Background Population surveys across the world have examined the impact of the COVID-19 pandemic on mental health. However, few have simultaneously examined independent cross-sectional data with longitudinal data, each of which have different strengths and weaknesses and facilitate the investigation of distinct research questions. This study aimed to investigate psychological distress and life satisfaction during the first and second lockdowns in the state of Victoria, Australia, and the social factors that may be affected by lockdowns and could affect mental health. Methods The VicHealth Victorian Coronavirus Wellbeing Impact Study included two 20-min opt-in online panel surveys conducted in May and September 2020 in Victoria, each with a sample of 2000 adults aged 18 + . A two-part study design was used: a repeated cross-sectional study of respondents who participated in Survey One and Survey Two, followed by a longitudinal nested cohort study. The primary exposures were social solidarity, social connectedness and staying connected with family and friends. Using logistic regression modelling, we explored the associations between our exposures and primary outcomes of psychological distress and life satisfaction with and without adjustment for covariates, both cross-sectionally and longitudinally. The results from the multivariable models were summarised using adjusted Odds Ratios (aOR), 95% Confidence Intervals (CI). Results Cross-sectional results indicated that the percentage of participants with low life satisfaction was significantly higher in the second survey sample (53%) compared to the first (47%). The percentage of participants with high psychological distress was higher but not significantly different between the two survey samples (14% first survey vs 16% second survey). Longitudinal study results indicated that lower social connectedness was significantly associated with higher psychological distress (aOR:3.3; 95% CI: 1.3–8.4) and lower life satisfaction (aOR:0.2; 95% CI: 0.1–0.4). Younger adults had higher psychological distress compared to older adults (aOR:6.8; 95% CI:1.5–31.1). Unemployment at the time of the first survey was significantly associated with lower life satisfaction at the second survey (aOR:0.5; 95% CI: 0.3–0.9). Conclusion This study supports the findings of other international studies. It also highlights the need to promote increased social connection and maintain it at times of isolation and separation, particularly amongst younger adults.

Determining the optimal price of products is essential, as it plays a critical role in improving a company’s profitability and market competitiveness. This requires the ability to calculate customers’ demand in the Fast Moving Consumer Goods (FMCG) industry as various effects exist between multiple products within a product category. The substitution effect is one of the challenging effects at retail stores, as it requires investigating an exponential number of combinations of price changes and the availability of other products. This paper suggests a systematic price decision support tool for demand prediction and price optimise in online and stationary retailers considering the substitution effect. Two procedures reflecting the product price changes and the demand correlation structure are introduced for demand prediction and price optimisation models. First, the developed demand prediction procedure is carried out considering the combination of price changes of all products reflecting the effect of substitution. Time series and different well-known machine learning approaches with hyperparameter tuning and rolling forecasting methods are utilised to select each product’s best demand forecast. Demand forecast results are used as input in the price optimisation model. Second, the developed price optimisation procedure is a constraint programming problem based on a week time frame and a product category level aggregation and is capable of maximising profit out of the many price combinations. The results using real-world transaction data with 12 products and 4 discount rates demonstrate that including some business rules as constraints in the proposed price optimisation model reduces the number of price combinations from 11,274,924 to 19,440 and execution time from 129.59 to 25.831 min. The utilisation of the presented price optimisation support tool enables the supply chain managers to identify the optimal discount rate for individual products in a timely manner, resulting in a net profit increase.

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