A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development

Abstract

Background The COVID-19 outbreak, an event of global concern, has provided scientists the opportunity to use mathematical modeling to run simulations and test theories about the pandemic. Objective The aim of this study was to propose a full-scale individual-based model of the COVID-19 outbreak in Lombardy, Italy, to test various scenarios pertaining to the pandemic and achieve novel performance metrics. Methods The model was designed to simulate all 10 million inhabitants of Lombardy person by person via a simple agent-based approach using a commercial computer. In order to obtain performance data, a collision detection model was developed to enable cluster nodes in small cells that can be processed fully in parallel. Within this collision detection model, an epidemic model based mostly on experimental findings about COVID-19 was developed. Results The model was used to explain the behavior of the COVID-19 outbreak in Lombardy. Different parameters were used to simulate various scenarios relating to social distancing and lockdown. According to the model, these simple actions were enough to control the virus. The model also explained the decline in cases in the spring and simulated a hypothetical vaccination scenario, confirming, for example, the herd immunity threshold computed in previous works. Conclusions The model made it possible to test the impact of people’s daily actions (eg, maintaining social distance) on the epidemic and to investigate interactions among agents within a social network. It also provided insight on the impact of a hypothetical vaccine.

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

“…( 2021 ) Italy Compared to traditional compartmental models, a time-modulated Hawkes process to model the spread of COVID-19. GQ1, GQ2 Giacopelli ( 2021 ) Italy A full-scale individual-based model of the COVID-19 outbreak to test various scenarios about the pandemic and achieve novel performance metrics. GQ1, SQ1, SQ4 Haghighat ( 2021 ) Iran A combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized patients.…”

“…Giacopelli 2021 brings very few observations; ( b ) Malakar et al 2021 bring imprecise outcomes for cumulated COVID-19 cases during modeling; ( c ) Bushira et al 2021 have no outcomes for cumulative COVID-19 cases prediction; ( d ) Kuo et al 2021 have no outcomes for cumulative COVID-19 cases’ prediction (Ozik et al. 2021 ; Giacopelli 2021 ; Malakar 2021 ; Bushira and Ongala 2021 ; Kuo and Fu 2021 ) …”

Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities

“…• Unable to automatically account for changes in disease parameters Agent-Based [22][23][24][25][26][27] Simulation-based models that replicate the movement of individuals in a population, given probabilities for various disease parameters.…”

“…Epidemiological models for COVID-19 that forecast COVID-19 infections, hospitalizations, and deaths have been critical in guiding public health interventions [9][10][11][12], including surveillance testing and social distancing. These models include compartmental models [13][14][15][16][17][18][19][20][21], agent-based models [22][23][24][25][26][27], and differential equation models [28][29][30]. Compartmental models involve categorization into and transition between compartments such as "Susceptible", "Exposed", "Infected", and "Recovered" (SEIR).…”

An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: The case study of Lombardy region

“…Simulations carried out with an ABM show that delaying the second dose, at least for people younger than 65 years, could result in reduced cumulative mortality under certain conditions. Giacopelli [23] exploited a collision detection algorithm to develop a full-scale ABM over Lombardy (10 million agents) capable of running on a commercial PC. Assuming fully sterilizing immunity, his model suggests that herd immunity against COVID-19 can be achieved when 70% of the population is vaccinated.…”