Objective: To describe the epidemiology of COVID-19 in one region of New Zealand in the context of the national lockdown and provide a reference for comparing infection dynamics and control measures between SARS-Cov-2 strains.Methods: Epidemiological linking and analysis of COVID-19 cases and their close contacts residing in the geographical area served by the Southern District Health Board (SDHB).Results: From 13 March to 5 April 5 2020, 186 cases were laboratory-confirmed with wild-type Sars-Cov-2 in SDHB. Overall, 35•1% of cases were attributable to household transmission, 27•0% to non-household, 25•4% to overseas travel and 12•4% had no known epidemiological links. The highest secondary attack rate was observed in households during lockdown (15•3%, 95%CI 10•4-21•5). The mean serial interval in 50 exclusive infector-infectee pairs was 4•0 days (95%CI 3•2-4•7days), and the mean incubation period was 3.4 days (95%CI 2•7-4•2). Conclusions:The SARS-CoV-2 incubation period may be shorter than early estimates that were limited by uncertainties in exposure history or small sample sizes. Implications for public health:The continuation of household transmission during lockdown highlights the need for effective home-based quarantine guidance. Our findings of a short incubation period highlight the need to contact trace and isolate as rapidly as possible.

BackgroundCarcinogen exposure data can potentially guide the work of health and safety (H and S) regulators. This project aims to use CAREX Canada data to estimate carcinogen exposures in New Zealand industries. This requires the creation of a cross-walk between the countries’ industry classifications.MethodsAgile and big-data-science methodologies were used to construct two versions of an industry classification cross-walk from the 2006 Australian and New Zealand Standard Industrial Classification (ANZSIC06) to the Canadian version of the 2002 North American Industrial Classification (NAICS2002), used by CAREX Canada.Firstly, concordance files from government statistics bureaus cross-walked the path: ANZSIC06 ->International Standard Industrial Classification of All Economic Activities Rev4 ->NAICS2017->NAICS2012->NAICS2007->NAICS2002. The cross-walk accounted for ‘one-to-many-to-one’, non-machine formats, and missing/erroneous values.Secondly, a fuzzy data matching pipeline was designed. Data preparation removed redundant, stop, and common domain words, and lemmatised using morphological analysis (e.g. fishing to fish). Data matching used a hybrid algorithm combining ‘JaroWinkler-distance’ and a token-sort approach (i.e. ignoring the positional occurrence of words in a sentence) to match descriptions. A trial-and-error approach was used to assign weightings and concatenate the hierarchical industry classification levels to improve match accuracy. Python language was used for implementation.For each method, random samples of 50 matches were manually classified as either poor or sufficient by two people. Disagreements were discussed and consensus reached.ResultsThe concordance cross-walk sample had 52% (95% C.I. 38%–66%) sufficient matches compared to 84% (95% C.I. 74%–94%) for the fuzzy data matching pipeline cross-walk sample.ConclusionsCross-walking countries’ industry classifications using a fuzzy data matching pipeline was more accurate than using a concordance cross-walk. The pipeline is modular enough to easily include more components. This work is part of a vision to design a semantic big-data lake, enabling integration of any data relevant to H and S.