Translating surveillance data into incidence estimates

Abstract

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Risk assessment of the entry of Pantoea stewartii subsp. stewartii on maize seed imported by the EU from the USA

“…The Panel applied formulas of Bourhis et al. () to calculate the efficacy of single or dual field inspections. RRO4 – pest free place of production (two inspections, one early and one later in the season) …”

“…Later inspections can be useful to detect Stewart's wilt due to later infection. Later inspection is especially effective if disease incidence increases over the season (Bourhis et al., ). Inspections early in the season are reported to be easier because plants are smaller and seedling wilt is easily identified (F. Finelli, Phytosanitary Service Emilia Romagna, Italy, personal communication, December 2018).…”

“…An 1−α upper limit can also be calculated using Bayes’ rule (Bourhis et al., ). The result is:…”

“…Bourhis et al. () developed formulas for the case of multiple inspections in a field. They considered an epidemic that increased in incidence by a factor λ between inspections.…”

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…[81]), and statistical models are used for forecasting numbers of cases [82] or representing processes such as disease surveillance [83]. In this issue, Bourhis et al [84] link a statistical approach for estimating infection prevalence to the susceptible -infected compartmental model to account for temporal variations in the number of infected hosts during an outbreak.…”