Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study (Preprint)

Mapping Intimacies ◽

10.2196/preprints.20912 ◽

2020 ◽

Author(s):

Efthimios Kaxiras ◽

Georgios Neofotistos

Keyword(s):

Wave Model ◽

Disease Spread ◽

Multiple Wave ◽

Agent Based ◽

Future Evolution ◽

Response Index ◽

Infected Population ◽

Intervention Measures ◽

Agent Based Simulations ◽

Pandemic Response

BACKGROUND Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. OBJECTIVE The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. METHODS We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. RESULTS We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model’s results, an index value was assigned to each country, quantifying in an objective manner the country’s response to the pandemic. CONCLUSIONS Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

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Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study

Journal of Medical Internet Research ◽

10.2196/20912 ◽

2020 ◽

Vol 22 (7) ◽

pp. e20912 ◽

Cited By ~ 3

Author(s):

Efthimios Kaxiras ◽

Georgios Neofotistos

Keyword(s):

Wave Model ◽

Disease Spread ◽

Multiple Wave ◽

Agent Based ◽

Future Evolution ◽

Response Index ◽

Infected Population ◽

Intervention Measures ◽

Agent Based Simulations ◽

Pandemic Response

Background Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. Objective The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. Methods We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. Results We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model’s results, an index value was assigned to each country, quantifying in an objective manner the country’s response to the pandemic. Conclusions Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

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Modeling the Covid-19 Pandemic Response of the US States

10.1101/2020.06.24.20138982 ◽

2020 ◽

Author(s):

Georgios Neofotistos ◽

Efthimios Kaxiras

Keyword(s):

District Of Columbia ◽

State Level ◽

Wave Model ◽

The State ◽

Multiple Wave ◽

Us States ◽

Response Index ◽

Intervention Measures ◽

Pandemic Response ◽

The Waves

AbstractBackgroundThe United States of America (USA) has been the country worst affected, in absolute terms, by the Covid-19 pandemic. The country comprises 50 states under a federal system. The impact of the pandemic has resulted in different responses at the state level, which are driven by differing intervention policies, demographics, connectedness and other factors. Understanding the dynamics of the Covid-19 pandemic at the state level is essential in predicting its future evolution.ObjectiveOur objective is to identify and characterize multiple waves of the pandemic by analyzing the reported infected population curve in each of the 50 US states. Based on the intensity of the waves, characterized by declining, stationary, or increasing strengths, each state’s response can be inferred and quantified.MethodsWe apply a recently proposed multiple-wave model to fit the infected population data for each state in USA, and use the proposed Pandemic Response Index to quantify their response to the Covid-19 pandemic.ResultsWe have analyzed reported infected cases from each one of the 50 USA states and the District of Columbia, based on the multiple-wave model, and present the relevant parameters. Multiple waves have been identified and this model is found to describe the data better. Each of the states can be classified into one of three distinct classes characterized by declining, increasing, or stationary strength of the waves following the initial one. The effectiveness of intervention measures can be inferred by the peak intensities of the waves, and states with similar population characteristics can be directly compared. We estimate how much lower the number of infections might have been, if early and strict intervention measures had been imposed to stop the disease spread at the first wave, as was the case for certain states. Based on our model’s results, we compute the value of the Pandemic Response Index, a recently introduced metric for quantifying in an objective manner the response to the pandemic.ConclusionsOur results reveal a series of epidemic waves, characterizing USA’s pandemic response at the state level, and also infer to what extent the imposition of early intervention measures could have had on the spread and impact of the disease. As of June 11, 2020, only 19 states and the District of Columbia (40% of the total) clearly exhibit declining trends in the numbers of reported infected cases, while 13 states exhibit stationary and 18 states increasing trends in the numbers of reported cases.

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Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter

Journal of Business Economics ◽

10.1007/s11573-020-01022-9 ◽

2021 ◽

Author(s):

Kathrin Eismann

Keyword(s):

Social Media ◽

Agent Based ◽

Social Media Networks ◽

Agent Based Simulations

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.

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Regional Downscaling of Copernicus ERA5 Wave Data for Coastal Engineering Activities and Operational Coastal Services

Water ◽

10.3390/w13060859 ◽

2021 ◽

Vol 13 (6) ◽

pp. 859

Author(s):

Giorgio Bellotti ◽

Leopoldo Franco ◽

Claudia Cecioni

Keyword(s):

Time Series ◽

National Level ◽

Wave Model ◽

Wave Climate ◽

Tyrrhenian Sea ◽

Computational Domain ◽

Multiple Wave ◽

Space Resolution ◽

Wave Data ◽

Fundamental Information

Hindcasted wind and wave data, available on a coarse resolution global grid (Copernicus ERA5 dataset), are downscaled by means of the numerical model SWAN (simulating waves in the nearshore) to produce time series of wave conditions at a high resolution along the Italian coasts in the central Tyrrhenian Sea. In order to achieve the proper spatial resolution along the coast, the finite element version of the model is used. Wave data time series at the ERA5 grid are used to specify boundary conditions for the wave model at the offshore sides of the computational domain. The wind field is fed to the model to account for local wave generation. The modeled sea states are compared against the multiple wave records available in the area, in order to calibrate and validate the model. The model results are in quite good agreement with direct measurements, both in terms of wave climate and wave extremes. The results show that using the present modeling chain, it is possible to build a reliable nearshore wave parameters database with high space resolution. Such a database, once prepared for coastal areas, possibly at the national level, can be of high value for many engineering activities related to coastal area management, and can be useful to provide fundamental information for the development of operational coastal services.

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