Open Government Data (OGD) driven decision aid: a predictive model to monitor COVID-19 and support decisions in a Brazilian State

Abstract

The objective of this research is to build a forecasting model for the evolution of COVID-19 in the state to assist governmental decision-making. This study adopts the Continuous Intelligent Pandemic Monitoring (CIPM) methodology to evaluate the COVID-19 situation in the state of Santa Catarina, Brazil. By examining the data from the state of Santa Catarina, this research examines the reasonableness of current epidemic numbers by using different exogenous variables, determines the severity level of the pandemic in the cities, and simulates its impacts to guide the government in terms of adequate public policy enforcement. The results reveal that the model helps to understand the importance of open data, and highlights the relevance and social contribution of the availability of data in real-time. Additionally, the prediction model contributes to governmental and societal decision making, as it helps to understand the effects of the pandemic on society through the analysis of exogenous variables (Demographic density; Industrial jobs; Percentage of urban population; Territorial extension of the municipality; List of municipalities by region; GDP/Per capita).