(R2032) Modeling the Effect of Sanitation Effort on the Spread of Carrier-dependent Infectious Diseases due to Environmental Degradation

In this present study, an SIS model is proposed and analyzed to study the effect of sanitation effort in controlling the spread of carrier-dependent infectious disease in a human habitat due to environmental degradation. The dynamics of the model consist of six dependent variables, the susceptible population density, infective population density, carrier population density, cumulative density of environmental degradation and the density of sanitation effort applied on carrier population and degraded environment. In the modeling process, the carrier population density and sanitation effort are modeled logistically and the degradation of the environment is assumed to be directly proportional to the population in the habitat. The analysis of the model is performed by using the stability theory of differential equations and numerical simulations. The study of model shows that as the degradation of environment increases, the density of the carrier population increases which ultimately increases the infective population. Further, the result shows that by applying suitable sanitation effort on the carrier population density and on the cumulative density of environmental degradation, the carrier population density decreases and hence the infective population. Thus, it is very important to keep our environment clean by applying proper sanitation to prevent the spread of carrier-dependent infectious diseases

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

Dynamical behavior of a time-delayed infectious disease model with a non-linear incidence function under the effect of vaccination and treatment

When an infectious disease propagates throughout society, the incidence function may rise at first due to an increase in pathogenicity and then decrease due to inhibitory effects until it reaches saturation. Effective vaccination and treatment are very helpful for controlling the effects of such infectious diseases. To analyze the impacts of these diseases, we proposed a new compartmental model with a generalized non-linear incidence function, vaccination function, and treatment function, along with time delays in the respective functions, which show how its monotonic features influence the stability of the model. Fundamental properties of a model, such as positivity, boundedness, and the existence of equilibria, are examined in this work. The basic reproduction number has been computed, and correlative studies for local stability in view of the basic reproduction number have been examined at the disease-free and endemic equilibrium points. A delay-independent global stability result has been established, and to be more precise, we explicitly derived the result on global stability by restricting delay parameters within a very specific range. Furthermore, numerical simulations and some examples based on COVID-19 real-time data are pointed out to emphasize the significance of how the disease's dynamical behavior is characterized by various functions for controlling the spread of disease in a population and to justify the mathematical conclusions.Comment: 25 pages, 19 figure

Infectious Disease Transmission by Arline Travel

Improvements in aviation technology have led to considerable growth of domestic and international flights worldwide especially during the past four decades. Commercial flights have increased the movement of and have connected people from virtually all corners of the globe since the end of World War II to exceed 3 billion passengers a year since 2013: a sizable proportion of the global human population. Flight times have decreased considerably from the onset of commercial aviation and the range of airliners has extended substantially. A passenger harboring an infectious agent embarking a flight on one continent can be deplaning on another continent well within half a days’ time, in many cases, before manifesting any symptoms of disease. Furthermore, close proximity of passengers, some perhaps immunocompromised, during extended transcontinental flights, combined with relatively low air humidity (10-20%) and limited air replacements in the pressurized cabin may facilitate exchange of airborne infections. Respiratory pathogens including Mycobacterium tuberculosis, SARS coronavirus, human influenza and parainfluenza viruses and most recently SARS-CoV-2 are most likely candidates to convert aircrafts into atypical, unwitting fomites. Other infectious diseases such as enteric pathogens with an incubation time longer than the duration of any given flight may permit their asymptomatic host to rapidly disseminate an epidemic within or across continents. In this article we review documented precedents, engineering controls on commercial airliners and additional security measures employed on the ground intended to mitigate infectious disease spread and transmission potential through air travel

Cost-Effectiveness Analyses of Typhoid Interventions in Epidemic and Endemic Settings

Background: Typhoid fever is a major source of morbidity and mortality in developing countries, accounting for approximately 12-21 million infections, 119,000-269,000 deaths, and 2-23 million disability-adjusted life years (DALYs) annually. Typhoid fever is caused by infection with the bacteria Salmonella enterica serovar Typhi, which is mainly transmitted through fecal contamination of food or water. Due to these modes of transmission, most cases occur in low- and middle-income countries (LMICs) where sanitary conditions are poor and access to clean water and sanitation is not common. However, the scale of disease incidence is uncertain. Studies suggest that facility-based laboratory-confirmed estimates, the numbers used for reporting and decision-making, are considerably lower than the actual numbers. As a result, typhoid likely has an even higher global burden than is reported.While typhoid remains a major cause of morbidity and mortality, it is preventable. Interventions against typhoid exist, with varying degrees of efficacy and costs. Investments in water and sewer systems in the early 20th century are thought to have been responsible for the decline of typhoid in many developed countries; however, no economic evaluations have quantified the costs and impact of improvements in sanitation. Additionally, a typhoid conjugate vaccine (TCV) has been approved, but research regarding its long-term efficacy and use in outbreak settings is limited. Cost-effectiveness evaluations of TCVs recommend their use in endemic settings, but modelling suggests that vaccination alone will not eliminate disease. Methods & Results: Before we can evaluate the impact of interventions, we need accurate estimates of baseline disease incidence. Therefore, in Chapter 1, we developed a Bayesian framework to combine multiple data sources to estimate the population-based typhoid incidence based on passive surveillance data from Blantyre, Malawi; Kathmandu, Nepal; and Dhaka, Bangladesh. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval (CrI): 6.0-12.4) in Malawi, 14.4 (95% CrI: 9.3-24.9) in Nepal, and 7.0 (95% CrI: 5.6-9.2) in Bangladesh. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood-culture-confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever.In Chapter 2, we evaluated the cost-effectiveness of typhoid conjugate vaccine use in response to outbreaks of typhoid fever. We fit a modified version of an existing dynamic compartmental model of typhoid fever to Malawi outbreak data and evaluated preventive and reactive vaccination strategies. We then conducted a cost-effectiveness analysis using the net-benefits framework to compare no vaccination to routine vaccination at 9 months of age with and without a catch-up campaign up to 15 years old. We examined variations in outbreak definitions, delays in implementation of reactive vaccination, and the timing of preventive vaccination relative to the outbreak. We estimated that vaccination would prevent 15-60% of disability-adjusted life-years (DALYs) in the outbreak scenarios. Some form of routine vaccination with a catch-up campaign was preferred over no vaccination for willingness-to-pay (WTP) values of at least $110 per DALY averted. Countries where outbreaks of typhoid fever due to introduction of antimicrobial resistant strains are likely to occur should consider TCV introduction. Reactive vaccination can be a cost-effective strategy, but only if delays in vaccine deployment are minimal; otherwise, introduction of preventive routine immunization with a catch-up campaign should be considered. Lastly, in Chapter 3, we quantified the relationship between investments in water and sanitation infrastructure and long-term typhoid transmission rates using historical data from 16 U.S. cities. We fit two models for each city: (1) we modified a Time-series Susceptible-Infectious-Recovered (TSIR) model and extracted long-term transmission rates, and (2) we measured the association between the transmission rates and financial variables using hierarchical regression models. Overall historical$1 per capita ($16.13 in 2017) investments in the water supply were associated with approximately 5$1 increases in the overall sewer system investments were associated with estimated 6% (95% confidence interval: 4-9%) decreases. Conclusions: A combination of statistical and mathematical modeling permits us to evaluate the cost-effectiveness of typhoid interventions across settings. We are able to estimate the true population-based incidence of typhoid fever in Africa and Asia, weigh the costs and effects of vaccination strategies in an outbreak setting, and estimate the impact of water and sanitation investments in an endemic setting. These findings can help to inform decision-making regarding typhoid control and prevention. The results can play an essential role in making the case for improvements in water and sanitation and/or vaccination to reduce the global burden of typhoid fever

The development of stochastic models for relative risk estimation in constructing pneumonia disease mapping in Malaysia

Pneumonia is one of the leading causes of death for infectious diseases especially in developing countries. Conventionally, its spread is only being monitored based on the total number of cases recorded without considering geographical distribution. Alternatively, disease mapping can be constructed based on the relative risk that includes a geographical distribution. A good disease mapping relies on the accuracy of relative risk estimated from the best-fitted statistical model. Therefore, this study aims to develop an alternative method in estimating the pneumonia relative risk based on four stochastic models: Susceptible-Infected-Carriers (SIC), Susceptible-Infected Recovered (SIR), Susceptible-Carrier-Infected-Recovered (SCIR), and Susceptible- Vaccinated-Carrier-Infected-Recovered (SVCIR). These estimated relative risks are then compared with those of the existing methods: Standardized Mortality Ratio (SMR), Poisson-gamma and Besag, York and Mollie (BYM) models. There are four phases in this study. Firstly, four deterministic models that are suitable for pneumonia disease transmission are selected, from which the stochastic models are developed. Next, these four stochastic models are applied to estimate the relative risk for pneumonia disease by analyzing pneumonia data in Malaysia from the year 2010 until the year 2019. The performance of these four stochastic models and existing methods is evaluated by comparing their relative risk values. Finally, the pneumonia risk maps are then constructed based on the relative risk values obtained. Findings show that there is a large gap in relative risk estimation values when using the stochastic SVCIR model compared to other models. The relative risk values when using stochastic SVCIR model decrease from high-risk level to medium risk level and from medium risk level to low-risk level. This situation occurs since stochastic SVCIR model allows for the spatial correlation between the areas and includes extra information in the model such as vaccination and carrier components. Application of the models on the Malaysian data shows that Putrajaya is identified as the highest risk of contracting pneumonia. This is because Putrajaya is the smallest area with the highest population growth rate in Malaysia. In conclusion, these stochastic models demonstrate better performance compared to the conventional models. Furthermore, these models are applicable to other infectious diseases with similar transmission characteristics. The disease mapping may assist the government in prioritizing areas that need further attention in gearing towards a sustainable health system

Nonlinear Hierarchical Models for Longitudinal Experimental Infection Studies

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models