Some Simple Nosocomial Disease Transmission Models

2015 ◽  
Vol 77 (3) ◽  
pp. 460-469 ◽  
Author(s):  
Fred Brauer
Keyword(s):  
Disease Transmission ◽  
Nosocomial Disease ◽  
Transmission Models

scholarly journals Understanding mosquito host-choice behaviour: a new and low-cost method of identifying the sex of human hosts from mosquito blood meals

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fiona Teltscher ◽  
Sophie Bouvaine ◽  
Gabriella Gibson ◽  
Paul Dyer ◽  
Jennifer Guest ◽  
...  
Keyword(s):  
Blood Meal ◽  
Host Species ◽  
Disease Transmission ◽  
Low Cost ◽  
Host Choice ◽  
Epidemiological Models ◽  
Choice Behaviour ◽  
Vector Borne ◽  
Transmission Models ◽  
Blood Meals

Abstract Background Mosquito-borne diseases are a global health problem, causing hundreds of thousands of deaths per year. Pathogens are transmitted by mosquitoes feeding on the blood of an infected host and then feeding on a new host. Monitoring mosquito host-choice behaviour can help in many aspects of vector-borne disease control. Currently, it is possible to determine the host species and an individual human host from the blood meal of a mosquito by using genotyping to match the blood profile of local inhabitants. Epidemiological models generally assume that mosquito biting behaviour is random; however, numerous studies have shown that certain characteristics, e.g. genetic makeup and skin microbiota, make some individuals more attractive to mosquitoes than others. Analysing blood meals and illuminating host-choice behaviour will help re-evaluate and optimise disease transmission models. Methods We describe a new blood meal assay that identifies the sex of the person that a mosquito has bitten. The amelogenin locus (AMEL), a sex marker located on both X and Y chromosomes, was amplified by polymerase chain reaction in DNA extracted from blood-fed Aedes aegypti and Anopheles coluzzii. Results AMEL could be successfully amplified up to 24 h after a blood meal in 100% of An. coluzzii and 96.6% of Ae. aegypti, revealing the sex of humans that were fed on by individual mosquitoes. Conclusions The method described here, developed using mosquitoes fed on volunteers, can be applied to field-caught mosquitoes to determine the host species and the biological sex of human hosts on which they have blood fed. Two important vector species were tested successfully in our laboratory experiments, demonstrating the potential of this technique to improve epidemiological models of vector-borne diseases. This viable and low-cost approach has the capacity to improve our understanding of vector-borne disease transmission, specifically gender differences in exposure and attractiveness to mosquitoes. The data gathered from field studies using our method can be used to shape new transmission models and aid in the implementation of more effective and targeted vector control strategies by enabling a better understanding of the drivers of vector-host interactions.

scholarly journals The effects of flooding and weather conditions on leptospirosis transmission in Thailand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Karine Chalvet-Monfray ◽  
Anuwat Wiratsudakul ◽  
Charin Modchang
Keyword(s):  
Sensitivity Analysis ◽  
Public Education ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Multiplication Rate ◽  
High Degree ◽  
Transmission Models

AbstractThe epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.

scholarly journals Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation

2018 ◽  
Vol 38 (8) ◽  
pp. 930-941
Author(s):  
Peter J. Dodd ◽  
Jeff J. Pennington ◽  
Liza Bronner Murrison ◽  
David W. Dowdy
Keyword(s):  
Infectious Disease ◽  
Cost Effectiveness ◽  
Decision Trees ◽  
Disease Transmission ◽  
Time Scale ◽  
Compartmental Models ◽  
Diagnostic Process ◽  
Design Strategies ◽  
Diagnostic Algorithms ◽  
Transmission Models

Introduction. Cost-effectiveness models for infectious disease interventions often require transmission models that capture the indirect benefits from averted subsequent infections. Compartmental models based on ordinary differential equations are commonly used in this context. Decision trees are frequently used in cost-effectiveness modeling and are well suited to describing diagnostic algorithms. However, complex decision trees are laborious to specify as compartmental models and cumbersome to adapt, limiting the detail of algorithms typically included in transmission models. Methods. We consider an approximation replacing a decision tree with a single holding state for systems where the time scale of the diagnostic algorithm is shorter than time scales associated with disease progression or transmission. We describe recursive algorithms for calculating the outcomes and mean costs and delays associated with decision trees, as well as design strategies for computational implementation. We assess the performance of the approximation in a simple model of transmission/diagnosis and its role in simplifying a model of tuberculosis diagnostics. Results. When diagnostic delays were short relative to recovery rates, our approximation provided a good account of infection dynamics and the cumulative costs of diagnosis and treatment. Proportional errors were below 5% so long as the longest delay in our 2-step algorithm was under 20% of the recovery time scale. Specifying new diagnostic algorithms in our tuberculosis model was reduced from several tens to just a few lines of code. Discussion. For conditions characterized by a diagnostic process that is neither instantaneous nor protracted (relative to transmission dynamics), this novel approach retains the advantages of decision trees while embedding them in more complex models of disease transmission. Concise specification and code reuse increase transparency and reduce potential for error.

scholarly journals Machine learning approaches to calibrate individual-based infectious disease models

2021 ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Learning Approaches ◽  
Dimensional Parameter ◽  
Novel Approach ◽  
Transmission Models

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.One Sentence SummaryWe propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

scholarly journals Adaptive data-driven age and patch mixing in contact networks with recurrent mobility

2021 ◽  
Author(s):  
Jesse Knight ◽  
Huiting Ma ◽  
Amir Ghasemi ◽  
Mackenzie Hamilton ◽  
Kevin Brown ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Age Distribution ◽  
Age Groups ◽  
Data Driven ◽  
Infectious Disease Transmission ◽  
Mobility Data ◽  
Contact Patterns ◽  
Different Types ◽  
Mixing Patterns ◽  
Transmission Models

AbstractInfectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form a contact if they meet in patch C. We build upon their approach to address three potential gaps that remain. First, our approach includes a distribution of contacts by age that is responsive to underlying age distribution of the mixing pool. Second, different age distributions by contact type are also maintained in our approach, such that changes to the numbers of different types of contacts are appropriately reflected in changes to the overall age mixing patterns. Finally, we introduce and distinguish between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch; and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch. We describe in detail the steps required to implement our approach, and present results of an example application.Graphical Abstract

scholarly journals Modelling approaches for simple dynamic networks and applications to disease transmission models

2012 ◽  
Vol 468 (2141) ◽  
pp. 1332-1355 ◽  
Author(s):  
Istvan Z. Kiss ◽  
Luc Berthouze ◽  
Timothy J. Taylor ◽  
Péter L. Simon
Keyword(s):  
Steady State ◽  
Network Model ◽  
Disease Transmission ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Link Type ◽  
Dynamic Network Model ◽  
Pairwise Model ◽  
Closure Relations ◽  
Transmission Models

In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.

scholarly journals Mosquito species and age influence thermal performance of traits relevant to malaria transmission

2019 ◽  
Author(s):  
KL Miazgowicz ◽  
EA Mordecai ◽  
SJ Ryan ◽  
RJ Hall ◽  
J Owen ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Egg Production ◽  
Life History Traits ◽  
Anopheles Stephensi ◽  
Mosquito Species ◽  
Urban Malaria ◽  
Temperature Dependent ◽  
Biting Rate ◽  
Transmission Models ◽  
Target Intervention

AbstractModels predicting disease transmission are a vital tool in the control of mosquito populations and malaria reduction as they can target intervention efforts. We compared the performance of temperature-dependent transmission models when mosquito life history traits were allowed to change across the lifespan of Anopheles stephensi, the urban malaria mosquito, to models parameterized with commonly derived estimates of lifetime trait values. We conducted an experiment on adult female An. stephensi to generate daily per capita values for lifespan, egg production, and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated based on daily rates versus directly observed lifetime values. Incorporating these temperature-trait relationships into an expression governing transmission suitability, relative R0(T), model resulted in minor differences in the breadth of suitable temperatures for Plasmodium falciparum transmission between the two models constructed from only An. stephensi trait data, but a substantial increase in breadth compared to a previously published model consisting of trait data from multiple mosquito species. Overall this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based environmental suitability predictions of transmission.

scholarly journals Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

2020 ◽  
Author(s):  
Kathy Leung ◽  
Joseph T Wu ◽  
Gabriel M Leung
Keyword(s):  
Real Time ◽  
Disease Transmission ◽  
Human Mobility ◽  
Population Mobility ◽  
Mobility Data ◽  
Physical Mixing ◽  
Real Time Tracking ◽  
Case Data ◽  
Transmission Models ◽  
New Framework

AbstractDigital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We developed a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we were able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e. no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings showed that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

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