Susanta Kumar Ghosh^1,2* and Chaitali Ghosh³

¹Former ICMR-National Institute of Malaria Research, Bangalore, India
²Resource Person (External Support) on Dengue, Government of Karnataka, India
³Tata Institute for Genetics and Society, Bangalore, India

*Corresponding Author: Susanta Kumar Ghosh, Former ICMR-National Institute of Malaria Research, Bangalore, India.

Received: January 15, 2024; Published: January 24, 2025

Abstract

Epidemiology is the backbone of any disease dynamics, its management and control within specific time and space in a specific setup. In recent years, advancements on digital and spatiotemporal epidemiology have made it possible for fast, efficient data capturing and analyses to take proper steps by the respective policy makers and programme managers. Digital epidemiology uses digital data based on the digital sources. On the other hand, spatiotemporal epidemiology uses the recorded survey data using recent advanced technologies of spatial, statistical and transmission modelling methods uncovering relationships between infectious disease pattern, and host or environmental conditions, generating detailed hotspots or clusters covering disease morbidity and mortality within a specific time. The recent one-in-a-century coronavirus disease 2019 (Covid-19) pandemic has shaken the health system, and at the same time taught us how quickly to find solutions to tackle this disease. Several apps and digital platforms have been developed during the Covid-19 pandemic and enabled contact tracing, information sharing, symptom tracking for effective management and containment. The similar strategies may be followed for many other infectious diseases such as AIDS, Ebola, Influenza, tuberculosis, malaria, dengue, Zika and Mpox including non-communicable diseases. India has made significant contributions forming a single window central dashboard containing the data of Covid-19 cases collected from all over the country. Day-to-day data collection and its analysis helped to defeat the challenge covering over 1.4 billion people. Use of artificial intelligence (AI) and machine learning (ML) and automation for disease modelling, diagnostics, vaccine development and delivery would make the systems more resilient for management of future epidemics. However, ethics, integrity, data protection need utmost priority in the digital era.

Keywords:Digital Epidemiology; Spatiotemporal Epidemiology; Public Health; Digital Surveillance; Prevention; Monitoring; Management; Evaluation; Apps; Geographic Information System; Geographic Positioning System; Artificial Intelligence; Machine Learning, Automation

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Citation

Citation: Susanta Kumar Ghosh and Chaitali Ghosh. “Digital and Spatiotemporal Epidemiology: Principles and Scopes in Infectious Diseases".Acta Scientific Microbiology 8.2 (2025): 62-71.

Copyright

Copyright: © 2025 Susanta Kumar Ghosh and Chaitali Ghosh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.