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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?
Introduction
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.1 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.2
These epidemics arise among communities living in overwhelming poverty, affected by conflict and regular population …