Simone E Carter, Nina Gobat, Jérôme Pfaffmann Zambruni, Juliet Bedford, Esther van Kleef, Thibaut Jombart, Mathias Mossoko, Dorothée Bulemfu Nkakirande, Carlos Navarro Colorado, Steve Ahuka-Mundeke
Social sciences research for epidemic response has evolved to provide critical evidence needed for outbreak prevention and control and is most impactful when included as part of a multidisciplinary, integrated package.
Outbreak analytics is a data science which encompasses multiple methods in epidemiological analysis and modelling to inform outbreak response.
We propose to complement this with data and analytical approaches from multiple disciplines to provide a holistic understanding which not only maps and models epidemiological data but seeks to provide context and an understanding for potential cause and effect, thus creating an integrated multidisciplinary outbreak analytics (IMOA) model.
Drawing on this experience, we have identified four questions to shape IMOA: (1) What are the impacts on healthcare-seeking behaviour, changing trends in service perception, and the availability, access and use of health services? (2) What are the perceptions and behaviours of healthcare workers and what impact does this have on outbreak dynamics? (3) What are individual and community understanding, perceptions and practices relevant to adapting public health and social measures? (4) What mechanisms are used to include gender and what impacts do these have on outbreak dynamics?
COVID-19 is but one of many public health crises facing the people of the Democratic Republic of the Congo (DRC). On 25 June 2020, the DRC government announced the end of the country’s largest Ebola outbreak on record and the second largest Ebola outbreak worldwide, a mere few weeks after a new outbreak (11th) started on 1 June 2020, in Mbandaka, Equateur Province. In 2019, measles claimed the lives of over 6000 people including 4500 children under the age of 5, malaria killed 17 000 individuals, and cholera outbreaks affected 20 of 26 provinces, resulting in 31 000 cases.
These epidemics arise among communities living in overwhelming poverty, affected by conflict and regular population displacement. The DRC is not a unique situation. Across many parts of the world, governments, humanitarian responders and communities confront multiple challenges where the response to public health emergencies must compete for human, financial and health service resources. Humanitarian responses to these crises are hampered by weak public health structures, poorly resourced health systems, and in some regions protracted insecurity and conflict. Tackling epidemics under these complex social, political and economic realities requires integrated and multidisciplinary sources of data and evidence to inform response strategies.
The use of social and behavioural sciences evidence in outbreak response has increased over the recent years, gaining particular traction since the 2014–2016 Ebola epidemic in West Africa. Social science analyses for epidemic response have evolved to provide critical evidence needed for outbreak prevention and control and are most impactful when included as part of a multidisciplinary, integrated package. Outbreak analytics is a data science which encompasses multiple methods in epidemiological analysis and modelling to inform outbreak response. We propose to complement this with data and analytical approaches from multiple disciplines to provide a holistic understanding which not only maps and models epidemiological data to, for example, assess trends, burden and risk factors, but seeks to provide context and an understanding for potential cause and effect, thus creating an integrated multidisciplinary outbreak analytics (IMOA) model.
An important case for this was demonstrated during the Ebola outbreak in Eastern DRC. The Social Sciences Analytics Cell (CASS) was established under the Ministry of Health (MoH) and led by UNICEF, in collaboration with national and international, academic and humanitarian partners. The CASS is the first field-based, multiactor, operational research mechanism providing rapid social and behavioural sciences evidence used to systematically inform real-time epidemiological analyses, government and response decision-making in an outbreak. Working hand in hand with (field) epidemiologists, statisticians and modellers, the CASS provided invaluable insights into observations from epidemiological data and analyses, helping not only to direct and refine epidemiological models, but also shedding lights on unexpected analytical results through accelerated indepth investigations in the field. By bringing social, behavioural and health services analyses to the research group, the CASS provides an essential complement to outbreak analytics, creating a mechanism for producing IMOA for programmatic and strategic decision-making. During the Eastern DRC Ebola outbreak, the CASS conducted 57 studies, many in response to direct requests from the DRC MoH, and codeveloped 112 evidence-based recommendations with MoH and response actors to guide response strategies. A monitoring system to track recommendations allowed researchers and decision-makers to demonstrate accountability and evidence-based decision-making. Examples of integrated studies are summarised in table 1.
Drawing on this experience, the CASS has identified four priority questions to shape IMOA agendas in humanitarian settings. These priorities have been identified to improve effectiveness and accountability of humanitarian programmes implemented during COVID-19, aligning with the social science research agenda set out by the WHO COVID-19 Research Roadmap.