A dynamic microsimulation model for epidemics

Abstract

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.

Keywords: COVID-19; Coronavirus; Dynamics; Microsimulation; SEIR; Spatial-interaction.

Fig. 1

Schematic showing the structural components of the SEIR model.

Fig. 2

The process of disease spread using the dynamic microsimulation model.

Fig. 3

Example output from augmented SPENSER dataset proportion of time spent at home, proportion of time spent at work, the percentage of key workers and the percentage of individuals with underlying health conditions (doctor diagnosed CVD, high blood pressure, diabetes, COPD and a BMI greater than 40) for the MSOAs in the five Local Authority Districts that comprise Devon.

Fig. 4

Geolytix retail point location coverage in the UK. The colours on the points represent one of four different floorspace bands. Flow lines connect origin MSOA zones to Geolytix retail points according to the modelled trip probabilities. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Trip probabilities from MSOA origin zones to Geolytix retail points (black dots). The colour of the flow line represents the magnitude of the trip probability from <0.0094 (blue) to >0.0226 (red). The map on the left contains the Geolytix retail points, which are omitted for clarity in the map on the right. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

Relative to a baseline of Jan 3-Feb 6, 2020 the proportion of time spent outside home (blue) and at home (orange). The official lockdown date March 23, 2020 is shown with the dashed black line. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7

Predicted daily number of COVID cases under a baseline scenario and a scenario where lockdown effects were a week earlier, by age group. Each of the scenarios were run 1,000 times to capture variation in the stochastic elements of the model. The daily number of cases from each of these runs is shown as a transparent line. The median value of all of the runs of each scenario is shown in bold. The dashed lines show the 95% uncertainty intervals for each scenario. For the baseline scenario Google Mobility data is used to invoke lockdown effects on reducing individuals time outside their home, for the experimental scenario where lockdown is a week earlier we shift the Google Mobility data a week earlier.

Fig. 8

Predicted proportion of individuals in each age group, colours represent each disease status. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9

Predicted percentage of each MSOA that was infected with COVID during the first 70 days of the outbreak under two scenarios, the baseline scenario and an experimental scenario where lockdown was shifted a week earlier.

Fig. 10

Screenshot of the GUI, showing point cloud visualisation and time-series charts.