Infectious Diseases In Population Research Articles

Modelling Infectious Disease Dynamics: A Robust Computational Approach for Stochastic SIRS with Partial Immunity and an Incidence Rate

For decades, understanding the dynamics of infectious diseases and halting their spread has been a major focus of mathematical modelling and epidemiology. The stochastic SIRS (susceptible–infectious–recovered–susceptible) reaction–diffusion model is a complicated but crucial computational scheme due to the combination of partial immunity and an incidence rate. Considering the randomness of individual interactions and the spread of illnesses via space, this model is a powerful instrument for studying the spread and evolution of infectious diseases in populations with different immunity levels. A stochastic explicit finite difference scheme is proposed for solving stochastic partial differential equations. The scheme is comprised of predictor–corrector stages. The stability and consistency in the mean square sense are also provided. The scheme is applied to diffusive epidemic models with incidence rates and partial immunity. The proposed scheme with space’s second-order central difference formula solves deterministic and stochastic models. The effect of transmission rate and coefficient of partial immunity on susceptible, infected, and recovered people are also deliberated. The deterministic model is also solved by the existing Euler and non-standard finite difference methods, and it is found that the proposed scheme forms better than the existing non-standard finite difference method. Providing insights into disease dynamics, control tactics, and the influence of immunity, the computational framework for the stochastic SIRS reaction–diffusion model with partial immunity and an incidence rate has broad applications in epidemiology. Public health and disease control ultimately benefit from its application to the study and management of infectious illnesses in various settings.

Implementation of a Curriculum to Optimize Hygiene Behaviors Among Refugees and Migrants Being Resettled

Health knowledge and behaviors can impact well-being and promote community integration post-arrival for refugees and migrants being resettled. Health and hygiene curricula are efficient and accessible mechanisms to mitigate the risk of chronic and infectious diseases in populations being resettled. This report summarizes a successful interagency/interdisciplinary Uganda-based collaboration between the International Organization for Migration, U.S. Centers for Disease Control and Prevention, and the University of Minnesota. The project's objective was to co-create and align a health curriculum and education messages across the resettlement continuum. Due to timing, the curriculum expanded to include COVID-19 prevention education, thus broadening the initiative's impact. Since the initial implementation, thousands of US-bound applicants have had exposure to the curriculum modules. The curriculum is now the primary tool for health education during pre-departure procedures in Kampala, Uganda, with implementations planned in other countries. The modular format and standardized non-technical language facilitated uptake by medical and non-medical personnel. Incorporating end users in curriculum development supports the early adoption and sustainability of the project. The interagency/interdisciplinary partnership strengthens systems and supports resource sharing to optimize the health and well-being of persons resettling in the United States.

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BackgroundThe spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated.ResultsIn this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC.ConclusionsMethodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.

Pediatric Infectious Diseases Encountered During Wartime-Part 1: Experiences and Lessons Learned From Armed Conflict in the Modern Era.

Purpose of ReviewArmed conflicts occur globally, with some regions experiencing heightened instability for many years. A better understanding of the infectious disease impact on children in armed conflict will allow aid organizations to anticipate and mitigate the most serious problems.Recent FindingsArmed conflicts are estimated to have caused approximately 30 million civilian deaths during the past 27 years, with two-thirds occurring in women and children. Children are extremely vulnerable to the mass population displacements, experiencing a combined loss of safety, nutrition, shelter, hygiene, and health care. Under these circumstances, the emergence and prevalence of multiple infectious diseases can result in heightened morbidity and mortality long after active conflict ceases.SummaryFactors leading to increased infectious diseases in populations in crisis due to armed conflict and lessons learned from recent outbreaks are discussed in detail. Acute respiratory infections, diphtheria, measles, varicella, and cholera are a few of the more common infectious diseases that take advantage of populations displaced or disrupted by conflict. Key issues include the ability of countries or non-governmental organizations (NGOs) to keep up with basic childhood immunizations, and how rapidly disease outbreaks are recognized and addressed with disease-specific interventions.

An Edge-Based Model of SEIR Epidemics on Static Random Networks.

Studies have been done using networks to represent the spread of infectious diseases in populations. For diseases with exposed individuals corresponding to a latent period, an SEIR model is formulated using an edge-based approach described by a probability generating function. The basic reproduction number is computed using the next generation matrix method and the final size of the epidemic is derived analytically. The SEIR model in this study is used to investigate the stochasticity of the SEIR dynamics. The stochastic simulations are performed applying continuous-time Gillespie's algorithm given Poisson and power law with exponential cut-off degree distributions. The resulting predictions of the SEIR model given the initial conditions match well with the stochastic simulations, validating the accuracy of the SEIR model. We varied the contribution of the disease parameters and the average degree of the network in order to investigate their effects on the spread of disease. We verified that the infection and the recovery rates show significant effects on the dynamics of the disease transmission. While the exposed rate delays the spread of the disease, increasing it towards infinity would lead to almost the same dynamics as that of an SIR case. A network with high average degree results to an early and higher peak of the epidemic compared to a network with low average degree. The results in this paper can be used as an alternative way of explaining the spread of disease and it provides implications on the control strategies applied to mitigate the disease transmission.

Nonlinear dynamics of infectious diseases via information-induced vaccination and saturated treatment

The present study of a nonlinear compartmental SVIR model accounts for the dynamics of infectious diseases in population. In this model, the effect of information on vaccination coverage is quantified when medical resources are limited during the epidemic outbreak. Model analysis is performed and the global asymptotic stability of the disease free equilibrium is established when treatment is available for all infective individuals. However, the existence of multiple endemic equilibria is observed due to saturation in medical treatment and information-induced vaccination coverage. If the basic reproduction number is greater than unity, a unique endemic equilibrium for the model is obtained under some parametric conditions. Using the geometric approach, the global asymptotic stability of this endemic equilibrium is established under a parametric constraint. Further, when medical resources are limited, the existence Hopf bifurcation is shown analytically, which infers the oscillatory persistence of disease within the population. Occurrence of Hopf–Hopf bifurcation is also investigated numerically. At first Hopf bifurcation threshold, an endemic equilibrium loses its stability and produces oscillations, and further, it regains its stability at another Hopf bifurcation threshold when oscillations disappear. Hence, multiple stability switches are observed due to the saturated treatment and information-induced vaccination. The effect of the fading of memory also plays a crucial role and gives rise to complex dynamical behaviour and also affects the stability properties. Numerically, different types of backward bifurcation are also examined. Extensive numerical experimentations are also carried out to further explore the model system.

Using social network analysis to inform disease control interventions

Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes.This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by “removing” a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics.This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations.

Towards a European Framework to Monitor Infectious Diseases among Migrant Populations: Design and Applicability.

There are limitations in our capacity to interpret point estimates and trends of infectious diseases occurring among diverse migrant populations living in the European Union/European Economic Area (EU/EEA). The aim of this study was to design a data collection framework that could capture information on factors associated with increased risk to infectious diseases in migrant populations in the EU/EEA. The authors defined factors associated with increased risk according to a multi-dimensional framework and performed a systematic literature review in order to identify whether those factors well reflected the reported risk factors for infectious disease in these populations. Following this, the feasibility of applying this framework to relevant available EU/EEA data sources was assessed. The proposed multidimensional framework is well suited to capture the complexity and concurrence of these risk factors and in principle applicable in the EU/EEA. The authors conclude that adopting a multi-dimensional framework to monitor infectious diseases could favor the disaggregated collection and analysis of migrant health data.

Status of the alien pathogen Paranosema locustae (Microsporidia) in grasshoppers (Orthoptera: Acridoidea) of the Argentine Pampas

After experimental introductions from North America in 1978–1982, the biocontrol agent Paranosema locustae became established in grasshopper communities of the western Pampas region of Argentina. The use and establishment of P. locustae in Argentina constitute both a case of neoclassical or new association biological control (use of an alien species against native pests) and a case of pathogen pollution (anthropogenic introduction–establishment of an infectious disease in populations of native species). Since P. locustae is a multihost pathogen among grasshoppers, its presence in the western Pampas represents an additional factor disrupting grasshopper communities according to the differential susceptibility of each species and possibly threatening some species. Microscopic examination of 504 grasshopper samples (the mean number of individuals per sample = 185) belonging to 43 species from 93 localities throughout the Pampas revealed an establishment area of approximate 90,000 km2 from about 35° North to 38° South and from 61° East to 65° West. Field infections by P. locustae have now been detected in 21 grasshopper species in the western Pampas. Susceptible species with geographic distributions mostly restricted to the establishment area and with numerically small populations, like the melanopline Scotussa daguerrei, are predicted to be the ones facing higher risks of negative impacts.

Epidemic curve and hazard function for occurrence of clinical equine influenza in a closed population of horses at a 3‐day event in southern Queensland, Australia, 2007

The risk of individuals becoming infected during an epidemic of infectious disease can vary as the disease progresses. Monitoring this risk may provide information about the dynamics of transmission. This study describes the epidemic curve for an epidemic of equine influenza (EI) in a closed population of horses predominantly immunologically naïve to EI at a 3-day event at Morgan Park in southern Queensland, Australia. The hazard function suggested that a subset of horses were at reduced risk of becoming infected. This highlights the importance, when modelling infectious disease in populations, of considering possible differences in the risk of infection among subgroups in the population.

The correlation between infectivity and incubation period of measles, estimated from households with two cases

The generation time of an infectious disease is the time between infection of a primary case and infection of a secondary case by the primary case. Its distribution plays a key role in understanding the dynamics of infectious diseases in populations, e.g. in estimating the basic reproduction number. Moreover, the generation time and incubation period distributions together characterize the effectiveness of control by isolation and quarantine. In modelling studies, a relation between the two is often not made specific, but a correlation is biologically plausible. However, it is difficult to establish such correlation, because of the unobservable nature of infection events. We have quantified a joint distribution of generation time and incubation period by a novel estimation method for household data with two susceptible individuals, consisting of time intervals between disease onsets of two measles cases. We used two such datasets, and a separate incubation period dataset. Results indicate that the mean incubation period and the generation time of measles are positively correlated, and that both lie in the range of 11–12 days, suggesting that infectiousness of measles cases increases significantly around the time of symptom onset. The correlation between times from infection to secondary transmission and to symptom onset could critically affect the predicted effectiveness of isolation and quarantine.

Strategies for the Development of New Vaccines

The reputation of vaccination rests on a two hundred year old history of success against major infectious diseases. In general, two achievements have been crucial to the success of vaccines: the induction of long-lasting immunological memory in individuals and the stimulation of a herd immunity that enhances control of infectious diseases in populations. However, when one reviews the vaccines now available it is apparent that most successes have been obtained when the microbe has a bacteremic or viremic phase during which it is susceptible to the action of neutralizing antibodies, and before replication in the particular organ to which it is tropic. Success has also been achieved against some agents replicating on respiratory or gastrointestinal mucosae, against which it has been possible to induce immune responses acting locally as well as systemically. Control of intracellular pathogens has not been achieved, except partly with the BCG vaccine against tuberculosis, and modern efforts are directed towards pathogens against which cellular immune responses are critical. Newer approaches in vaccine production such as nucleic acid immunization, vectors, reverse genetics and additional routes of administration may circumvent prior difficulties. The target of vaccination will shift towards adolescents, adults, patients in hospital and those with chronic diseases and possibly will extend to therapy as well as prevention. The major scientific problems to be solved are maintenance of immune memory, immaturity and post-maturity of the immune system, and adjuvants capable of stimulating selective cell types. Abstracts for SupplementInternational Journal of Infectious DiseasesVol. 14Preview Full-Text PDF Open Archive

Phylodynamics of Infectious Disease Epidemics

We present a formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations. Virus phylogenies have been used in many recent studies to infer properties of epidemics. These approaches rely on coalescent models that may not be appropriate for infectious diseases. We account for phylogenetic patterns of viruses in susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) models of infectious disease, and our approach may be a viable alternative to demographic models used to reconstruct epidemic dynamics. The method allows epidemiological parameters, such as the reproductive number, to be estimated directly from viral sequence data. We also describe patterns of phylogenetic clustering that are often construed as arising from a short chain of transmissions. Our model reproduces the moments of the distribution of phylogenetic cluster sizes and may therefore serve as a null hypothesis for cluster sizes under simple epidemiological models. We examine a small cross-sectional sample of human immunodeficiency (HIV)-1 sequences collected in the United States and compare our results to standard estimates of effective population size. Estimated prevalence is consistent with estimates of effective population size and the known history of the HIV epidemic. While our model accurately estimates prevalence during exponential growth, we find that periods of decline are harder to identify.

Race, Sex, and the Neglected Risks for Women and Girls in Sub-Saharan Africa

Global AIDS policy has failed to stop the spread of HIV in sub-Saharan Africa because prevention programs are limited by an unscientific theory of disease causation, which assumes that differences in HIV prevalence among populations are adequately explained by variation in rates of unsafe sexual behaviors. But differences in sexual behavior cannot explain 250-fold differences in HIV prevalence among countries and increasing divergence in incidence. Such disparities point to biological characteristics of the pathogen, the host, and the environment, all of which influence individual risk of infection and the spread of infectious diseases in populations. This paper explores the ways in which Western constructions of race and sexuality have drawn attention to sexual behavior alone and away from scientific evidence of biological risk factors that increase HIV transmission in poor populations. Much of the discussion addresses sub-Saharan Africa, but the biological implications could apply to poor people in other regions.

Infectious diseases and extinction risk in wild mammals.

: Parasite‐driven declines in wildlife have become increasingly common and can pose significant risks to natural populations. We used the IUCN Red List of Threatened and Endangered Species and compiled data on hosts threatened by infectious disease and their parasites to better understand the role of infectious disease in contemporary host extinctions. The majority of mammal species considered threatened by parasites were either carnivores or artiodactyls, two clades that include the majority of domesticated animals. Parasites affecting host threat status were predominantly viruses and bacteria that infect a wide range of host species, including domesticated animals. Counter to our predictions, parasites transmitted by close contact were more likely to cause extinction risk than those transmitted by other routes. Mammal species threatened by parasites were not better studied for infectious diseases than other threatened mammals and did not have more parasites or differ in four key traits demonstrated to affect parasite species richness in other comparative studies. Our findings underscore the need for better information concerning the distribution and impacts of infectious diseases in populations of endangered mammals. In addition, our results suggest that evolutionary similarity to domesticated animals may be a key factor associated with parasite‐mediated declines; thus, efforts to limit contact between domesticated hosts and wildlife could reduce extinction risk.

Cost-Effectiveness of Partner Pharmacotherapy in Screening Women for Asymptomatic Infection with Chlamydia Trachomatis

Objective To assess the cost-effectiveness of pharmacotherapy for male partners in screening women for asymptomatic infection with Chlamydia trachomatis (CT). Methods and Data A pharmacoeconomic decision analysis model was constructed for the health outcomes of a CT screening program, such as averted cases of pelvic inflammatory disease and infertility (major outcomes). Reinfection in the absence of partner pharmacotherapy was included in the model. Cost-effectiveness from a societal perspective was estimated for prevalence data from a selective opportunistic screening program in Amsterdam. For diagnosis of asymptomatic CT infection a Ligase Chain Reaction (LCR) test on urine was used; for pharmacotherapy of women and partners azithromycin was used. By linking health outcomes with health care costs and productivity losses, averted costs were estimated. Cost-effectiveness was expressed as net costs per major outcome averted. Results Partner pharmacotherapy reduces net costs per major outcome averted of the screening program by approximately 50%. Sensitivity analysis indicates significant improvements in cost-effectiveness of the screening program, even when relevant assumptions are varied. Within the broader framework of the screening program, partner pharmacotherapy is a cost-saving activity. Conclusions Inclusion of partner pharmacotherapy provides significant improvements in overall cost-effectiveness of the CT screening program among women aged 15 to 29. Partner pharmacotherapy lowers net costs per major outcome averted to the realm where implementation of the screening program should be considered. Considering the cost-saving potential, male partner pharmacotherapy should be pursued within the broader framework of a CT screening program for women. Reinfection should be included in any future pharmacoeconomic model of CT screening. Further work on this type of model should also be directed to linking cost-effectiveness to epidemiological models for the long-term spread of infectious diseases in populations.

The Mathematics of Infectious Diseases

Many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases. Threshold theorems involving the basic reproduction number $R_{0}$, the contact number $\sigma$, and the replacement number R are reviewed for the classic SIR epidemic and endemic models. Similar results with new expressions for $R_{0}$ are obtained for MSEIR and SEIR endemic models with either continuous age or age groups. Values of $R_{0}$ and $\sigma$ are estimated for various diseases including measles in Niger and pertussis in the United States. Previous models with age structure, heterogeneity, and spatial structure are surveyed.

Vaccination against the major infectious diseases

The reputation of vaccination rests on a 200-year-old history of success against major infectious diseases. That success has led to the doctrine of ‘for each disease, a vaccine’. Although some diseases have proved frustrating, this doctrine carries considerable truth. However, when one reviews the vaccines now available it is apparent that most successes have been obtained when the microbe has a bacteremic or viremic phase during which it is susceptible to the action of neutralizing antibodies, and before replication in the particular organ to which it is tropic. Poliomyelitis and infections by capsulated bacteria are examples where vaccination has worked efficiently. However, some success has also been achieved against agents replicating on respiratory or gastrointestinal mucosae. Influenza, pertussis and rotavirus vaccines are examples of such agents, against which it has been possible to induce immune responses acting locally as well as systemically. In addition, when bacteria produce disease through exotoxins, purification and chemical or genetic inactivation of those toxins has yielded highly efficacious vaccines. Control of intracellular pathogens has not been achieved, except partly with the BCG vaccine against tuberculosis, and modern efforts are directed towards pathogens against which cellular immune responses are critical. In general, two achievements have been crucial to the success of vaccines: the induction of long-lasting immunological memory in individuals and the stimulation of a herd immunity that enhances control of infectious diseases in populations.

THRESHOLD QUANTITIES FOR INFECTIOUS DISEASES IN PERIODIC ENVIRONMENTS

In this short note we give threshold quantities that determine the stability of the infection-free steady state for periodic deterministic systems that describe the spread of infectious diseases in populations whose individuals can be divided into a finite number of distinct groups. We concentrate on "micro-parasitic" infections, but the theory was originally developed for helminth infections. As an example, we treat a simple model of a vector transmitted infection with periodic recruitment in the vector population.

Automata network SIR models for the spread of infectious diseases in populations of moving individuals

Automata network SIR models for the spread of infectious diseases are studied. The local rule consists of two subrules. The first one, applied sequentially, describes the motion of the individuals, the second is synchronous and models infection and removal (or recovery). The spatial correlations created by the application of the second subrule are partially destroyed according to the degree of mixing of the population which follows from the application of the first subrule. One- and two-population models are considered. In the second case, individuals belonging to one population may be infected only by individuals belonging to the other population as is the case, for example, for the heterosexual propagation of a venereal disease. It is shown that the occurrence of the epidemic in one population may be triggered by the occurrence of the epidemic in the other population. The emphasis is on the influence of the degree of mixing of the individuals which follows from their diffusive motion. In particular, the asymptotic behaviours for very small and very large mixing are determined. When the degree of mixing tends to infinity the correlations are completely destroyed and the time evolution of the epidemic is then correctly predicted by the mean-field approximation.