scholarly journals Modelling disease transmission from touchscreen user interfaces

2021 ◽  
Vol 8 (7) ◽  
pp. 210625
Author(s):  
Andrew Di Battista ◽  
Christos Nicolaides ◽  
Orestis Georgiou
Keyword(s):  
User Interfaces ◽  
Disease Transmission ◽  
Essential Role ◽  
Stochastic Simulations ◽  
Reproductive Number ◽  
Human Computer Interactions ◽  
Queuing Network ◽  
Closed Population ◽  
Technological Strategies ◽  
Counterintuitive Result

The extensive use of touchscreens for all manner of human–computer interactions has made them plausible instruments of touch-mediated disease transmission. To that end, we employ stochastic simulations to model human–fomite interaction with a distinct focus on touchscreen interfaces. The timings and frequency of interactions from within a closed population of infectious and susceptible individuals was modelled using a queuing network. A pseudo-reproductive number R was used to compare outcomes under various parameter conditions. We then apply the simulation to a specific real-world scenario; namely that of airport self-check-in and baggage drop. A counterintuitive result was that R decreased with increased touch rates required for touchscreen interaction. Additionally, as one of few parameters to be controlled, the rate of cleaning/disinfecting screens plays an essential role in mitigating R , though alternative technological strategies could prove more effective. The simulation model developed provides a foundation for future advances in more sophisticated fomite disease-transmission modelling.

scholarly journals Modelling disease transmission from touchscreen user interfaces

2020 ◽  
Author(s):  
Andrew Di Battista ◽  
Christos Nicolaides ◽  
Orestis Georgiou
Keyword(s):  
User Interfaces ◽  
Real World ◽  
Disease Transmission ◽  
Stochastic Simulations ◽  
Alternative Methods ◽  
Reproductive Number ◽  
Human Computer Interactions ◽  
Queuing Network ◽  
Closed Population ◽  
Computer Interactions

AbstractThe extensive use of touchscreens for all manner of human-computer interactions has made them plausible instruments of touch-mediated disease transmission. To that end, we employ stochastic simulations to model human-fomite interaction with a distinct focus on touchscreen interfaces. The timings and frequency of interactions from within a closed population of infectious and susceptible individuals was modelled using a basic queuing network. A pseudo reproductive number (R) was used to compare outcomes under various parameter conditions. We also expanded the simulation to a specific real-world scenario; namely airport self check-in and baggage drop. Results revealed that the required rate of cleaning/disinfecting of screens to effectively mitigate R can be inordinately high. This suggests that revised or alternative methods should be considered.

scholarly journals Increasing Effectiveness of Genetically Modifying Mosquito Populations: Risk Assessment of Releasing Blood-Fed Females

2021 ◽  
Author(s):  
Siyang Xia ◽  
Jonah Ury ◽  
Jeffrey R. Powell
Keyword(s):  
Disease Transmission ◽  
Stochastic Simulations ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Short Term ◽  
Resistance Level ◽  
Long Term Effect ◽  
Release Program ◽  
Parameter Values

Releasing mosquito refractory to pathogens has been proposed as a means of controlling mosquito-borne diseases. A recent modeling study demonstrated that instead of the conventional male-only releases, adding blood-fed females to the release population could significantly increase the program’s efficiency, hastening the decrease in disease transmission competence of the target mosquito population and reducing the duration and costs of the release program. However, releasing female mosquitoes presents a short-term risk of increased disease transmission. To quantify this risk, we constructed a Ross–MacDonald model and an individual-based stochastic model to estimate the increase in disease transmission contributed by the released blood-fed females, using the mosquito Aedes aegypti and the dengue virus as a model system. Under baseline parameter values informed by empirical data, our stochastic models predicted a 1.1–5.5% increase in dengue transmission during the initial release, depending on the resistance level of released mosquitoes and release size. The basic reproductive number (R0) increased by 0.45–3.62%. The stochastic simulations were then extended to 10 releases to evaluate the long-term effect. The overall reduction of disease transmission was much greater than the number of potential infections directly contributed by the released females. Releasing blood-fed females with males could also outperform conventional male-only releases when the release strain is sufficiently resistant, and the release size is relatively small. Overall, these results suggested that the long-term benefit of releasing blood-fed females often outweighs the short-term risk.

scholarly journals Mask-Ematics: Modeling the Effects of Masks in COVID-19 Transmission in High-Risk Environments

Epidemiologia ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 207-226
Author(s):  
Anthony Morciglio ◽  
Bin Zhang ◽  
Gerardo Chowell ◽  
James M. Hyman ◽  
Yi Jiang
Keyword(s):  
Public Health ◽  
High Risk ◽  
Disease Transmission ◽  
Large Fraction ◽  
Critical Mass ◽  
Transmission Model ◽  
Reproductive Number ◽  
High Quality ◽  
Moderate Quality ◽  
Epidemic Size

The COVID-19 pandemic has placed an unprecedented burden on public health and strained the worldwide economy. The rapid spread of COVID-19 has been predominantly driven by aerosol transmission, and scientific research supports the use of face masks to reduce transmission. However, a systematic and quantitative understanding of how face masks reduce disease transmission is still lacking. We used epidemic data from the Diamond Princess cruise ship to calibrate a transmission model in a high-risk setting and derive the reproductive number for the model. We explain how the terms in the reproductive number reflect the contributions of the different infectious states to the spread of the infection. We used that model to compare the infection spread within a homogeneously mixed population for different types of masks, the timing of mask policy, and compliance of wearing masks. Our results suggest substantial reductions in epidemic size and mortality rate provided by at least 75% of people wearing masks (robust for different mask types). We also evaluated the timing of the mask implementation. We illustrate how ample compliance with moderate-quality masks at the start of an epidemic attained similar mortality reductions to less compliance and the use of high-quality masks after the epidemic took off. We observed that a critical mass of 84% of the population wearing masks can completely stop the spread of the disease. These results highlight the significance of a large fraction of the population needing to wear face masks to effectively reduce the spread of the epidemic. The simulations show that early implementation of mask policy using moderate-quality masks is more effective than a later implementation with high-quality masks. These findings may inform public health mask-use policies for an infectious respiratory disease outbreak (such as one of COVID-19) in high-risk settings.

scholarly journals Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Navavat Pipatsart ◽  
Wannapong Triampo ◽  
Charin Modchang
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Random Network ◽  
Network Models ◽  
Transmission Probability ◽  
Stochastic Simulations ◽  
Random Networks ◽  
Cumulative Number ◽  
Network Adaptation ◽  
Infectious Disease Dynamics

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.

Modeling and stability analysis of the spread of novel coronavirus disease COVID-19

2021 ◽  
pp. 2150035
Author(s):  
A. George Maria Selvam ◽  
Jehad Alzabut ◽  
D. Abraham Vianny ◽  
Mary Jacintha ◽  
Fatma Bozkurt Yousef
Keyword(s):  
Fractional Order ◽  
Disease Transmission ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Basic Reproductive Number ◽  
Phase Portraits ◽  
Transmission Model ◽  
Reproductive Number ◽  
Discrete Version ◽  
Novel Coronavirus

Towards the end of 2019, the world witnessed the outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (COVID-19), a new strain of coronavirus that was unidentified in humans previously. In this paper, a new fractional-order Susceptible–Exposed–Infected–Hospitalized–Recovered (SEIHR) model is formulated for COVID-19, where the population is infected due to human transmission. The fractional-order discrete version of the model is obtained by the process of discretization and the basic reproductive number is calculated with the next-generation matrix approach. All equilibrium points related to the disease transmission model are then computed. Further, sufficient conditions to investigate all possible equilibria of the model are established in terms of the basic reproduction number (local stability) and are supported with time series, phase portraits and bifurcation diagrams. Finally, numerical simulations are provided to demonstrate the theoretical findings.

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

scholarly journals Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule

2020 ◽  
Author(s):  
Jody R Reimer ◽  
Sharia M Ahmed ◽  
Benjamin Brintz ◽  
Rashmee U Shah ◽  
Lindsay T Keegan ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Diagnostic Testing ◽  
Prediction Rule ◽  
Population Level ◽  
Clinical Prediction Rule ◽  
Transmission Model ◽  
Reproductive Number ◽  
Clinical Prediction ◽  
Health Goals ◽  
Novel Approach

Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.

scholarly journals Co-circulating mumps lineages at multiple geographic scales

2018 ◽  
Author(s):  
Shirlee Wohl ◽  
Hayden C. Metsky ◽  
Stephen F. Schaffner ◽  
Anne Piantadosi ◽  
Meagan Burns ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Local Community ◽  
Epidemiological Data ◽  
Viral Disease ◽  
The United States ◽  
Reproductive Number ◽  
Multiple Introductions ◽  
Global Context ◽  
The Us ◽  
Short Time

AbstractDespite widespread vaccination, eleven thousand mumps cases were reported in the United States (US) in 2016–17, including hundreds in Massachusetts, primarily in college settings. We generated 203 whole genome mumps virus (MuV) sequences from Massachusetts and 15 other states to understand the dynamics of mumps spread locally and nationally, as well as to search for variants potentially related to vaccination. We observed multiple MuV lineages circulating within Massachusetts during 2016–17, evidence for multiple introductions of the virus to the state, and extensive geographic movement of MuV within the US on short time scales. We found no evidence that variants arising during this outbreak contributed to vaccine escape. Combining epidemiological and genomic data, we observed multiple co-circulating clades within individual universities as well as spillover into the local community. Detailed data from one well-sampled university allowed us to estimate an effective reproductive number within that university significantly greater than one. We also used publicly available small hydrophobic (SH) gene sequences to estimate migration between world regions and to place this outbreak in a global context, but demonstrate that these short sequences, historically used for MuV genotyping, are inadequate for tracing detailed transmission. Our findings suggest continuous, often undetected, circulation of mumps both locally and nationally, and highlight the value of combining genomic and epidemiological data to track viral disease transmission at high resolution.

scholarly journals Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

2020 ◽  
Author(s):  
Matt J Keeling ◽  
Louise Dyson ◽  
Glen Guyver-Fletcher ◽  
Alex Holmes ◽  
Malcolm G Semple ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Data Streams ◽  
Disease Transmission ◽  
Disease Outbreaks ◽  
Epidemiological Data ◽  
Reproductive Number ◽  
Short Term ◽  
Statistical Tool ◽  
The Many ◽  
The Uk

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the basic reproductive ratio, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first-wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

scholarly journals Intrinsic and realized generation intervals in infectious-disease transmission

2015 ◽  
Vol 282 (1821) ◽  
pp. 20152026 ◽  
Author(s):  
David Champredon ◽  
Jonathan Dushoff
Keyword(s):  
Public Health ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Interval Data ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Infectious Disease Transmission ◽  
Generation Interval ◽  
Public Health Strategies ◽  
The Relationship

The generation interval is the interval between the time when an individual is infected by an infector and the time when this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modelling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the ‘intrinsic’ one typically used in mathematical models and the ‘forward’ and ‘backward’ ones typically observed from contact-tracing data, looking, respectively, forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models.

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