ArticleRoyal Society open science2020
Improved susceptible-infectious-susceptible epidemic equations based on uncertainties and autocorrelation functions.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Stochastic modelling of viral infection spread via a Partial Integro-Differential Equation.Infectious Disease Modelling · 2025Article
- General SIR model for visible and hidden epidemic dynamics.Frontiers in artificial intelligence · 2025Article
- Theoretical Epidemiology Needs Urgent Attention in China.China CDC weekly · 2024Article
- Endemic characteristics of SARS-CoV-2 infection.Scientific reports · 2023Article
- Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface.Infectious Disease Modelling · 2023Article
- Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study.Chaos, solitons, and fractals · 2022Article
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Authors and funding
3 authors.
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Abstract
Compartmental equations are primary tools in the study of disease spreading processes. They provide accurate predictions for large populations but poor results whenever the integer nature of the number of agents is evident. In the latter instance, uncertainties are relevant factors for pathogen transmission. Starting from the agent-based approach, we investigate the role of uncertainties and autocorrelation functions in the susceptible-infectious-susceptible (SIS) epidemic model, including their relationship with epidemiological variables. We find new differential equations that take uncertainties into account. The findings provide improved equations, offering new insights on disease spreading processes.
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