Between-host dynamics (Epidemiology)

2021 ◽  
pp. 281-316
Author(s):  
Paul Schmid-Hempel
Keyword(s):  
Infectious Disease ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Threshold Size ◽  
Contact Rates ◽  
Immunological Markers ◽  
Individual Based Models ◽  
Infective Disease ◽  
Host Parasite ◽  
Immune Defences

Epidemiology is the population dynamics of host–parasite systems. The spread of an infective disease is analysed with several tools. The SIR model (susceptible, infected, recovered hosts) is a standard model, with the basic reproductive number (R 0) as a characteristic. Diseases, in general, spread if R 0 > 1, which suggests a threshold size for host populations, and also for endemic maintenance or periodic outbreaks. Furthermore, spatial heterogeneity or the distribution of infections among hosts affects an epidemic. Individual-based models can follow the fate of infections more closely. Network analysis provides insights into transmission and contact rates. Models also describe the epidemics of vectored diseases, or of macroparasitic infections. Molecular epidemiology uses genetic markers or genomes to follow the spread of an infectious disease; phylodynamics reconstructs transmission chains, especially for viral diseases. Immunoepidemiology studies how immune defences affect an epidemic and identifies immunological markers for the study of infectious disease dynamics.

scholarly journals Huanglongbing Model under the Control Strategy of Discontinuous Removal of Infected Trees

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1164
Author(s):  
Weiwei Ling ◽  
Pinxia Wu ◽  
Xiumei Li ◽  
Liangjin Xie
Keyword(s):  
Infectious Disease ◽  
Global Stability ◽  
Control Strategy ◽  
Theoretical Basis ◽  
Dynamic Properties ◽  
Transmission Mechanism ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Vector Borne ◽  
Citrus Trees

By using differential equations with discontinuous right-hand sides, a dynamic model for vector-borne infectious disease under the discontinuous removal of infected trees was established after understanding the transmission mechanism of Huanglongbing (HLB) disease in citrus trees. Through calculation, the basic reproductive number of the model can be attained and the properties of the model are discussed. On this basis, the existence and global stability of the calculated equilibria are verified. Moreover, it was found that different I0 in the control strategy cannot change the dynamic properties of HLB disease. However, the lower the value of I0, the fewer HLB-infected citrus trees, which provides a theoretical basis for controlling HLB disease and reducing expenditure.

AN EPIDEMIOLOGICAL MODEL WITH MULTIPLE ENDEMIC STATES

2015 ◽  
Vol 23 (supp01) ◽  
pp. S17-S31
Author(s):  
GEISER VILLAVICENCIO-PULIDO ◽  
IGNACIO BARRADAS ◽  
LUNA BEATRIZ
Keyword(s):  
Infectious Disease ◽  
Backward Bifurcation ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Force Of Infection

We present a model describing the dynamics of an infectious disease for which the force of infection is diminished through a reaction of the susceptible to the number of infected individuals. We show that, even though the structure of the model is a simple one, different kinds of backward bifurcation can appear for values of the basic reproductive number bigger than one. Under some conditions on the parameters, multiple endemic equilibria may appear for values of the basic reproductive number less or greater than one.

scholarly journals An artificially simulated outbreak of a respiratory infectious disease

2020 ◽  
Author(s):  
Zuiyuan Guo ◽  
Shuang Xu ◽  
Libo Tong ◽  
Botao Dai ◽  
Yuandong Liu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Attack Rate ◽  
General Pattern ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Developmental Trend ◽  
Collision Model ◽  
Military Camp ◽  
Epidemiological Characteristics

Abstract Background Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 =3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusions The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

scholarly journals An artificially simulated outbreak of a respiratory infectious disease

2020 ◽  
Author(s):  
Zuiyuan Guo ◽  
Shuang Xu ◽  
Libo Tong ◽  
Botao Dai ◽  
Yuandong Liu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Attack Rate ◽  
General Pattern ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Developmental Trend ◽  
Collision Model ◽  
Military Camp ◽  
Epidemiological Characteristics

Abstract Background Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 =3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusions The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

scholarly journals Population density and basic reproductive number of COVID-19 across United States counties

2020 ◽  
Author(s):  
Karla Therese L. Sy ◽  
Laura F. White ◽  
Brooke Nichols
Keyword(s):  
Population Density ◽  
Transmission Probability ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Main Mode ◽  
Cross Sectional ◽  
Rate Of Spread ◽  
Contact Rates ◽  
Random Intercept ◽  
Private Transportation

AbstractThe basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties, and whether population density could be used as a proxy for contact rates. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0. We also assessed whether this association was differential across county-level main mode of transportation-to-work percentage. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. The effect of population density and R0 was not modified by private transportation use. Differential R0 by population density can assist in more accurate predictions of the rate of spread of SARS-CoV-2 in areas that do not yet have active cases.Article Summary LineU.S. counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density.

scholarly journals Quantifying Uncertainty in Infectious Disease Mechanistic Models

2021 ◽  
Author(s):  
Lucy D’Agostino McGowan ◽  
Kyra H Grantz ◽  
Eleanor Murray
Keyword(s):  
Infectious Disease ◽  
Data Uncertainty ◽  
Basic Reproductive Number ◽  
Sensitivity Analyses ◽  
Reproductive Number ◽  
Mechanistic Models ◽  
Statistical Uncertainty ◽  
Stochastic Uncertainty ◽  
Quantifying Uncertainty

Abstract This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

scholarly journals An artificially simulated outbreak of a respiratory infectious disease

2019 ◽  
Author(s):  
Zuiyuan Guo ◽  
Shuang Xu ◽  
Libo Tong ◽  
Botao Dai ◽  
Yuandong Liu
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Attack Rate ◽  
General Pattern ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Developmental Trend ◽  
Collision Model ◽  
Military Camp ◽  
Epidemiological Characteristics

Abstract Background Outbreaks of respiratory infectious diseases often take place in crowded places. To understand the spreading pattern of an outbreak of a respiratory infectious disease and provide a theoretical basis for the targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between the onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeds 1.5, the median attack rate increases sharply; when R 0 =3, with a delay in the TOI, the attack rate increases gradually and eventually remains stable. If the IOI exceeds 2.3 days, the median attack rate will also increase dramatically. If the IR exceeds 0.5, the median of the attack rate nears zero. The median generation time was 8.26 days (95% CI: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and the R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusion The random collision model not only simulates how an epidemic spreads with superior precision but also allows more flexibility in the settings of the exposure population’s activities and different types of infectious diseases, which is conducive to furthering the exploration of the epidemiological characteristics of epidemic outbreaks.

scholarly journals Population density and basic reproductive number of COVID-19 across United States counties

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249271
Author(s):  
Karla Therese L. Sy ◽  
Laura F. White ◽  
Brooke E. Nichols
Keyword(s):  
United States ◽  
Population Density ◽  
Disease Transmission ◽  
Household Income ◽  
The United States ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Main Mode ◽  
Contact Rates ◽  
Mode Of Transportation

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R0 among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km2 was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R0 by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R0 was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R0 estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

scholarly journals Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 41
Author(s):  
Elizabeth Hunter ◽  
John D. Kelleher
Keyword(s):  
Complex System ◽  
Infectious Disease ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Disease Spread ◽  
Reproductive Number ◽  
Agent Based Model ◽  
School Closures ◽  
Agent Based

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

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