Panel Data Analysis via Mechanistic Models

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences

Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.

Global Chronic obstructive pulmonary disease burden attributable to air pollution from 1990 to 2019

BACKGROUND

The disease burden attributable to chronic obstructive pulmonary disease (COPD) is significant worldwide. Some studies have linked exposure to air pollution to COPD, but there has been little research on this.

METHODS

We aimed to assess the COPD-related disease burden attributable to air pollution from multiple epidemiological perspectives. This study conducted a three-stage analysis. Firstly, we reported on the burden of disease worldwide in 2019 by different subgroups including sex, age, region, and country. Secondly, we studied the trends in disease burden from 1990 to 2019. Finally, we explored the association of some national indicators with disease burden to look for risk factors.

RESULTS

In 2019, the death number of COPD associated with air pollution accounted for 2.32% of the total global death, and the number of DALY accounted for 1.12% of the global DALY. From 1990 to 2019, the death number of COPD associated with air pollution increased peaked at 1.41 million in 1993, fluctuated, and then declined. We found the same temporal pattern of DALY. The corresponding age-standardized rates had been falling. At the same time, the burden of COPD associated with air pollution was also affected by some national indicators.

CONCLUSIONS

This study indicated that air pollution-related COPD contributed to a significant global disease burden. We called for health policymakers to take action and interventions targeting vulnerable countries and susceptible populations.

Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models

Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data are collected and reported. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally-efficient workflow we call Profile-Wise Analysis (PWA) that addresses all three steps in a unified way. Recently-developed methods for constructing 'profile-wise' prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters. We then demonstrate how to combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. Our three case studies illustrate practical aspects of the workflow, focusing on ordinary differential equation (ODE) mechanistic models with both Gaussian and non-Gaussian noise models. While the case studies focus on ODE-based models, the workflow applies to other classes of mathematical models, including partial differential equations and simulation-based stochastic models. Open-source software on GitHub can be used to replicate the case studies.

Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study

Circulation of seasonal influenza is the product of complex interplay among multiple drivers, yet characterizing the underlying mechanism remains challenging. Leveraging the diverse seasonality of A(H3N2) virus and abundant climatic space across regions in China, we quantitatively investigated the relative importance of population susceptibility, climatic factors, and antigenic change on the dynamics of influenza A(H3N2) through an integrative modelling framework. Specifically, an absolute humidity driven multiscale transmission model was constructed for the 2013/2014, 2014/2015 and 2016/2017 influenza seasons that were dominated by influenza A(H3N2). We revealed the variable impact of absolute humidity on influenza transmission and differences in the occurring timing and magnitude of antigenic change for those three seasons. Overall, the initial population susceptibility, climatic factors, and antigenic change explained nearly 55% of variations in the dynamics of influenza A(H3N2). Specifically, the additional variation explained by the initial population susceptibility, climatic factors, and antigenic change were at 33%, 26%, and 48%, respectively. The vaccination program alone failed to fully eliminate the summer epidemics of influenza A(H3N2) and non-pharmacological interventions were needed to suppress the summer circulation. The quantitative understanding of the interplay among driving factors on the circulation of influenza A(H3N2) highlights the importance of simultaneous monitoring of fluctuations for related factors, which is crucial for precise and targeted prevention and control of seasonal influenza.

Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics

The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.

Ambient high temperature exposure and global disease burden during 1990-2019: An analysis of the Global Burden of Disease Study 2019

BACKGROUND

A warming climate throughout the 21st century makes ambient high temperature exposure a major threat to population health worldwide. Mitigating the health impact of high temperature requires a timely, comprehensive and reliable assessment of disease burden globally, regionally and temporally.

AIM

Based on Global Burden of Disease (GBD) Study 2019, this study aimed to evaluate the disease burden attributable to high temperature from various epidemiology perspectives.

METHODS

A three-stage analysis was undertaken to investigate the number and age-standardized rates of death and disability-adjusted life years (DALY) attributable to high temperature from GBD Study 2019. First, we reported the high temperature-related disease burden for the whole world and for different groups by gender, age, region, country and disease. Second, we examined the temporal trend of the disease burden attributable to high temperature from 1990 to 2019. Finally, we explored if and how the high temperature-related disease burden was modified by a number of country-level indicators.

RESULTS

Globally, high temperature accounted for 0.54% of death and 0.46% of DALY in 2019, equating to the age-standardized rates of death and DALY (per 100,000 population) of 3.99 (95% uncertainty interval (UI): 2.88, 5.93) and 156.81 (95% UI: 107.98, 261.98), respectively. In 2019, the high temperature-related DALY and death rates were the highest for lower respiratory infections, although they showed a downward trend. In contrast, during 1990-2019, high temperature-related non-communicable diseases burden exhibited an upward trend. Meanwhile, the disease burden attributable to high temperature varied spatially, with the heaviest burden in regions with low sociodemographic index (SDI) and the lightest burden in regions with high SDI. In addition, high temperature-related disease burden appeared to be higher in a country with a higher population density and PM_2.5 concentration background but lower in a country with a higher density of greenness.

CONCLUSION

This study for the first time provided a comprehensive understanding of the global disease burden attributable to high temperature, underscoring the policy priority to protect human health worldwide in the context of global warming with particular attention to vulnerable countries or regions as well as susceptible population and diseases.

Synchronized nonpharmaceutical interventions for the control of COVID-19

The world is experiencing an ongoing pandemic of coronavirus disease-2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In attempts to control the pandemic, a range of nonpharmaceutical interventions (NPIs) has been implemented worldwide. However, the effect of synchronized NPIs for the control of COVID-19 at temporal and spatial scales has not been well studied. Therefore, a meta-population model that incorporates essential nonlinear processes was constructed to uncover the transmission characteristics of SARS-CoV-2 and then assess the effectiveness of synchronized NPIs on COVID-19 dynamics in China. Regional synchronization of NPIs was observed in China, and it was found that a combination of synchronized NPIs (the travel restrictions, the social distancing and the infection isolation) prevented 93.7% of SARS-CoV-2 infections. The use of synchronized NPIs at the time of the Wuhan lockdown may have prevented as much as 38% of SARS-CoV-2 infections, compared with the unsynchronized scenario. The interconnectivity of the epicenter, the implementation time of synchronized NPIs, and the number of regions considered all affected the performance of synchronized NPIs. The results highlight the importance of using synchronized NPIs in high-risk regions for the control of COVID-19 and shed light on effective strategies for future pandemic responses.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11071-021-06505-0.

The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections

Hosts defend themselves against pathogens by mounting an immune response. Fully understanding the immune response as a driver of host disease and pathogen evolution requires a quantitative account of its impact on parasite population dynamics. Here, we use a data-driven modeling approach to quantify the birth and death processes underlying the dynamics of infections of the rodent malaria parasite, Plasmodium chabaudi, and the red blood cells (RBCs) it targets. We decompose the immune response into 3 components, each with a distinct effect on parasite and RBC vital rates, and quantify the relative contribution of each component to host disease and parasite density. Our analysis suggests that these components are deployed in a coordinated fashion to realize distinct resource-directed defense strategies that complement the killing of parasitized cells. Early in the infection, the host deploys a strategy reminiscent of siege and scorched-earth tactics, in which it both destroys RBCs and restricts their supply. Late in the infection, a "juvenilization" strategy, in which turnover of RBCs is accelerated, allows the host to recover from anemia while holding parasite proliferation at bay. By quantifying the impact of immunity on both parasite fitness and host disease, we reveal that phenomena often interpreted as immunopathology may in fact be beneficial to the host. Finally, we show that, across mice, the components of the host response are consistently related to each other, even when infections take qualitatively different trajectories. This suggests the existence of simple rules that govern the immune system's deployment.