Coupled Map Lattice Model for Insects and Spreadable Substances

Rodrigues, Luiz Alberto Díaz; Varriale, Maria Cristina; Godoy, Wesley Augusto Conde; Mistro, Diomar Cristina

doi:10.1007/978-3-319-06877-0_7

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…According to the Coupled Map Network (CMN) theory, e.g. [18] and references therein, each site x = (x i , x j ) of this network is represented by integer coordinates x i = is, x j = js, where s is the size of the site,…”

Section: Mathematical Modelingmentioning

confidence: 99%

Mathematical Modeling and Georeferenced Forecasting for the COVID-19 at the State of RS, Brazil

Marques

Cezaro²,

Alves³

et al. 2023

TCAM

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In this contribution, we present a predictive tool developed to help in the management of the evolution of the COVID-19 pandemic situation in Rio Grande do Sul (RS) - Brazil. This tool is the result of georeferenced data analysis, mathematical modeling, and parameter calibration for the dynamics of a SIR-type model defined on a spatial structure that allows distinct subpopulations to interact, similar to the controlled distancing (A_l for l = 1, ···, 21) groupings proposed by the RS government and public health authorities. The predictive analysis, updated biweekly, provides three distinct scenarios per month (milder, average, and severe) and is made available as WebSIGs (Geographic Information System - GIS). The forecast of the average scenario for each Al group is the result of a simulation of the proposed SIRtype dynamics with calibrated parameters derived from an augmented Lagrangian maximum a posteriori estimation and data on the number of infected cases made available by the RS Health Secretariat. The milder and severe scenarios are obtained from the average scenario, with changes in the contagion rates of each Al group. When compared to the number of infections reported in each Al group, the modeling predictions for a biweekly time window (the first two weeks) were quite satisfactory, with errors ranging from 0% to 5.13%, gradually increasing over time. Therefore, we suggest a biweekly re-calibration of the parameters and corresponding forecasts as a wise strategy.

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Section: Mathematical Modelingmentioning

confidence: 99%

Mathematical Modeling and Georeferenced Forecasting for the COVID-19 at the State of RS, Brazil

Marques

Cezaro²,

Alves³

et al. 2023

TCAM

View full text Add to dashboard Cite

show abstract

“…Currently, the importance of spatial explicit approaches has been well recognized in theoretical ecology, although the space has been neglected by theoretical models in entomology in recent decades (Rodrigues et al, 2014). We used computer modelling with spatial explicit as a tool for understanding the behavioural process.…”

Section: Introductionmentioning

confidence: 99%