scholarly journals How do pathogen evolution and host heterogeneity interact in disease emergence?

2006 ◽  
Vol 273 (1605) ◽  
pp. 3075-3083 ◽  
Author(s):  
Andrew Yates ◽  
Rustom Antia ◽  
Roland R Regoes
Keyword(s):  
Infectious Diseases ◽  
Real World ◽  
Host Population ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Disease Emergence ◽  
Adaptive Changes ◽  
Vaccination Strategies ◽  
Evolutionary Changes ◽  
Host Heterogeneity

Heterogeneity in the parameters governing the spread of infectious diseases is a common feature of real-world epidemics. It has been suggested that for pathogens with basic reproductive number R 0 >1, increasing heterogeneity makes extinction of disease more likely during the early rounds of transmission. The basic reproductive number R 0 of the introduced pathogen may, however, be less than 1 after the introduction, and evolutionary changes are then required for R 0 to increase to above 1 and the pathogen to emerge. In this paper, we consider how host heterogeneity influences the emergence of both non-evolving pathogens and those that must undergo adaptive changes to spread in the host population. In contrast to previous results, we find that heterogeneity does not always make extinction more likely and that if adaptation is required for emergence, the effect of host heterogeneity is relatively small. We discuss the application of these ideas to vaccination strategies.

scholarly journals Emerging infectious pathogens of wildlife

2001 ◽  
Vol 356 (1411) ◽  
pp. 1001-1012 ◽  
Author(s):  
A. Dobson ◽  
J. Foufopoulos
Keyword(s):  
United States ◽  
Anthropogenic Activities ◽  
Disease Outbreaks ◽  
The United States ◽  
Host Population ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Emergent Pathogen ◽  
Insight Into

The first part of this paper surveys emerging pathogens of wildlife recorded on the ProMED Web site for a 2–year period between 1998 and 2000. The majority of pathogens recorded as causing disease outbreaks in wildlife were viral in origin. Anthropogenic activities caused the outbreaks in a significant majority of cases. The second part of the paper develops some matrix models for quantifying the basic reproductive number, R 0 , for a variety of potential types of emergent pathogen that cause outbreaks in wildlife. These analyses emphasize the sensitivity of R 0 to heterogeneities created by either the spatial structure of the host population, or the ability of the pathogens to utilize multiple host species. At each stage we illustrate how the approach provides insight into the initial dynamics of emergent pathogens such as canine parvovirus, Lyme disease, and West Nile virus in the United States.

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 Effect of daily human movement on some characteristics of dengue dynamics

2020 ◽  
Author(s):  
Mayra R. Tocto-Erazo ◽  
Daniel Olmos-Liceaga ◽  
José A. Montoya-Laos
Keyword(s):  
Mathematical Modeling ◽  
Infectious Diseases ◽  
Numerical Simulations ◽  
High Activity ◽  
Human Movement ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Key Factor ◽  
Low Activity ◽  
Outbreak Size

Human movement is a key factor in infectious diseases spread such as dengue. Here, we explore a mathematical modeling approach based on a system of ordinary differential equations to study the effect of human movement on characteristics of dengue dynamics such as the existence of endemic equilibria, and the start, duration, and amplitude of the outbreak. The model considers that every day is divided into two periods: high-activity and low-activity. Periodic human movement between patches occurs in discrete times. Based on numerical simulations, we show unexpected scenarios such as the disease extinction in regions where the local basic reproductive number is greater than 1. In the same way, we obtain scenarios where outbreaks appear despite the fact that the local basic reproductive numbers in these regions are less than 1 and the outbreak size depends on the length of high-activity and low-activity periods.

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

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 Predicted impact of vaccination and active case finding measures to control epidemic of Coronavirus Disease 2019 (COVID-19) in a migrant-populated area in Thailand

2021 ◽  
Author(s):  
Rapeepong Suphanchaimat ◽  
Natthaprang Nittayasoot ◽  
Panithee Thammawijaya ◽  
Pard Teekasap ◽  
Kamnuan Ungchusak
Keyword(s):  
Vaccination Coverage ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Initial Population ◽  
Deterministic System ◽  
Single Measure ◽  
Vaccination Strategies ◽  
Epidemic Area ◽  
The Impact ◽  
Second Wave

Abstract Background: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March-May 2020 and was now facing the second wave of COVID-19 since December 2020. For the second wave, the intensity was more pronounced. The area faced the greatest hit was Samut Sakhon, a main migrant-receiving province in the country. Thus, the Thai Ministry of Public Health (MOPH) was now considering the initiation of vaccination strategies in combination with active face finding (ACF) in the epidemic area. The objective of this study was to assess the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted.Methods: The study obtained data mainly from the Division of Epidemiology, Department of Disease Control (DDC), MOPH. Deterministic system dynamics and compartmental models were exercised. Basic reproductive number (R0) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size.Results: The findings showed that the greater the vaccination coverage was, the smaller the size of incident and cumulative cases. Compared with no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R0 was smaller (⁓53% and ⁓51% when R0 equated 2 and 1.5 respectively).Conclusion: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage was, the greater the volume of cases could be saved. Though we demonstrated the benefit of vaccination strategies in this setting, the actual implementation needs to consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value.

scholarly journals An antigenic diversification threshold for falciparum malaria transmission at high endemicity

2020 ◽  
Author(s):  
Qixin He ◽  
Mercedes Pascual
Keyword(s):  
Infectious Diseases ◽  
Malaria Transmission ◽  
Basic Reproductive Number ◽  
Dynamic Strain ◽  
Reproductive Number ◽  
Apparent Paradox ◽  
Large Reservoir ◽  
Antigenic Diversity ◽  
Major Antigen ◽  
The One

AbstractIn malaria and several other important infectious diseases, high prevalence occurs concomitantly with incomplete immunity. This apparent paradox poses major challenges to malaria elimination in highly endemic regions, where asymptomatic Plasmodium falciparum infections are present across all age classes creating a large reservoir that maintains transmission. This reservoir is in turn enabled by extreme antigenic diversity of the parasite and turnover of new variants. We present here the concept of a threshold in local pathogen diversification that defines a sharp transition in transmission intensity below which new antigen-encoding genes generated by either recombination or migration cannot establish. Transmission still occurs below this threshold, but diversity of these genes can neither accumulate nor recover from interventions that further reduce it. An analytical expectation for this threshold is derived and compared to numerical results from a stochastic individual-based model of malaria transmission that incorporates the major antigen-encoding multigene family known as var. This threshold we call Rdiv; it is complementary to the one defined by the classic basic reproductive number of infectious diseases, R0, which does not easily apply under large and dynamic strain diversity. This new threshold concept can be exploited for effective malaria control and applied more broadly to other pathogens with large multilocus antigenic diversity.

scholarly journals Infectious disease dynamics: what characterizes a successful invader?

2001 ◽  
Vol 356 (1410) ◽  
pp. 901-910 ◽  
Author(s):  
Robert M. May ◽  
Sunetra Gupta ◽  
Angela R. McLean
Keyword(s):  
Emerging Infectious Diseases ◽  
Infectious Agent ◽  
Immune Deficiency Syndrome ◽  
Deficiency Syndrome ◽  
Host Population ◽  
Hiv And Aids ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Species Barrier ◽  
Successful Invader

Against the background of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) and other potentially emerging (or re–emerging) infectious diseases, this review will focus on the properties which enable an infectious agent to establish and maintain itself within a specified host population. We shall emphasize that for a pathogen to cross a species barrier is one thing, but for it successfully to maintain itself in the new population is must have a ‘basic reproductive number’, R 0 , which satisfies R 0 > 1. We shall further discuss how behavioural factors interweave with the basic biology of the production of transmission stages by the pathogen, all subject to possible secular changes, to determine the magnitude of R 0 . Although primarily focusing on HIV and AIDS, we shall review wider aspects of these questions.

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 antigenic diversification threshold for falciparum malaria transmission at high endemicity

2021 ◽  
Vol 17 (2) ◽  
pp. e1008729
Author(s):  
Qixin He ◽  
Mercedes Pascual
Keyword(s):  
Infectious Diseases ◽  
Malaria Transmission ◽  
Basic Reproductive Number ◽  
Dynamic Strain ◽  
Reproductive Number ◽  
Apparent Paradox ◽  
Large Reservoir ◽  
Antigenic Diversity ◽  
Major Antigen ◽  
The One

In malaria and several other important infectious diseases, high prevalence occurs concomitantly with incomplete immunity. This apparent paradox poses major challenges to malaria elimination in highly endemic regions, where asymptomatic Plasmodium falciparum infections are present across all age classes creating a large reservoir that maintains transmission. This reservoir is in turn enabled by extreme antigenic diversity of the parasite and turnover of new variants. We present here the concept of a threshold in local pathogen diversification that defines a sharp transition in transmission intensity below which new antigen-encoding genes generated by either recombination or migration cannot establish. Transmission still occurs below this threshold, but diversity of these genes can neither accumulate nor recover from interventions that further reduce it. An analytical expectation for this threshold is derived and compared to numerical results from a stochastic individual-based model of malaria transmission that incorporates the major antigen-encoding multigene family known as var. This threshold corresponds to an “innovation” number we call Rdiv; it is different from, and complementary to, the one defined by the classic basic reproductive number of infectious diseases, R0, which does not easily apply under large and dynamic strain diversity. This new threshold concept can be exploited for effective malaria control and applied more broadly to other pathogens with large multilocus antigenic diversity.

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