Joint estimation of the effective reproduction number and daily incidence in the presence of aggregated and missing data

Abstract

Disease surveillance is an integral component of government policy, allowing public health professionals to monitor transmission of infectious diseases and appropriately apply interventions. To aid with surveillance efforts, there has been extensive development of mathematical models to help inform policy decisions, However, these mathematical models rely upon data streams that are expensive and often only practical for high income countries. With a growing focus on equitable public health tools there is a dire need for development of mathematical models that are equipped to handle the data stream challenges prevalent in low and middle income countries, where data is often incomplete and subject to aggregation. To address this need, we develop a mathematical model for the joint estimation of the effective reproduction number and daily incidence of an infectious disease using incomplete and aggregated data. Our investigation demonstrates that this novel mathematical model is robust across a variety of reduced data streams, making it suitable for application in diverse regions.Author summaryMonitoring the transmission of infectious diseases is an important part of government policy that is often hindered by limitations in data streams. This is especially true in low and middle income countries where health sectors have less funding. In this work we develop a mathematical model to enhance disease surveillance by overcoming these data limitations, providing accurate inferences of relevant epidemiological parameters.