Transmission Dynamics Of Infection Research Articles

346 Investigating the potential of incorporating indirect genetic effects into genetic evaluations of dairy calf disease traits

Abstract The goal of infectious disease control is often local eradication, but this is theoretically impossible to achieve based on classical quantitative genetic theory. Current methods focus on the susceptibility of individuals to disease and assume that exposure to a pathogen is 1) constant over time, 2) equal among individuals, and (3) due entirely to the environment. For this reason, it is likely that conventional genetic methods are capturing only a fraction of genetic variation in disease occurrence. The incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in infectious disease traits. This stems from the ability to include the positive feed-back dynamics of infectious disease transmission. From an epidemiolocal perspective, both susceptibility and infectivity also have indirect genetic effects (IGE), because the genotype of one individual impacts the risk of infection of other individuals, and this can drastically affect the rate and direction of response to selection. Epidemiological models unravel the genetic heterogeneity in both susceptibility and infectivity traits, and account for the impact each animal has on its contemporaries compared with conventional quantitative genetic approaches. From a calf health perspective, there is a shift towards group housing dairy calves, primarily due to welfare and social acceptability. In turn, this will lead to increased animal interactions from a young age. Therefore, the aim of this study was to investigate the potential of incorporating IGE into current quantitative approaches to determine its impact on selection potential. Our study looked at two common infectious diseases in calves on dairy farms: respiratory problems (RESP) and diarrhea (DIAR). Producer-recorded data comprised of 19,445 records collected on 34 herds that group housed calves between 2007 and 2020. Calves were allocated into pen groups based on birth dates and herd specific housing practices. Original phenotypes were split into 10 records, each representing a week of life, to determine when exactly calves became sick and infective. Several scenarios were investigated with respect to the time an animal was infective for following a disease case (1 or 2 wk), and the maximum difference in weeks between animal birth dates to be included within the same pen (2 to 5 wk). Variance components were estimated using a generalized linear mixed model fitting a complementary-log-log link function with offset for exposure. Initial heritability estimates for susceptibility on the observed scale for DIAR ranged from 0.03 to 0.07, and from 0.02 to 0.03 for RESP across scenarios. While heritability estimates for infectivity are currently being investigated, the results for susceptibility highlight the potential for incorporating IGE into genetic evaluation of disease traits and provide an introduction towards incorporating epidemiological theory into classical quantitative approaches to better respect the transmission dynamics of infections.

DETERMINISTIC MODEL OF ZIKA-VIRUS WITH CARRIER MOTHER AND RESERVOIRS: A MATHEMATICAL ANALYSIS APPROACH

In this paper, a mathematical model for the Zika virus is suggested to investigate the transmission dynamics of infection based on humans, pregnant carrier mother, infected children and the reservoir (primates) in three connected populations. Vertical and direct transmissions from all people to primates are considered in the proposed model. The Zika virus then spreads from this reservoir of infection via the nonhuman primate population (infected mosquitoes) to other entities. This virus can be passed on to the human population through an infected mosquito. Therefore, the new model with ten compartmental models has been normalized as follows: The normalized model is analyzed in depth to explore linkages between mosquitoes, humans, and primates on the dynamics of Zika-Virus transmission. The mathematical analysis comprises positivity and boundedness of solutions, determination of the basic reproduction number R0 via next-generation matrix approach, existence and stability of all equilibria as well as sensitivity analysis. Local and Global Stability of the Disease-free Equilibrium. Finally, numerical simulations are performed to verify the analytical results obtained and exhibit the contribution of different model parameters on disease transmission dynamics. The results prove that the interaction of forest mosquitoes with primates has a significant effect on human-Zika-Virus transmission dynamics among the susceptible population due to transitions to forested areas. Moreover, the findings suggest that the transmission probabilities and biting rates of mosquitoes on humans and primates are major parameters in transmitting the disease.

Pig herd management and infection transmission dynamics: a challenge for modellers

Pig herd management and infection transmission dynamics: a challenge for modellers

Combining wastewater surveillance and case data in estimating the time-varying effective reproduction number

Wastewater surveillance has been increasingly acknowledged as a useful tool for monitoring transmission dynamics of infections of public health concern, including the coronavirus disease (COVID-19). While a range of models have been proposed to estimate the time-varying effective reproduction number (Rt) utilizing clinical data, few have harnessed the viral concentration in wastewater samples to do so, leaving uncertainties about the potential precision gains with its use. In this study, we developed a Bayesian hierarchical model which simultaneously reconstructed the latent infection trajectory and estimated Rt. Focusing on the 2022 and early 2023 COVID-19 transmission trends in Singapore, where mass community wastewater surveillance has become routine, we performed estimations using a spectrum of data sources, including reported case counts, hospital admissions, deaths, and wastewater viral loads. We further explored the performance of our wastewater model across various scenarios with different sampling strategies. The results showed consistent estimates derived from models employing diverse data streams, while models incorporating more wastewater samples exhibited greater uncertainty and variation in the inferred Rts. Additionally, our analysis revealed prominent day-of-the-week effect in reported case counts and substantial temporal variations in ascertainment rates. In response to these findings, we advocate for a hybrid approach leveraging both clinical and wastewater surveillance data to account for changes in case-ascertainment rates. Furthermore, our study demonstrates the possibility of reducing sampling frequency or sample size without compromising estimation accuracy for Rt, highlighting the potential for optimizing resource allocation in surveillance efforts while maintaining robust insights into the transmission dynamics of infectious diseases.

Towards models of infection transmission dynamics

Towards models of infection transmission dynamics

Mathscr {S}mathscr {E}mathscr {I}mathscr {A}mathscr {R}mathscr {S} model for analyzing mathscr {C}mathscr {O}mathscr {V}mathscr {I}mathscr {D}-19 pandemic process via uppsi -Caputo fractional derivative and numerical simulation

The objective of this study is to develop the mathscr {S}mathscr {E}mathscr {I}mathscr {A}mathscr {R}mathscr {S} epidemic model for mathscr {C}mathscr {O}mathscr {V}mathscr {I}mathscr {D}-{textbf {19}} utilizing the uppsi -Caputo fractional derivative. The reproduction number (breve{mathscr {R}}_0) is calculated utilizing the next generation matrix method. The equilibrium points of the model are computed, and both the local and global stability of the disease-free equilibrium point are demonstrated. Sensitivity analysis is discussed to describe the importance of the parameters and to demonstrate the existence of a unique solution for the model by applying a fixed point theorem. Utilizing the fractional Euler procedure, an approximate solution to the model is obtained. To study the transmission dynamics of infection, numerical simulations are conducted by using MatLab. Both numerical methods and simulations can provide valuable insights into the behavior of the system and help in understanding the existence and properties of solutions. By placing the values texttt{t}, ln (texttt{t}) and sqrt{texttt{t}} instead of uppsi , the derivatives of the Caputo and Caputo–Hadamard and Katugampola appear, respectively, to compare the results of each with real data. Besides, these simulations specifically with different fractional orders to examine the transmission dynamics. At the end, we come to the conclusion that the simulation utilizing Caputo derivative with the order of 0.95 shows the prevalence of the disease better. Our results are new which provide a good contribution to the current research on this field of research.

A MATHEMATICAL MODEL FOR TUBERCULOSIS INFECTION TRANSMISSION DYNAMICS IN THE PRESENCE OF TESTING AND THERAPY, ISOLATION AND TREATMENT

In this study, a mathematical model for Tuberculosis infection transmission dynamics is developed by incorporating testing and therapy of latent individuals, the isolation of infectious individuals and the treatment of the isolated individuals. The basic reproduction number was computed using the next generation matrix method. Analysis of the model at the disease-free equilibrium state and the endemic equilibrium states shows that it is locally and globally asymptomatically stable whenever the basic reproduction number is less than unity at the disease -free equilibrium state and locally and globally asymptotically stable whenever the basic reproduction number is greater than unity. The result from the sensitivity index of show that the infection transmission parameter and other control parameters such as early detection and therapy, the isolation of infected individuals and treatment are crucial parameters to tuberculosis management. It is shown from numerical simulations that the early detection and therapy, isolation and treatment of infected individuals will reduce the infection transmission. Further numerical results show that the combination of early detection and therapy, isolation and treatment of infectious individuals will decrease the infection transmission and its eventual eradication from the human population.

Wastewater surveillance could serve as a pandemic early warning system for COVID‐19 and beyond

AbstractWastewater‐based surveillance can be used as an early warning system to identify COVID‐19 outbreaks because the viral load can be observed in sewage before it is clinically verified. Wastewater surveillance of SARS‐CoV‐2 can trace the transmission dynamics of infection in communities when using the scale of a wastewater diversion and treatment system. Using this early detection method can help protect human health and mitigate socio‐economic losses. It can help quantify the epidemiological data of a given population in real‐time and circumvent the need for other epidemiological indicators. There are challenges in using this technique in areas with underdeveloped sewerage infrastructure. It is especially the case in developing nations where uniform protocols for viral detection are lacking, and wastewater is heterogeneous because of environmental and operational conditions. This article explains the need for and importance of wastewater‐based surveillance for SARS‐CoV‐2. It lays out the most recent methodological approaches for detecting SARS‐CoV‐2 in municipal wastewater and outlines the main challenges associated with wastewater‐based epidemiology (WBE). The article includes a case study of surveillance work across India to demonstrate how a developing nation manages research and locational challenges. The socio‐economic, ethical, and policy dimensions of WBE for SARS‐CoV‐2 are also discussed.This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > Sustainable Engineering of Water Engineering Water > Methods

A mathematical model for transmission dynamics of COVID-19 infection.

In this paper, a mathematical model of COVID-19 has been proposed to study the transmission dynamics of infection by taking into account the role of symptomatic and asymptomatic infected individuals. The model has also considered the effect of non-pharmaceutical interventions (NPIs) in controlling the spread of virus. The basic reproduction number ( ) has been computed and the analysis shows that for , the disease-free state becomes globally stable. The conditions of existence and stability for two other equilibrium states have been obtained. Transcritical bifurcation occurs when basic reproduction number is one (i.e. ). It is found that when asymptomatic cases get increased, infection will persist in the population. However, when symptomatic cases get increased as compared to asymptomatic ones, the endemic state will become unstable and infection may eradicate from the population. Increasing NPIs decrease the basic reproduction number and hence, the epidemic can be controlled. As the COVID-19 transmission is subject to environmental fluctuations, the effect of white noise has been considered in the deterministic model. The stochastic differential equation model has been solved numerically by using the Euler-Maruyama method. The stochastic model gives large fluctuations around the respective deterministic solutions. The model has been fitted by using the COVID-19 data of three waves of India. A good match is obtained between the actual data and the predicted trajectories of the model in all three waves of COVID-19. The findings of this model may assist policymakers and healthcare professionals in implementing the most effective measures to prevent the transmission of COVID-19 in different settings.

A robust study of the transmission dynamics of malaria through non-local and non-singular kernel

<abstract><p>It is valuable to measure the epidemiological significance of malaria, since there has been a growing interest in reducing malaria through improved local and national health care systems. We formulate the dynamics of malaria infection via a fractional framework to understand the intricate transmission route of malaria and to identify the role of memory for the control of malaria. The model is investigated for basic results, moreover, the basic reproduction number is determined symbolized by $ \mathcal{R}_0 $. We have shown the local stability of the disease-free steady-state of the system for for $ \mathcal{R}_0 < 1 $. The existence and uniqueness of the solution of the system are examined. The Adams Bashforth approach in fractional form is applied to analyse the numerical outcomes of the mathematical model. Furthermore, in order to realise more efficiently, the Atangana-Baleanu (ABC) fractional nonlocal operator, which was just invented, is used. The stability of the system is investigated through the fixed-point theorems of Krasnoselskii and Banach. The behaviour of the approximation solution is illustrated in terms of graphs across various fractional values and other factors of the systems. After all, a brief analysis of the simulation's findings is provided to explain how infection transmission dynamics occur in society.</p></abstract>

MgpB genotyping and genetic diversity for antimicrobial resistance of Mycoplasma genitalium.

Introduction. Antimicrobial resistance (AMR) among Mycoplasma genitalium is a global issue. Understanding the transmission dynamics of infection is an important factor in reducing the occurrence of AMR.Hypothesis/Gap Statement. There is limited information on the genotyping and AMR traits of M. genitalium.Aims. Single-locus sequence-based (SLSB) mgpB sequence typing and genetic diversity analyses of AMR M. genitalium isolated from patients in the Republic of Korea were performed to clarify the transmission dynamics and eludicate proper management.Methodology. Sanger sequencing of mgpB, 23S rRNA, parC and gyrA genes from a total of 103 M. genitalium-positive specimens from 89 patients was carried out.Results. Twenty-seven different mgpB genotypes (GTs) were identified; 12 had been reported previously and 15 had not. GT7 and GT8 occurred frequently (n=38, 36.89 %, and n=16, 15.53 %, respectively). The genetic diversity of the AMR-determining sites was randomly dispersed among the different GTs. However, these GTs were classified into two phylogenetically distinct clusters that were significantly correlated with patient age and genetic diversity at positions 2058 and 2059 in the 23S rRNA gene. The GTs of 20 consecutive samples from 6 patients were compared to investigate temporal changes in GTs. One specimen changed its GT during follow-up, suggesting a new infection.Conclusions. mgpB sequence typing can be a reliable tool for epidemiological studies. Two clusters have different characteristics in terms of genetic diversity. The cluster with genetic diversity in the AMR-determining site may be explained by the high prevalence of the specimens and subsequent antimicrobial exposure during the study period.

A mesoscale agent based modeling framework for flow-mediated infection transmission in indoor occupied spaces

The ongoing Covid-19 pandemic, and its associated public health and socioeconomic burden, has reaffirmed the necessity for a comprehensive understanding of flow-mediated infection transmission in occupied indoor spaces. This is an inherently multiscale problem, and suitable investigation approaches that can enable evidence-based decision-making for infection control strategies, interventions, and policies; will need to account for flow physics, and occupant behavior. Here, we present a mesoscale infection transmission model for human occupied indoor spaces, by integrating an agent-based human interaction model with a flow physics model for respiratory droplet dynamics and transport. We outline the mathematical and algorithmic details of the modeling framework, and demonstrate its validity using two simple simulation scenarios that verify each of the major sub-models. We then present a detailed case-study of infection transmission in a model indoor space with 60 human occupants; using a systematic set of simulations representing various flow scenarios. Data from the simulations illustrate the utility and efficacy of the devised mesoscale model in resolving flow-mediated infection transmission; and elucidate key trends in infection transmission dynamics amongst the human occupants.

Clustering of Covid-19 infections among healthcare workers: Experience from a tertiary care center in Saudi Arabia

IntroductionCoronavirus infectious disease 2019 (COVID-19) had a significant impact on healthcare workers (HCWs) worldwide. Understanding the dynamics of infection transmission is important to develop strategies to prevent its spread.MethodsA retrospective study of a cohort of HCWs with COVID-19 from a single tertiary care hospital during the first wave of the pandemic. Epidemiological investigations and identification of clusters of infection were done prospectively.ResultsA total of 326 HCWs had COVID-19 based on positive polymerase chain reaction tests for SARS-CoV-2. Ten clusters of infection were identified; nine clusters had HCWs as the index cases while one cluster had a patient as the index case. The largest cluster involved 15 transmissions, and one cluster included a secondary transmission. Sharing accommodation and social gatherings were the commonest epidemiological links. The majority of infected HCWs had mild infections, 23 (6%) required hospital admission and 3 (1%) required intensive care; all fully recovered. Majority of infections (80%) were community-acquired. Living in shared accommodation was associated with COVID-19 (120/690 versus 206/1610, P value = .01) while working in COVID-19 designated wards/units was not associated with COVID-19 (52/297 vs 274/2003, P value = .13).ConclusionsClustering of COVID-19 was common among HCWs and related to shared accommodation and social gatherings, infection was of mild severity, and was not associated with caring for COVID-19 patients.

Sexually Transmitted Infection Transmission Dynamics During the Coronavirus Disease 2019 (COVID-19) Pandemic Among Urban Gay, Bisexual, and Other Men Who Have Sex With Men.

BackgroundThe impact of coronavirus disease 2019 (COVID-19) mitigation measures on sexually transmitted infection (STI) transmission and racial disparities remains unknown. Our objectives were to compare sex and drug risk behaviors, access to sexual health services, and STI positivity overall and by race during the COVID-19 pandemic compared with pre-pandemic among urban sexual minority men (MSM).MethodsSexually active MSM aged 18–45 years were administered a behavioral survey and STI testing every 3-months. Participants who completed at least 1 during-pandemic (April 2020–December 2020) and 1 pre-pandemic study visit (before 13 March 2020) that occurred less than 6 months apart were included. Regression models were used to compare during- and pre-pandemic visit outcomes.ResultsOverall, among 231 MSM, reports of more than 3 sex partners declined(pandemic-1: adjusted prevalence ratio 0.68; 95% confidence interval: .54–.86; pandemic-2: 0.65, .51–.84; pandemic-3: 0.57, .43–.75), substance use decreased (pandemic-1: 0.75, .61–.75; pandemic-2: 0.62, .50–.78; pandemic-3: 0.61, .47–.80), and human immunodeficiency virus/preexposure prophylaxis care engagement (pandemic-1: 1.20, 1.07–1.34; pandemic-2: 1.24, 1.11–1.39; pandemic-3: 1.30, 1.16–1.47) increased. STI testing decreased (pandemic-1: 0.68, .57–.81; pandemic-2: 0.78, .67–.92), then rebounded (pandemic-3: 1.01, .87–1.18). Nei­ther Chlamydia (pandemic-2: 1.62, .75–3.46; pandemic-3: 1.13, .24–1.27) nor gonorrhea (pandemic-2: 0.87, .46 1.62; pandemic-3: 0.56, .24–1.27) positivity significantly changed during vs pre-pandemic. Trends were mostly similar among Black vs. non-Black MSM.ConclusionsWe observed sustained decreases in STI risk behaviors but minimal change in STI positivity during compared with pre-pandemic. Our findings underscore the need for novel STI prevention strategies that can be delivered without in-person interactions.

Transient dynamics of infection transmission in a simulated intensive care unit.

Healthcare-associated infections (HAIs) remain a serious public health problem. In previous work, two models of an intensive care unit (ICU) showed that differing population structures had markedly different rates of Staphylococcus aureus (MRSA) transmission. One explanation for this difference is the models having differing long-term equilbrium dynamics, resulting from different basic reproductive numbers, R0. We find in this system however that this is not the case, and that both models had the same value for R0. Instead, short-term, transient dynamics, characterizing a series of small, self-limiting outbreaks caused by pathogen reintroduction were responsible for the differences. These results show the importance of these short-term factors for disease systems where reintroduction events are frequent, even if they are below the epidemic threshold. Further, we examine how subtle changes in how a hospital is organized—or how a model assumes a hospital is organized—in terms of the admission of new patients may impact transmission rates. This has implications for both novel pathogens introduced into ICUs, such as Ebola, MERS or COVID-19, as well as existing healthcare-associated infections such as carbapenem-resistant Enterobacteriaceae.

SIR model for propagation of COVID-19 in the Paraíba's State (Brazil)

This work aims to apply the SIR-type compartmental model (Susceptible - Infected - Removed) in the evolution of Covid-19 in Paraíba's State and Campina Grande City. For that, the parameters of the model were considered to be variable during time evolution, within an appropriate range. The system of differential equations was solved numerically using the Euler method. The parameters were obtained by adjusting the model to the infected data provided by the Paraíba Health Department. According to the results obtained, the model describes the infected population well. There was a reduction in the effective reproduction number in Paraíba and the town of Campina Grande. It is noteworthy that understanding the dynamics of infection transmission and evaluating the effectiveness of control measures is crucial to assess the potential for sustained transmission to occur in new areas. The model can also be applied to describe epidemic dynamics in other regions and countries.

Scenario analysis for programmatic tuberculosis control in Bangladesh: a mathematical modelling study

Tuberculosis (TB) is a major public health problem in Bangladesh. Although the National TB control program of Bangladesh is implementing a comprehensive expansion of TB control strategies, logistical challenges exist, and there is significant uncertainty concerning the disease burden. Mathematical modelling of TB is considered one of the most effective ways to understand the dynamics of infection transmission and allows quantification of parameters in different settings, including Bangladesh. In this study, we present a two-strain mathematical modelling framework to explore the dynamics of drug-susceptible (DS) and multidrug-resistant (MDR) TB in Bangladesh. We calibrated the model using DS and MDR-TB annual incidence data from Bangladesh from years 2001 to 2015. Further, we performed a sensitivity analysis of the model parameters and found that the contact rate of both strains had the largest influence on the basic reproduction numbers {text{R}}_{{0{text{s}}}} and {text{R}}_{{0{text{m}}}} of DS and MDR-TB, respectively. Increasingly powerful intervention strategies were developed, with realistic impact and coverage determined with the help of local staff. We simulated for the period from 2020 to 2035. Here, we projected the DS and MDR-TB burden (as measured by the number of incident cases and mortality) under a range of intervention scenarios to determine which of these scenario is the most effective at reducing burden. Of the single-intervention strategies, enhanced case detection is the most effective and prompt in reducing DS and MDR-TB incidence and mortality in Bangladesh and that with GeneXpert testing was also highly effective in decreasing the burden of MDR-TB. Our findings also suggest combining additional interventions simultaneously leads to greater effectiveness, particularly for MDR-TB, which we estimate requires a modest investment to substantially reduce, whereas DS-TB requires a strong sustained investment.

Mathematical modeling for the outbreak of the coronavirus (COVID-19) under fractional nonlocal operator

A mathematical model for the spread of the COVID-19 disease based on a fractional Atangana–Baleanu operator is studied. Some fixed point theorems and generalized Gronwall inequality through the AB fractional integral are applied to obtain the existence and stability results. The fractional Adams–Bashforth is used to discuss the corresponding numerical results. A numerical simulation is presented to show the behavior of the approximate solution in terms of graphs of the spread of COVID-19 in the Chinese city of Wuhan. We simulate our table for the data of Wuhan from February 15, 2020 to April 25, 2020 for 70 days. Finally, we present a debate about the followed simulation in characterizing how the transmission dynamics of infection can take place in society.

China’s Reactions to COVID-19 Outbreaks in Wuhan before Lockdown: The Impact of Three Weeks Delay.

Background: Wuhan, China was the original epicenter of COVID-19 pandemic The goal of the current study is to understand the infection transmission dynamics be

Dynamics of Population Immunity Due to the Herd Effect in the COVID-19 Pandemic.

The novel Coronavirus 2 Severe Acute Respiratory Syndrome (SARS-Cov-2) has led to the Coronavirus Disease 2019 (COVID-19) pandemic, which has surprised health authorities around the world, quickly producing a global health crisis. Different actions to cope with this situation are being developed, including confinement, different treatments to improve symptoms, and the creation of the first vaccines. In epidemiology, herd immunity is presented as an area that could also solve this new global threat. In this review, we present the basis of herd immunology, the dynamics of infection transmission that induces specific immunity, and how the application of immunoepidemiology and herd immunology could be used to control the actual COVID-19 pandemic, along with a discussion of its effectiveness, limitations, and applications.