor by unfractionated heparin (UFH). Using a novel approach presented here, a value of K = 3.53 × 10 M is calculated for SARS-CoV-2 from the IC for inhibition by UFH of SARS-CoV-2 plaque formation in cell culture together with the dissociation constant K of 0.73 × 10 M reported for heparin binding to SARS-CoV-2 SGP trimer. Such a high K limits the effectiveness of competitive inhibitors such as UFH. The second is the attachment of a nanoparticle such as a zinc oxide tetrapod (ZnOT) to the virus shell as for herpes simplex virus (HSV). The increase in molecular weight through ZnOT attachment is predicted to decrease K by orders of magnitude by making the entropy change (ΔS) on immobilisation of the ZnOT:virus complex on cell binding more negative than for the virus alone. According to the model, ZnOT acts synergistically with UFH at the IC of 33 μg/cm which together decrease viral infectivity by 61,000-fold compared to the two-fold and three-fold decreases predicted for UFH alone at the IC and for ZnOT alone respectively. According to the model here, UFH alone at its peak deliverable dose to the lung of 1,000 μg/cm only decreases infectivity by 31-fold. Practicable approaches to target and decrease ΔS for respiratory viruses should therefore be considered. The combination of decreasing ΔS together with blocking the interaction of virus surface protein with its host cell receptor may achieve synergistic effects for faecal-oral viruses and HSV. The third is reversible noncompetitive inhibition of the viral main protease (M) for which the decrease in p is assumed to be proportional to the decrease in enzyme activity as predicted by enzyme kinetic equations for a given concentration of inhibitor which binds to M with dissociation constant K. Virologists reporting viral inhibition studies are urged to report the concentration of cells in the cell culture experiment as this is a key parameter in estimating K here.

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

There is a need to evaluate and minimize the risk of novel coronavirus infections at mass gathering events, such as sports. In particular, to consider how to hold mass gathering events, it is important to clarify how the local infection prevalence, the number of spectators, the capacity proportion, and the implementation of preventions affect the infection risk. In this study, we used an environmental exposure model to analyze the relationship between infection risk and infection prevalence, the number of spectators, and the capacity proportion at mass gathering events in football and baseball games. In addition to assessing risk reduction through the implementation of various preventive measures, we assessed how face-mask-wearing proportion affects infection risk. Furthermore, the model was applied to estimate the number of infectors who entered the stadium and the number of newly infected individuals, and to compare them with actual reported cases. The model analysis revealed an 86-95% reduction in the infection risk due to the implementation of face-mask wearing and hand washing. Under conditions in which vaccine effectiveness was 20% and 80%, the risk reduction rates of infection among vaccinated spectators were 36% and 96%, respectively. Among the individual measures, face-mask wearing was particularly effective, and the infection risk increased as the face-mask-wearing proportion decreased. A linear relationship was observed between infection risk at mass gathering events and the infection prevalence. Furthermore, the number of newly infected individuals was also dependent on the number of spectators and the capacity proportion independent of the infection prevalence, confirming the importance of considering spectator capacity in infection risk management. These results highlight that it is beneficial for organisers to ensure prevention compliance and to mitigate or limit the number of spectators according to the prevalence of local infection. Both the estimated and reported numbers of newly infected individuals after the events were small, below 10 per 3-4 million spectators, despite a small gap between these numbers.

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.

The 2020 Olympic/Paralympic Games have been postponed to 2021, due to the COVID-19 pandemic. We developed a model that integrated source-environment-receptor pathways to evaluate how preventive efforts can reduce the infection risk among spectators at the opening ceremony of Tokyo Olympic Games. We simulated viral loads of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emitted from infectors through talking/coughing/sneezing and modeled temporal environmental behaviors, including virus inactivation and transfer. We performed Monte Carlo simulations to estimate the expected number of newly infected individuals with and without preventive measures, yielding the crude probability of a spectator being an infector among the 60,000 people expected to attend the opening ceremony. Two indicators, i.e., the expected number of newly infected individuals and the newly infected individuals per infector entry, were proposed to demonstrate the extent of achievable infection risk reduction levels by implementing possible preventive measures. A no-prevention scenario produced 1.5-1.7 newly infected individuals per infector entry, whereas a combination of cooperative preventive measures by organizers and the spectators achieved a 99% risk reduction, corresponding to 0.009-0.012 newly infected individuals per infector entry. The expected number of newly infected individuals was calculated as 0.005 for the combination of cooperative preventive scenarios with the crude probability of a spectator being an infector of 1 × 10. Based on our estimates, a combination of cooperative preventions between organizers and spectators is required to prevent a viral spread at the Tokyo Olympic/Paralympic Games. Further, under the assumption that society accepts < 10 newly infected persons traced to events held during the entire Olympic/Paralympic Games, we propose a crude probability of infectors of < 5 × 10 as a benchmark for the suppression of the infection. This is the first study to develop a model that can assess the infection risk among spectators due to exposure pathways at a mass gathering event.

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

Driven by the quantitative estimate of risk via the dose-response models, quantitative microbial risk assessment has been used successfully for public health interventions. The dose-response models are derived starting from an average exposed dose of infectious particles, this dictates the of dose data units required. Then dose-response data from animal model experiments are used to optimize these mechanistic dose-response models. For hepatitis A (Hep-A), the only available dose-response data use grams of feces for dose units. Therefore, to develop a dose-response model for Hep-A a method of converting these doses in grams of feces into infectious particles, while accounting for the uncertainty of this conversion is needed. This research develops a method to couple data simulation with the likelihood estimation method for model optimization to accomplish this. This adapted method uses data simulation to model the doses as viral particles while accounting for the within-group variability of this simulation. Then these simulated doses, coupled with the original dose-response data, are used to optimize the mechanistic dose-response models. This method results in a more computationally rigorous means of modeling these types of dose-response data. The resulting dose-response model for Hep-A is also more appropriate to use than the current option for Hep-A risk models.