Applied Stochastic Solutions, Dynamic Analysis, and Mathematical Models for Issues in Demography, Epidemiology, and Environmetrics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1173

Special Issue Editors


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Guest Editor
1. Chair of Empirical Methods in Social Science and Demography, Faculty of Economics and Sociology, University of Rostock, 18057 Rostock, Germany
2. Department of Health Monitoring and Biometrics, aQua-Institut, 37073 Göttingen, Germany
Interests: forecasting; time series analysis; multivariate methods; stochastics; demography; epidemiology; econometrics; social insurance; gerontology

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Guest Editor
Department of Marine Sciences, Faculty of the Environment, University of the Aegean, University Hill, GR81100 Mytilene, Lesvos Island, Greece
Interests: complexity/diversity and stability; species coexistence; community ecology; conservation biology; biostatistics
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Special Issue Information

Dear Colleagues,

Many real-world phenomena, be they social, ecological, or biological in general, do not follow predefined patterns but rather exhibit random behavior. To name a few examples, we do not know ex ante the number of migrants arriving at a certain destination during a given period; neither do we know the damages and associated costs that will arise for an insurance company due to natural hazards (e.g., hurricanes) before they occur. We also do not know how a specific individual will respond to a certain treatment for a disease, or even if/when the individual will contract the disease. These examples illustrate that phenomena are often stochastic, although they are often modeled using deterministic methods that typically identify a mean or median outcome. However, uncertainty or risk are often not quantified at all or may only be quantified by scenario analyses that cover only a minor share of possible scenarios and commonly do not assign probabilities to these scenarios.

This Special Issue stresses the engineering and application of stochastic approaches for real-world applications, such as in economics, sociology, geography, epidemiology, biometry, or ecology. You are cordially invited to submit papers related to all aspects of stochasticity in real-world applications. These might be approaches in forecasting, the estimation of bias due to underdetection (e.g., in migration or disease research), or interpolation and imputation methods. Another current example is how to appropriately include stochasticity in disease modeling. Other applications involve the modeling of natural disasters. This list is not exhaustive, and fitting papers that present either theoretical or empirical approaches to deal with issues in fields associated with demography, epidemiology, biometry, or environmetrics are welcome. Please note that innovative approaches that develop novel ideas instead of applying established approaches to new data or topics are especially encouraged. The major intention of this Special Issue is to advance methodological standards, although illustrative applications of established methods to a broad readership are also welcome.

Authors who would like to make sure that their paper concept fits the scope of the Special Issue are highly encouraged to send a proposal (about 1–2 pages) to Patrizio Vanella ([email protected]) on their intended paper beforehand. We look forward to your submissions.

Dr. Patrizio Vanella
Dr. Giorgos Kokkoris
Guest Editors

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Keywords

  • stochasticity
  • demography
  • epidemiology
  • biometrics
  • econometrics
  • applied mathematics
  • statistics
  • orecasting
  • extrapolation
  • interpolation
  • imputation
  • error assessment
  • bias estimation
  • multicollinearity

Published Papers (2 papers)

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Research

25 pages, 1734 KiB  
Article
Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations
by Jan Weymeirsch, Julian Ernst and Ralf Münnich
Mathematics 2024, 12(10), 1550; https://doi.org/10.3390/math12101550 - 16 May 2024
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Abstract
Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. [...] Read more.
Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. However, estimating such models on the regional level is often not feasible due to limited sample size and lack of geographic information. Simply applying the model estimated at the national level to all geographies leads to biased state transitions due to regional differences in level and distribution. In this paper, we introduce a model-based alignment method to adapt predicted probabilities obtained from a nationally estimated model to subregions by integrating known marginal distributions to re-introduce regional heterogeneity and create more realistic trajectories, particularly in small areas. We show that the model-adjusted transition probabilities can capture region-specific patterns and lead to improved projections. Our findings are useful to researchers who want to harmonise model outputs with external information, in particular for the field of microsimulation. Full article
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21 pages, 2864 KiB  
Article
Impact of Demographic Developments and PCV13 Vaccination on the Future Burden of Pneumococcal Diseases in Germany—An Integrated Probabilistic Differential Equation Approach
by Myka Harun Sarajan, Kahkashan Mahreen, Patrizio Vanella and Alexander Kuhlmann
Mathematics 2024, 12(6), 796; https://doi.org/10.3390/math12060796 - 8 Mar 2024
Viewed by 665
Abstract
Streptococcus pneumonia is the primary cause of morbidity and mortality in infants and children globally. Invasive pneumococcal disease (IPD) incidence is affected by various risk factors such as age and comorbidities. Additionally, this bacterium is a major cause of community-acquired pneumonia (CAP), leading [...] Read more.
Streptococcus pneumonia is the primary cause of morbidity and mortality in infants and children globally. Invasive pneumococcal disease (IPD) incidence is affected by various risk factors such as age and comorbidities. Additionally, this bacterium is a major cause of community-acquired pneumonia (CAP), leading to higher rates of hospitalization, especially among older adults. Vaccination with pneumococcal conjugate vaccines (PCVs) has proven effective, but the demographic transition in Germany poses a challenge. This study introduces a novel stochastic approach by integrating a population forecast model into a transmission dynamic model to investigate the future burden of pneumococcal diseases in three age groups (0–4, 5–59, and 60 and older). Our simulations, presented through mean predictions and 75% prediction intervals, indicate that implementing PCV13 (13-valent pneumococcal conjugate vaccine) until the year 2050 results in reduced cases of IPD and CAP in all age groups compared to scenarios without infant vaccination. However, cases with non-vaccine serotypes may persist at higher levels compared to scenarios without infant vaccination. Consequently, there may be a need for improvement in the current national vaccine policy, such as implementing the use of higher-valent PCVs and strengthening adult vaccination uptake. Full article
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