Modeling the impact of immigration on the epidemiology of tuberculosis

A geometric approach to the impact of immigration of people infected with communicable diseases

We construct a set of new epidemiological thresholds to address the general problem of spreading and containment of a transmissible disease with influx of infected individuals (i.e., when the classic R0 is no longer meaningful). We provide analytical properties of these indices and illustrate their usefulness in a compartmental model of COVID-19 with data taken from Chile showing a good predictive potential when contrasted with the recorded disease behavior. This geometric approach and the associated analytical and numerical results break new ground in that they allow us to quantify the severity of an immigration of infectious individuals into a community, and identification of the key parameters that are capable of changing or reversing the spread of an infectious disease in specific models.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Demographic Control Measure Implications of Tuberculosis Infection for Migrant Workers across Taiwan Regions

A sharp increase in migrant workers has raised concerns for TB epidemics, yet optimal TB control strategies remain unclear in Taiwan regions. This study assessed intervention efforts on reducing tuberculosis (TB) infection among migrant workers. We performed large-scale data analyses and used them to develop a control-based migrant worker-associated susceptible-latently infected-infectious-recovered (SLTR) model. We used the SLTR model to assess potential intervention strategies such as social distancing, early screening, and directly observed treatment, short-course (DOTS) for TB transmission among migrant workers and locals in three major hotspot cities from 2018 to 2023. We showed that social distancing was the best single strategy, while the best dual measure was social distancing coupled with early screening. However, the effectiveness of the triple strategy was marginally (1-3%) better than that of the dual measure. Our study provides a mechanistic framework to facilitate understanding of TB transmission dynamics between locals and migrant workers and to recommend better prevention strategies in anticipation of achieving WHO's milestones by the next decade. Our work has implications for migrant worker-associated TB infection prevention on a global scale and provides a knowledge base for exploring how outcomes can be best implemented by alternative control measure approaches.

Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic

The current COVID-19 pandemic has demonstrated the vulnerability of healthcare systems worldwide. When combined with natural disasters, pandemics can further strain an already exhausted healthcare system. To date, frameworks for quantifying the collective effect of the two events on hospitals are nonexistent. Moreover, analytical methods for capturing the dynamic spatiotemporal variability in capacity and demand of the healthcare system posed by different stressors are lacking. Here, we investigate the combined impact of wildfire and pandemic on a network of hospitals. We combine wildfire data with varying courses of the spread of COVID-19 to evaluate the effectiveness of different strategies for managing patient demand. We show that losing access to medical care is a function of the relative occurrence time between the two events and is substantial in some cases. By applying viable mitigation strategies and optimizing resource allocation, patient outcomes could be substantially improved under the combined hazards.

The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans

Background

Tuberculosis (TB), a preventable and curable disease, is claimed as the second largest number of fatalities, and there are 9,025 cases reported in the United States in 2018. Many researchers have done a lot of research and achieved remarkable results, but TB is still a severe problem for human beings. The study is a further exploration of the prevention and control of tuberculosis.

Methods

In the paper, we propose a new dynamic model to study the transmission dynamics of TB, and then use global differential evolution and local sequential quadratic programming (DESQP) optimization algorithm to estimate parameters of the model. Finally, we use Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) to analyze the influence of parameters on the basic reproduction number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal R_{0}$\end{document}R0) and the total infectious (including the diagnosed, undiagnosed and incomplete treatment infectious), respectively.

Results

According to the research, the basic reproduction number is computed as 2.3597 from 1984 to 2018, which means TB is also an epidemic in the US. The diagnosed rate is 0.6082, which means the undiagnosed will be diagnosed after 1.6442 years. The diagnosed will recover after an average of 1.9912 years. Moreover, some diagnosed will end the treatment after 1.7550 years for some reason. From the study, it’s shown that 2.40% of the recovered will be reactivated, and 13.88% of the newborn will be vaccinated. However, the immune system will be lost after about 19.6078 years.

Conclusion

Through the results of this study, we give some suggestions to help prevent and control the TB epidemic in the United States, such as prolonging the protection period of the vaccine by developing new and more effective vaccines to prevent TB; using the Chemoprophylaxis for incubation patients to prevent their conversion into active TB; raising people’s awareness of the prevention and control of TB and treatment after illness; isolating the infected to reduce the spread of TB. According to the latest report in the announcement that came at the first WHO Global Ministerial Conference on Ending tuberculosis in the Sustainable Development Era, we predict that it is challenging to control TB by 2030.

Backward bifurcation and hysteresis in models of recurrent tuberculosis

An epidemiological model is presented that provides a comprehensive description of the transmission pathways involved for recurrent tuberculosis (TB), whereby cured individuals can become reinfected. Our main goal is to determine conditions that lead to the appearance of a backward bifurcation. This occurs when an asymptotically stable infection free equilibrium concurrently exists with a stable non-trivial equilibria even though the basic reproduction number R₀ is less than unity. Although, some 10-30% cases of TB are recurrent, the role of recurrent TB as far as the formation of backward bifurcation is concerned, is rarely if ever studied. The model used here incorporates progressive primary infection, exogenous reinfection, endogenous reactivation and recurrent TB as transmission mechanisms that contribute to TB progression. Unlike other studies of TB dynamics that make use of frequency dependent transmission rates, our analysis provides exact backward bifurcation threshold conditions without resorting to commonly applied approximations and simplifying assumptions. Exploration of the model through analytical and numerical analysis reveal that recurrent TB is sometimes capable of triggering hysteresis effects which allow TB to persist when R₀ < 1 even though there is no backward bifurcation. Furthermore, recurrent TB can independently induce backward bifurcation phenomena if it exceeds a certain threshold.

Comparing Drivers and Dynamics of Tuberculosis in California, Florida, New York, and Texas

RATIONALE

There is substantial state-to-state heterogeneity in tuberculosis (TB) in the United States; better understanding this heterogeneity can inform effective response to TB at the state level, the level at which most TB control efforts are coordinated.

OBJECTIVES

To characterize drivers of state-level heterogeneity in TB epidemiology in the four U.S. states that bear half the country's TB burden: California, Florida, New York, and Texas.

METHODS

We constructed an individual-based model of TB in the four U.S. states and calibrated the model to state-specific demographic and age- and nativity-stratified TB incidence data. We used the model to infer differences in natural history of TB and in future projections of TB.

MEASUREMENTS AND MAIN RESULTS

We found that differences in both demographic makeup (particularly the size and composition of the foreign-born population) and TB transmission dynamics contribute to state-level differences in TB epidemiology. The projected median annual rate of decline in TB incidence in the next decade was substantially higher in Texas (3.3%; 95% range, -5.6 to 10.9) than in California (1.7%; 95% range, -3.8 to 7.1), Florida (1.5%; 95% range, -7.4 to 14), and New York (1.9%; 95% range, -6.4 to 9.8). All scenarios projected a flattening of the decline in TB incidence by 2025 without additional resources or interventions.

CONCLUSIONS

There is substantial state-level heterogeneity in TB epidemiology in the four states, which reflect both demographic factors and potential differences in the natural history of TB. These differences may inform resource allocation decisions in these states.

Analysis of Transmission and Control of Tuberculosis in Mainland China, 2005-2016, Based on the Age-Structure Mathematical Model

Tuberculosis (TB), an air-borne infectious disease, is a major public-health problem in China. The reported number of the active tuberculosis cases is about one million each year. The morbidity data for 2005–2012 reflect that the difference in morbidity based on age group is significant, thus the role of age-structure on the transmission of TB needs to be further developed. In this work, based on the reported data and the observed morbidity characteristics, we propose a susceptible-exposed-infectious-recovered (SEIR) epidemic model with age groupings, involving three categories: children, the middle-aged, and senior to investigate the role of age on the transmission of tuberculosis in Mainland China from 2005 to 2016. Then, we evaluated the parameters by the Least Square method and simulated the model and it had good alignment with the reported infected TB data in Mainland China. Furthermore, we estimated the basic reproduction number R0 of 1.7858, with an obtained 95% confidence interval for R0 of (1.7752,1.7963) by Latin hypercube sampling, and we completed a sensitivity analysis of R0 in terms of some parameters. Our study demonstrates that diverse age groups have different effects on TB. Two effective measures were found that would help reach the goals of the World Health Organization (WHO) End TB Strategy: an increase in the recovery rate and the reduction in the infectious rate of the senior age group.

Current and future trends in tuberculosis incidence in New York City: a dynamic modelling analysis

BACKGROUND

After steady decline since the 1990s, tuberculosis (TB) incidence in New York City (NYC) and the United States (US) has flattened. The reasons for this trend and the implications for the future trajectory of TB in the US remain unclear.

METHODS

We developed a compartmental model of TB in NYC, parameterized with detailed epidemiological data. We ran the model under five alternative scenarios representing different explanations for recent declines in TB incidence. We evaluated each scenario's relative likelihood by comparing its output to available data. We used the most likely scenarios to explore drivers of TB incidence and predict future trajectories of the TB epidemic in NYC.

FINDINGS

Demographic changes and declining TB transmission alone were insufficient to explain recent trends in NYC TB incidence. Only scenarios that assumed contemporary changes in TB dynamics among the foreign-born - a declining rate of reactivation or a decrease in imported subclinical TB - could accurately describe the trajectory of TB incidence since 2007. In those scenarios, the projected decline in TB incidence from 2015 to 2025 varied from minimal [2·0%/year (95% credible interval 0·4-3·5%)] to similar to 2005 to 2009 trends [4·4%/year (2·5-6·4%)]. The primary factor differentiating optimistic from pessimistic projections was the degree to which improvements in TB dynamics among the foreign-born continued into the coming decade.

INTERPRETATION

Further progress against TB in NYC requires additional focus on the foreign-born population. Absent additional intervention in this group, TB incidence may not decline further.

Social support and HIV/STDs infections among a probability-based sample of rural married migrant women in Shandong Province, China

Background

The increasing population of marriage-based migrant women is disproportionally affected by AIDS/STDs in China, and social support plays a critical role. This study aims to describe the social support level received by married migrant women in rural areas in Shandong province in comparison to non-migrant local women, identifies the relevant factors of this social support condition among married migrant women, and observes the correlation between social support level and infection status of AIDS and STDs among this group.

Methods

A probability-based sample of 1,076 migrant and 1,195 local women were included in the study. A pre-tested field questionnaire was administered to participants through a direct face-to-face interview. Questionnaire contained questions on socio-demographic information, AIDS and STDs prevalence information and Social Support Rating Scale (SSRS) which measures objective support, subjective support, and utilization of social support.

Results

Compared to local women, married migrant women had lower levels of social support in most dimensions. Multi-variable analysis revealed that relationship with spouse, family average income, number of children, education, engagement and claimed reasons of moving have various correlations with one or all dimensions of social support scores. Higher social support is also related to awareness of infection status of HIV and STDs among this group.

Conclusion

Our findings provide further evidence that married migrant women have lower levels of social support which may be related to some social characteristics and their awareness status of AIDS and STDs infection status and that targeted interventions need to be developed for this population.

Predicting U.S. tuberculosis case counts through 2020

In 2010, foreign-born persons accounted for 60% of all tuberculosis (TB) cases in the United States. Understanding which national groups make up the highest proportion of TB cases will assist TB control programs in concentrating limited resources where they can provide the greatest impact on preventing transmission of TB disease. The objective of our study was to predict through 2020 the numbers of U.S. TB cases among U.S.-born, foreign-born and foreign-born persons from selected countries of birth. TB case counts reported through the National Tuberculosis Surveillance System from 2000–2010 were log-transformed, and linear regression was performed to calculate predicted annual case counts and 95% prediction intervals for 2011–2020. Data were analyzed in 2011 before 2011 case counts were known. Decreases were predicted between 2010 observed and 2020 predicted counts for total TB cases (11,182 to 8,117 [95% prediction interval 7,262–9,073]) as well as TB cases among foreign-born persons from Mexico (1,541 to 1,420 [1,066–1,892]), the Philippines (740 to 724 [569–922]), India (578 to 553 [455–672]), Vietnam (532 to 429 [367–502]) and China (364 to 328 [249–433]). TB cases among persons who are U.S.-born and foreign-born were predicted to decline 47% (4,393 to 2,338 [2,113–2,586]) and 6% (6,720 to 6,343 [5,382–7,476]), respectively. Assuming rates of declines observed from 2000–2010 continue until 2020, a widening gap between the numbers of U.S.-born and foreign-born TB cases was predicted. TB case count predictions will help TB control programs identify needs for cultural competency, such as languages and interpreters needed for translating materials or engaging in appropriate community outreach.

Modeling the spread of tuberculosis in semiclosed communities

We address the problem of long-term dynamics of tuberculosis (TB) and latent tuberculosis (LTB) in semiclosed communities. These communities are congregate settings with the potential for sustained daily contact for weeks, months, and even years between their members. Basic examples of these communities are prisons, but certain urban/rural communities, some schools, among others could possibly fit well into this definition. These communities present a sort of ideal conditions for TB spread. In order to describe key relevant dynamics of the disease in these communities, we consider a five compartments SEIR model with five possible routes toward TB infection: primary infection after a contact with infected and infectious individuals (fast TB), endogenous reactivation after a period of latency (slow TB), relapse by natural causes after a cure, exogenous reinfection of latently infected, and exogenous reinfection of recovered individuals. We discuss the possible existence of multiple endemic equilibrium states and the role that the two types of exogenous reinfections in the long-term dynamics of the disease could play.

Epidemiological models of Mycobacterium tuberculosis complex infections

The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.

Influence of temporary migration on the transmission of infectious diseases in a migrants' home village

The number of temporary migrant workers from rural areas to urban areas in emerging market economies like China has increased dramatically since the early 1980s. Temporary migrant workers have been labeled as the major driving force for the rising incidence of infectious diseases in cities. However, it has not been well recognized that temporary migration indeed may have tremendous impacts on the spread of infectious diseases in migrants' home villages. In this paper, by proposing a delay differential equation model, we provide a framework to study the influence of temporary migration on the transmission of infectious diseases in a migrant workers' home village. The model is shown to admit a unique positive equilibrium which is locally asymptotically stable and is globally asymptotically stable under certain conditions. This implies that the disease always persists at a constant level. Considering tuberculosis as an example, we explore various disease prevention and control strategies numerically to demonstrate how migration related parameters affect the early outbreak of the disease. We find that a single control strategy, such as reducing the migration time period alone, has little effect on reducing the disease endemic level. For disease prevention and control, temporary migrant workers should be identified as the top target group, and a combination of several prevention strategies should be implemented.

Modelling tuberculosis trends in the USA

We present a mathematical transmission model of tuberculosis in the USA. The model is calibrated to recent trends of declining incidence in the US-born and foreign-born populations and is used in assessing relative impacts of treatment of latently infected individuals on elimination time, where elimination is defined as annual incidence <1 case/million. Provided current control efforts are maintained, elimination in the US-born population can be achieved before the end of this century. However, elimination in the foreign-born population is unlikely in this timeframe even with higher rates of targeted testing and treatment of residents of and immigrants to the USA with latent tuberculosis infection. Cutting transmission of disease as an interim step would shorten the time to elimination in the US-born population but foreign-born rates would remain above the elimination target.

TRAVELING PATTERN INDUCED BY MIGRATION IN AN EPIDEMIC MODEL

The main work in spatial epidemiology is the study of spatial variation in disease risk or incidence, including the spatial patterns of the populations. Spread of diseases in human populations can exhibit different patterns for spatially explicit approaches. In this paper, we investigate an epidemic model with both diffusion and migration. In the previous work (Sun et al., J Stat Mech P11011, 2007), we studied the model only with diffusion and obtained stationary Turing pattern. However, combined with migration, the model will exhibit typical traveling pattern, which is shown by both mathematical analysis and numerical simulations. The results obtained well extend the finding of pattern formation in the epidemic model and may well explain the field observed in the real world.

A mathematical model for evaluating tuberculosis screening strategies

In order to better predict epidemic of tuberculosis (TB) and evaluate the effect of TB control strategies, we added the effect of transmission from permanent residents to migrants to our previous TB model. We simulated the behavior of TB transmission by the extended model. The numerical simulation indicated that the basic reproductive numbers must be less than one in both permanent residents and migrants in order to eliminate the disease from total population. We also evaluated the Canada's TB screening strategy and observe that TB is sensitive to the strategy. Our study suggests that immigrants have a considerable influence on the overall transmission dynamics behavior of tuberculosis. The effective TB control measures should incorporate migrant as well the permanent residents.

Modeling toxoplasmosis spread in cat populations under vaccination

In this paper we present an epidemiological model to study the transmission dynamics of toxoplasmosis in a cat population under a continuous vaccination schedule. We explore the dynamics of toxoplasmosis at the population level using a mathematical model that includes the effect of oocyst, since the probability of acquisition of Toxoplasma Gondii infection depends on the environmental load of the parasite. This model considers indirectly the infection of prey through the oocyst shedding by cats. We prove that the basic reproduction number R(0) is a threshold value that completely determines the global dynamics and the outcome of the disease. Numerical computer simulations are presented to investigate different scenarios. These simulations show the effectiveness of a constant vaccination program.

Spatial analysis of tuberculosis cases in migrants and permanent residents, Beijing, 2000-2006

To determine the role of the migrant population in the transmission of tuberculosis (TB), we investigated the distribution and magnitude of TB in permanent residents and migrant populations of Beijing, People's Republic of China, from 2000 through 2006. An exploratory spatial data analysis was applied to detect the "hot spots" of TB among the 2 populations. Results, using the data obtained from 2004-2006, showed that people who migrated from the western, middle, and eastern zones of China had a significantly higher risk of having TB than did permanent residents. These findings indicate that population fluctuations have affected the rate of TB prevalence in Beijing, and interventions to control TB should include the migrant population.

Spatial analysis of tuberculosis cases in migrants and permanent residents, Beijing, 2000-2006

To determine the role of the migrant population in the transmission of tuberculosis (TB), we investigated the distribution and magnitude of TB in permanent residents and migrant populations of Beijing, People's Republic of China, from 2000 through 2006. An exploratory spatial data analysis was applied to detect the "hot spots" of TB among the 2 populations. Results, using the data obtained from 2004-2006, showed that people who migrated from the western, middle, and eastern zones of China had a significantly higher risk of having TB than did permanent residents. These findings indicate that population fluctuations have affected the rate of TB prevalence in Beijing, and interventions to control TB should include the migrant population.