Population-level Models Research Articles

Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.

Heavy-tailed abundance distributions from stochastic Lotka-Volterra models.

Microbial communities found in nature are composed of many rare species and few abundant ones, as reflected by their heavy-tailed abundance distributions. How a large number of species can coexist in those complex communities and why they are dominated by rare species is still not fully understood. We show how heavy-tailed distributions arise as an emergent property from large communities with many interacting species in population-level models. To do so, we rely on generalized Lotka-Volterra models for which we introduce a global maximal capacity. This maximal capacity accounts for the fact that communities are limited by available resources and space. In a parallel ad hoc approach, we obtain heavy-tailed abundance distributions from logistic models, without interactions, through specific distributions of the parameters. We expect both mechanisms, interactions between many species and specific parameter distributions, to be relevant to explain the observed heavy tails.

Consistency of ranking was evaluated as new measure for prediction model stability: longitudinal cohort study

ObjectiveClinical risk prediction models are generally assessed on population level with a lack of measures that evaluate their stability at predicting risks of individual patients. This study evaluated the use of ranking as a measure to assess individual level stability between risk prediction models. Study Design and SettingA large patient cohort (3.66 million patients with 0.11 million cardiovascular events) extracted from the Clinical Practice Research Datalink was used in the exemplar of cardiovascular disease risk prediction. ResultsIt was found that 15 models (including machine learning and statistical models) had similar population-level model performance (C statistics about 0.88). For patients with high absolute risks, the models were more consistent in ranking of risk predictions (interquartile range (IQR) of differences in rank percentiles -0.6 to 1.0), but inconsistent in absolute risk (IQR of differences in absolute risk -18.8 to 9.0). At low risk, the reverse was true with inconsistent ranking but more consistent absolute risk. ConclusionConsistency of ranking of individual risk predictions is a useful measure to assess risk prediction models providing complementary information to absolute risk stability. Model developing guidelines including “TRIPOD” and “PROBAST” should incorporate ranking to assess individual level stability between risk prediction models.

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes

Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions. To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data. This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016. A random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions. Discrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars. This study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario. In this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.

Analytic Characterization of the Herpes Simplex Virus Type 2 Epidemic in the United States, 1950-2050.

Background We analytically characterized the past, present, and future levels and trends of the national herpes simplex virus type 2 (HSV-2) epidemic in the United States.MethodsA population-level mathematical model was constructed to describe HSV-2 transmission dynamics and was fitted to the data series of the National Health and Nutrition Examination Survey.ResultsOver 1950–2050, antibody prevalence (seroprevalence) increased rapidly from 1960, peaking at 19.9% in 1983 in those aged 15–49 years, before reversing course to decline to 13.2% by 2020 and 8.5% by 2050. Incidence rate peaked in 1971 at 11.9 per 1000 person-years, before declining by 59% by 2020 and 70% by 2050. Annual number of new infections peaked at 1 033 000 in 1978, before declining to 667 000 by 2020 and 600 000 by 2050. Women were disproportionately affected, averaging 75% higher seroprevalence, 95% higher incidence rate, and 71% higher annual number of infections. In 2020, 78% of infections were acquired by those 15–34 years of age.ConclusionsThe epidemic has undergone a major transition over a century, with the greatest impact in those 15–34 years of age. In addition to 47 million prevalent infections in 2020, high incidence will persist over the next 3 decades, adding >600 000 new infections every year.

The Comprehensive Communal Trauma Intervention Model (CCTIM), an Innovative Transdisciplinary Population-Level Model for Treating Trauma-Induced Illness and Mental Health in Global Vulnerable Communities: Palestine, a Case Study.

This paper explores how Western medicine may not fully understand and address post-traumatic stress disorder (PTSD) and other trauma-induced illnesses in a culturally appropriate manner in marginalized communities and offers a theoretical framework to develop comprehensive, effective, and sustainable solutions that comprehensively address and treat the trauma on both a collective and individual level. Focused on Palestinians, this paper discusses the collective trauma Palestinians experienced and how it manifests in transgenerational effects on the body and mind that may be post-traumatic stress disorder (PTSD) or perhaps another distinct condition that is yet to be codified in the Western medical lexicon. It describes local alternatives to Western medical diagnostic tools like the "ease to disease" diagnostic scale and the sociopolitical context-in this case, the Palestinian fight for karamah, or dignity-from which such alternatives arise. Based on these findings, a novel theoretical framework, the comprehensive communal trauma intervention model (CCTIM), a truly transdisciplinary population-level model for treating mental health in vulnerable communities globally, is proposed. It articulates the need to address the root cause of collective trauma, make modifications to the healthcare system, and cultivate strategic equity-oriented and research-based partnerships.

Estimation of the Basic Reproduction Number (R0) for COVID-19 Epidemic in District Level Named Rewa M.P. [INDIA

Objectives: COVID-19 was announced as a global pandemic by World Health Organization (WHO) owing to its highly contagious and pathogenicity that has been rapidly spreading throughout the world since its first reported outbreak in China in December, 2019.The aim of this study was to estimate the basic reproduction number (R0) and forecast the situation to understand the future transmission dynamics of the COVID-19 outbreak at a district level.Study design: Retrospective cohort study.Methods: We used two data sources for the study. Firstly, contact history of laboratory confirmed SARS-CoV-2 infected individuals & Secondly, the publicly available daily case count data were used together by applying approach named population level model (PLM) for estimating R0 and project the size of the epidemic over the next 365days as per SIR model with help of python.Results: The peak of the R0 = 2.3 was attained in the month of September 2020. Then subsequent reduction toward the end of September can be attributed to aggressive testing, more contact tracing, and isolation measures implemented in the Rewa district during that month so currently in month of November up to 22nd R0=1.8 reported according to our study.Conclusions: Combination of Public health measures such as social distancing, rigorous contact tracing & testing, and isolation of contacts and the vulnerable should be effective to reduce the instantaneous R0 value and could thereby reduce the final outbreak size.Funding Statement: There is no source of funding/ sponsor for this study.Declaration of Interests: There is no conflict of interest.Ethics Approval Statement: Ethical clearance was obtained from Institutional Ethical Committee.

Using epidemiological evidence to forecast population need for early treatment programmes in mental health: a generalisable Bayesian prediction methodology applied to and validated for first-episode psychosis in England.

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable. To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections. We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16-64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for 'suspected psychosis'. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels. A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623-8597; observed 8038, difference of 74 [0.92%]). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10383-11740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively. Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.

Projected Estimates of Opioid Mortality After Community-Level Interventions

The United States is experiencing a crisis of opioid overdose. In response, the US Department of Health and Human Services has defined a goal to reduce overdose mortality by 40% by 2022. To identify specific combinations of 3 interventions (initiating more people to medications for opioid use disorder [MOUD], increasing 6-month retention with MOUD, and increasing naloxone distribution) associated with at least a 40% reduction in opioid overdose in simulated populations. This decision analytical model used a dynamic population-level state-transition model to project outcomes over a 2-year horizon. Each intervention scenario was compared with the counterfactual of no intervention in simulated urban and rural communities in Massachusetts. Simulation modeling was used to determine the associations of community-level interventions with opioid overdose rates. The 3 examined interventions were initiation of more people to MOUD, increasing individuals' retention with MOUD, and increasing distribution of naloxone. Data were analyzed from July to November 2020. Reduction in overdose mortality, medication treatment capacity needs, and naloxone needs. No single intervention was associated with a 40% reduction in overdose mortality in the simulated communities. Reaching this goal required use of MOUD and naloxone. Achieving a 40% reduction required that 10% to 15% of the estimated OUD population not already receiving MOUD initiate MOUD every month, with 45% to 60%% retention for at least 6 months, and increased naloxone distribution. In all feasible settings and scenarios, attaining a 40% reduction in overdose mortality required that in every month, at least 10% of the population with OUD who were not currently receiving treatment initiate an MOUD. In this modeling study, only communities with increased capacity for treating with MOUD and increased MOUD retention experienced a 40% decrease in overdose mortality. These findings could provide a framework for developing community-level interventions to reduce opioid overdose death.

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales.

Assessing the long-term consequences of sub-lethal anthropogenic disturbance on wildlife populations requires integrating data on fine-scale individual behavior and physiology into spatially and temporally broader, population-level inference. A typical behavioral response to disturbance is the cessation of foraging, which can be translated into a common metric of energetic cost. However, this necessitates detailed empirical information on baseline movements, activity budgets, feeding rates and energy intake, as well as the probability of an individual responding to the disturbance-inducing stressor within different exposure contexts. Here, we integrated data from blue whales (Balaenoptera musculus) experimentally exposed to military active sonar signals with fine-scale measurements of baseline behavior over multiple days or weeks obtained from accelerometry loggers, telemetry tracking and prey sampling. Specifically, we developed daily simulations of movement, feeding behavior and exposure to localized sonar events of increasing duration and intensity and predicted the effects of this disturbance source on the daily energy intake of an individual. Activity budgets and movements were highly variable in space and time and among individuals, resulting in large variability in predicted energetic intake and costs. In half of our simulations, an individual's energy intake was unaffected by the simulated source. However, some individuals lost their entire daily energy intake under brief or weak exposure scenarios. Given this large variation, population-level models will have to assess the consequences of the entire distribution of energetic costs, rather than only consider single summary statistics. The shape of the exposure-response functions also strongly influenced predictions, reinforcing the need for contextually explicit experiments and improved mechanistic understanding of the processes driving behavioral and physiological responses to disturbance. This study presents a robust approach for integrating different types of empirical information to assess the effects of disturbance at spatio-temporal and ecological scales that are relevant to management and conservation.

Preventing Opioid Overdose: From Prediction to Operationalization

The opioid epidemic remains a significant public health challenge in the US. A catalyst for reducing the incidence of opioid-related harm could be the development and operationalization of patient risk stratification models. Prior work has focused on the statistical performance of such models, usually tailored to specific adverse events or sub-populations, without considering operational implications. A particular challenge in this context is predicting the most severe outcomes (fatal overdoses) due to imbalanced datasets. We develop a single population-level model for predicting a full range of adverse opioid-related events. We further explore the capacity requirements of interventions based on our model and address salient implementation trade-offs. We partner with Staten Island Performing Provider System to access claims data and electronic health records for the patient population on Staten Island. We develop a machine learning model for predicting adverse opioid-related outcomes. Subsequently, we conduct a rolling horizon analysis to evaluate the capacity requirements of intervention policies leveraging the model. Finally, we quantify the trade-off in predictive performance against concerns that arise in implementation, such as interpretability, data delay, and prediction horizon length. Our single model risk stratification framework achieves an Area Under the Receiver Operating characteristic Curve (AU-ROC) of 0.95, 0.87, 0.83 for the outcomes of any adverse opioid event, opioid overdose, and fatal opioid overdose, respectively. This model can be used to identify a small intervention cohort (1% of the highest-risk patients) which includes the majority (69%) of adverse opioid events. Our results suggest that predictive performance does not need to be sacrificed to satisfy implementation constraints. Accurate risk stratification models are operationally feasible for implementation, even in the absence of training data on fatal overdoses. The implementation of such models allows for targeted interventions with limited intervention capacity and in the presence of operational constraints.

Characterizing the type 2 diabetes mellitus epidemic in Jordan up to 2050

We aimed to characterize the type 2 diabetes mellitus (T2DM) epidemic and the role of key risk factors in Jordan between 1990–2050, and to forecast the T2DM-related costs. A recently-developed population-level T2DM mathematical model was adapted and applied to Jordan. The model was fitted to six population-based survey data collected between 1990 and 2017. T2DM prevalence was 14.0% in 1990, and projected to be 16.0% in 2020, and 20.6% in 2050. The total predicted number of T2DM cases were 218,326 (12,313 were new cases) in 1990, 702,326 (36,941 were new cases) in 2020, and 1.9 million (79,419 were new cases) in 2050. Out of Jordan’s total health expenditure, 19.0% in 1990, 21.1% in 2020, and 25.2% in 2050 was forecasted to be spent on T2DM. The proportion of T2DM incident cases attributed to obesity was 55.6% in 1990, 59.5% in 2020, and 62.6% in 2050. Meanwhile, the combined contribution of smoking and physical inactivity hovered around 5% between 1990 and 2050. Jordan’s T2DM epidemic is predicted to grow sizably in the next three decades, driven by population ageing and high and increasing obesity levels. The national strategy to prevent T2DM needs to be strengthened by focusing it on preventive interventions targeting T2DM and key risk factors.

Cost of Increasing Years‐of‐Life‐Gained (YLG) Using Fecal Immunochemical Testing as a Population‐Level Screening Model in a Rural Appalachian Population

Given the innovation of fecal immunochemical testing (FIT) to detect polyps in the rectum and colon for removal by colonoscopy, it is important to determine the cost per Life-Year Gained (LYG) when using FIT as a population-level screening model. This is particularly true for medically underserved rural populations. Accordingly, the purpose of this study was to make this determination among rural Appalachians experiencing isolation and economic challenges. The study occurred in an 8-county area of southeastern Kentucky. Kits were distributed to 1,424 residents. Seven hundred thirty-two kits (51.4%) were completed and returned. A Markov decision-analytic model was developed using PrecisionTree 7.6. Reactive test results occurred for 144 of the completed kits (19.7%). Thirty-seven colonoscopies were verified, with 15 of these indicating precancerous changes or actual cancer. Program costs were estimated at $461,952, with the average cost per person screened estimated at $324. Cost per LYG was $7,912. In contrast to an average cost per LYG of $17,200, our findings suggest a highly favorable cost-effectiveness ratio for this population of medically underserved rural residents. Cost-benefit analyses suggest that the screening program begins to yield positive net benefits at the stage when project recipients undergo colonoscopy, suggesting that this is the key step for behavioral intervention and intensified outreach.

Reactivation of latent infections with migration shapes population-level disease dynamics.

Annual migration is common across animal taxa and can dramatically shape the spatial and temporal patterns of infectious disease. Although migration can decrease infection prevalence in some contexts, these energetically costly long-distance movements can also have immunosuppressive effects that may interact with transmission processes in complex ways. Here, we develop a mechanistic model for the reactivation of latent infections driven by physiological changes or energetic costs associated with migration (i.e. 'migratory relapse') and its effects on disease dynamics. We determine conditions under which migratory relapse can amplify or reduce infection prevalence across pathogen and host traits (e.g. infectious periods, virulence, overwinter survival, timing of relapse) and transmission phenologies. We show that relapse at either the start or end of migration can dramatically increase prevalence across the annual cycle and may be crucial for maintaining pathogens with low transmissibility and short infectious periods in migratory populations. Conversely, relapse at the start of migration can reduce the prevalence of highly virulent pathogens by amplifying culling of infected hosts during costly migration, especially for highly transmissible pathogens and those transmitted during migration or the breeding season. Our study provides a mechanistic foundation for understanding the spatio-temporal patterns of relapsing infections in migratory hosts, with implications for zoonotic surveillance and understanding how infection patterns will respond to shifts in migratory propensity associated with environmental change. Further, our work suggests incorporating within-host processes into population-level models of pathogen transmission may be crucial for reconciling the range of migration-infection relationships observed across migratory species.

Engineering recurrent neural networks from task-relevant manifolds and dynamics.

Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.

The dynamics of trait variance in multi-species communities

In this paper, we establish the explicit connection between deterministic trait-based population-level models (in the form of partial differential equations) and species-level models (in the form of ordinary differential equations), in the context of eco-evolutionary systems. In particular, by starting from a population-level model of density distributions in trait space, we derive what amounts to an extension of the typical models at the species level known from adaptive dynamics literature, to account not only for abundance and mean trait values, but also explicitly for trait variances. Thus, we arrive at an explicitly polymorphic model at the species level. The derivations make precise the relationship between the parameters in the two classes of models and allow us to distinguish between notions of fitness on the population and species levels. Through a formal stability analysis, we see that exponential growth of an eigenvalue in the trait covariance matrix corresponds to a breakdown of the underlying assumptions of the species-level model. In biological terms, this may be interpreted as a speciation event: that is, we obtain an explicit notion of the blow-up of the variance of (possibly a linear combination of) traits as a precursor to speciation. Moreover, since evolutionary volatility of the mean trait value is proportional to trait variance, this provides a notion that species at the cusp of speciation are also the most adaptive. We illustrate these concepts and considerations using a numerical simulation.

Heterogeneous Projection of Disruptive Malware Prevalence in Mobile Social Networks

Segregating the latency phase from the actual disruptive phase of certain mobile malware grades offers more opportunities to effectively mitigate the viral spread in its early stages. Inspired by epidemiology, in this letter, a stochastic propagation model that accounts for infection latency of disruptive malware in both personal and spatial social links between constituent mobile network user pairs is proposed. To elucidate the true impact of unique user attributes on the virulence of the proposed spreading process, heterogeneity in transition rates is also considered in an approximated mean-field epidemic network model. Furthermore, derivations for the system equilibrium and stability analysis are provided. Simulation results showcase the viability of our model in contrasting between latent and disruptive infection stages with respect to a homogeneous population-level benchmark model.

Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles

Fall-related hip fractures are a major public health issue. While individual-level risk assessment tools exist, population-level predictive models could catalyze innovation in large-scale interventions. This study presents a hierarchical probabilistic model that predicts population-level hip fracture risk based on Factor of Risk (FOR) principles. Model validation demonstrated that FOR output aligned with a published dataset categorized by sex and hip fracture status. The model predicted normalized FOR for 100000 individuals simulating the Canadian older-adult population. Predicted hip fracture risk was higher for females (by an average of 38%), and increased with age (by15% per decade). Potential applications are discussed.

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.

Estimating costs and benefits of stroke management: A population-based simulation model

The paper demonstrates how a system dynamics approach can support strategic planning of health care services and can in particular help to balance cost-effectiveness considerations with budget impact considerations when assessing a comprehensive package of stroke care interventions in Singapore. A population-level system dynamics model is used to investigate 12 intervention scenarios based on six stroke interventions (a public information campaign, thrombolysis, endovascular therapy, acute stroke unit (ASU), out-of-hospital rehabilitation, and secondary prevention). Primary outcomes included cumulative discounted costs and quality-adjusted life years (QALYs) gained, as well as cumulative net monetary benefit by 2030. All intervention scenarios result in an increase in net monetary benefit by 2030; much of these gains were realized through improved post-acute care. Findings highlight the importance of coordination of care, and affirms the economic value of current stroke interventions.