LAUSR

Background: Artificial Intelligence (AI) and data science research are promising tools to better inform public policy and public health responses, promoting automation and affordability. During the COVID-19 pandemic, AI has been an aid to forecast outbreak spread globally. The overall aim of the study is to contribute to the ongoing public health, socioeconomic, and communication challenges caused by COVID-19. Protocol: COLEV is a five-pronged interdisciplinary mixed methods project based on AI and data science from an inclusive perspective of age and gender to develop, implement, and communicate useful evidence for COVID-19-related response and recovery in Colombia. The first objective is identification of stakeholders’ preferences, needs, and their use of AI and data science relative to other forms of evidence. The second objective will develop locally relevant mathematical models that will shed light on the possible impact, trajectories, geographical spread, and uncertainties of disease progression as well as risk assessment. The third objective focuses on estimating the effect of COVID-19 on other diseases, gender disparities and health system saturation. The fourth objective aims to analyze popular social networks to identify health-related trending interest and users that act as ‘super spreaders’ for information and misinformation. Finally, the fifth objective, aims at designing disruptive cross-media communication strategies to confront mis- and dis-information around COVID-19. To understand stakeholders’ perspectives, we will use semi-structured interviews and ethnographic work. Daily cases and deaths of COVID-19 reported from the National Surveillance System (INS) of Colombia will be used for quantitative analysis, and data regarding the online conversation will be obtained from Facebook and Twitter. Conclusions: COLEV intends to facilitate the dialogue between academia and health policymakers. The results of COLEV will inform on the responsible, safe and ethical use of AI and data science for decision-making in the context of sanitary emergencies in deeply unequal settings.