Innovations in Biostatistics and Epidemiological Modelling for Tackling Global Disease Burden
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Infectious Diseases, Chronic Diseases, and Disease Prevention".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 20
Special Issue Editor
Interests: Bayesian statistical methods and their application to epidemiology and health; addressing maternal and child health and a variety of health-related health inequalities, in law and middle-income countries, using large scale data
* Professor Khaled Khatab, Chair of Statistics, specialises in advanced statistical modelling for global health, focusing on child health, infectious diseases, and health inequalities, with impactful work in LMICs using Bayesian and geospatial methods.
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Special Issue Information
Dear Colleagues,
The persistent and evolving challenge of accurately quantifying and addressing the global burden of diseases remains central to advancing public health, particularly as the world confronts growing inequalities, climate-related health threats, and emerging epidemics. The exponential increase in the availability of large-scale health data, ranging from national surveys to routine health information systems and novel digital sources, provides unparalleled opportunities for disease monitoring and evaluation. However, these opportunities also introduce substantial methodological complexities, especially when dealing with big data involving multiple variables and dimensions that interact in non-linear and context-specific ways. Classical statistical models often fall short in capturing this complexity and may obscure the intricate associations between multiple risk factors, socio-demographic variables, and geographical determinants that contribute to the persistence and distribution of diseases, particularly in under-resourced areas. Therefore, there is an urgent need for advanced biostatistical tools such as Bayesian frameworks, spatial and spatiotemporal models, machine learning algorithms, and causal inference methods capable of navigating these complexities, uncovering hidden associations, and informing more precise and equitable public health interventions. This Special Issue will showcase cutting-edge developments in biostatistics and epidemiology, with a particular focus on methodological innovations and their practical application to disease burden estimation, health inequality assessment, risk prediction, and intervention evaluation. We especially desire contributions addressing the unique challenges faced by low- and middle-income countries (LMICs), where data limitations and health disparities often complicate evidence generation. We welcome original research articles, methodological innovations, simulation studies, systematic reviews, and comparative assessments that advance the frontiers of biostatistics and epidemiology for informing global and local health decision-making.
Prof. Dr. Khaled Khatab
Guest Editor
Manuscript Submission Information
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Keywords
- disease burden estimation
- biostatistics
- spatial epidemiology
- bayesian modelling
- survival and longitudinal analysis
- global health
- low- and middle-income countries
- machine learning
- health inequality
- causal inference
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