Correction: Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach

Abstract

This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multisourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.

Maritime transportation and people mobility in the early diffusion of COVID-19 in Croatia

“…Other methods were previously employed to study spreading of infection diseases through interaction between different geographical regions. It is worth mentioning the different methods among which lattice simulation using SIR models with space extension and interaction using Bayesian maximum entropy theory ( 38 ), lattice spatio-temporal modeling framework integrating SIR and log-Gaussian Cox process (LGCP) process ( 39 ), graph modelization where policies to curb the spreading are tested by removing individuals ( 40 ) or agent-based simulations of aerosol and pedestrian trails to track the spreading at the level of an airport ( 41 ). The eRG method is more economical as it only considers the total number of infections, and it has been used in conjunction with airborne traffic to study the early diffusion of COVID-19 in the United States ( 42 ).…”

Spatio-temporal impact of self-financed rotavirus vaccination on rotavirus and acute gastroenteritis hospitalisations in the valencia region, spain.

“…In fact, a number of key studies have shown that the spread of infectious diseases are heterogeneously distributed in space because places differ in their environmental and population characteristics (18) (19). Consequently, epidemiological studies are often confounded by complex and dynamic spatio-temporal processes (20) , (18). RV vaccine uptake and hospitalisations could, therefore, vary from time to time and between places for different reasons, including complex interaction of population demographics, socioeconomic inequalities, environmental factors, circulation of RV strains and their interactions across space and time (21).…”

Spatio-Temporal Impact of Self-Financed Rotavirus Vaccination on Rotavirus and Acute Gastroenteritis Hospitalisations in the Valencia Region, Spain.

“…In fact, a number of key studies have shown that the spread of infectious diseases are heterogeneously distributed in space because places differ in their environmental and population characteristics (18) (19). Consequently, epidemiological studies are often confounded by complex and dynamic spatio-temporal processes (20) , (18).…”