Harnessing the Potential of Digital Data for Infectious Disease Surveillance in sub-Saharan Africa
Creators: name: Ahmad, Amena A.
name: Ahmady-Moghaddam, Nima
name: Becher, Heiko
name: Belau, Matthias H.
name: Boenecke, Juliane
name: Brinkel, Johanna
name: Clemen, Thomas
name: Dretvić, Daria
name: Himmel, Mirko
name: Kreppel, Katharina
name: Leal Filho, Walter
name: Lenfers, Ulfia A.
name: May, Jürgen
name: Mustafa, Ummul-Khair
name: Nyambo, Devotha
name: Pacoe, Luba
name: Pohlmann, Jennifer
name: Reintjes, Ralf
name: Sauli, Elingarami
name: Streit, Wolfgang
name: Ströbele, Jonathan
Publisher: name: Zenodo
Year: 2022
Subjects: Digital Surveillance
Open Data
Outbreak Detection
Tanzania

description: Despite efforts by the WHO to support local surveillance strategies in developing countries, there is a lack of robust public health surveillance frameworks. As a result, early infectious disease outbreak detection and response remain a significant challenge for local health systems in low-resource settings such as sub-Saharan African countries. In contrast, the growing digital infrastructure, especially in the mobile phone sector, and the global availability of extensive digital data offer promising solutions to enhance and strengthen epidemiological surveillance. Yet, there is little insight into concepts of utilisation and transfer into local public health practice. Using Tanzania as an example, a novel electronic surveillance and early outbreak alert framework is being developed that links signals on emerging diseases with relevant contextual Open Data for rapid outbreak risk assessment. The concept focuses on haemorrhagic fever diseases, specifically dengue virus disease, which is increasingly spreading in sub-Saharan Africa. A data stack framework forms the core of the system, which augments electronic information on the occurrence of acute haemorrhagic fever syndrome, e.g., collected via mobile phone-based surveillance tools, with openly available socio-ecological context data specific to dengue. Preliminary results on the data and information flow within the surveillance framework are presented and strategies for an automated indicator-based risk assessment for dengue outbreaks will be discussed, supplemented by an agent-based simulation framework to model possible short-term outbreak scenarios. In addition, adequate data inputs, identified through an appraisal of various data sources available for Tanzania, are outlined. The framework could serve as a blueprint for designing locally implementable early warning and decision support systems integrated with existing digital surveillance infrastructure.
description: Corresponding Author: Juliane Boenecke (juliane.boenecke@bnitm.de) Funding: German Federal Ministry of Education and Research (BMBF) / CONNECT Education-Research-Innovation (Grant ID: 01DU20005)
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