Agent-Based Modeling of COVID-19 Transmission: A Case Study of Housing Densities in Sankalitnagar, Ahmedabad DOI Creative Commons

Molly French,

Amit Patel, Abid Qureshi

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(6), P. 208 - 208

Published: June 17, 2024

The differential transmission of COVID-19 depending on the socio-economic status a neighborhood is well established. For example, several studies have shown that was higher in poorer and denser neighborhoods than wealthier ones. However, what less known how this varied rate interacted with established health measures, i.e., face masks lockdowns, context developing countries to reduce pandemic cases hence resulted fewer deaths. This study uses an Agent-Based Model (ABM) simulation examine impacts mitigation efforts (i.e., lockdowns combined masks) across single Ahmedabad, city state Gujarat, India. model parameterized using real-world population data, which allows us simulate spread find conditions most closely match realities spring 2020. Consequently, can be used understand impact nation-wide lockdown COVID Ahmedabad as function housing density. Thus, invaluable insight into effectiveness measure derived. Further information about by neighborhood, other factors impacted it, ascertained.

Language: Английский

Deterministic optimal control compartmental model for COVID-19 infection DOI

Barbara Fosua Afful,

Godfred Agyemang Safo,

D. Marri

et al.

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 20, 2025

Language: Английский

Citations

2
Why Gender and Sex Matter in Infectious Disease Modelling: A Conceptual Framework DOI Creative Commons
Diane Auderset, Julien Riou, Carole Clair

et al.

SSM - Population Health, Journal Year: 2025, Volume and Issue: 30, P. 101775 - 101775

Published: March 12, 2025

The COVID-19 pandemic underscored the differential impact of infectious diseases across population groups, with gender and sex identified as important dimensions influencing transmission health outcomes. Sex-related biological factors, such differences in immune response comorbidities, contribute to men's heightened severity risks, while norms roles influence exposure patterns, adherence prevention measures, healthcare access, women's higher reported infection rates certain contexts. Despite widely observed gender/sex disparities, disease models frequently overlook key dimensions, leading gaps understanding potential blind spots public interventions. This paper develops a conceptual framework based on Susceptible-Exposed-Infectious-Recovered/Deceased (SEIR/D) compartmental model map pathways through which may susceptibility, exposure, transmission, recovery, mortality. Using narrative review modelling, epidemiological, clinical studies, this identifies characterises main social mechanisms matter-including gendered occupational preventive disparities healthcare-seeking behaviour-alongside sex-based severity. also examines gender-related variations epidemiological surveillance data, highlighting testing uptake hospitalisation referrals that could outputs. By synthesising these insights, provides theoretical foundation for integrating into models. It advocates interdisciplinary collaboration between modellers, scientists, clinicians advance gender- sex-sensitive modelling approaches. Accounting can enhance predictive accuracy, inform intervention strategies, promote equity response.

Language: Английский

Citations

1
Asymptotic Analysis of Optimal Vaccination Policies DOI Creative Commons
Matthew J. Penn, Christl A. Donnelly

Bulletin of Mathematical Biology, Journal Year: 2023, Volume and Issue: 85(3)

Published: Jan. 20, 2023

Abstract Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such is complicated, resultant solution may be difficult to explain policy-makers public. The key novelty this paper derivation leading-order optimal policy under multi-group susceptible–infected–recovered dynamics two different cases. Firstly, it considers case small vulnerable subgroup population shows that (in asymptotic limit) vaccinate group first, regardless properties other groups. Then, vaccine supply transforms problem into simple knapsack by linearising final size equations. Both these cases are then explored further through numerical examples, which show solutions also directly useful for realistic parameter values. Moreover, findings give some general principles will help public understand reasoning behind programs more generic

Language: Английский

Citations

12
Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach DOI Creative Commons
Cheng Cheng, Elayaraja Aruchunan, Noor Aziz

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 15, 2025

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance understanding of temporal evolution infectious diseases. This work integrates differential equations with deep predict time-varying parameters in SEIRV model. Experimental results based on reported data from China between January 1, and December 2022, demonstrate that proposed method can accurately learn future states. Our hybrid SEIRV-DNNs also be applied other diseases such as influenza dengue, some modifications compartments accommodate related control measures. approach will facilitate improving predictive modeling optimizing public health intervention strategies.

Language: Английский

Citations

0
Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter DOI Creative Commons
Matteo Perini, Teresa K. Yamana, Marta Galanti

et al.

Epidemics, Journal Year: 2025, Volume and Issue: unknown, P. 100818 - 100818

Published: Jan. 1, 2025

Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics pandemics, such as COVID-19. Effective modeling transmission in interconnected populations helps inform public health responses interventions across regions. We developed a novel metapopulation model simulating respiratory virus North America region, specifically 96 states, provinces, territories Canada, Mexico, United States. The is informed by COVID-19 case data, which assimilated using Ensemble Adjustment Kalman filter (EAKF), Bayesian inference algorithm. Additionally, commuting mobility data used to build adjust network movement locations on daily basis. This model-inference system provides estimates dynamics, infection rates, ascertainment rates each from January 2020 March 2021. results highlight differences among three countries. structure enables rapid simulation at large scale, assimilation method makes responsive changes dynamics. can serve versatile platform other diseases American region.

Language: Английский

Citations

0
Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model DOI Creative Commons

Yanwen Shao,

Tsz Kin Wan,

Kei Hang Katie Chan

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 10, 2025

Since December 2019, cases of COVID-19 have spread globally, caused millions deaths and huge economic losses. To investigate the impact different factors predict future trend, this study collects relevant data for 15 countries, containing 44 features in about 900 days, which can be classified into four groups: pandemic information, characteristics climate, prevention policies. Through selection several important features, we identified that stronger on increase new groups. Then, use a long-time span to by training long short-term memory (LSTM) model, support vector regressor (SVR) temporal convolutional network (TCN), among LSTM possessed best performance offered good generalization ability. Under metric explained variance scores (EVS), prediction performances were most accurate Germany (0.864), Italy (0.860) United States (0.766). Overall, results may provide insight predictions number more countries/regions offer some insightful recommendation governments carry out effective policies prevent COVID-19.

Language: Английский

Citations

0
Optimizing overlapping non-pharmaceutical interventions with a socio-demographic model DOI Creative Commons
Guido Benedetti, Ryan Weightman, Benedetto Piccoli

et al.

Bollettino dell Unione Matematica Italiana, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Language: Английский

Citations

0
A mathematical model for assessing vaccination's efficacy as a preventative strategy against newly emerging COVID-19 variants DOI
Akeem Olarewaju Yunus,

Morufu Oyedunsi Olayiwola

Vacunas, Journal Year: 2025, Volume and Issue: unknown, P. 500392 - 500392

Published: April 1, 2025

Language: Английский

Citations

0
Booster Dose Vaccination and Dynamics of COVID-19 Pandemic in the Fifth Wave: An Efficient and Simple Mathematical Model for Disease Progression DOI Creative Commons
Thitiya Theparod,

Pannathon Kreabkhontho,

Watchara Teparos

et al.

Vaccines, Journal Year: 2023, Volume and Issue: 11(3), P. 589 - 589

Published: March 3, 2023

Mathematical studies exploring the impact of booster vaccine doses on recent COVID-19 waves are scarce, leading to ambiguity regarding significance doses.A mathematical model with seven compartments was used determine basic and effective reproduction numbers proportion infected people during fifth wave COVID-19. Using next-generation matrix, we computed parameter, Rt.During wave, reproductive number in Thailand calculated be R0= 1.018691. Analytical analysis revealed both local global stability disease-free equilibrium presence an endemic equilibrium. A dose-dependent decrease percentage individuals observed vaccinated population. The simulation results matched real-world data patients, establishing suitability model. Furthermore, our suggested that who had received vaccinations a better recovery rate death lowest among those dose. dose reduced over time, suggesting efficacy 0.92.Our study employed rigorous analytical approach accurately describe dynamics Thailand. Our findings demonstrated administering can significantly increase rate, resulting lower reduction individuals. These have important implications for public health policymaking, as they provide useful information more forecasting pandemic improving efficiency interventions. Moreover, contributes ongoing discourse effectiveness mitigating pandemic. Essentially, suggests substantially reduce spread virus, supporting case widespread campaigns.

Language: Английский

Citations

10
Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning DOI Creative Commons
Péter Polcz, István Z. Reguly, Kálmán Tornai

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(5), P. e1013028 - e1013028

Published: May 8, 2025

Optimal intervention planning is a critical part of epidemiological control, which difficult to attain in real life situations. Ordinary differential equation (ODE) models can be used optimize control but the results not easily translated interventions highly complex environments. Agent-based methods on other hand allow detailed modeling environment optimization precluded by large number parameters. Our goal was combine advantages both approaches, i.e., The epidemic objectives are expressed as time-dependent reference for infected people. To track this reference, model predictive controller (MPC) designed with compartmental ODE prediction compute optimal level stringency interventions, later specific actions such mobility restriction, quarantine policy, masking rules, school closure. effects transmission rate pathogen, and hence their stringency, computed using PanSim, an agent-based simulator that contains environment. realism practical applicability method demonstrated wide range discrete measures taken into account. Moreover, change between applied during consecutive intervals also minimized. We found combined strategy able efficiently COVID-19-like process, terms incidence, virulence, infectiousness surprisingly sparse (e.g. 21 day) regimes. At same time, approach proved robust even scenarios significant uncertainties, unknown rate, uncertain time probability constants. high performance computation allows test cases run. proposed computational framework reused management unexpected pandemic events customized needs any country.

Language: Английский

Citations

0