RESUMO
A useful parametric specification for the expected value of an epidemiological process is revived, and its statistical and empirical efficacy are explored. The Richards' curve is flexible enough to adapt to several growth phenomena, including recent epidemics and outbreaks. Here, two different estimation methods are described. The first, based on likelihood maximisation, is particularly useful when the outbreak is still ongoing and the main goal is to obtain sufficiently accurate estimates in negligible computational run-time. The second is fully Bayesian and allows for more ambitious modelling attempts such as the inclusion of spatial and temporal dependence, but it requires more data and computational resources. Regardless of the estimation approach, the Richards' specification properly characterises the main features of any growth process (e.g. growth rate, peak phase etc.), leading to a reasonable fit and providing good short- to medium-term predictions. To demonstrate such flexibility, we show different applications using publicly available data on recent epidemics where the data collection processes and transmission patterns are extremely heterogeneous, as well as benchmark datasets widely used in the literature as illustrative.
Assuntos
Teorema de Bayes , Humanos , Funções Verossimilhança , Modelos Estatísticos , Epidemias/estatística & dados numéricos , Surtos de Doenças/estatística & dados numéricos , Modelos Epidemiológicos , Métodos EpidemiológicosRESUMO
There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph's edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.
Assuntos
Doenças Transmissíveis , Simulação por Computador , Epidemias , Conceitos Matemáticos , Humanos , Epidemias/estatística & dados numéricos , Doenças Transmissíveis/epidemiologia , Doenças Transmissíveis/transmissão , Modelos Epidemiológicos , Modelos BiológicosRESUMO
BACKGROUND: Since May 7 2022, mpox has been endemic in many countries which has attracted the attention of health authorities in various countries and made control decisions, in which vaccination is the mainstream strategy. However, the shortage of vaccine doses and the reduction of protective efficacy have led to unresolved issues such as vaccine allocation decisions and evaluation of transmission scale. METHODS: We developed an epidemiological model to describe the prevalence of the mpox virus in New York City and calibrated the model to match surveillance data from May 19 to November 3, 2022. Finally, we adjusted the model to simulate and compare several scenarios of non-vaccination and pre-pandemic vaccination. RESULTS: Relative to the status quo, if vaccination is not carried out, the number of new infections increases to about 385%, and the transmission time will be extended to about 350%, while if vaccinated before the epidemic, the number of new infections decreases to 94.2-96%. CONCLUSIONS: The mpox outbreak in New York City may be linked to the Pride event. However, with current vaccine coverage, there will be no more large-scale outbreaks of mpox, even if there is another similar activity. For areas with limited vaccines, priority is given to high-risk groups in the age group [34-45] years as soon as possible.
Assuntos
Surtos de Doenças , Humanos , Cidade de Nova Iorque/epidemiologia , Surtos de Doenças/prevenção & controle , Adulto , Pessoa de Meia-Idade , Adulto Jovem , Adolescente , Criança , Idoso , Vacinação/estatística & dados numéricos , Pré-Escolar , Mpox/epidemiologia , Mpox/prevenção & controle , Lactente , Masculino , Feminino , Modelos Epidemiológicos , Idoso de 80 Anos ou mais , Vacinas contra Influenza/administração & dosagem , Recém-Nascido , Fatores Etários , PrevalênciaRESUMO
Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their calibration is not straightforward, since many factors contribute to the rapid change of the transmission dynamics. For example, there might be changes in the individual awareness, the imposition of non-pharmacological interventions and the emergence of new variants. As a consequence, model parameters such as the transmission rate are doomed to vary in time, making their assessment more challenging. Here, we propose to use Physics-Informed Neural Networks (PINNs) to track the temporal changes in the model parameters and the state variables. PINNs recently gained attention in many engineering applications thanks to their ability to consider both the information from data (typically uncertain) and the governing equations of the system. The ability of PINNs to identify unknown model parameters makes them particularly suitable to solve ill-posed inverse problems, such as those arising in the application of epidemiological models. Here, we develop a reduced-split approach for the implementation of PINNs to estimate the temporal changes in the state variables and transmission rate of an epidemic based on the SIR model equation and infectious data. The main idea is to split the training first on the epidemiological data, and then on the residual of the system equations. The proposed method is applied to five synthetic test cases and two real scenarios reproducing the first months of the Italian COVID-19 pandemic. Our results show that the split implementation of PINNs outperforms the joint approach in terms of accuracy (up to one order of magnitude) and computational times (speed up of 20%). Finally, we illustrate that the proposed PINN-method can also be adopted to produced short-term forecasts of the dynamics of an epidemic.
Assuntos
COVID-19 , Redes Neurais de Computação , Humanos , COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/prevenção & controle , Modelos Epidemiológicos , Biologia Computacional/métodos , Epidemias/estatística & dados numéricos , Epidemias/prevenção & controle , SARS-CoV-2 , Simulação por Computador , AlgoritmosRESUMO
Pathogen epidemics are key threats to human and wildlife health. Across systems, host protection from pathogens following initial exposure is often incomplete, resulting in recurrent epidemics through partially-immune hosts. Variation in population-level protection has important consequences for epidemic dynamics, but how acquired protection influences inter-individual heterogeneity in susceptibility and its epidemiological consequences remains understudied. We experimentally investigated whether prior exposure (none, low-dose, or high-dose) to a bacterial pathogen alters host heterogeneity in susceptibility among songbirds. Hosts with no prior pathogen exposure had little variation in protection, but heterogeneity in susceptibility was significantly augmented by prior pathogen exposure, with the highest variability detected in hosts given high-dose prior exposure. An epidemiological model parameterized with experimental data found that heterogeneity in susceptibility from prior exposure more than halved epidemic sizes compared with a homogeneous population with identical mean protection. However, because infection-induced mortality was also greatly reduced in hosts with prior pathogen exposure, reductions in epidemic size were smaller than expected in hosts with prior exposure. These results highlight the importance of variable protection from prior exposure and/or vaccination in driving population-level heterogeneity and epidemiological dynamics.
Assuntos
Doenças das Aves , Animais , Suscetibilidade a Doenças , Doenças das Aves/epidemiologia , Doenças das Aves/microbiologia , Interações Hospedeiro-Patógeno , Modelos EpidemiológicosRESUMO
Models with several levels of mixing (households, workplaces), as well as various corresponding formulations for R 0 , have been proposed in the literature. However, little attention has been paid to the impact of the distribution of the population size within social structures, effect that can help plan effective interventions. We focus on the influence on the model outcomes of teleworking strategies, consisting in reshaping the distribution of workplace sizes. We consider a stochastic SIR model with two levels of mixing, accounting for a uniformly mixing general population, each individual belonging also to a household and a workplace. The variance of the workplace size distribution appears to be a good proxy for the impact of this distribution on key outcomes of the epidemic, such as epidemic size and peak. In particular, our findings suggest that strategies where the proportion of individuals teleworking depends sublinearly on the size of the workplace outperform the strategy with linear dependence. Besides, one drawback of the model with multiple levels of mixing is its complexity, raising interest in a reduced model. We propose a homogeneously mixing SIR ODE-based model, whose infection rate is chosen as to observe the growth rate of the initial model. This reduced model yields a generally satisfying approximation of the epidemic. These results, robust to various changes in model structure, are very promising from the perspective of implementing effective strategies based on social distancing of specific contacts. Furthermore, they contribute to the effort of building relevant approximations of individual based models at intermediate scales.
Assuntos
COVID-19 , Conceitos Matemáticos , Processos Estocásticos , Local de Trabalho , Humanos , Local de Trabalho/estatística & dados numéricos , COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/prevenção & controle , Características da Família , Epidemias/estatística & dados numéricos , Densidade Demográfica , Modelos Epidemiológicos , Modelos Biológicos , Simulação por Computador , Número Básico de Reprodução/estatística & dados numéricos , SARS-CoV-2RESUMO
The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.
Assuntos
Número Básico de Reprodução , COVID-19 , Saúde Pública , SARS-CoV-2 , COVID-19/transmissão , COVID-19/epidemiologia , Humanos , Índia/epidemiologia , SARS-CoV-2/isolamento & purificação , Análise Custo-Benefício , Pandemias/prevenção & controle , Modelos Epidemiológicos , Modelos TeóricosRESUMO
In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India.
Assuntos
Algoritmos , COVID-19 , SARS-CoV-2 , COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/prevenção & controle , Humanos , Índia/epidemiologia , Pandemias , Fatores de Tempo , Modelos Epidemiológicos , Modelos EstatísticosRESUMO
Hand, foot and mouth disease (HFMD) is a Class C infectious disease that carries particularly high risk for preschool children and is a leading cause of childhood death in some countries. We mimic the periodic outbreak of HFMD over a 2-year period-with differing amplitudes-and propose a dynamic HFMD model that differentiates transmission between mature and immature individuals and uses two possible optimal-control strategies to minimize case numbers, total costs and deaths. We parameterized the model by fitting it to HFMD data in mainland China from January 2011 to December 2018, and the basic reproduction number was estimated as 0.9599. Sensitivity analysis demonstrates that transmission between immature and mature individuals contributes substantially to new infections. Increasing the isolation rates of infectious individuals-particularly mature infectious individuals-could greatly reduce the outbreak risk and potentially eradicate the disease in a relatively short time period. It follows that we have a reasonable chance of controlling HFMD if we can reduce transmission in children under 7 and isolate older infectious individuals.
Assuntos
Número Básico de Reprodução , Surtos de Doenças , Doença de Mão, Pé e Boca , Conceitos Matemáticos , Modelos Biológicos , Estações do Ano , Doença de Mão, Pé e Boca/transmissão , Doença de Mão, Pé e Boca/epidemiologia , Doença de Mão, Pé e Boca/prevenção & controle , China/epidemiologia , Humanos , Número Básico de Reprodução/estatística & dados numéricos , Surtos de Doenças/prevenção & controle , Surtos de Doenças/estatística & dados numéricos , Pré-Escolar , Criança , Lactente , Fatores Etários , Simulação por Computador , Isolamento de Pacientes/estatística & dados numéricos , Modelos EpidemiológicosRESUMO
The emergence of multi-disease epidemics presents an escalating threat to global health. In response to this serious challenge, we present an innovative stochastic susceptible-vaccinated-infected-recovered epidemic model that addresses the dynamics of two diseases alongside intricate vaccination strategies. Our novel model undergoes a comprehensive exploration through both theoretical and numerical analyses. The stopping time concept, along with appropriate Lyapunov functions, allows us to explore the possibility of a globally positive solution. Through the derivation of reproduction numbers associated with the stochastic model, we establish criteria for the potential extinction of the diseases. The conditions under which one or both diseases may persist are explained. In the numerical aspect, we derive a computational scheme based on the Milstein method. The scheme will not only substantiate the theoretical results but also facilitate the examination of the impact of parameters on disease dynamics. Through examples and simulations, we have a crucial impact of varying parameters on the system's behavior.
Assuntos
Epidemias , Humanos , Processos Estocásticos , Simulação por Computador , Número Básico de Reprodução , Modelos Biológicos , Vacinação , Modelos EpidemiológicosRESUMO
To explore the influence of state changes on brucellosis, a stochastic brucellosis model with semi-Markovian switchings and diffusion is proposed in this paper. When there is no switching, we introduce a critical value R s and obtain the exponential stability in mean square when R s < 1 by using the stochastic Lyapunov function method. Sudden climate changes can drive changes in transmission rate of brucellosis, which can be modelled by a semi-Markov process. We study the influence of stationary distribution of semi-Markov process on extinction of brucellosis in switching environment including both stable states, during which brucellosis dies out, and unstable states, during which brucellosis persists. The results show that increasing the frequencies and average dwell times in stable states to certain extent can ensure the extinction of brucellosis. Finally, numerical simulations are given to illustrate the analytical results. We also suggest that herdsmen should reduce the densities of animal habitation to decrease the contact rate, increase slaughter rate of animals and apply disinfection measures to kill brucella.
Assuntos
Brucelose , Simulação por Computador , Cadeias de Markov , Conceitos Matemáticos , Modelos Biológicos , Processos Estocásticos , Brucelose/transmissão , Brucelose/epidemiologia , Brucelose/microbiologia , Animais , Humanos , Modelos Epidemiológicos , Brucella/patogenicidade , Mudança ClimáticaRESUMO
Epidemic models of susceptibles, exposed, infected, recovered and deceased (SΕIRD) presume homogeneity, constant rates and fixed, bilinear structure. They produce short-range, single-peak responses, hardly attained under restrictive measures. Tuned via uncertain I,R,D data, they cannot faithfully represent long-range evolution. A robust epidemic model is presented that relates infected with the entry rate to health care units (HCUs) via population averages. Model uncertainty is circumvented by not presuming any specific model structure, or constant rates. The model is tuned via data of low uncertainty, by direct monitoring: (a) of entries to HCUs (accurately known, in contrast to delayed and non-reliable I,R,D data) and (b) of scaled model parameters, representing population averages. The model encompasses random propagation of infections, delayed, randomly distributed entries to HCUs and varying exodus of non-hospitalized, as disease severity subdues. It closely follows multi-pattern growth of epidemics with possible recurrency, viral strains and mutations, varying environmental conditions, immunity levels, control measures and efficacy thereof, including vaccination. The results enable real-time identification of infected and infection rate. They allow design of resilient, cost-effective policy in real time, targeting directly the key variable to be controlled (entries to HCUs) below current HCU capacity. As demonstrated in ex post case studies, the policy can lead to lower overall cost of epidemics, by balancing the trade-off between the social cost of infected and the economic contraction associated with social distancing and mobility restriction measures.
Assuntos
COVID-19 , Epidemias , Humanos , Epidemias/estatística & dados numéricos , COVID-19/transmissão , COVID-19/epidemiologia , COVID-19/prevenção & controle , Conceitos Matemáticos , Modelos Epidemiológicos , Doenças Transmissíveis/epidemiologia , Doenças Transmissíveis/transmissão , SARS-CoV-2 , Número Básico de Reprodução/estatística & dados numéricos , Doença pelo Vírus Ebola/epidemiologia , Doença pelo Vírus Ebola/transmissão , Doença pelo Vírus Ebola/prevenção & controle , Política de SaúdeRESUMO
Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.
Assuntos
Número Básico de Reprodução , COVID-19 , Simulação por Computador , Conceitos Matemáticos , Modelos Biológicos , Pandemias , SARS-CoV-2 , COVID-19/transmissão , COVID-19/epidemiologia , COVID-19/prevenção & controle , Humanos , Brasil/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Modelos Epidemiológicos , Quarentena/estatística & dados numéricosRESUMO
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
Assuntos
COVID-19 , SARS-CoV-2 , COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/prevenção & controle , Humanos , Pandemias , Modelos Epidemiológicos , Epidemias/estatística & dados numéricosRESUMO
Bluetongue virus (BT) is a vector-borne virus that causes a disease, called bluetongue, which results in significant economic loss and morbidity in sheep, cattle, goats and wild ungulates across all continents of the world except Antarctica. Despite the geographical breadth of its impact, most BT epidemiological models are informed by parameters derived from the 2006-2009 BTV-8 European outbreak. The aim of this study was to develop a highly adaptable model for BT which could be used elsewhere in the world, as well as to identify the parameters which most influence outbreak dynamics, so that policy makers can be properly informed with the most current information to aid in disease planning. To provide a framework for future outbreak modelling and an updated parameterisation that reflects natural variation in infections, a newly developed and parameterised two-host, two-vector species ordinary differential equation model was formulated and analysed. The model was designed to be adaptable to be implemented in any region of the world and able to model both epidemic and endemic scenarios. It was parameterised using a systematic literature review of host-to-vector and vector-to-host transmission rates, host latent periods, host infectious periods, and vaccine protection factors. The model was demonstrated using the updated parameters, with South Africa as a setting based on the Western Cape's known cattle and sheep populations, local environmental parameters, and Culicoides spp. presence data. The sensitivity analysis identified that the duration of the infectious period for sheep and cows had the greatest impact on the outbreak length and number of animals infected at the peak of the outbreak. Transmission rates from cows and sheep to C. imicola midges greatly influenced the day on which the peak of the outbreak occurred, along with the duration of incubation period, and infectious period for cows. Finally, the protection factor of the vaccine had the greatest influence on the total number of animals infected. This knowledge could aid in the development of control measures. Due to gradual climate and anthropological change resulting in alterations in vector habitat suitability, BT outbreaks are likely to continue to increase in range and frequency. Therefore, this research provides an updated BT modelling framework for future outbreaks around the world to explore transmission, outbreak dynamics and control measures.
Assuntos
Vírus Bluetongue , Bluetongue , Doenças dos Bovinos , Surtos de Doenças , Animais , Bovinos , Bluetongue/epidemiologia , Bluetongue/transmissão , Bluetongue/virologia , Bluetongue/prevenção & controle , Vírus Bluetongue/patogenicidade , Doenças dos Bovinos/epidemiologia , Doenças dos Bovinos/virologia , Doenças dos Bovinos/transmissão , Ceratopogonidae/virologia , Surtos de Doenças/prevenção & controle , Surtos de Doenças/veterinária , Cabras/virologia , Insetos Vetores/virologia , Ovinos/virologia , África do Sul/epidemiologia , Modelos EpidemiológicosRESUMO
The resurgence of monkeypox causes considerable healthcare risks needing efficient immunization programs. This work investigates the monkeypox disease dynamics in the UK, focusing on the impact of vaccination under real data. The key difficulty is to correctly predict the spread of the disease and evaluate the success of immunization efforts. We construct a mathematical model for monkeypox infection and extend it to the fractional case considering the Caputo derivative. The analysis ensures the positivity, boundedness, and uniqueness of the solution for the non-integer system. We conduct a local asymptotical stability analysis (LAS) at the disease-free equilibrium (DFE) D0, showing the result for R0<1. Additionally, we demonstrate the existence of multiple endemic equilibria and provide conditions for backward bifurcation, which are illustrated graphically. Using real case data from the UK, we estimate model parameters via the nonlinear least square method. Our results show that, without vaccination, R2≈0.8, whereas vaccination reduces it to R2v=0.48. We perform sensitivity analysis to identify key parameters influencing disease elimination, presenting the outcomes through graphs. To solve numerically the fractional model, we outline a numerical scheme and provide detailed results under various parameter assumptions. Our findings suggest that high vaccine efficacy, a low waning rate of the vaccines, and increased vaccination of the infected people can significantly reduce the future cases of monkeypox in the UK. The present study offers a comprehensive framework for monkeypox dynamics and informs public health strategies for effective disease control and prevention.
Assuntos
Vacinação , Humanos , Modelos Biológicos , Reino Unido/epidemiologia , Doenças Transmissíveis/transmissão , Doenças Transmissíveis/epidemiologia , Modelos EpidemiológicosRESUMO
Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic.
Assuntos
Febre Aftosa , Humanos , Febre Aftosa/epidemiologia , Reino Unido/epidemiologia , Análise Espacial , Modelos Epidemiológicos , Epidemias , Doenças Transmissíveis/epidemiologia , Simulação por Computador , Modelos EstatísticosRESUMO
Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information. Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk. We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them to produce results in line with, or superior to, parametric spatial ILMs. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease.
Assuntos
Teorema de Bayes , Febre Aftosa , Cadeias de Markov , Método de Monte Carlo , Humanos , Febre Aftosa/epidemiologia , Febre Aftosa/transmissão , Reino Unido/epidemiologia , Análise Espacial , Modelos Epidemiológicos , Simulação por Computador , Modelos EstatísticosRESUMO
This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic's spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city's 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments.