Microplanning for Designing Vaccination Campaigns in Low-Resource Settings: A Geospatial Artificial Intelligence-Based Framework to Tackle COVID-19

Background: Existing campaign-based healthcare delivery programs used for immunization often fall short of established health coverage targets due to the lack of accurate population estimates and location. A microplan, an integrated set of detailed planning components, can be used to identify this information in order to support programs such as equitable COVID-19 vaccination.  Methods: We present a series of steps necessary to create an artificial intelligence-based framework for automated microplanning, and our pilot implementation of this analysis tool across 29 countries of the Americas. Further, we describe our processes for generating a conceptual framework, creating customized catchment areas, and estimate the up-do-date population to support microplanning of health campaigns.  Findings: Through our application of the present framework, we found that 68 million individuals across the 29 countries are within five km of a health facility. The number of health facilities analyzed ranged from 2 in Peru to 789 in Argentina, while the total population within five km ranged from 1,233 in Peru to 15,304,439 in Mexico.  Interpretation: Our results demonstrate the feasibility of using this methodological framework to support the development of customized microplans for health campaigns using open-source data in multiple countries. The pandemic is demanding an improved capacity to generate successful, efficient immunization campaigns; we believe that the steps described here will increase the automation of microplans in low resource settings. Funding Statement: Funding is not applicable for this study. Declaration of Interests: All authors filled the ICME Form for Disclosure of Potential Conflicts of Interest, and for all authors there were nothing to disclose.