Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand

Abstract

Timely 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 clinical testing, healthcare-seeking behaviour or access to care. We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods. We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand’s second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. Estimating R, 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. To make informed public health decisions about infectious diseases, it is important to understand the number of infections in the community. Reported cases, however, underestimate the number of infections and the degree of underestimation likely changes with time. Wastewater data provides an alternative data source that does not depend on testing practices. Here, we combined wastewater observations of SARS-CoV-2 with reported cases to estimate the reproduction number (how quickly infections are increasing or decreasing) and the case ascertainment rate (the fraction of infections reported as cases). We apply the model to Aotearoa New Zealand and demonstrate that the second wave of infections in July 2022 had approximately the same number of infections as the first wave in March 2022 despite reported cases being 50% lower. Watson et. al construct a state-space model to assess disease transmission from virus concentration data in wastewater. Using data from New Zealand they estimate infection and relative case ascertainment rates during several waves of infection with the omicron SARS-CoV-2 variant.