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Auckland is New Zealand's largest city and is the main international entry point to New Zealand, with most of the nation's Managed Isolation and Quarantine Facilities. Consequently, it is the place most likely for a COVID-19 outbreak to occur and, accordingly, has been the focus of efforts to monitor SARS-CoV-2 via wastewater-based epidemiology. Historically, wastewater-based epidemiology has mainly been applied at the catchment or sewershed scale, with samples collected at wastewater treatment plants. COVID-19 has necessitated a re-evaluation of this broad-scale approach to wastewaterbased epidemiology in New Zealand, where there is a need for more detailed information to better target the public health response. Using Auckland as a case study, this paper assesses the spatial and temporal extent of the city's wastewater network to inform the selection of strategic neighbourhoodscale sampling sites for wastewater-based epidemiology. Sample site selection criteria included topology and connectivity of the sewer network, the capacity to record sewer flow, limited rain infiltration, resident population, and accessibility. Six sites were identified that provide an immediate opportunity for neighbourhood-scale monitoring. Reflecting on the analysis required for selecting appropriate monitoring locations, the paper moves to critically discuss the key unknowns and research needs associated with conducting neighbourhoodscale wastewater-based epidemiology. Population mobility poses challenges for estimating population size and for capturing a positive SARS-CoV-2 signal in wastewater at this scale. There is a need to determine the full spectrum of residence times within the sewer network to design representative sampling and the implications of disregarding residence times in the current sampling regimes are poorly understood. Hydraulic models require refinement to accurately account for variable residence times and topological features in the network (e.g., holding tanks and pumping stations). Addressing these challenges is urgent and ongoing to realise the benefits of neighbourhood-scale wastewater-based epidemiology in response to COVID-19 and beyond. [ABSTRACT FROM AUTHOR]

Governments around the world are seeking to slow the spread of Covid-19 by implementing measures that encourage, or mandate, changes in people's behaviour. These changes include the wearing of face masks, social distancing, and testing and self-isolating when unwell. The success of these measures depends on the commitment of individuals to change their behaviour accordingly. Understanding and predicting the motivation of individuals to change their behaviour is therefore critical in assessing the likely effectiveness of these measures in slowing the spread of the virus. In this paper we draw on a novel framework, the I3 Compliance Response Framework, to understand and predict the motivation of residents in Auckland, New Zealand, to comply with measures to prevent the spread of Covid-19. The Framework is based on two concepts. The first uses the involvement construct to predict the motivation of individuals to comply. The second separates the influence of the policy measure from the influence of the policy outcome on the motivation of individuals to comply. In short, the Framework differentiates between the strength of individuals' motivation and their beliefs about the advantages and disadvantages of policy outcomes and policy measures. We found this differentiation was useful in predicting an individual's possible behavioural responses to a measure and discuss how it could assist government agencies to develop strategies to enhance compliance. [ABSTRACT FROM AUTHOR]

Background: Serological assays that detect antibodies to SARS-CoV-2 are critical for determining past infection and investigating immune responses in the COVID-19 pandemic. We established ELISA-based immunoassays using locally produced antigens when New Zealand went into a nationwide lockdown and the supply chain of diagnostic reagents was a widely held domestic concern. The relationship between serum antibody binding measured by ELISA and neutralising capacity was investigated using a surrogate viral neutralisation test (sVNT). Methods: A pre-pandemic sera panel (n = 113), including respiratory infections with symptom overlap with COVID-19, was used to establish assay specificity. Sera from PCR-confirmed SARS-CoV-2 patients (n = 21), and PCR-negative patients with respiratory symptoms suggestive of COVID-19 (n = 82) that presented to the two largest hospitals in Auckland during the lockdown period were included. A two-step IgG ELISA based on the receptor binding domain (RBD) and spike protein was adapted to determine seropositivity, and neutralising antibodies that block the RBD/hACE-2 interaction were quantified by sVNT. Results: The calculated cut-off (>0.2) in the two-step ELISA maximised specificity by classifying all pre-pandemic samples as negative. Sera from all PCR-confirmed COVID-19 patients were classified as seropositive by ELISA _7 days after symptom onset. There was 100% concordance between the two-step ELISA and the sVNT with all 7+ day sera from PCR-confirmed COVID-19 patients also classified as positive with respect to neutralising antibodies. Of the symptomatic PCR-negative cohort, one individual with notable travel history was classified as positive by two-step ELISA and sVNT, demonstrating the value of serology in detecting prior infection. Conclusions: These serological assays were established and assessed at a time when human activity was severely restricted in New Zealand. This was achieved by generous sharing of reagents and technical expertise by the international scientific community, and highly collaborative efforts of scientists and clinicians across the country. The assays have immediate utility in supporting clinical diagnostics, understanding transmission in high-risk cohorts and underpinning longer-term 'exit' strategies based on effective vaccines and therapeutics. [ABSTRACT FROM AUTHOR]

The city of Auckland, New Zealand (Tāmaki Makaurau, Aotearoa), sits on top of an active volcanic field. Seismic stations in and around the city monitor activity of the Auckland Volcanic Field (AVF) and provide data to image its subsurface. The seismic sensors – some positioned at the surface and others in boreholes – are generally noisier during the day than during nighttime. For most stations, weekdays are noisier than weekends, proving human activity contributes to recordings of seismic noise, even on seismographs as deep as 384 m below the surface and as far as 15 km from Auckland's Central Business District. Lockdown measures in New Zealand to battle the spread of COVID-19 allow us to separate sources of seismic energy and evaluate both the quality of the monitoring network and the level of local seismicity. A matched-filtering scheme based on template matching with known earthquakes improved the existing catalogue of five known local earthquakes to 35 for the period between 1 November 2019 and 15 June 2020. However, the Level-4 lockdown from 25 March to 27 April – with its drop in anthropogenic seismic noise above 1 Hz – did not mark an enhanced detection level. Nevertheless, it may be that wind and ocean swell mask the presence of weak local seismicity, particularly near surface-mounted seismographs in the Hauraki Gulf that show much higher levels of noise than the rest of the local network. [ABSTRACT FROM AUTHOR]