The Impact of Movement on Disease Dynamics in a Multi-city Compartmental Model Including Residency Patch

2016 ◽  
pp. 255-265
Author(s):  
Diána Knipl
Keyword(s):  
Compartmental Model ◽  
Disease Dynamics ◽  
The Impact

scholarly journals Impact of Population Mixing Between a Vaccinated Majority and Unvaccinated Minority on Disease Dynamics. Implications for SARS-CoV-2

2021 ◽  
Author(s):  
Ashleigh Tuite ◽  
Afia Amoako ◽  
David Fisman
Keyword(s):  
Compartmental Model ◽  
Vaccine Development ◽  
Health And Safety ◽  
Infection Risk ◽  
Base Case ◽  
Disease Dynamics ◽  
Population Mixing ◽  
Contact Frequency ◽  
Random Mixing ◽  
The Impact

Background: The speed of vaccine development has been a singular achievement during the SARS-CoV-2 pandemic. However, anti-vaccination movements and disinformation efforts have resulted in suboptimal uptake of available vaccines. Vaccine opponents often frame their opposition in terms of the rights of the unvaccinated. Our objective was to explore the impact of mixing of vaccinated and unvaccinated populations on risk among vaccinated individuals. Methods: We constructed a simple Susceptible-Infectious-Recovered (SIR) compartmental model of a respiratory infectious disease with two connected sub-populations: vaccinated individuals and unvaccinated individuals (Figure 1). We modeled the non-random mixing of these two groups using a matrix approach with a mixing constant varied to simulate a spectrum of patterns ranging from random mixing to complete assortativity. We evaluated the dynamics of an epidemic within each subgroup, and in the population as a whole, and also evaluated the contact-frequency-adjusted contribution of unvaccinated individuals to risk among the vaccinated. Results: As expected, the relative risk of infection was markedly higher among unvaccinated individuals than among vaccinated individuals. However, the contact-adjusted contribution of unvaccinated individuals to infection risk during the epidemic was disproportionate with unvaccinated individuals contributing to infection risk among the vaccinated at a rate up to 6.4 times higher than would have been expected based on contact numbers alone in the base case. As assortativity increased the final attack rate decreased among vaccinated individuals, but the contact-adjusted contribution to risk among vaccinated individuals derived from contact with unvaccinated individuals increased. Interpretation: While risk associated with avoiding vaccination during a virulent pandemic accrues chiefly to the unvaccinated, the choices of these individuals are likely to impact the health and safety of vaccinated individuals in a manner disproportionate to the fraction of unvaccinated individuals in the population.

scholarly journals A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mohammed A. Aba Oud ◽  
Aatif Ali ◽  
Hussam Alrabaiah ◽  
Saif Ullah ◽  
Muhammad Altaf Khan ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Reproduction Number ◽  
Model Parameters ◽  
Disease Dynamics ◽  
Threshold Parameter ◽  
Fractional Model ◽  
Infected People ◽  
Fractional Models ◽  
Disease Epidemics ◽  
The Impact

AbstractCOVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained $\mathcal{R}_{0} \approx 1.50$ R 0 ≈ 1.50 . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

scholarly journals Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
A. Corberán-Vallet ◽  
F. J. Santonja ◽  
M. Jornet-Sanz ◽  
R.-J. Villanueva
Keyword(s):  
Discrete Time ◽  
Compartmental Model ◽  
Predictive Distribution ◽  
Model Parameters ◽  
Immunization Program ◽  
Posterior Predictive Distribution ◽  
Vaccination Strategies ◽  
Proposed Model ◽  
Routine Immunization Program ◽  
The Impact

We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health’s great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox incidence, which facilitates the computation of point forecasts and prediction intervals.

scholarly journals Optimal control approaches for combining medicines and mosquito control in tackling dengue

2020 ◽  
Vol 7 (4) ◽  
pp. 181843 ◽  
Author(s):  
Thomas Rawson ◽  
Kym E. Wilkins ◽  
Michael B. Bonsall
Keyword(s):  
Optimal Control ◽  
Disease Control ◽  
Mosquito Control ◽  
Large Population ◽  
Human Movement ◽  
Mitigation Strategies ◽  
Disease Dynamics ◽  
Vector Ecology ◽  
Dengue Disease ◽  
The Impact

Dengue is a debilitating and devastating viral infection spread by mosquito vectors, and over half the world’s population currently live at risk of dengue (and other flavivirus) infections. Here, we use an integrated epidemiological and vector ecology framework to predict optimal approaches for tackling dengue. Our aim is to investigate how vector control and/or vaccination strategies can be best combined and implemented for dengue disease control on small networks, and whether these optimal strategies differ under different circumstances. We show that a combination of vaccination programmes and the release of genetically modified self-limiting mosquitoes (comparable to sterile insect approaches) is always considered the most beneficial strategy for reducing the number of infected individuals, owing to both methods having differing impacts on the underlying disease dynamics. Additionally, depending on the impact of human movement on the disease dynamics, the optimal way to combat the spread of dengue is to focus prevention efforts on large population centres. Using mathematical frameworks, such as optimal control, are essential in developing predictive management and mitigation strategies for dengue disease control.

scholarly journals COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios

2020 ◽  
Vol 49 (5) ◽  
pp. 1443-1453 ◽  
Author(s):  
Henrik Sjödin ◽  
Anders F Johansson ◽  
Åke Brännström ◽  
Zia Farooq ◽  
Hedi Katre Kriit ◽  
...  
Keyword(s):  
Intensive Care ◽  
Excess Mortality ◽  
Compartmental Model ◽  
Health Care Demand ◽  
Radiation Model ◽  
The Usa ◽  
Community Transmission ◽  
Health Demands ◽  
Health Demand ◽  
The Impact

Abstract Background While the COVID-19 outbreak in China now appears suppressed, Europe and the USA have become the epicentres, both reporting many more deaths than China. Responding to the pandemic, Sweden has taken a different approach aiming to mitigate, not suppress, community transmission, by using physical distancing without lockdowns. Here we contrast the consequences of different responses to COVID-19 within Sweden, the resulting demand for care, intensive care, the death tolls and the associated direct healthcare related costs. Methods We used an age-stratified health-care demand extended SEIR (susceptible, exposed, infectious, recovered) compartmental model for all municipalities in Sweden, and a radiation model for describing inter-municipality mobility. The model was calibrated against data from municipalities in the Stockholm healthcare region. Results Our scenario with moderate to strong physical distancing describes well the observed health demand and deaths in Sweden up to the end of May 2020. In this scenario, the intensive care unit (ICU) demand reaches the pre-pandemic maximum capacity just above 500 beds. In the counterfactual scenario, the ICU demand is estimated to reach ∼20 times higher than the pre-pandemic ICU capacity. The different scenarios show quite different death tolls up to 1 September, ranging from 5000 to 41 000, excluding deaths potentially caused by ICU shortage. Additionally, our statistical analysis of all causes excess mortality indicates that the number of deaths attributable to COVID-19 could be increased by 40% (95% confidence interval: 0.24, 0.57). Conclusion The results of this study highlight the impact of different combinations of non-pharmaceutical interventions, especially moderate physical distancing in combination with more effective isolation of infectious individuals, on reducing deaths, health demands and lowering healthcare costs. In less effective mitigation scenarios, the demand on ICU beds would rapidly exceed capacity, showing the tight interconnection between the healthcare demand and physical distancing in the society. These findings have relevance for Swedish policy and response to the COVID-19 pandemic and illustrate the importance of maintaining the level of physical distancing for a longer period beyond the study period to suppress or mitigate the impacts from the pandemic.

scholarly journals Compositional cyber-physical epidemiology of COVID-19

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jin Woo Ro ◽  
Nathan Allen ◽  
Weiwei Ai ◽  
Debi Prasad ◽  
Partha S. Roop
Keyword(s):  
New Zealand ◽  
Government Policy ◽  
Cyber Physical Systems ◽  
Published Data ◽  
Disease Dynamics ◽  
Physical Systems ◽  
The Government ◽  
The Impact ◽  
First Time ◽  
New Framework

Abstract The COVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured. We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems. Consequently, we term the new framework Compositional Cyber-Physical Epidemiology. We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.

scholarly journals Co-infection does not predict disease signs in Gopherus tortoises

2017 ◽  
Vol 4 (10) ◽  
pp. 171003 ◽  
Author(s):  
Chava L. Weitzman ◽  
Ryan Gov ◽  
Franziska C. Sandmeier ◽  
Sarah J. Snyder ◽  
C. Richard Tracy
Keyword(s):  
Dna Sequences ◽  
Host Species ◽  
Disease Ecology ◽  
Clinical Signs ◽  
Infection Intensity ◽  
The United States ◽  
Host Immune System ◽  
Disease Dynamics ◽  
Upper Respiratory Tract ◽  
The Impact

In disease ecology, the host immune system interacts with environmental conditions and pathogen properties to affect the impact of disease on the host. Within the host, pathogens may interact to facilitate or inhibit each other's growth, and pathogens interact with different hosts differently. We investigated co-infection of two Mycoplasma and the association of infection with clinical signs of upper respiratory tract disease in four congeneric tortoise host species ( Gopherus ) in the United States to detect differences in infection risk and disease dynamics in these hosts. Mojave Desert tortoises had greater prevalence of Mycoplasma agassizii than Texas tortoises and gopher tortoises, while there were no differences in Mycoplasma testudineum prevalence among host species. In some host species, the presence of each pathogen influenced the infection intensity of the other; hence, these two mycoplasmas interact differently within different hosts, and our results may indicate facilitation of these bacteria. Neither infection nor co-infection was associated with clinical signs of disease, which tend to fluctuate across time. From M. agassizii DNA sequences, we detected no meaningful differentiation of haplotypes among hosts. Experimental inoculation studies and recurrent resampling of wild individuals could help to decipher the underlying mechanisms of disease dynamics in this system.

A COMPARTMENTAL MODEL OF SLEEPING SICKNESS IN CENTRAL AFRICA

1996 ◽  
Vol 04 (04) ◽  
pp. 459-477 ◽  
Author(s):  
MARC ARTZROUNI ◽  
JEAN-PAUL GOUTEUX
Keyword(s):  
Compartmental Model ◽  
Equilibrium Points ◽  
Central Africa ◽  
Sleeping Sickness ◽  
Tsetse Flies ◽  
Reproduction Rate ◽  
Basic Reproduction Rate ◽  
The Stability ◽  
The Impact ◽  
Percent Decrease

We present a five-variable compartmental model for the spread of Trypanosoma brucei gambiense, the parasite responsible for the transmission (through tsetse flies) of sleeping sickness in Central Africa. The model’s equilibrium points depend on two “summary parameters”: gr, the proportion removed among human infectives, and R0, the basic reproduction rate. Stability results are obtained for the origin but not for other equilibrium points. A two-variable simplified version of the model is presented and the stability of all its equilibrium points can be investigated analytically. Both models are applied to the Niari focus of Central Africa and used to test the impact of a vector control strategy. The models’ results are in agreement with the extinction of the epidemic that was brought about by a fifty percent decrease in vector density.

scholarly journals THE DYNAMICS OF THE RISK OF THE MORBIDITY AND THE ADAPTATION PROCESS AS INDICES OF THE IMPACT OF LOCAL ENVIRONMENTAL FACTORS ON THE POPULATION

2018 ◽  
Vol 97 (2) ◽  
pp. 124-131 ◽  
Author(s):  
V. M. Prusakov ◽  
Aleksandra V. Prusakova
Keyword(s):  
High Risk ◽  
Environmental Factors ◽  
Infectious Diseases ◽  
Absolute Magnitude ◽  
The Body ◽  
Adaptation Process ◽  
Disease Dynamics ◽  
Local Factors ◽  
The Impact

There were studied: The role of the disease dynamics at the background area in the formation of the risk for childhood morbidity in the study area; the value of indices of the long-term wavelike risk dynamics and the corresponding adaptation process for the identification and classification of mass non-infectious diseases. The waviness dynamics of the children morbidity risk is caused by the wave-like nature of the disease dynamics in the study and background areas. The disease risk level is formed not only by differences in the incidence rates of the background and study areas but also from differences in phases of high and low non-specific resistance of children contingent in these territories. The different character of the dynamics of the risk for the disease and related waviness of the adaptation process among children reflects the existence of differences in exposure to characteristics of local environmental factors in each territory. The average risk of disease, around which there are carried out annual fluctuations risks and phase states of the adaptation process, and the corresponding levels of reactivity and resistance of the body are the result of the absolute magnitude of the impact of local factors on the study area. The average relative risk of the morbidity, around which there are carried out annual fluctuations risks and phase states of the adaptation process is an integral index of the level of mass non-infectious diseases and the degree of severity of the medical and environmental situation, the level of reactivity and work mismatch of the body subsystems of children and the degree of their intensity. This is the measure of the absolute magnitude of the impact of local factors. The waviness to the development of states of high and low resistance is both always an index of antistress activation responses (or non-specifically high resistance state) and relative to the average force of impact factors (for the observed reactivity level). On the basis of the accounting for the level of the risk, there is suggested the classification of infectious diseases, including 1) the background or relatively satisfactory morbidity, 2) mass morbidity with the increased risk, 3) mass incidence of the high-risk, and 4) a mass incidence of the very high risk.

scholarly journals Modelling the impact of extending dose intervals for COVID-19 vaccines in Canada

2021 ◽  
Author(s):  
Austin Nam ◽  
Raphael Ximenes ◽  
Man Wah Yeung ◽  
Sharmistha Mishra ◽  
Jianhong Wu ◽  
...  
Keyword(s):  
Time Horizon ◽  
Compartmental Model ◽  
Age Groups ◽  
Dose Interval ◽  
Base Case ◽  
Third Wave ◽  
Older Individuals ◽  
High Efficacy ◽  
The Impact ◽  
Epidemiologic Features

AbstractBackgroundDual dose SARS-CoV-2 vaccines demonstrate high efficacy and will be critical in public health efforts to mitigate the COVID-19 pandemic and its health consequences; however, many jurisdictions face very constrained vaccine supply. We examined the impacts of extending the interval between two doses of mRNA vaccines in Canada in order to inform deliberations of Canada’s National Advisory Committee on Immunization.MethodsWe developed an age-stratified, deterministic, compartmental model of SARS-CoV-2 transmission and disease to reproduce the epidemiologic features of the epidemic in Canada. Simulated vaccination comprised mRNA vaccines with explicit examination of effectiveness against disease (67% [first dose], 94% [second dose]), hospitalization (80% [first dose], 96% [second dose]), and death (85% [first dose], 96% [second dose]) in adults aged 20 years and older. Effectiveness against infection was assumed to be 90% relative to the effectiveness against disease. We used a 6-week mRNA dose interval as our base case (consistent with early program rollout across Canadian and international jurisdictions) and compared extended intervals of 12 weeks, 16 weeks, and 24 weeks. We began vaccinations on January 1, 2021 and simulated a third wave beginning on April 1, 2021.ResultsExtending mRNA dose intervals were projected to result in 12.1-18.9% fewer symptomatic cases, 9.5-13.5% fewer hospitalizations, and 7.5-9.7% fewer deaths in the population over a 12-month time horizon. The largest reductions in hospitalizations and deaths were observed in the longest interval of 24 weeks, though benefits were diminishing as intervals extended. Benefits of extended intervals stemmed largely from the ability to accelerate coverage in individuals aged 20-74 years as older individuals were already prioritized for early vaccination. Conditions under which mRNA dose extensions led to worse outcomes included: first-dose effectiveness < 65% against death; or protection following first dose waning to 0% by month three before the scheduled 2nd dose at 24-weeks. Probabilistic simulations from a range of likely vaccine effectiveness values did not result in worse outcomes with extended intervals.ConclusionUnder real-world effectiveness conditions, our results support a strategy of extending mRNA dose intervals across all age groups to minimize symptomatic cases, hospitalizations, and deaths while vaccine supply is constrained.

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