scholarly journals Asymptomatic individuals can increase the final epidemic size under adaptive human behavior

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baltazar Espinoza ◽  
Madhav Marathe ◽  
Samarth Swarup ◽  
Mugdha Thakur
Keyword(s):  
Planning Horizon ◽  
Infection Risk ◽  
Epidemiological Models ◽  
Epidemic Size ◽  
Behavioral Adaptations ◽  
Future State ◽  
Final Epidemic Size ◽  
Asymptomatic Individuals ◽  
The Impact ◽  
Finite Planning Horizon

AbstractInfections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves—and be perceived by others—as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system’s future state over a finite planning horizon. We found that individuals’ risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals’ behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

Adaptive Human Behavior in Epidemics: the Impact of Risk Misperception on the Spread of Epidemics

2021 ◽  
Author(s):  
Baltazar Espinoza ◽  
Madhav Marathe ◽  
Samarth Swarup ◽  
Mugdha Thakur
Keyword(s):  
Planning Horizon ◽  
Infection Risk ◽  
Epidemiological Models ◽  
Epidemic Size ◽  
Behavioral Adaptations ◽  
Final Epidemic Size ◽  
Asymptomatic Infections ◽  
Asymptomatic Individuals ◽  
The Impact ◽  
Finite Planning Horizon

Abstract Infections produced by pre-symptomatic and asymptomatic (non-symptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals unaware of the infection risk they pose to others, may perceive themselves --and being perceived by others-- as not representing risk of infection. Yet many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates individuals' behavioral decisions based on a projection of the future system's state over a finite planning horizon. We found that individuals' risk misperception in the presence of asymptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of asymptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

scholarly journals Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study

2021 ◽  
Author(s):  
Sarafa A. Iyaniwura ◽  
Musa Rabiu ◽  
Jummy F. David ◽  
Jude D. Kong
Keyword(s):  
Public Health ◽  
Reproduction Number ◽  
Health Policies ◽  
Public Health Policies ◽  
Indirect Transmission ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
The Impact ◽  
Size Relation ◽  
Pharmaceutical Interventions

AbstractAdherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that direct transmission has more effect on the reproduction number and final epidemic size, relative to indirect transmission. In addition, we showed that there is a significant benefit in adhering to the COVID-19 NPIs.

scholarly journals Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study

2021 ◽  
Vol 18 (6) ◽  
pp. 8905-8932
Author(s):  
Sarafa A. Iyaniwura ◽  
◽  
Musa Rabiu ◽  
Jummy F. David ◽  
Jude D. Kong ◽  
...  
Keyword(s):  
Public Health ◽  
Compartmental Model ◽  
Health Policies ◽  
Public Health Policies ◽  
Indirect Transmission ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
The Impact ◽  
Size Relation ◽  
Pharmaceutical Interventions

<abstract><p>Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that there is a significant benefit in adhering to the COVID-19 NPIs.</p></abstract>

scholarly journals The impact of spatial and social structure on an SIR epidemic on a weighted multilayer network

2022 ◽  
Author(s):  
Ágnes Backhausz ◽  
István Z. Kiss ◽  
Péter L. Simon
Keyword(s):  
Major Effect ◽  
Final Size ◽  
Multilayer Network ◽  
Sir Epidemic ◽  
Epidemic Size ◽  
Key Factor ◽  
Community Transmission ◽  
Final Epidemic Size ◽  
Hungarian Population ◽  
The Impact

AbstractA key factor in the transmission of infectious diseases is the structure of disease transmitting contacts. In the context of the current COVID-19 pandemic and with some data based on the Hungarian population we develop a theoretical epidemic model (susceptible-infected-removed, SIR) on a multilayer network. The layers include the Hungarian household structure, with population divided into children, adults and elderly, as well as schools and workplaces, some spatial embedding and community transmission due to sharing communal spaces, service and public spaces. We investigate the sensitivity of the model (via the time evolution and final size of the epidemic) to the different contact layers and we map out the relation between peak prevalence and final epidemic size. When compared to the classic compartmental model and for the same final epidemic size, we find that epidemics on multilayer network lead to higher peak prevalence meaning that the risk of overwhelming the health care system is higher. Based on our model we found that keeping cliques/bubbles in school as isolated as possible has a major effect while closing workplaces had a mild effect as long as workplaces are of relatively small size.

scholarly journals Estimating the Final Epidemic Size for COVID-19 Outbreak using Improved Epidemiological Models

2020 ◽  
Author(s):  
Rajesh Ranjan
Keyword(s):  
Normal Distribution ◽  
Infection Rate ◽  
Recent Trend ◽  
European Countries ◽  
Epidemiological Models ◽  
Epidemic Size ◽  
Slow Decline ◽  
Geographical Regions ◽  
The Us ◽  
Final Epidemic Size

AbstractFinal epidemic sizes of different geographical regions due to COVID-19 are estimated using logistic, SIR and generalized SEIR models. These models use different parameters which are estimated using non-linear fits from the available data. It is found that both SIR and generalized SEIR models give similar estimations for regions where curves show signs of flattening. A study of these models with data from China indicates that in such cases these estimates may be more reliable. However, recent trend indicate that unlike China, the decline in infection rate for the US and other European countries is very slow, and does not follow a symmetric normal distribution. Hence a correction is introduced to account for this very slow decline based on the data from Italy. The estimates with all these models are presented for the most affected countries due to COVID-19. According to these models, the final epidemic size in the US, Italy, Spain, and Germany could be 1.1, 0.22, 0.24 and 0.19 million respectively. Also, it is expected that curves for most of the geographical regions will flatten by the middle of May 2020.

scholarly journals Mobility Restrictions and the Control of COVID-19

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Charles Perrings ◽  
Baltazar Espinoza
Keyword(s):  
Public Health ◽  
High Risk ◽  
Disease Risk ◽  
Final Size ◽  
Epidemic Size ◽  
First Response ◽  
Risk Areas ◽  
Final Epidemic Size ◽  
The Impact ◽  
Different Levels

A recent study on the impact of mobility controls on the final size of epidemics by Espinoza, Castillo-Chavez, and Perrings (2020) found that mobility restrictions between areas experiencing different levels of disease risk and with different public health infrastructures do not always reduce the final epidemic size. Indeed, restrictions on the mobility of people from high-risk to low-risk areas can increase, not reduce, the total number of infections. Since the first response of many countries to the COVID-19 pandemic was to implement mobility restrictions, it is worth bearing in mind the implications of the Espinoza result when considering the effectiveness of such restrictions.

scholarly journals Predictions for COVID-19 Outbreak in India using epidemiological models

2020 ◽  
Author(s):  
Rajesh Ranjan
Keyword(s):  
Daily Basis ◽  
Epidemic Size ◽  
The Us ◽  
Major Outbreak ◽  
Community Transmission ◽  
Final Epidemic Size ◽  
Short And Long Term ◽  
The Impact ◽  
Geographical Locations

AbstractCOVID-19 data from India is compared against several countries as well as key states in the US with a major outbreak, and it is found that the basic reproduction number R0 for India is in the expected range of 1.4-3.9. Further, the rate of growth of infections in India is very close to that in Washington and California. Exponential and classic susceptible-infected-recovered (SIR) models based on available data are used to make short and long-term predictions on a daily basis. Based on the SIR model, it is estimated that India will enter equilibrium by the end of May 2020 with the final epidemic size of approximately 13,000. However, this estimation will be invalid if India enters the stage of community transmission. The impact of social distancing, again with the assumption of no community transmission, is also assessed by comparing data from different geographical locations.

scholarly journals Optimal timing for social distancing during an epidemic

2020 ◽  
Author(s):  
Oscar Patterson-Lomba
Keyword(s):  
Reproduction Number ◽  
Decision Makers ◽  
Optimal Time ◽  
Optimal Timing ◽  
Epidemiological Models ◽  
Pharmacological Interventions ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Epidemic Size ◽  
Final Epidemic Size

AbstractSocial distancing is an effective way to contain the spread of a contagious disease, particularly when facing a novel pathogen and no pharmacological interventions are available. In such cases, conventional wisdom suggests that social distancing measures should be introduced as soon as possible after the beginning of an outbreak to more effectively mitigate the spread of the disease. Using a simple epidemiological model we show that, however, there is in fact an optimal time to initiate a temporal social distancing intervention if the goal is to reduce the final epidemic size or “flatten” the epidemic curve. The optimal timing depends strongly on the effective reproduction number (R0) of the disease, such that as the R0 increases, the optimal time decreases non-linearly. Additionally, if pharmacological interventions (e.g., a vaccine) become available at some point during the epidemic, the sooner these interventions become available the sooner social distancing should be initiated to maximize its effectiveness. Although based on a simple model, we hope that these insights inspire further investigations within the context of more complex and data-driven epidemiological models, and can ultimately help decision makers to improve temporal social distancing policies to mitigate the spread of epidemics.

scholarly journals Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 463
Author(s):  
Narjiss Sallahi ◽  
Heesoo Park ◽  
Fedwa El Mellouhi ◽  
Mustapha Rachdi ◽  
Idir Ouassou ◽  
...  
Keyword(s):  
Public Health ◽  
A Priori ◽  
Epidemiological Models ◽  
Sir Epidemic Model ◽  
Gulf Region ◽  
Epidemiological Modeling ◽  
Public Health Policies ◽  
Sir Epidemic ◽  
The Impact ◽  
Case Data

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework

2020 ◽  
Author(s):  
Antonio Sánchez Herguedas ◽  
Adolfo Crespo Márquez ◽  
Francisco Rodrigo Muñoz
Keyword(s):  
Preventive Maintenance ◽  
Recurrence Relations ◽  
Direct Calculation ◽  
Planning Horizon ◽  
Practical Implementation ◽  
Practical Application ◽  
Mathematical Solution ◽  
Accumulated Reward ◽  
Finite Planning Horizon

Abstract This paper describes the optimization of preventive maintenance (PM) over a finite planning horizon in a semi-Markov framework. In this framework, the asset may be operating, and providing income for the asset owner, or not operating and undergoing PM, or not operating and undergoing corrective maintenance following failure. PM is triggered when the asset has been operating for τ time units. A number m of transitions specifies the finite horizon. This system is described with a set of recurrence relations, and their z-transform is used to determine the value of τ that maximizes the average accumulated reward over the horizon. We study under what conditions a solution can be found, and for those specific cases the solution τ* is calculated. Despite the complexity of the mathematical solution, the result obtained allows the analyst to provide a quick and easy-to-use tool for practical application in many real-world cases. To demonstrate this, the method has been implemented for a case study, and its accuracy and practical implementation were tested using Monte Carlo simulation and direct calculation.

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