Estimating time of infection using prior serological and individual information can greatly improve incidence estimation of human and wildlife infections

Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using arenavirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays.
Author Summary Human and wildlife diseases can be tracked by looking at incidence, which is the number of new infections per time unit (typically day, week or month). While theoretically this would only be a matter of counting the number of newly infected individuals, in reality these data are difficult to acquire due to limited sampling possibilities and undetectable cases. This means that a method must be used to estimate the real incidence using a limited amount of data. For many infections, the concentration and quality of antibodies changes predictably over time, which means that one could use the antibody level at any point in time to back-calculate how much time passed since the infection entered the body. Other information, such as the age of the individual, or the presence of the pathogen, can also help to estimate when an individual became infected. Improving on existing methods, we developed a method that allows the use of a wide range of information sources for estimating individual time since infection. Using arenavirus infection in mice, we show that this method works well when sufficient background data are available, and that it can greatly improve the estimation of incidence patterns.