An Agent-Based Model of COVID-19 Epidemic: A Case of Barking and Dagenham, London, UK

The novel coronavirus disease 2019 (COVID-19) has swept across the globe, taken countless lives and grievously wounded the economy. Nonpharmaceutical interventions (NPIs) have been implemented worldwide, and lockdown has been regarded as the most effective NPI. To assess the effectiveness of lockdown control, in this project we proposed an agent-based model (ABM) that simulates the spread of COVID-19 with and without lockdown intervention in Barking and Dagenham, London. In the study, we integrated geographical information with ABM technology to simulate individuals’ interactions in various activities via a geospatial context. By doing so, we could provide infection and death case numbers simulated from the individual-based susceptible-exposed-infected-recovered epidemic framework, where each person is part of the transmission chain. The results suggested lockdown could effectively flatten the COVID-19 curve but was not effective for long periods. This prototype could be accommodated easily for other diseases or locations by adjusting parameters or changing the input spatial data set, which could help promote the public’s understanding of disease spread dynamics and urge others to take better steps towards pandemic prevention and control

City size based scaling of the urban internal nodes layout

The size of a city is not only essential for depicting the scale of the urban system, but also crucial to support the prosperity, order, and high-speed developments. However, its relation to the underlying urban structure has not been empirically investigated in detail. To examine the impact of city size on the city structure and quantify structural features, in this study, a statistical analysis was performed based on network science and an interdisciplinary theoretical system. To obtain the statistical law of internal node layout, the urban system was regarded as a complete graph weighted by the Euclidean distance. The relationship between the urban internal nodes layout (points of interest data, Weibo check-in data, and central point of road intersection data) and the city size was established. The results confirmed the existence of statistical laws in the layout of urban spatial elements, and explored the relationship between the changes in urban node network structure and inequality. This study provided a new perspective of urban structure to understand the complexity of the city, and suggested an approach to adjust this structure to narrow down the gap between the urban and rural areas

Optimal decision in MC supply chain with overconfident retailer based on the newsvendor model

In this paper, we analyze the optimal order-quantity decisions in a supply chain with mass customization (MC) manufacturer and overconfident retailers. First, we consider a newsvendor model in which an unbiased retailer sells mass customized products. The retailer needs to make order quantity decisions before the selling season. Meanwhile, the supplier is a mass customization manufacturer and implements modular production. The supply process is uncertain, as the real quantity the retailer received is the order quantity multiplied by a random yield rate. Second, two overconfident models are considered and theorems are proposed. In the first model, the behavioral bias of overconfidence only affects the retailer’s judgment of variance of market demand. In the second model, the behavior bias of overconfidence affects not only the retailer’s cognition of the variance of market demand, but also his cognition of the expectation of market demand. In addition, the relationship between the optimal decisions and the modularity level is obtained. Finally, we provide managerial insights for the decision makers of the retailers and the manufacturers on order quantity and modularity level, respectively

The major food options in Singapore (coordinates and opening hours; collected year 2023)

Fast food providers in Singapore:Representative fast food chains, including McDonald's, KFC, and Burger King.Non-fast food outlets included food courts, hawker centers, and coffee shops.Data sources:We primarily used the Singapore government's centralized database (data.gov.sg) for food courts and hawker centers due to its comprehensiveness and accuracy.Google Maps served as a supplementary source for any missing data.Opening hours:We obtained opening hours directly from the websites of branded fast food chains.For other types of outlets, we employed address-matching techniques in the Google Maps database to retrieve opening hours.Data collection timeline:Data collection occurred around the first quarter of 2023, with specific dates for each category:February 2023: McDonald's and KFCJanuary 2023: Burger KingMarch 2023: Coffee shopsApril 2023: Food courts and hawker centers</p