Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread.

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model's predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced. This paper adapts an existing, large-scale, individual-based COVID-19 model to explore the benefits of updating the model in pseudo-real time. We use Approximate Bayesian Computation (ABC) to dynamically recalibrate the model's parameter values as new data emerge. ABC offers advantages over alternative calibration methods by providing information about the uncertainty associated with particular parameter values and the resulting COVID-19 predictions through posterior distributions. Analysing such distributions is crucial in fully understanding a model and its outputs. We find that forecasts of future disease infection rates are improved substantially by incorporating up-to-date observations and that the uncertainty in forecasts drops considerably in later simulation windows (as the model is provided with additional data). This is an important outcome because the uncertainty in model predictions is often overlooked when models are used in policy.

Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city's center and low in the outskirts. This contrasts with the city's population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. 'What-if' scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal.

Using social media data to understand the impact of the COVID-19 pandemic on urban green space use.

Having access to and visiting urban green space (UGS) improves liveability and provides considerable benefits to residents. However, traditional methods of investigating UGS visitation, such as questionnaires and social surveys, are usually time- and resource-intensive, and frequently provide less transferable, site-specific outcomes. This study uses social media data (Twitter) to examine spatio-temporal changes in UGS use in London associated with COVID-19 related lockdowns. It compares georeferenced Tweets posted in a 3 month period from 23 March to 23 June for 3 years covering the first lockdown in the UK in 2020, with Tweets for the same period in 2019 and 2021. The results show that (1) the land-use type of Public Park and Garden was the most frequently visited type of UGS, which may be correlated with these UGS areas remaining opening during the lockdown period; (2) the usage of UGS decreased in central London and increased in other areas during lockdown, which may correlated with working from home restrictions; (3) activities were positively associated with Physical activities maybe as a result of allowing people to take a single daily exercise, and (4) people spent more time in UGS areas on weekdays than weekends compared to pre-lockdown. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.