scholarly journals Modelling Excess Mortality in Covid-19-Like Epidemics

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1236
Author(s):  
Zdzislaw Burda
Keyword(s):  
Disease Transmission ◽  
Mitigation Strategies ◽  
System Capacity ◽  
Infectious Period ◽  
Cumulative Number ◽  
Epidemic Spreading ◽  
Social Contacts ◽  
Local Modes ◽  
Hybrid Strategy ◽  
The Usa

We develop an agent-based model to assess the cumulative number of deaths during hypothetical Covid-19-like epidemics for various non-pharmaceutical intervention strategies. The model simulates three interrelated stochastic processes: epidemic spreading, availability of respiratory ventilators and changes in death statistics. We consider local and non-local modes of disease transmission. The first simulates transmission through social contacts in the vicinity of the place of residence while the second through social contacts in public places: schools, hospitals, airports, etc., where many people meet, who live in remote geographic locations. Epidemic spreading is modelled as a discrete-time stochastic process on random geometric networks. We use the Monte–Carlo method in the simulations. The following assumptions are made. The basic reproduction number is R0=2.5 and the infectious period lasts approximately ten days. Infections lead to severe acute respiratory syndrome in about one percent of cases, which are likely to lead to respiratory default and death, unless the patient receives an appropriate medical treatment. The healthcare system capacity is simulated by the availability of respiratory ventilators or intensive care beds. Some parameters of the model, like mortality rates or the number of respiratory ventilators per 100,000 inhabitants, are chosen to simulate the real values for the USA and Poland. In the simulations we compare ‘do-nothing’ strategy with mitigation strategies based on social distancing and reducing social mixing. We study epidemics in the pre-vacine era, where immunity is obtained only by infection. The model applies only to epidemics for which reinfections are rare and can be neglected. The results of the simulations show that strategies that slow the development of an epidemic too much in the early stages do not significantly reduce the overall number of deaths in the long term, but increase the duration of the epidemic. In particular, a hybrid strategy where lockdown is held for some time and is then completely released, is inefficient.

scholarly journals Estimating cost-benefit of quarantine length for COVID-19 mitigation

2020 ◽  
Author(s):  
Andreas Eilersen ◽  
Kim Sneppen
Keyword(s):  
Short Duration ◽  
Disease Transmission ◽  
Social Groups ◽  
Cost Benefit ◽  
Relative Effect ◽  
Mitigation Strategies ◽  
Disease Spread ◽  
Social Contacts ◽  
Agent Based ◽  
The Cost

ABSTRACTBackgroundThe international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent.MethodsIn this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures.ResultsWe find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost-benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown of workplaces.ConclusionsA targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its relative effect increases when supplemented with other measures that reduce disease transmission.

Crowded Health

2019 ◽  
Author(s):  
William O’Toole ◽  
Dr Stephen Luke ◽  
Travis Semmens ◽  
Dr Jason Brown ◽  
Andrew Tatrai
Keyword(s):  
Health Service ◽  
Disease Transmission ◽  
Mitigation Strategies ◽  
System Capacity ◽  
Health Service Provision ◽  
Medical Risk ◽  
Health Service Providers ◽  
Large Numbers ◽  
Risk Of Disease ◽  
Early Decision

Crowds carry real health risks. By definition, crowds bring large numbers of people in to close proximity and confined spaces. The risk of injury is real, due to accident, crush or malice and the medical risk of disease transmission and demographic-specific presentations must also be considered. Selecting health service providers is a key early decision. Consulting with local ambulance and health services to build relationships and to seek advice on local providers, legislative requirements and existing health system capacity is time well spent. It is critical that the provider(s) chosen have the skills, resources and experience to service the event and predictable escalation. Pre-hospital health service provision is a niche industry and is variably regulated. The accumulation of clinical, command and logistical experience takes many years and is a truly heuristic process. A tiered service delivery model, discussed further below, should be adopted with centralized call-taking and management of resources. Finalizing the size, scope and cost of this model can be a time-consuming and stressful process. This will be informed by the health risk assessment, with mitigation strategies according to ALARP principles, although high consequence outcomes (long tail risks) like cardiac arrest and major trauma will require additional resources.

scholarly journals Cost–benefit of limited isolation and testing in COVID-19 mitigation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andreas Eilersen ◽  
Kim Sneppen
Keyword(s):  
Short Duration ◽  
Disease Transmission ◽  
Social Groups ◽  
Cost Benefit ◽  
Mitigation Strategies ◽  
Disease Spread ◽  
Agent Based Model ◽  
Social Contacts ◽  
Agent Based ◽  
The Cost

Abstract The international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent. In this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures. We find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost–benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown. A targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its effect increases when testing is more widespread.

scholarly journals Addressing the COVID-19 transmission in inner Brazil by a mathematical model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
G. B. Almeida ◽  
T. N. Vilches ◽  
C. P. Ferreira ◽  
C. M. C. B. Fortaleza
Keyword(s):  
Mathematical Model ◽  
Disease Transmission ◽  
Abiotic Factors ◽  
Severe Pneumonia ◽  
Mitigation Strategies ◽  
Social Distancing ◽  
Control Disease ◽  
Set Up ◽  
Novel Coronavirus ◽  
Public Health Strategies

AbstractIn 2020, the world experienced its very first pandemic of the globalized era. A novel coronavirus, SARS-CoV-2, is the causative agent of severe pneumonia and has rapidly spread through many nations, crashing health systems and leading a large number of people to death. In Brazil, the emergence of local epidemics in major metropolitan areas has always been a concern. In a vast and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for inner Brazil, and what can we do to control infection transmission in each of these locations? Here, a mathematical model is proposed to simulate disease transmission among individuals in several scenarios, differing by abiotic factors, social-economic factors, and effectiveness of mitigation strategies. The disease control relies on keeping all individuals’ social distancing and detecting, followed by isolating, infected ones. The model reinforces social distancing as the most efficient method to control disease transmission. Moreover, it also shows that improving the detection and isolation of infected individuals can loosen this mitigation strategy. Finally, the effectiveness of control may be different across the country, and understanding it can help set up public health strategies.

scholarly journals COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ye Emma Zohner ◽  
Jeffrey S. Morris
Keyword(s):  
Web Application ◽  
The United States ◽  
Mitigation Strategies ◽  
Time Lags ◽  
Policy Makers ◽  
Major Health ◽  
Dynamic Representation ◽  
Graphical Displays ◽  
The Usa ◽  
User Friendly

Abstract Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

Drought spatiotemporal propagation via land feedbacks

2021 ◽  
Author(s):  
Diego G. Miralles ◽  
Dominik L. Schumacher ◽  
Jessica Keune ◽  
Paul A. Dirmeyer
Keyword(s):  
Soil Moisture ◽  
Water Vapor ◽  
Mitigation Strategies ◽  
Soil Drought ◽  
The Usa ◽  
Forecast Models ◽  
Atmospheric Feedbacks ◽  
Dry Out ◽  
Water And Food Security ◽  
The Impact

<p>The predicted increase in drought occurrence and intensity will pose serious threats to global future water and food security. This was hinted by several historically unprecedented droughts over the last two decades, taking place in Europe, Australia, Amazonia or the USA. It has been hypothesised that the strength of these events responded to self-reinforcement processes related to land–atmospheric feedbacks: as rainfall deficits dry out soil and vegetation, the evaporation of land water is reduced, then the local air becomes too dry to yield rainfall, which further enhances drought conditions. Despite the 'local' nature of these feedbacks, their consequences can be remote, as downwind regions may rely on evaporated water transported by winds from drought-affected locations. Following this rationale, droughts may not only self-reinforce locally, due to land atmospheric feedbacks, but <em>self-propagate</em> in the downwind direction, always conditioned on atmospheric circulation. This propagation is not only meteorological but relies on soil moisture drought, and may lead to a downwind cascading of impacts on water resources. However, a global capacity to observe these processes is lacking, and thus our knowledge of how droughts start and evolve, and how this may change as climate changes, remains limited. Furthermore, climate and forecast models are still immature when it comes to representing the influences of land on rainfall.</p><p>Here, the largest global drought events are studied to unravel the role of land–atmosphere feedbacks during the spatiotemporal propagation of these events. We based our study on satellite and reanalysis records of soil moisture, evaporation, air humidity, winds and precipitation, in combination with a Lagrangian framework that can map water vapor trajectories and explore multi-dimensional feedbacks. We estimate the reduction in precipitation in the direction of drought propagation that is caused by the upwind soil moisture drought, and isolate this effect from the influence of potential evaporation and circulation changes. By doing so, the downwind lack of precipitation caused by upwind soil drought via water vapor deficits, and hence the impact of drought self-propagation, is determined. We show that droughts occurring in dryland regions are particularly prone to self-propagate, as evaporation there tends to respond strongly to enhanced soil stress and precipitation is frequently convective. This kind of knowledge may be used to improve climate and forecast models and can be exploited to develop geo-engineering mitigation strategies to help prevent drought events from aggravating during their early stages.</p>

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.

scholarly journals Predictive accuracy of a hierarchical logistic model of cumulative SARS-CoV-2 case growth until May 2020

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Levente Kriston
Keyword(s):  
Disease Transmission ◽  
Logistic Model ◽  
Predictive Accuracy ◽  
Future Research ◽  
Cumulative Number ◽  
Epidemiological Models ◽  
First Case ◽  
Distribution Of Resources ◽  
The One ◽  
Models Of Disease

Abstract Background Infectious disease predictions models, including virtually all epidemiological models describing the spread of the SARS-CoV-2 pandemic, are rarely evaluated empirically. The aim of the present study was to investigate the predictive accuracy of a prognostic model for forecasting the development of the cumulative number of reported SARS-CoV-2 cases in countries and administrative regions worldwide until the end of May 2020. Methods The cumulative number of reported SARS-CoV-2 cases was forecasted in 251 regions with a horizon of two weeks, one month, and two months using a hierarchical logistic model at the end of March 2020. Forecasts were compared to actual observations by using a series of evaluation metrics. Results On average, predictive accuracy was very high in nearly all regions at the two weeks forecast, high in most regions at the one month forecast, and notable in the majority of the regions at the two months forecast. Higher accuracy was associated with the availability of more data for estimation and with a more pronounced cumulative case growth from the first case to the date of estimation. In some strongly affected regions, cumulative case counts were considerably underestimated. Conclusions With keeping its limitations in mind, the investigated model may be used for the preparation and distribution of resources during the initial phase of epidemics. Future research should primarily address the model’s assumptions and its scope of applicability. In addition, establishing a relationship with known mechanisms and traditional epidemiological models of disease transmission would be desirable.

Climate change advocacy: exploring links between student empowerment and civic engagement

2020 ◽  
Vol 21 (6) ◽  
pp. 1209-1231
Author(s):  
Alicia B. Wodika ◽  
Wendi K. Middleton
Keyword(s):  
Climate Change ◽  
Service Learning ◽  
Data Collection ◽  
Civic Engagement ◽  
Climate Change Adaptation ◽  
Mitigation Strategies ◽  
Student Experiences ◽  
Content Type ◽  
The Usa ◽  
Adaptation And Mitigation

Purpose This study identified the attitudes and behaviors of college students regarding their advocacy for climate change adaptation and mitigation. Specifically, perceptions of climate change were assessed and advocacy activities were identified for climate change and/or other topics. Design/methodology/approach Using convenience sampling, students (n = 440) from three universities in the Midwest, the USA, completed surveys assessing their level of agreement with activities related to civic engagement, climate change and policy. Semantic differential scales focused on “learning about climate change,” “advocating for climate change mitigation” and “advocating for climate change adaptation.” Three open-ended questions were used to identify student experiences with civic engagement and/or service-learning, as well as topics in which they advocate and how they advocate. Findings Regarding advocacy in general, over 50% of the sample did not advocate for any topic, with 24.5% of students stating they advocated for the environment/climate change. Students who identified as female, democratic and 1st or 2nd year in school were more likely to be engaged with environmental advocacy. Regarding civic engagement, seniors were more actively engaged with their communities and also more likely to vote in local, state and national elections. Research limitations/implications Time of data collection was a potential limitation with schools conducting data collection at different time periods. Students who identified more progressive politically were also more likely to participate in the study. Originality/value While research exists regarding student civic engagement levels, this research project identified ways in which students engaged in advocacy, identifying potential links with civic engagement and enhanced participation in climate change adaptation and mitigation strategies.

scholarly journals Speech can produce jet-like transport relevant to asymptomatic spreading of virus

2020 ◽  
Vol 117 (41) ◽  
pp. 25237-25245 ◽  
Author(s):  
Manouk Abkarian ◽  
Simon Mendez ◽  
Nan Xue ◽  
Fan Yang ◽  
Howard A. Stone
Keyword(s):  
Social Interactions ◽  
Disease Transmission ◽  
Laboratory Experiments ◽  
Scaling Laws ◽  
Mitigation Strategies ◽  
Directed Transport ◽  
Transport Distance ◽  
Long Time ◽  
Phonetic Features

Many scientific reports document that asymptomatic and presymptomatic individuals contribute to the spread of COVID-19, probably during conversations in social interactions. Droplet emission occurs during speech, yet few studies document the flow to provide the transport mechanism. This lack of understanding prevents informed public health guidance for risk reduction and mitigation strategies, e.g., the “6-foot rule.” Here we analyze flows during breathing and speaking, including phonetic features, using orders-of-magnitude estimates, numerical simulations, and laboratory experiments. We document the spatiotemporal structure of the expelled airflow. Phonetic characteristics of plosive sounds like “P” lead to enhanced directed transport, including jet-like flows that entrain the surrounding air. We highlight three distinct temporal scaling laws for the transport distance of exhaled material including 1) transport over a short distance (<0.5 m) in a fraction of a second, with large angular variations due to the complexity of speech; 2) a longer distance, ∼1 m, where directed transport is driven by individual vortical puffs corresponding to plosive sounds; and 3) a distance out to about 2 m, or even farther, where sequential plosives in a sentence, corresponding effectively to a train of puffs, create conical, jet-like flows. The latter dictates the long-time transport in a conversation. We believe that this work will inform thinking about the role of ventilation, aerosol transport in disease transmission for humans and other animals, and yield a better understanding of linguistic aerodynamics, i.e., aerophonetics.

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