A compartmental model for Schistosoma japonicum transmission dynamics in the Philippines.

Schistosomiasis is a chronic and debilitating neglected tropical disease (NTD), second only to malaria as one of the most devastating parasitic diseases. Caused by a parasitic flatworm of the genus Schistosoma, infection occurs when skin comes in contact with contaminated freshwater that contains schistosome-hosting snails. The disease continues to be endemic in many regions of the Philippines, where it poses a significant public health challenge due to a lack of healthcare resources. In the Philippines, additional mammalian reservoirs for the S. japonicum parasite, especially bovines such as carabaos, also facilitate the spread of schistosomiasis. We extend existing compartment models to include human, snail, bovine, and free-living Schistosoma for a comprehensive look at the transmission dynamics of the disease. Sensitivity analysis of model parameters shows that the carabaos themselves can sustain the endemicity of schistosomiasis. Thus, we consider the control method of farming mechanization to avoid contaminated freshwater sources. We find that a reduction of contaminated water contacts by at least 77% will break the transmission cycle and eliminate the disease. However, reducing the contact by about 70% will still result in decrease of human schistosomiasis prevalence to under 1% in 15 years or less. Achieving such high reduction of contact rates could be a daunting task, especially in rural areas. Still, the potential to eliminate or at least reduce the schistosomiasis prevalence should be considered an additional benefit of mechanization efforts in the Philippines.

Epidemiological Model for Conventional Tobacco Control Measures and Tobacco Endgame Policies.

Epidemiological models, also known as host-agent-vector-environment models, are utilized in public health to gain insights into disease occurrence and to formulate intervention strategies. In this paper, we propose an epidemiological model that incorporates both conventional measures and tobacco endgame policies. Our model suggests that conventional measures focus on relationships among agent-vector-host-environment components, whereas endgame policies inherently aim to change or eliminate those components at a fundamental level. We also found that the vector (tobacco industry) and environment (physical and social surroundings) components were insufficiently researched or controlled by both conventional measures and tobacco endgame policies. The use of an epidemiological model for tobacco control and the tobacco endgame is recommended to identify areas that require greater effort and to develop effective intervention measures.

Dosimetric analysis of a compartmental model for radioligand uptake in tumor lesions.

Radioligand therapy is a targeted cancer therapy that delivers radiation to tumor cells based on the expression of specific markers on the cell surface. It has become an important treatment option in metastasized neuroendocrine tumors and advanced prostate cancer. The analysis of absorbed doses in radioligand therapies has gained much attention and remains a challenging task due to individual pharmacokinetics. As an alternative to the often used sum of exponential functions in intra-therapeutic dosimetry, a basic compartmental model for the pharmacokinetics of radioligands is described and analyzed in this paper. In its simplest version, the model behavior is determined by the uptake capacity and the association constant and can be solved analytically. The model is extended with rates for excretion from the source compartment and externalization from the lesion compartment. Numerical calculations offer an insight into the quantitative effects of the model parameters on the absorbed dose in the tumor lesion. This analysis helps understanding the importance of clinically relevant factors, e.g. the effect on absorbed doses of modified radioligands that bind to albumin. Using clinical data, the potential application in intra-therapeutic dosimetry is illustrated and compared to the bi-exponential function which lacks a mechanistical basis. While the compartmental model is found to constitute a feasible alternative in these examples, this has to be confirmed by further clinical studies.

Multi compartmental 3D breast cancer disease model-recapitulating tumor complexity in in-vitro.

Breast cancer is the most common ailment among women. In 2020, it had the highest incidence of any type of cancer. Many Phase II and III anti-cancer drugs fail due to efficacy, durability, and side effects. Thus, accelerated drug screening models must be accurate. In-vivo models have been used for a long time, but delays, inconsistent results, and a greater sense of responsibility among scientists toward wildlife have led to the search for in-vitro alternatives. Stromal components support breast cancer growth and survival. Multi-compartment Transwell models may be handy instruments. Co-culturing breast cancer cells with endothelium and fibroblasts improves modelling. The extracellular matrix (ECM) supports native 3D hydrogels in natural and polymeric forms. 3D Transwell cultured tumor spheroids mimicked in-vivo pathological conditions. Tumor invasion, migration, Trans-endothelial migration, angiogenesis, and spread are studied using comprehensive models. Transwell models can create a cancer niche and conduct high-throughput drug screening, promising future applications. Our comprehensive shows how 3D in-vitro multi compartmental models may be useful in producing breast cancer stroma in Transwell culture.

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.

Epidemiological model for the construction of scenarios of the dissemination of COVID-19 in Codó-MA / Modelo epidemiológico para construção de cenários da disseminação da COVID-19 em Codó-MA / Modelo epidemiológico para la construcción de escenarios de la diseminación del COVID-19 en Codó-MA

Background and objectives: due to the increase in the number of cases of the new coronavirus in the city of Codó-MA, there was a need to carry out a study on the spread of COVID-19 in the municipality in order to have a better knowledge and understanding of the problem. A study was carried out on the spread of COVID-19 in the city of Codó-MA, comparing the quantitative data on the number of cases in 2020 and 2021 between May and July and using the epidemiological model Susceptible-Infectious-Isolated-Recovered (SIQR). Methods: we collected daily data from the epidemiological bulletins made available by the Municipal Health Department of Codó (SEMUS-Codó), we chose the SIQR compartmental model to carry out the simulations, we assumed hypotheses and estimated the parameters in order to design the scenarios. We simulated scenarios such as social distancing of healthy individuals and social isolation of infected individuals. Results: in early 2020, cases increased more frequently than in early 2021, and approximately 20% of those infected were in social isolation. According to projections, more than 80% of cases of COVID-19 were not accounted for in Codó. In 2021, there was greater underreporting than in 2020, approximately 82% and 85%, respectively. Conclusion: from the results, the authors conclude that the social isolation of those infected is a more efficient method to contain an epidemic than the total blockade of the population and that the high number of underreported cases is because most of these cases are asymptomatic.(AU)

Justificativa e objetivos: devido ao aumento do número de casos do novo coronavírus na cidade de Codó-MA, viu-se a necessidade para fazer um estudo sobre a propagação da COVID-19 no município para a ter melhor conhecimento e entendimento do problema. Foi realizado um estudo sobre a disseminação da COVID-19 na cidade de Codó-MA, sendo comparados os dados quantitativos dos números de casos nos anos de 2020 e 2021 entre os meses de maio e julho e utilizando o modelo epidemiológico Suscetíveis-Infecciosos-Isolados-Recuperados (SIQR). Métodos: coletamos os dados diários dos boletins epidemiológicos disponibilizados pela Secretaria Municipal de Saúde de Codó (SEMUS- Codó), escolhemos o modelo compartimental SIQR para a realização das simulações, supomos hipóteses e estimamos os parâmetros para podermos projetar os cenários. Simulamos cenários, tais como distanciamento social dos indivíduos sadios e isolamento social dos indivíduos infectados. Resultados: no início de 2020, os casos aumentaram com mais frequência do que no início de 2021, e aproximadamente 20% dos infectados estavam em isolamento social. De acordo com as projeções, mais de 80% dos casos de COVID-19 não foram contabilizados em Codó. Em 2021, houve maior subnotificação do que em 2020, aproximadamente 82% e 85%, respectivamente. Conclusão: a partir dos resultados, os autores concluem que o isolamento social dos infectados é um método mais eficiente para conter uma epidemia do que o bloqueio total da população e que o alto número de casos subnotificados são porque a maioria desses casos são assintomáticos.(AU)

Antecedentes y objetivos debido al incremento en el número de casos del nuevo coronavirus en la ciudad de Codó-MA, surgió la necesidad de realizar un estudio sobre la propagación del COVID-19 en el municipio con el fin de tener un mejor conocimiento y comprensión de el problema. Se realizó un estudio sobre la propagación del COVID-19 en la ciudad de Codó-MA, comparando datos cuantitativos del número de casos en 2020 y 2021 entre mayo y julio y utilizando el modelo epidemiológico Susceptible-Infeccioso-Aislado-Recuperado (SIQR). Métodos: recolectamos datos diarios de los boletines epidemiológicos que pone a disposición la Secretaría Municipal de Salud de Codó (SEMUS-Codó), elegimos el modelo compartimental SIQR para realizar las simulaciones, asumimos hipótesis y estimamos los parámetros para poder diseñar los escenarios. Simulamos escenarios como el distanciamiento social de personas sanas y el aislamiento social de personas infectadas. Resultados: a principios de 2020, los casos aumentaron con más frecuencia que a principios de 2021, y aproximadamente el 20% de los infectados se encontraban en aislamiento social. Según proyecciones, en Codó no se contabilizaron más del 80% de los casos de COVID-19. En 2021 hubo mayor subregistro que en 2020, aproximadamente 82% y 85%, respectivamente. Conclusión: de los resultados, los autores concluyen que el aislamiento social de los contagiados es un método más eficiente para contener una epidemia que el bloqueo total de la población y que el alto número de casos subregistrados se debe a que la mayoría de estos casos son asintomáticos.(AU)

An Epidemiological Model to Estimate the Prevalence of Diffuse Large B-Cell Lymphoma in the United States.

BACKGROUND: Prevalence is reflective of disease incidence and survival, and defined as the number of patients living with active disease. In diseases such as diffuse large B-cell lymphoma (DLBCL) with treatments with curative potential, a proportion of patients are cured, leading to a need for accurate, contemporary estimates of DLBCL prevalence to gauge the impact of the rapidly emerging treatment landscape. METHODS: Data from Surveillance, Epidemiology, and End Results (SEER) from 2000-2018 were utilized to develop an epidemiological model of incidence, survival, and cure, to estimate the current prevalent DLBCL population requiring active management in the United States (US). A variety of estimates were explored regarding cure rate and timing, based on a companion analysis of MarketScan data for treatment patterns and survival in incident DLBCL patients, and conditional survival analysis of SEER data. RESULTS: Across scenarios, with estimated cure ranging from 52.8% and 68.9%, and timing of cure ranging from 1 and 20 years post diagnosis, the estimated prevalence ranged from 63,883 to 142,889. With an assumption of no cure, estimated prevalence was 179,475. DISCUSSION: Prevalence estimates of DLBCL varied almost 3-fold, depending on specific cure adjustments made. Further understanding of DLBCL prevalence, for newly diagnosed and relapsed and/or refractory disease, is important to characterize the impact of emerging treatment options and related health care burden.

Deep learning via LSTM models for COVID-19 infection forecasting in India.

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

Modeling for COVID-19 college reopening decisions: Cornell, a case study.

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

Vancomycin pharmacokinetic parameters in patients undergoing hematopoietic stem cell transplantation.

INTRODUCTION: Current guidelines on vancomycin dosing lack specific recommendations about its dosing in hematopoietic stem cell transplant (HSCT) patients, the objective of the current study was to compare vancomycin pharmacokinetic variables in this population with those of general population. METHODS: A prospective study was designed and the calculated parameters of vancomycin pharmacokinetic were compared with individualized parameters. Two trough levels before 4th and 5th doses and a peak level after the 4th dose, were taken. All patients received a dose of 15 mg/kg of vancomycin two or three times a day. Pharmacokinetic parameters were calculated using a one compartmental model. The association between different variables and of acute kidney injury (AKI) development and achievement of target levels were also evaluated. RESULTS: A significant difference was observed between population Volume of distribution (Vd) and individualized Vd (mean 57.33 L vs 162.86 L, p value 0.019) and trough and peak levels (p values 0.0001 and 0.001; for mean trough and peak levels respectively). The achievement of the recommended trough levels and area under the concentration time curve per minimum inhibitory concentration (AUC24/MIC) was very low (5/71 and 24/71 patients respectively). No significant differences were observed between population and individualized clearance and rate of elimination of vancomycin (p values of 0.092 and 0.55 respectively). Concomitant receipt of cyclosporine was significantly related with development of AKI (p value 0.046). CONCLUSION: The dosing methods which use population-based pharmacokinetic variables does not result in desired therapeutic levels in HSCT patients, mainly because of larger vancomycin volume of distribution.

Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) models to accurately identify patients with HBV, using only easily obtained demographic data from a population-based data set. APPROACH AND RESULTS: We identified participants with data on HBsAg, birth year, sex, race/ethnicity, and birthplace from 10 cycles of the National Health and Nutrition Examination Survey (1999-2018) and divided them into two cohorts: training (cycles 2, 3, 5, 6, 8, and 10; n = 39,119) and validation (cycles 1, 4, 7, and 9; n = 21,569). We then developed and tested our two models. The overall cohort was 49.2% male, 39.7% White, 23.2% Black, 29.6% Hispanic, and 7.5% Asian/other, with a median birth year of 1973. In multivariable logistic regression, the following factors were associated with HBV infection: birth year 1991 or after (adjusted OR [aOR], 0.28; p < 0.001); male sex (aOR, 1.49; p = 0.0080); Black and Asian/other versus White (aOR, 5.23 and 9.13; p < 0.001 for both); and being USA-born (vs. foreign-born; aOR, 0.14; p < 0.001). We found that the ML model consistently outperformed the LR model, with higher area under the receiver operating characteristic values (0.83 vs. 0.75 in validation cohort; p < 0.001) and better differentiation of high- and low-risk persons. CONCLUSIONS: Our ML model provides a simple, targeted approach to HBV screening, using only easily obtained demographic data.

A Compartmental Model for the Iron Trafficking Across the Blood-Brain Barriers in Neurodegenerative Diseases.

Iron accumulation in the brain is supposed to play a central role in the induction of oxidative stress and consequently in neurodegeneration. The sensitive balance of iron in the brain is maintained by the brain barriers system, i.e., the blood-brain barrier between the blood and brain interstitial fluid and the blood-cerebrospinal fluid barrier between the blood and cerebrospinal fluid (CSF). In this work, we proposed a three-compartmental mathematical model simulating iron trafficking between blood, CSF, and cerebral space, describing the direction of fluxes based on the structural and functional characteristics of the brain barriers system. Different techniques of sensitivity analysis were used to evaluate the most important parameters, providing an indication for the most relevant biological functions that potentially affect the physiological transport of iron across brain barriers.

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.

Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease in China, Japan, and Korea. This study aimed to estimate the monthly SFTS occurrence and the monthly number of SFTS cases in the geographical area in Korea using epidemiological data including demographic, geographic, and meteorological factors. Important features were chosen through univariate feature selection. Two models using machine learning methods were analyzed: the classification model in machine learning (CMML) and regression model in machine learning (RMML). We developed a novel model incorporating the CMML results into RMML, defined as modified-RMML. Feature importance was computed to assess the contribution of estimating the number of SFTS cases using modified-RMML. Aspect to the accuracy of the novel model, the performance of modified-RMML was improved by reducing the MSE for the test data as 12.6-52.2%, compared to the RMML using five machine learning methods. During the period of increasing the SFTS cases from May to October, the modified-RMML could give more accurate estimation. Computing the feature importance, it is clearly observed that climate factors such as average maximum temperature, precipitation as well as mountain visitors, and the estimation of SFTS occurrence obtained from CMML had high Gini importance. The novel model incorporating CMML and RMML models improves the accuracy of the estimation of SFTS cases. Using the model, climate factors, including temperature, relative humidity, and mountain visitors play important roles in transmitting SFTS in Korea. Our findings highlighted that the guidelines for mountain visitors to prevent SFTS transmissions should be addressed. Moreover, it provides important insights for establishing control interventions that predict early identification of SFTS cases.

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.

Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model.

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys' prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys' prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys' prior.

A newly developed and externally validated non-clinical score accurately predicts 10-year cardiovascular disease risk in the general adult population.

Inclusion of clinical parameters limits the application of most cardiovascular disease (CVD) prediction models to clinical settings. We developed and externally validated a non-clinical CVD risk score with a clinical extension and compared the performance to established CVD risk scores. We derived the scores predicting CVD (non-fatal and fatal myocardial infarction and stroke) in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (n = 25,992, cases = 683) using competing risk models and externally validated in EPIC-Heidelberg (n = 23,529, cases = 692). Performance was assessed by C-indices, calibration plots, and expected-to-observed ratios and compared to a non-clinical model, the Pooled Cohort Equation, Framingham CVD Risk Scores (FRS), PROCAM scores, and the Systematic Coronary Risk Evaluation (SCORE). Our non-clinical score included age, gender, waist circumference, smoking, hypertension, type 2 diabetes, CVD family history, and dietary parameters. C-indices consistently indicated good discrimination (EPIC-Potsdam 0.786, EPIC-Heidelberg 0.762) comparable to established clinical scores (thereof highest, FRS: EPIC-Potsdam 0.781, EPIC-Heidelberg 0.764). Additional clinical parameters slightly improved discrimination (EPIC-Potsdam 0.796, EPIC-Heidelberg 0.769). Calibration plots indicated very good calibration with minor overestimation in the highest decile of predicted risk. The developed non-clinical 10-year CVD risk score shows comparable discrimination to established clinical scores, allowing assessment of individual CVD risk in physician-independent settings.

Validation of a spatial agent-based model for Taenia solium transmission ("CystiAgent") against a large prospective trial of control strategies in northern Peru.

BACKGROUND: The pork tapeworm (Taenia solium) is a parasitic helminth that imposes a major health and economic burden on poor rural populations around the world. As recognized by the World Health Organization, a key barrier for achieving control of T. solium is the lack of an accurate and validated simulation model with which to study transmission and evaluate available control and elimination strategies. CystiAgent is a spatially-explicit agent based model for T. solium that is unique among T. solium models in its ability to represent key spatial and environmental features of transmission and simulate spatially targeted interventions, such as ring strategy. METHODS/PRINCIPAL FINDINGS: We validated CystiAgent against results from the Ring Strategy Trial (RST)-a large cluster-randomized trial conducted in northern Peru that evaluated six unique interventions for T. solium control in 23 villages. For the validation, each intervention strategy was replicated in CystiAgent, and the simulated prevalences of human taeniasis, porcine cysticercosis, and porcine seroincidence were compared against prevalence estimates from the trial. Results showed that CystiAgent produced declines in transmission in response to each of the six intervention strategies, but overestimated the effect of interventions in the majority of villages; simulated prevalences for human taenasis and porcine cysticercosis at the end of the trial were a median of 0.53 and 5.0 percentages points less than prevalence observed at the end of the trial, respectively. CONCLUSIONS/SIGNIFICANCE: The validation of CystiAgent represented an important step towards developing an accurate and reliable T. solium transmission model that can be deployed to fill critical gaps in our understanding of T. solium transmission and control. To improve model accuracy, future versions would benefit from improved data on pig immunity and resistance, field effectiveness of anti-helminthic treatment, and factors driving spatial clustering of T. solium infections including dispersion and contact with T. solium eggs in the environment.

Measuring the COVID-19 Mortality Burden in the United States : A Microsimulation Study.

BACKGROUND: Fully assessing the mortality burden of the COVID-19 pandemic requires measuring years of life lost (YLLs) and accounting for quality-of-life differences. OBJECTIVE: To measure YLLs and quality-adjusted life-years (QALYs) lost from the COVID-19 pandemic, by age, sex, race/ethnicity, and comorbidity. DESIGN: State-transition microsimulation model. DATA SOURCES: Health and Retirement Study, Panel Study of Income Dynamics, data on excess deaths from the Centers for Disease Control and Prevention, and nursing home death counts from the Centers for Medicare & Medicaid Services. TARGET POPULATION: U.S. population aged 25 years and older. TIME HORIZON: Lifetime. PERSPECTIVE: Individual. INTERVENTION: COVID-19 pandemic through 13 March 2021. OUTCOME MEASURES: YLLs and QALYs lost per 10 000 persons in the population. The estimates account for the age, sex, and race/ethnicity of decedents, along with obesity, smoking behavior, lung disease, heart disease, diabetes, cancer, stroke, hypertension, dementia, and nursing home residence. RESULTS OF BASE-CASE ANALYSIS: The COVID-19 pandemic resulted in 6.62 million QALYs lost (9.08 million YLLs) through 13 March 2021, with 3.6 million (54%) lost by those aged 25 to 64 years. The greatest toll was on Black and Hispanic communities, especially among men aged 65 years or older, who lost 1138 and 1371 QALYs, respectively, per 10 000 persons. Absent the pandemic, 38% of decedents would have had average or above-average life expectancies for their subgroup defined by age, sex, and race/ethnicity. RESULTS OF SENSITIVITY ANALYSIS: Accounting for uncertainty in risk factors for death from COVID-19 yielded similar results. LIMITATION: Estimates may vary depending on assumptions about mortality and quality-of-life projections. CONCLUSION: Beyond excess deaths alone, the COVID-19 pandemic imposed a greater life expectancy burden on persons aged 25 to 64 years, including those with average or above-average life expectancies, and a disproportionate burden on Black and Hispanic communities. PRIMARY FUNDING SOURCE: National Institute on Aging.

Strongly Divergent Impact of Adherence Patterns on Efficacy of Colorectal Cancer Screening: The Need to Refine Adherence Statistics.

INTRODUCTION: The performance of colorectal cancer (CRC) screening programs depends on the adherence to screening offers. However, identical adherence levels may result from varying patterns of the population's screening behavior. We quantified the effects of different adherence patterns on the long-term performance of CRC screening for annual fecal immunochemical testing and screening colonoscopy at 10-year intervals. METHODS: Using a multistate Markov model, we simulated scenarios where, while at the same overall adherence level, a certain proportion of the population adheres to all screening offers (selective adherence) or the entire population uses the screening offers at some point(s) of time, albeit not in the recommended frequency (sporadic adherence). Key outcomes for comparison were the numbers of prevented CRC cases and prevented CRC deaths after 50 simulated years. RESULTS: For screening with annual fecal immunochemical testing at adherence levels of 10%-50%, ratios of prevented CRC cases (CRC deaths) resulting from a sporadic vs a selective pattern ranged from 1.8 to 4.4 (1.9-5.3) for men and from 1.7 to 3.6 (1.8-4.4) for women, i.e., up to 4-5 times more CRC cases and deaths were prevented when the population followed a sporadic instead of a selective adherence pattern. Comparisons of simulated scenarios for screening colonoscopy revealed similar patterns. DISCUSSION: Over a lifelong time frame, large numbers of irregular screening attendees go along with much larger preventive effects than small numbers of perfectly adhering individuals. In clinical practice, efforts to reach as many people as possible at least sporadically should be prioritized over efforts to maximize adherence to repeat screening offers.