scholarly journals An Economist’s Guide to Epidemiology Models of Infectious Disease

2020 ◽  
Vol 34 (4) ◽  
pp. 79-104
Author(s):  
Christopher Avery ◽  
William Bossert ◽  
Adam Clark ◽  
Glenn Ellison ◽  
Sara Fisher Ellison
Keyword(s):  
Infectious Disease ◽  
Political Economy ◽  
Disease Transmission ◽  
Sir Model ◽  
Disease Suppression ◽  
Disease Spread ◽  
High Profile ◽  
Models Of Disease ◽  
Susceptible Populations

We describe the structure and use of epidemiology models of disease transmission, with an emphasis on the susceptible/infected/recovered (SIR) model. We discuss high-profile forecasts of cases and deaths that have been based on these models, what went wrong with the early forecasts, and how they have adapted to the current COVID pandemic. We also offer three distinct areas where economists would be well positioned to contribute to or inform this epidemiology literature: modeling heterogeneity of susceptible populations in various dimensions, accommodating endogeneity of the parameters governing disease spread, and helping to understand the importance of political economy issues in disease suppression.

scholarly journals The attainment of herd immunity with a reducing risk of contact infection in modelling 

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Hishamshah Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Model ◽  
Force Of Infection ◽  
Initial Stage ◽  
And Control ◽  
Model Features

Abstract The risk of contact infection among susceptible individuals in a randomly mixed population can be reduced by the presence of immune individuals and this principle forms the fundamental of herd immunity. The conventional susceptible-infectious-recovered (SIR) model features an infection-induced herd immunity model, but does not include the reducing risk of contact infection among susceptible individuals in the transmission model, therefore tends to overestimate the transmission dynamics of infectious diseases. Here we show that the reducing risk of contact infection among susceptible individuals can be achieved by incorporating the proportion of susceptible individuals (model A) or the inverse of proportion of recovered individuals (model B) in the force of infection of the SIR model. We numerically simulated the conventional SIR model and both new SIR models A and B under the exact condition with a basic reproduction number of 3·0. Prior to the numerical simulation, the threshold for the eradication of infectious disease through herd immunity was expected to be 0·667 (66·7%) for all three models. All three models performed likewise at the initial stage of disease transmission. In the conventional SIR model, the infectious disease subsided when 94·0 % of the population had been infected and recovered, way above the expected threshold for eradication and control of the infectious disease. Both models A and B simulated the infectious disease to diminish when 66·7% and 75·6% of the population had been infected, showing herd immunity might protect more susceptible individuals from the infectious disease as compared to the projection generated by the conventional SIR. Our study shows that model A provides a better framework for modelling herd immunity through vaccination, while model B provides a better framework for modelling herd immunity through infection. Both models overcome the insufficiency of the conventional SIR model in attaining the effect of herd immunity in modelling outputs, which is important and relevant for modelling infectious disease, such as the COVID-19 in a randomly mixed population.

scholarly journals Modelling song popularity as a contagious process

2021 ◽  
Vol 477 (2253) ◽  
pp. 20210457
Author(s):  
Dora P. Rosati ◽  
Matthew H. Woolhouse ◽  
Benjamin M. Bolker ◽  
David J. D. Earn
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Disease Transmission ◽  
Sir Model ◽  
Sir Epidemic Model ◽  
Infectious Disease Transmission ◽  
Download Time ◽  
Sir Epidemic ◽  
Popular Songs ◽  
Song Preferences

Popular songs are often said to be ‘contagious’, ‘infectious’ or ‘viral’. We find that download count time series for many popular songs resemble infectious disease epidemic curves. This paper suggests infectious disease transmission models could help clarify mechanisms that contribute to the ‘spread’ of song preferences and how these mechanisms underlie song popularity. We analysed data from MixRadio, comprising song downloads through Nokia cell phones in Great Britain from 2007 to 2014. We compared the ability of the standard susceptible–infectious–recovered (SIR) epidemic model and a phenomenological (spline) model to fit download time series of popular songs. We fitted these same models to simulated epidemic time series generated by the SIR model. Song downloads are captured better by the SIR model, to the same extent that actual SIR simulations are fitted better by the SIR model than by splines. This suggests that the social processes underlying song popularity are similar to those that drive infectious disease transmission. We draw conclusions about song popularity within specific genres based on estimated SIR parameters. In particular, we argue that faster spread of preferences for Electronica songs may reflect stronger connectivity of the ‘susceptible community’, compared with the larger and broader community that listens to more common genres.

scholarly journals Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

2020 ◽  
Vol 16 (12) ◽  
pp. e1008447
Author(s):  
Christopher M. Pooley ◽  
Glenn Marion ◽  
Stephen C. Bishop ◽  
Richard I. Bailey ◽  
Andrea B. Doeschl-Wilson
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Statistical Power ◽  
Genetic Effect ◽  
Software Tool ◽  
Disease Spread ◽  
Simultaneous Estimation ◽  
Parameter Estimates ◽  
Infectious Disease Transmission ◽  
Wide Range

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.

scholarly journals Fixed Point Analysis of Kermack Mckendrick SIR Model

10.29007/pl65 ◽  
2018 ◽  
Author(s):  
Fenny Narsingani ◽  
Mahendra B Prajapati ◽  
Pravin Himmatlal Bhathawala
Keyword(s):  
Infectious Disease ◽  
Fixed Point ◽  
Threshold Value ◽  
Sir Model ◽  
Disease Spread ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Point Analysis ◽  
Transmission Of Disease

Public health is constantly under risk due to growing microorganisms. Infectious disease spread rapidly among the population in contact and so people take the different steps to reduce the transmission of disease. Compartmental model such as SIR model developed by W. Kermack and G Mckendrick are modeled for the progress of epidemic. Fixed point analysis has been applied to mathematical models of compartmental infectious disease models for understanding the long term outcome of disease. We have applied the analysis to the spread of infectious disease and obtained the threshold value and this threshold value helps us to predict when epidemic peaks.

scholarly journals Infectious disease transmission and contact networks in wildlife and livestock

2015 ◽  
Vol 370 (1669) ◽  
pp. 20140107 ◽  
Author(s):  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Wild Animal ◽  
Disease Spread ◽  
Farmed Fish ◽  
Infectious Disease Transmission ◽  
Network Modelling ◽  
Flexible Tool ◽  
Contact Networks ◽  
Contact Data

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

scholarly journals Spatial diffusion of COVID-19: An econometric-based approach.

2021 ◽  
Author(s):  
Phani Devarakonda ◽  
Ravi Sadasivuni ◽  
Jianhong Wu ◽  
David Shaw
Keyword(s):  
New York ◽  
Spatial Data ◽  
Disease Transmission ◽  
Activity Patterns ◽  
Epidemiological Studies ◽  
Behavioral Pattern ◽  
Disease Spread ◽  
Spatial Diffusion ◽  
Novel Approach ◽  
Susceptible Populations

An understanding of how infected-susceptible populations interact is critical to identify underlying causal factors and disease transmission patterns of infectious diseases. Disease transmission patterns are dynamic, non-linear, and spatially complex. This anisotropic characteristic of disease spread necessitates the ideal solution to be sensitive to the geographic context. A Spatial Diffusion Model (SDM) to predict interaction potential and COVID-19 risk probability is developed by adapting the Newtonian gravity model. This novel approach overcomes the limitations of existing epidemiological studies by characterizing the behavioral patterns of the infected population to model the spatiotemporal transmission of disease across the geographic space. The proposed model is robust as it couples a multicriteria behavioral pattern to enhance predictive capability. The model shows an 83.74% correlation with the observational COVID-19 case data. The highest risk patterns for COVID-19 are predicted in the neighborhoods of New York City (NYC), exhibiting clustered socioeconomic disparities along with racial and ethnic heterogeneity. Policymakers can use these results to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions. This study emphasizes the need for improved spatial epidemiological models including enhanced depictions of human activity patterns and the need to integrate spatial data with advanced mathematical models.

scholarly journals Revealing mechanisms of infectious disease transmission through empirical contact networks

2017 ◽  
Author(s):  
Pratha Sah ◽  
Michael Otterstatter ◽  
Stephan T. Leu ◽  
Sivan Leviyang ◽  
Shweta Bansal
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Network Models ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Contact Networks ◽  
Transmission Mechanisms ◽  
Disease Information ◽  
Crithidia Bombi ◽  
Transmission Pathways

AbstractThe spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling infectious disease dynamics through contact networks is sometimes challenging, however, due to a limited understanding of pathogen transmission routes and infectivity. We developed a novel tool, INoDS (Identifying Network models of infectious Disease Spread) that estimates the predictive power of empirical contact networks to explain observed patterns of infectious disease spread. We show that our method is robust to partially sampled contact networks, incomplete disease information, and enables hypothesis testing on transmission mechanisms. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumble bee colonies and Salmonella in wild Australian sleepy lizard populations. The performance of INoDS in synthetic and complex empirical systems highlights its role in identifying transmission pathways of novel or neglected pathogens, as an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

scholarly journals DNA Epidemic Model Construction and Dynamics Optimization

2020 ◽  
Vol 14 (3) ◽  
pp. 97-117
Author(s):  
Yinglian Zhou
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Optimization Problems ◽  
Disease Model ◽  
Sir Model ◽  
Test Results ◽  
Infectious Disease Model ◽  
Exchange Information ◽  
Complex Optimization ◽  
Dna Algorithm

In order to solve some complex optimization problems, the SIR-DNA algorithm was constructed based on the DNA-based SIR (susceptible-infectious-recovered) infectious disease model. Since infectious diseases attack a very small part of the individual's genes, the number of variables per treatment is small; thus, the natural dimensionality reduction of the algorithm is achieved. Based on the DNA-SIR infectious disease model, different infections can be distinguished in the pathogenesis of viruses. The mechanisms of disease transmission are described by the SIR model, and these are used to construct operators such as SS, SI, II, IR, RR, and RS, so that individuals can naturally exchange information naturally through disease transmission. The test results show that the algorithm has the characteristics of strong search ability and has a high convergence speed for solving complex optimization problems.

scholarly journals Protection Strategy for Edge-Weighted Graphs in Disease Spread

2021 ◽  
Vol 11 (11) ◽  
pp. 5115
Author(s):  
Ronald Manríquez ◽  
Camilo Guerrero-Nancuante ◽  
Carla Taramasco
Keyword(s):  
Infectious Disease ◽  
Weighted Graph ◽  
Sir Model ◽  
Disease Spread ◽  
Weighted Graphs ◽  
Fake News ◽  
Scale Free ◽  
Protection Strategy ◽  
Immunization Strategies ◽  
Scale Free Networks

Fake news, viruses on computer systems or infectious diseases on communities are some of the problems that are addressed by researchers dedicated to study complex networks. The immunization process is the solution to these challenges and hence the importance of obtaining immunization strategies that control these spreads. In this paper, we evaluate the effectiveness of the DIL-Wα ranking in the immunization of nodes that are attacked by an infectious disease that spreads on an edge-weighted graph using a graph-based SIR model. The experimentation was done on real and scale-free networks and the results illustrate the benefits of this ranking.

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