Research on risk contagion mechanism of big fintech based on the SIRS model.

In recent years, while the relationship between the new financial institutions, represented by financial technology companies, and the traditional financial institutions(banks, securities, insurance, etc.) has been steadily enhanced, a New Relational Network has silently emerged. Along with the rapid expansion of big fintech companies, the possibility of financial risk breeding and spreading in the New Relational Network is also rising. This article analyzes and simulates the risk contagion mechanism of big fintech risks based on the SIRS epidemic model. The study's findings imply that: when the number of big fintech companies infected with risk exceeds the risk threshold, the big fintech risk will spread in the New Relational Network. At this time, the number of big fintech companies infected with risk can be reduced below the threshold by enhancing the risk warning, risk management, risk buffering and blocking capabilities, and timely improving risk prevention and control measures in the post-infection phase. It means that the big fintech risk is controlled. For big fintech risks, proactive interventions are more effective than post-incident response measures. This paper makes the following recommendations for preventing big fintech risks: creating a risk monitoring and early warning system to raise the Big Fintech companies' direct immunization rates; strengthening the big fintech companies' risk management and risk mitigation capabilities; enhancing the internal and external supervision to achieve sustainable development of big fintech companies.

Mathematical assessment of monkeypox disease with the impact of vaccination using a fractional epidemiological modeling approach.

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox.

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model.

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.

Modeling the future of HIV in Turkey: Cost-effectiveness analysis of improving testing and diagnosis.

AIMS: This study aimed to determine HIV incidence and prevalence in Turkey and to estimate the cost-effectiveness of improving testing and diagnosis in the next 20 years. BACKGROUND: HIV incidence in Turkey has been rapidly increasing in the last decade with a particularly high rate of infection for younger populations, which underscores the urgent need for a robust prevention program and improved testing capacity for HIV. METHODS: We developed a dynamic compartmental model of HIV transmission and progression among the Turkish population aged 15-64 and assessed the effect of improving testing and diagnosis. The model generated the number of new HIV cases by transmission risk and CD4 level, HIV diagnoses, HIV prevalence, continuum of care, the number of HIV-related deaths, and the expected number of infections prevented from 2020 to 2040. We also explored the cost impact of HIV and the cost-effectiveness of improving testing and diagnosis. RESULTS: Under the base case scenario, the model estimated an HIV incidence of 13,462 cases in 2020, with 63% undiagnosed. The number of infections was estimated to increase by 27% by 2040, with HIV incidence in 2040 reaching 376,889 and HIV prevalence 2,414,965 cases. Improving testing and diagnosis to 50%, 70%, and 90%, would prevent 782,789, 2,059,399, and 2,336,564 infections-32%, 85%, and 97% reduction in 20 years, respectively. Improved testing and diagnosis would reduce spending between $1.8 and $8.8 billion. CONCLUSIONS: In the case of no improvement in the current continuum of care, HIV incidence and prevalence will significantly increase over the next 20 years, placing a significant burden on the Turkish healthcare system. However, improving testing and diagnosis could substantially reduce the number of infections, ameliorating the public health and disease burden aspects.

Multiobjective optimization to assess dengue control costs using a climate-dependent epidemiological model.

Arboviruses, diseases transmitted by arthropods, have become a significant challenge for public health managers. The World Health Organization highlights dengue as responsible for millions of infections worldwide annually. As there is no specific treatment for the disease and no free-of-charge vaccine for mass use in Brazil, the best option is the measures to combat the vector, the Aedes aegypti mosquito. Therefore, we proposed an epidemiological model dependent on temperature, precipitation, and humidity, considering symptomatic and asymptomatic dengue infections. Through computer simulations, we aimed to minimize the amount of insecticides and the social cost demanded to treat patients. We proposed a case study in which our model is fitted with real data from symptomatic dengue-infected humans in an epidemic year in a Brazilian city. Our multiobjective optimization model considers an additional control using larvicide, adulticide, and ultra-low volume spraying. The work's main contribution is studying the monetary cost of the actions to combat the vector demand versus the hospital cost per confirmed infected, comparing approaches with and without additional control. Results showed that the additional vector control measures are cheaper than the hospital treatment without the vector control would be.

The patterns and driving forces of dengue invasions in China.

BACKGROUND: Global connectivity and environmental change pose continuous threats to dengue invasions from worldwide to China. However, the intrinsic relationship on introduction and outbreak risks of dengue driven by the landscape features are still unknown. This study aimed to map the patterns on source-sink relation of dengue cases and assess the driving forces for dengue invasions in China. METHODS: We identified the local and imported cases (2006-2020) and assembled the datasets on environmental conditions. The vector auto-regression model was applied to detect the cross-relations of source-sink patterns. We selected the major environmental drivers via the Boruta algorithm to assess the driving forces in dengue outbreak dynamics by applying generalized additive models. We reconstructed the internal connections among imported cases, local cases, and external environmental drivers using the structural equation modeling. RESULTS: From 2006 to 2020, 81,652 local dengue cases and 12,701 imported dengue cases in China were reported. The hotspots of dengue introductions and outbreaks were in southeast and southwest China, originating from South and Southeast Asia. Oversea-imported dengue cases, as the Granger-cause, were the initial driver of the dengue dynamic; the suitable local bio-socioecological environment is the fundamental factor for dengue epidemics. The Bio8 [odds ratio (OR) = 2.11, 95% confidence interval (CI): 1.67-2.68], Bio9 (OR = 291.62, 95% CI: 125.63-676.89), Bio15 (OR = 4.15, 95% CI: 3.30-5.24), normalized difference vegetation index in March (OR = 1.27, 95% CI: 1.06-1.51) and July (OR = 1.04, 95% CI: 1.00-1.07), and the imported cases are the major drivers of dengue local transmissions (OR = 4.79, 95% CI: 4.34-5.28). The intermediary effect of an index on population and economic development to local cases via the path of imported cases was detected in the dengue dynamic system. CONCLUSIONS: Dengue outbreaks in China are triggered by introductions of imported cases and boosted by landscape features and connectivity. Our research will contribute to developing nature-based solutions for dengue surveillance, mitigation, and control from a socio-ecological perspective based on invasion ecology theories to control and prevent future dengue invasion and localization.

The effect of medical innovation on the cost-effectiveness of Covid 19-related policies in the United States using a SIR model.

BACKGROUND: During 2020-21, the United States used a multifaceted approach to control SARS-CoV-2 (Covid-19) and reduce mortality and morbidity. This included non-medical interventions (NMIs), aggressive vaccine development and deployment, and research into more effective approaches to medically treat Covid-19. Each approach had both costs and benefits. The objective of this study was to calculate the Incremental Cost Effectiveness Ratio (ICER) for three major Covid-19 policies: NMIs, vaccine development and deployment (Vaccines), and therapeutics and care improvements within the hospital setting (HTCI). METHODS: To simulate the number of QALYs lost per scenario, we developed a multi-risk Susceptible-Infected-Recovered (SIR) model where infection and fatality rates vary between regions. We use a two equation SIR model. The first equation represents changes in the number of infections and is a function of the susceptible population, the infection rate and the recovery rate. The second equation shows the changes in the susceptible population as people recover. Key costs included loss of economic productivity, reduced future earnings due to educational closures, inpatient spending and the cost of vaccine development. Benefits included reductions in Covid-19 related deaths, which were offset in some models by additional cancer deaths due to care delays. RESULTS: The largest cost is the reduction in economic output associated with NMI ($1.7 trillion); the second most significant cost is the educational shutdowns, with estimated reduced lifetime earnings of $523B. The total estimated cost of vaccine development is $55B. HTCI had the lowest cost per QALY gained vs "do nothing" with a cost of $2,089 per QALY gained. Vaccines cost $34,777 per QALY gained in isolation, while NMIs alone were dominated by other options. HTCI alone dominated most alternatives, except the combination of HTCI and Vaccines ($58,528 per QALY gained) and HTCI, Vaccines and NMIs ($3.4 m per QALY gained). CONCLUSIONS: HTCI was the most cost effective and was well justified under any standard cost effectiveness threshold. The cost per QALY gained for vaccine development, either alone or in concert with other approaches, is well within the standard for cost effectiveness. NMIs reduced deaths and saved QALYs, but the cost per QALY gained is well outside the usual accepted limits.

The impact of positive and negative information on SIR-like epidemics in delayed multiplex networks.

In order to better study the interaction between epidemic propagation and information diffusion, a new coupling model on multiplex networks with time delay is put forward in this paper. One layer represents the information diffusion about epidemics. There is not only information about the positive prevention of infectious diseases but also negative preventive information. Meanwhile, the dissemination of information at this layer will be influenced by the mass media, which can convey positive and reliable preventive measures to help the public avoid exposure to contagion. The other layer represents the transmission of infectious diseases, and the public in this layer no longer only exchange information related to infectious diseases in the virtual social network like the information layer but spread infectious diseases through contact among people. The classical SIR model is used to model for epidemic propagation. Since each infected individual needs to spend enough time to recover, the infected one at one time does not necessarily change to the recovered one at the next time, so time delay is an essential factor to be considered in the model. Based on the microscopic Markov chain approach, this paper obtains an explicit expression for epidemic threshold in the two-layered multiplex networks with time delay, which reveals some main factors affecting epidemic threshold. In particular, the time delay has a noticeable effect on the epidemic threshold to some extent. Finally, the influence of these main factors on the epidemic threshold and their interaction are proved through numerical simulations.

Data needs for integrated economic-epidemiological models of pandemic mitigation policies.

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has led to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.

Worker Protection Scenarios for General Analytical Testing Facility under Several Infection Propagation Risks: Scoping Review, Epidemiological Model and ISO 31000.

Infectious disease is a risk threating industrial operations and worker health. In gastrointestinal disease cases, outbreak is sporadic, and propagation is often terminated within certain populations, although cases in industrial sites are continuously reported. The ISO 31000 international standard for risk management, an epidemiological triad model, and a scoping review were the methods used to establish response procedures (scenarios) to protect workers from the risk of the propagation of a gastrointestinal disease. First, human reservoirs and transmission routes were identified as controllable risk sources based on a scoping review and the use of a triad model. Second, the possibility of fomite- or surface-mediated transmission appeared to be higher based on environmental characterization. Thus, the propagation could be suppressed using epidemiological measures categorized by reservoirs (workers) or transmission routes during a primary case occurrence. Next, using results of a matrix, a strengths-weaknesses-opportunities-threats analysis and a scoping review, the risk treatment option was determined as risk taking and sharing. According to epidemiology of gastrointestinal infections, systematic scenarios may ensure the efficacy of propagation control. Standardized procedures with practicality and applicability were established for categorized scenarios. This study converged ISO 31000 standards, an epidemiological model, and scoping review methods to construct a risk management scenario (non-pharmaceutical intervention) optimized for the unique characteristics of a specific occupational cluster.

Epidemiological model based periodic intervention policies for COVID-19 mitigation in the United Kingdom.

As the UK, together with numerous countries in the world, moves towards a new phase of the COVID-19 pandemic, there is a need to be able to predict trends in sufficient time to limit the pressure faced by the National Health Service (NHS) and maintain low hospitalisation levels. In this study, we explore the use of an epidemiological compartmental model to devise a periodic adaptive suppression/intervention policy to alleviate the pressure on the NHS. The proposed model facilitates the understanding of the progression of the specific stages of COVID-19 in communities in the UK including: the susceptible population, the infected population, the hospitalised population, the recovered population, the deceased population, and the vaccinated population. We identify the parameters of the model by relying on past data within the period from 1 October 2020 to 1 June 2021. We use the total number of hospitalised patients and the fraction of those infected who are being admitted to hospital to develop adaptive policies: these modulate the recommended level of social restriction measures and realisable vaccination target adjustments. The analysis over the period 1 October 2020 to 1 June 2021 demonstrates our periodic adaptive policies have the potential to reduce the hospitalisation by 58% on average per month. In a further prospective analysis over the period August 2021 to May 2022, we analyse several future scenarios, characterised by the relaxation of restrictions, the vaccination ineffectiveness and the gradual decay of the vaccination-induced immunity within the population. In addition, we simulate the surge of plausible variants characterised by an higher transmission rate. In such scenarios, we show that our periodic intervention is effective and able to maintain the hospitalisation rate to a manageable level.

Mitigating an Epidemic of Resistance with Integrated Disease Management Tactics: Conflicting Management Recommendations from Insecticide Resistance and Epidemiological Models.

Insect-transmitted plant pathogens threaten crop production worldwide. Because a single feeding bout may be sufficient for a vector to transmit a pathogen that kills the plant, treatment thresholds for vectors of plant pathogens are low. For many vector species, overreliance on chemical controls has resulted in evolution of insecticide resistance. Analysis of complementary insecticide resistance and epidemiological models indicated that tactics for delaying resistance evolution conflict with tactics for limiting pathogen spread. Insecticide resistance models support maintaining untreated refuges that serve as a source of susceptible insects that reduce the likelihood of mating among rare resistant insects. In contrast, epidemiological models indicate that movement of vectors from untreated areas to insecticide-treated areas contributes to pathogen spread. Accordingly, epidemiological models support area-wide insecticide spray programs, although resistance models indicate that such an approach is likely to lead to rapid resistance. To mitigate risk of insecticide resistance, additional management approaches must be integrated into plant disease management strategies. The resistance and epidemiological models were used to evaluate effects of integrating application of insecticides with two additional management strategies: deployment of partially resistant plants (plants that are not immune to infection but have lower acquisition and inoculation rates than susceptible plants) and mating disruption (reduced vector birth rate in mating disruption-treated areas). Deployment of partially resistant plants reduced the risk that untreated areas served as a source of inoculative vectors. Mating disruption reduced the risk of resistance by suppressing growth of insecticide-resistant populations and benefited disease management by reducing vector abundance. Simulation results indicated that by targeting multiple aspects of the plant-pathogen-vector system, pathogen spread could be suppressed and resistance delayed. Implementation of such an approach will require innovations in vector control and sustained efforts in plant breeding.

Return to normal pre-COVID-19 life is delayed by inequitable vaccine allocation and SARS-CoV-2 variants.

As a result of the COVID-19 pandemic, whether and when the world can reach herd immunity and return to normal life and a strategy for accelerating vaccination programmes constitute major concerns. We employed Metropolis-Hastings sampling and an epidemic model to design experiments based on the current vaccinations administered and a more equitable vaccine allocation scenario. The results show that most high-income countries can reach herd immunity in less than 1 year, whereas low-income countries should reach this state after more than 3 years. With a more equitable vaccine allocation strategy, global herd immunity can be reached in 2021. However, the spread of SARS-CoV-2 variants means that an additional 83 days will be needed to reach global herd immunity and that the number of cumulative cases will increase by 113.37% in 2021. With the more equitable vaccine allocation scenario, the number of cumulative cases will increase by only 5.70% without additional vaccine doses. As SARS-CoV-2 variants arise, herd immunity could be delayed to the point that a return to normal life is theoretically impossible in 2021. Nevertheless, a more equitable global vaccine allocation strategy, such as providing rapid vaccine assistance to low-income countries/regions, can improve the prevention of COVID-19 infection even though the virus could mutate.

Qualitative and quantitative analysis of the COVID-19 pandemic by a two-side fractional-order compartmental model.

Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R0 can be obtained, which is helpful for estimating the severity of an outbreak qualitatively. Sensitivity analysis of R0 is performed to identify and rank key epidemiological parameters. Based on the real data of the United States, numerical tests reveal that the model with both left-hand side fractional derivative and right-hand side fractional integral terms has a better forecast ability for the epidemic trend in the next ten days. Our extensive computational results also quantitatively reveal that non-pharmaceutical interventions, such as isolation, stay at home, strict control of social distancing, and rapid testing can play an important role in preventing the pandemic of the disease. Thus, the two-side fractional models are proposed in this paper can successfully capture the change rule of COVID-19, which provide a strong tool for understanding and analyzing the trend of the outbreak.

The cost-effectiveness of social distancing measures for mitigating the COVID-19 pandemic in a highly-populated country: A case study in Indonesia.

BACKGROUND: As one of the strategies to mitigate the COVID-19 pandemic, social distancing (SD) measures are recommended to control disease spread and reduce the attack rate. Therefore, this study aims to estimate the costs and effects of SD measures through school closures, workforce, and community contact reductions for mitigating the COVID-19 pandemic in Indonesia. METHODS: Two mitigation scenarios of SD for 1 month and continuous SD were compared with the baseline (no intervention). A modified Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model accounting for disease spread during the latent period was applied by considering a 1-year time horizon. The costs of healthcare, school closures, and productivity loss due to disease as well as intervention were considered to estimate the total pandemic cost among all scenarios. RESULTS: In a comparison with the baseline, the result showed that total savings in scenarios of SD for 1 month and continuous SD was approximately $415 billion and $699 billion, respectively, while the averted deaths were 4.6 million and 8.5 million, respectively. Sensitivity analysis showed that basic reproduction number, infectious period, daily wage, incubation period, daily ICU admission cost, and case fatality rate were the most influential parameters affecting the savings and the number of averted deaths. CONCLUSIONS: SD measures through school closures, workforce, and community contact reductions were concluded to be cost-saving. Increasing the duration of social distancing tends to increase both the savings and the number of averted deaths.

Modelling the cost-effectiveness of essential and advanced critical care for COVID-19 patients in Kenya.

BACKGROUND: Case management of symptomatic COVID-19 patients is a key health system intervention. The Kenyan government embarked to fill capacity gaps in essential and advanced critical care (ACC) needed for the management of severe and critical COVID-19. However, given scarce resources, gaps in both essential and ACC persist. This study assessed the cost-effectiveness of investments in essential and ACC to inform the prioritisation of investment decisions. METHODS: We employed a decision tree model to assess the incremental cost-effectiveness of investment in essential care (EC) and investment in both essential and ACC (EC +ACC) compared with current healthcare provision capacity (status quo) for COVID-19 patients in Kenya. We used a health system perspective, and an inpatient care episode time horizon. Cost data were obtained from primary empirical analysis while outcomes data were obtained from epidemiological model estimates. We used univariate and probabilistic sensitivity analysis to assess the robustness of the results. RESULTS: The status quo option is more costly and less effective compared with investment in EC and is thus dominated by the later. The incremental cost-effectiveness ratio of investment in essential and ACC (EC+ACC) was US$1378.21 per disability-adjusted life-year averted and hence not a cost-effective strategy when compared with Kenya's cost-effectiveness threshold (US$908). CONCLUSION: When the criterion of cost-effectiveness is considered, and within the context of resource scarcity, Kenya will achieve better value for money if it prioritises investments in EC before investments in ACC. This information on cost-effectiveness will however need to be considered as part of a multicriteria decision-making framework that uses a range of criteria that reflect societal values of the Kenyan society.

When and why direct transmission models can be used for environmentally persistent pathogens.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

Smart investment of virus RNA testing resources to enhance Covid-19 mitigation.

A variety of mitigation strategies have been employed against the Covid-19 pandemic. Social distancing is still one of the main methods to reduce spread, but it entails a high toll on personal freedom and economic life. Alternative mitigation strategies that do not come with the same problems but are effective at preventing disease spread are therefore needed. Repetitive mass-testing using PCR assays for viral RNA has been suggested, but as a stand-alone strategy this would be prohibitively resource intensive. Here, we suggest a strategy that aims at targeting the limited resources available for viral RNA testing to subgroups that are more likely than the average population to yield a positive test result. Importantly, these pre-selected subgroups include symptom-free people. By testing everyone in these subgroups, in addition to symptomatic cases, large fractions of pre- and asymptomatic people can be identified, which is only possible by testing-based mitigation. We call this strategy smart testing (ST). In principle, pre-selected subgroups can be found in different ways, but for the purpose of this study we analyze a pre-selection procedure based on cheap and fast virus antigen tests. We quantify the potential reduction of the epidemic reproduction number by such a two-stage ST strategy. In addition to a scenario where such a strategy is available to the whole population, we analyze local applications, e.g. in a country, company, or school, where the tested subgroups are also in exchange with the untested population. Our results suggest that a two-stage ST strategy can be effective to curb pandemic spread, at costs that are clearly outweighed by the economic benefit. It is technically and logistically feasible to employ such a strategy, and our model predicts that it is even effective when applied only within local groups. We therefore recommend adding two-stage ST to the portfolio of available mitigation strategies, which allow easing social distancing measures without compromising public health.

Pharmacokinetics and Safety of Single-Dose Amphotericin B Colloidal Dispersion in Healthy Chinese Subjects and Population Pharmacokinetic/Pharmacodynamic Analysis to Inform Clinical Efficacy in Invasive Infections Caused by Candida albicans.

PURPOSE: Amphotericin B colloidal dispersion (ABCD) is a less toxic formulation of amphotericin B for the treatment of invasive fungal infections. The pharmacokinetic (PK) profile and safety of a generic ABCD were investigated after a single dose (0.5 to 1.5 mg/kg) administered as an intravenous infusion in 30 healthy Chinese subjects. METHODS: PK data from healthy Chinese male subjects were applied for developing a population PK model to predict the PK profiles of standard doses (3 or 4 mg/kg) in patients. A 5000-time Monte Carlo simulation of AUC0-24/MIC target was implemented to determine the probability of target attainment (PTA) and cumulative fraction of response (CFR) under standard doses. FINDINGS: The PK profiles of intravenous administration of ABCD were best described by a 3-compartmental model with a time-varying clearance and a dose-dependent volume of distribution in the peripheral compartment. PK/pharmacodynamic (PK/PD) analysis revealed that 3 or 4 mg/kg ABCD once a day resulted in favorable CRF (>98%) with 2-log reduction of Candida albicans. A high PTA (>90%) was achieved at MIC ≤2 mg/L for the dosing regimen of ABCD 3 mg/kg and 4 mg/kg for MIC ≤4 mg/L. IMPLICATIONS: PK/PD analysis indicated that a favorable efficacy of ABCD could be reached at a dose of 3 or 4 mg/kg once daily for 14 to 28 days to treat invasive fungal infections caused by C albicans. ClinicalTrials.gov identifier: NCT03577509.