Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand

Abstract

AbstractBackgroundTimely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by human behaviours. Here, we investigated how these two data sources can be combined to inform estimates of the instantaneous reproduction number,R, and track changes in the CAR over time.MethodsWe constructed a state-space model that we solved using sequential Monte Carlo methods. The observed data are the levels of SARS-CoV-2 in wastewater and reported case incidence. The hidden states that we estimate areRand CAR. Model parameters are estimated using the particle marginal Metropolis Hastings algorithm.FindingsWe analysed data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates thatRpeaked at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaked around 12 March 2022. Accounting for reduced CAR, we estimate that New Zealand’s second Omicron wave in July 2022 was similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 was approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. The CAR in subsequent waves around November 2022 and April 2023 was estimated to be comparable to that in the second Omicron wave.InterpretationThis work on wastewater-based epidemiology (WBE) can be used to give insight into key epidemiological quantities. EstimatingR, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time, which may be increasingly useful as intensive pandemic surveillance programmes are wound down.FundingNew Zealand Ministry of Health, Department of Prime Minister and Cabinet, the Royal Society Te Apārangi, Imperial College London, and University of Oxford.Research in ContextEvidence before this studyThere has been a substantial increase in the number of publications focusing on wastewater-based epidemiology (WBE) in recent years, particularly during the COVID-19 pandemic. We searched PubMed for “wastewater based epidemiology” and found fewer than 10 papers per year prior to 2014 with a drastic increase to 463 in 2022. Approximately 52% of the WBE publications are related to COVID-19 (“wastewater based epidemiology” AND (“SARS-CoV-2” OR “COVID-19”)). Many studies have focused on detecting SARS-CoV-2 in wastewater systems but only 10 have estimated the reproduction number (“wastewater based epidemiology” AND (“SARS-CoV-2” OR “COVID-19”) AND “reproduction number”). No previous work has combined WBE with reported case data to estimate (relative) case ascertainment rate (“waste-water based epidemiology” AND (“SARS-CoV-2” OR “COVID-19”) AND “case ascertainment rate”). Previous work has estimated the reproduction number from reported cases assuming constant under-ascertainment but the issue of time-varying case ascertainment has not yet been addressed, except to demonstrate the effect of a pre-determined change in case ascertainment.Added value of this studyWe present a model that, for the first time, enables reported case information to be combined with wastewater data to estimate epidemiology quantities. This work further demonstrates the utility of WBE; the reproduction number can be estimated in the absence of reported case information (although results are more reliable when case data are included), and wastewater data include information that, when combined with case data, can be used to estimate the time-varying relative case ascertainment rate.Implications of all the available evidenceIn order to make informed and timely public health decisions about infectious diseases, it is important to understand the number of infections in the community. WBE provides a useful source of data that is not impacted by time-varying testing practices. Wastewater data can be quantitatively combined with case information to better understand the state of an epidemic. In order to determine the absolute case ascertainment rate (rather than the relative rate calculated in this work), there is a need for infection prevalence surveys to calibrate model results against.