Integrating testing volume into bandit algorithms for infectious disease surveillance.

Mobile testing services provide opportunities for active surveillance of infectious diseases for hard-to-reach and/or high-risk individuals who do not know their disease status. Identifying as many infected individuals as possible is important for mitigating disease transmission. Recently, multi-armed bandit sampling approaches have been adapted and applied in this setting to maximize the cumulative number of positive tests collected over time. However, these algorithms have not considered the possibility of variability in the number of tests administered across testing sites. What impact this variability has on the ability of these approaches to maximize yield is currently unknown. Therefore, we investigate this question by extending existing sampling frameworks to directly account for variability in testing volume while also maintaining the computational tractability of the previous methods. Through a simulation study based on human immunodeficiency virus infection characteristics in the Republic of the Congo (Congo-Brazzaville) as well as an application to COVID-19 testing data in Connecticut, we find improved long- and short-term performances of the new methods compared to several existing approaches. Based on these findings and the ease of computation, we recommend use of the newly developed methods for active surveillance of infectious diseases when variability in testing volume may be present.