Abstract
Background: Extreme Weather Events (EWE’) in KwaZulu-Natal (KZN) province, South Africa are increasingly common causing disease outbreaks, loss of homes, property and life with pressure on the health system. An Early Warning System (EWS) intervention is needed to alert communities and government, to increase preparedness for floods, improving resilience of health services to predict, detect and respond to emergencies and potential disease outbreaks.
Objectives: To improve the preparedness/response of the healthcare systems/disaster management organisations and reduce healthcare burden. The EWS based on Artificial Intelligence (AI) and sensor technology for four vulnerable communities will be developed for implementation in two districts: eThekwini and Ugu districts of KZN province. An AI-based solution will give vulnerable communities, health officials and policy makers early warning/alerts that they can use to make informed decisions, thereby limiting the impact of EWE’s.
Approach: The study sites are eThekwini and Ugu Health Districts. The study population comprises the community and health facilities in selected sites in the two health districts one urban and one rural. This transdisciplinary project will build on prior engagement with communities/health providers (e.g., communities, professionals, policy makers) integrating a participatory, co-creation approach. An over-arching intervention development framework will guide this project with evaluation conducted via a longitudinal, mixed methods approach in four work packages (WP’s). These consist of a systematic review on the impact of climate change on disease outbreaks (WP 1), an assessment of the disease burden at primary care clinics in Ugu and eThekwini (WP 2), health needs and community experiences at the time of an extreme weather event (WP 3), an assessment of the health systems resilience in responding to an extreme weather event. WP 4 will apply an overarching Realist Evaluation and SROI approach (focus groups, interviews and questionnaires) to evaluate the efficiency, acceptability and cost effectiveness of EWS impact on health outcomes during flooding in KZN province. EWS will be conducted in (WP 4). Ethics for the project has been sought from the relevant ethical committees.
Results: The data analysis will be informed by the data collected. Descriptive and inferential statistical analysis will be conducted on questionnaire data to understand pathogen leading to disease outbreaks and risk management strategies for health systems during flooding. The project will design, development and launch a dashboard for a command-and-control centre to enable EWE and disease outbreak alert/warning for selected vulnerable communities and establish communication with healthcare organisations (input to WP3 for resource management), vulnerable communities, community leaders and disaster management organisations based on a cascaded structure model. WP3 will develop training and mentor healthcare workers on how to ensure a resilient health system and respond in EWE’s. Finally, WP4 will capture the social cost benefit analysis of the EWS intervention to take account of the economic, environmental and social value of the EWS for communities and disaster risk reduction and the associated value.
Implications: Based on the study findings, recommendations will be made to the Department of Health with respect to an early warning system and health systems resilience.