A Hybridized Stochastic SIR-Vasicek Model in Evaluating a Pandemic Emergency Financing Facility

In the context of the present global health crisis, we examine the design and valuation of a pandemic emergency financing facility (PEFF) akin to a catastrophe (CAT) bond. While a CAT bond typically enables fund generation to the insurers and re-insurers after a disaster happens, a PEFF or pandemic bond's payout is linked to random thresholds that keep evolving as the pandemic continues to unfold. The subtle difference in the timing and structure of the funding payout between the usual CAT bond and PEFF complicates the valuation of the latter. We address this complication, and our analysis identifies certain aspects in the PEFF's design that must be simplified and strengthened so that this financial instrument is able to serve the intent of its original creation. An extension of the compartmentalized deterministic epidemic model-which describes the random number of people in three classes: susceptible (S), infected (I), and removed (R) or SIR for short-to its stochastic analog is put forward. At time t, S(t), I(t), and R (t) satisfy a system of interacting stochastic differential equations in our extended framework. The payout is triggered when the number of infected people exceeds a predetermined threshold. A CAT-bond pricing setup is developed with the Vasicek-based financial risk factor correlated with the SIR dynamics for the PEFF valuation. The probability of a pandemic occurrence during the bond's term to maturity is calculated via a Poisson process. Our sensitivity analyses reveal that the SIR's disease transmission and recovery rates, as well as the interest rate's mean-reverting level, have a substantial effect on the bond price. Our proposed synthesized model was tested and validated using a Canadian COVID-19 dataset during the early development of the pandemic. We illustrate that the PEFF's payout could occur as early as seven weeks after the official declaration of the pandemic, and the deficiencies of the most recent PEFF sold by an international financial institution could be readily rectified.

Flattening the curve

We quantify attening the curve under the assumption of a soft quarantine in the spread of a contagious viral disease in a society. In particular, the maximum daily infection rate is expected to drop by twice the percentage drop in the virus reproduction number. The same percentage drop is expected for the maximum daily hospitalization or fatality rate. A formula for the expected maximum daily fatality rate is given. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Modeling the impact of the Omicron infection wave in Germany.

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Modeling the drivers of oscillations in COVID-19 data on college campuses.

PURPOSE: Incorporating human behavior in a disease model can explain the oscillations in COVID-19 data which occur more rapidly than can be explained by variants alone on college campuses. METHODS: Dampened oscillations emerge by supplementing a simple disease model with a risk assessment function, which depends on the current number of infected individuals in the student population and the institutional public health policies. After accounting for a rapid disease impulse due to social gatherings, we achieve sustained oscillations that follow the trend of 2020/2021 COVID-19 data as reported on the COVID-19 dashboards of US post-secondary institutions. RESULTS: This adjustment to the epidemiological model can provide an intuitive way of understanding rapid oscillations based on human risk perception and institutional policies. More risk-averse communities experience lower disease-level equilibria and less oscillations within the system, while communities that are less responsive to changes in the number of infected individuals exhibit larger amplitude and frequency of the oscillations. CONCLUSIONS: Community risk assessment plays an important role in COVID-19 management in college settings. Improving the ability of individuals to rapidly and conservatively respond to changes in community disease levels may help assist in self-regulating these oscillations to levels well below thresholds for emergency management.

Effect of Transmission and Vaccination on Time to Dominance of Emerging Viral Strains: A Simulation-Based Study.

We studied the effect of transmissibility and vaccination on the time required for an emerging strain of an existing virus to dominate in the infected population using a simulation-based experiment. The emergent strain is assumed to be completely resistant to the available vaccine. A stochastic version of a modified SIR model for emerging viral strains was developed to simulate surveillance data for infections. The proportion of emergent viral strain infections among the infected was modeled using a logistic curve and the time to dominance (TTD) was recorded for each simulation. A factorial experiment was implemented to compare the TTD values for different transmissibility coefficients, vaccination rates, and initial vaccination coverage. We discovered a non-linear relationship between TTD and the relative transmissibility of the emergent strain for populations with low vaccination coverage. Furthermore, higher vaccination coverage and high vaccination rates in the population yielded significantly lower TTD values. Vaccinating susceptible individuals against the current strain increases the susceptible pool of the emergent virus, which leads to the emergent strain spreading faster and requiring less time to dominate the infected population.

Modeling Cardiac SARS-CoV-2 Infection with Human Pluripotent Stem Cells.

Although SARS-CoV-2, the causative virus of the global COVID-19 pandemic, primarily affects the respiratory tract, it is now recognized to have broad multi-organ tropism and systemic effects. Early reports indicated that SARS-CoV-2 infection could lead to cardiac damage, suggesting the virus may directly impact the heart. Cardiac cell types derived from human pluripotent stem cells (hPSCs) enable mechanistic interrogation of SARS-CoV-2 infection in human cardiac tissue. PURPOSE OF REVIEW: To review the studies published since the emergence of the COVID-19 pandemic which utilize hPSCs and their cardiovascular derivative cell types to interrogate the tropism and effects of SARS-CoV-2 infection in the heart, as well as explore potential therapies. RECENT FINDINGS: Recent studies reveal that SARS-CoV-2 is capable of infecting and replicating within hPSC-derived cardiomyocytes and sinoatrial nodal cells, but not as extensively in their non-parenchymal counterparts. Additionally, they show striking viral effects on cardiomyocyte structure, transcriptional activity, and survival, along with potential mechanisms and therapeutic targets. Cardiac models derived from hPSCs are a viable platform to study the impact of SARS-CoV-2 on cardiac tissue and may lead to novel mechanistic insight as well as therapeutic interventions.

Quantifying Uncertainty in Mechanistic Models of Infectious Disease.

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

Intestinal organoids: A versatile platform for modeling gastrointestinal diseases and monitoring epigenetic alterations.

Considering the significant limitations of conventional 2D cell cultures and tissue in vitro models, creating intestinal organoids has burgeoned as an ideal option to recapitulate the heterogeneity of the native intestinal epithelium. Intestinal organoids can be developed from either tissue-resident adult stem cells (ADSs) or pluripotent stem cells (PSCs) in both forms induced PSCs and embryonic stem cells. Here, we review current advances in the development of intestinal organoids that have led to a better recapitulation of the complexity, physiology, morphology, function, and microenvironment of the intestine. We discuss current applications of intestinal organoids with an emphasis on disease modeling. In particular, we point out recent studies on SARS-CoV-2 infection in human intestinal organoids. We also discuss the less explored application of intestinal organoids in epigenetics by highlighting the role of epigenetic modifications in intestinal development, homeostasis, and diseases, and subsequently the power of organoids in mirroring the regulatory role of epigenetic mechanisms in these conditions and introducing novel predictive/diagnostic biomarkers. Finally, we propose 3D organoid models to evaluate the effects of novel epigenetic drugs (epi-drugs) on the treatment of GI diseases where epigenetic mechanisms play a key role in disease development and progression, particularly in colorectal cancer treatment and epigenetically acquired drug resistance.

The changing health impact of vaccines in the COVID-19 pandemic: A modeling study.

Much of the world's population had already been infected with COVID-19 by the time the Omicron variant emerged at the end of 2021, but the scale of the Omicron wave was larger than any that had come before or has happened since, and it left a global imprinting of immunity that changed the COVID-19 landscape. In this study, we simulate a South African population and demonstrate how population-level vaccine effectiveness and efficiency changed over the course of the first 2 years of the pandemic. We then introduce three hypothetical variants and evaluate the impact of vaccines with different properties. We find that variant-chasing vaccines have a narrow window of dominating pre-existing vaccines but that a variant-chasing vaccine strategy may have global utility, depending on the rate of spread from setting to setting. Next-generation vaccines might be able to overcome uncertainty in pace and degree of viral evolution.

Modeling of Respiratory Diseases Evolving with Fibrosis from Organoids Derived from Human Pluripotent Stem Cells.

Respiratory disease is one of the leading causes of morbidity and mortality worldwide. There is no cure for most diseases, which are treated symptomatically. Hence, new strategies are required to deepen the understanding of the disease and development of therapeutic strategies. The advent of stem cell and organoid technology has enabled the development of human pluripotent stem cell lines and adequate differentiation protocols for developing both airways and lung organoids in different formats. These novel human-pluripotent-stem-cell-derived organoids have enabled relatively accurate disease modeling. Idiopathic pulmonary fibrosis is a fatal and debilitating disease that exhibits prototypical fibrotic features that may be, to some extent, extrapolated to other conditions. Thus, respiratory diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or the one caused by SARS-CoV-2 may reflect some fibrotic aspects reminiscent of those present in idiopathic pulmonary fibrosis. Modeling of fibrosis of the airways and the lung is a real challenge due to the large number of epithelial cells involved and interaction with other cell types of mesenchymal origin. This review will focus on the status of respiratory disease modeling from human-pluripotent-stem-cell-derived organoids, which are being used to model several representative respiratory diseases, such as idiopathic pulmonary fibrosis, cystic fibrosis, chronic obstructive pulmonary disease, and COVID-19.

Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination.

In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.

Building Blood Vessel Chips with Enhanced Physiological Relevance

Blood vessel chips are bioengineered microdevices, consisting of biomaterials, human cells, and microstructures, which recapitulate essential vascular structure and physiology and allow a well-controlled microenvironment and spatial-temporal readouts. Blood vessel chips afford promising opportunities to understand molecular and cellular mechanisms underlying a range of vascular diseases. The physiological relevance is key to these blood vessel chips that rely on bioinspired strategies and bioengineering approaches to translate vascular physiology into artificial units. Here, several critical aspects of vascular physiology are discussed, including morphology, material composition, mechanical properties, flow dynamics, and mass transport, which provide essential guidelines and a valuable source of bioinspiration for the rational design of blood vessel chips. The state-of-art blood vessel chips are also reviewed that exhibit important physiological features of the vessel and reveal crucial insights into the biological processes and disease pathogenesis, including rare diseases, with notable implications for drug screening and clinical trials. It is envisioned that the advances in biomaterials, biofabrication, and stem cells improve the physiological relevance of blood vessel chips, which, along with the close collaborations between clinicians and bioengineers, enable their widespread utility. © 2023 Wiley-VCH GmbH.

Induced pluripotent stem cells: Generation methods and a new perspective in COVID-19 research.

Induced pluripotent stem cells (iPSCs) exhibit an unlimited ability to self-renew and produce various differentiated cell types, thereby creating high hopes for both scientists and patients as a great tool for basic research as well as for regenerative medicine purposes. The availability and safety of iPSCs for therapeutic purposes require safe and highly efficient methods for production of these cells. Different methods have been used to produce iPSCs, each of which has advantages and disadvantages. Studying these methods would be very helpful in developing an easy, safe, and efficient method for the generation of iPSCs. Since iPSCs can be generated from somatic cells, they can be considered as valuable cellular resources available for important research needs and various therapeutic purposes. Coronavirus disease 2019 (COVID-19) is a disease that has endangered numerous human lives worldwide and currently has no definitive cure. Therefore, researchers have been rigorously studying and examining all aspects of COVID-19 and potential treatment modalities and various drugs in order to enable the treatment, control, and prevention of COVID-19. iPSCs have become one of the most attractive and promising tools in this field by providing the ability to study COVID-19 and the effectiveness of drugs on this disease outside the human body. In this study, we discuss the different methods of generation of iPSCs as well as their respective advantages and disadvantages. We also present recent applications of iPSCs in the study and treatment of COVID-19.

Induced Pluripotent Stem Cell-Derived Organoids: Their Implication in COVID-19 Modeling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a significant global health issue. This novel virus's high morbidity and mortality rates have prompted the scientific community to quickly find the best COVID-19 model to investigate all pathological processes underlining its activity and, more importantly, search for optimal drug therapy with minimal toxicity risk. The gold standard in disease modeling involves animal and monolayer culture models; however, these models do not fully reflect the response to human tissues affected by the virus. However, more physiological 3D in vitro culture models, such as spheroids and organoids derived from induced pluripotent stem cells (iPSCs), could serve as promising alternatives. Different iPSC-derived organoids, such as lung, cardiac, brain, intestinal, kidney, liver, nasal, retinal, skin, and pancreatic organoids, have already shown immense potential in COVID-19 modeling. In the present comprehensive review article, we summarize the current knowledge on COVID-19 modeling and drug screening using selected iPSC-derived 3D culture models, including lung, brain, intestinal, cardiac, blood vessels, liver, kidney, and inner ear organoids. Undoubtedly, according to reviewed studies, organoids are the state-of-the-art approach to COVID-19 modeling.

Agent-Based Simulation of Social Determinants of Health for Equitable COVID-19 Intervention

In the COVID-19 pandemic, one-size-flts-all interventions have been implemented based on COVID-19 disease models which simulate disease spread on a more generalized scale, lacking specificity for communities in different settings. This approach, not considering the important local health indicators Social Determinants of Health (SDOH), renders inequities and disparity in intervention effectiveness at the local level. This research answers the following questions: how specific SDOH risk profiles impact COVID-19 outbreak severity and how should interventions be implemented to achieve net positive health impact? A novel agent-based disease model was developed using NetLogo to simulate COVID-19 transmission and intervention using relevant SDOH in specific localities. The model is fitted with COVID-19 variant-specific constants such as susceptibility, mortality rate, recovery time, incubation period, mask efficacy, vaccine efficacy, and reinfection rate. Those constants are further calibrated with SDOH such as healthcare access (vaccination and booster rates) and social context (population size, population density, racial profile, and age demographics). Model inputs also include intervention used (mask mandate, testing and isolation, lockdown) and compliance rate to such interventions. The model was validated in Westchester County, NY for two different time periods with Alpha and Omicron yielding 84.2% and 68.5% accuracy respectively. Sensitivity analysis demonstrated: 1) a higher elderly population, lower young population, lower vaccination rate, and higher Hispanic and Black population were all factors that increased outbreak severity;2) all variants had similar death rate after reaching ~25% of population vaccinated;and 3) boosters affected Omicron more than other variants, especially in reducing breakthroughs. Scenario analyses were conducted for four U.S. counties: Hunterdon, NJ;Levy, FL;Monterey, CA;and Coles, IL. These analyses showed that 1) informed interventions based on localities' SDOH would dramatically reduce inequity, 2) interventions have higher impact in localities with higher risk SDOH, and 3) weighing other health and social economic consequences against predicted COVID-19 mortality can achieve holistic equity. The model enables local officials to assess the type, intensity, and timing of interventions to achieve maximum health outcomes. They can weigh the benefits of interventions against the socioeconomic or other risks of inequity to local populations. This research empowers local officials in diverse settings with an accessible modeling tool to remain nimble, stay conscious of health disparities, and better focus limited resources in health related decisions for their communities. © 2022 IEEE.

Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.

Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. Methods: We estimated the effective reproduction number (R t ) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified. Findings: The model-estimated mean R t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R t could have been less than 1. Conclusion: Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.

Modeling the impact of child vaccination (5-11 y) on overall COVID-19 related hospitalizations and mortality in a context of omicron variant predominance and different vaccination coverage paces in Brazil.

Background: Developing countries have experienced significant COVID-19 disease burden. With the emergence of new variants, particularly omicron, the disease burden in children has increased. When the first COVID-19 vaccine was approved for use in children aged 5-11 years of age, very few countries recommended vaccination due to limited risk-benefit evidence for vaccination of this population. In Brazil, ranking second in the global COVID-19 death toll, the childhood COVID-19 disease burden increased significantly in early 2022. This prompted a risk-benefit assessment of the introduction and scaling-up of COVID-19 vaccination of children. Methods: To estimate the potential impact of vaccinating children aged 5-11 years with mRNA-based COVID-19 vaccine in the context of omicron dominance, we developed a discrete-time SEIR-like model stratified in age groups, considering a three-month time horizon. We considered three scenarios: No vaccination, slow, and maximum vaccination paces. In each scenario, we estimated the potential reduction in total COVID-19 cases, hospitalizations, deaths, hospitalization costs, and potential years of life lost, considering the absence of vaccination as the base-case scenario. Findings: We estimated that vaccinating at a maximum pace could prevent, between mid-January and April 2022, about 26,000 COVID-19 hospitalizations, and 4200 deaths in all age groups; of which 5400 hospitalizations and 410 deaths in children aged 5-11 years. Continuing vaccination at a slow/current pace would prevent 1450 deaths and 9700 COVID-19 hospitalizations in all age groups in this same time period; of which 180 deaths and 2390 hospitalizations in children only. Interpretation: Maximum vaccination of children results in a significant reduction of COVID-19 hospitalizations and deaths and should be enforced in developing countries with significant disease incidence in children. Funding: This manuscript was funded by the Brazilian Council for Scientific and Technology Development (CNPq - Process # 402834/2020-8).

Modeling COVID-19 incidence with Google Trends.

Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal "loss of smell," "loss of taste," "fever" (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.