Estimation of COVID-19 Dynamics in the Different States of the United States during the First Months of the Pandemic

Rojas-Valenzuela, Ignacio; Valenzuela, Olga; Delgado-Márquez, Elvira; Rojas, Fernando

doi:10.3390/engproc2021005053

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…It is possible that virus diffusion patterns evolved over the analyzed time and differed between the waves, for example, in the case of the influenza epidemic studied in [ 16 ]. Moreover, as other studies addressing the COVID-19 pandemic distinguished and focused on its various phases [ 39 , 40 , 41 ], this may indicate that analyzing the whole pandemic in a single procedure may cause bias. If so, the adopted calculation of a single correlation coefficient value between states and prefectures in a too-long period could result in a hindered information extraction process.…”

Section: Discussionmentioning

confidence: 99%

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Davahli

Karwowski

Fiok

et al. 2022

Biology

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Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

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Section: Discussionmentioning

confidence: 99%

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Davahli

Karwowski

Fiok

et al. 2022

Biology

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“…That said, including them would likely increase the number of infections and thus deaths for “no-measures” scenario and in maybe for the “uniform” scenario. A second limitation is that we assumed that the virus would spread homogenously throughout the population, while in the reality, transmission occurs mainly within clusters where local characteristics, time, and distance play an important role ( 39 ). A model based on cluster-wise transmission would be complex and difficult due to the need of detailed data.…”

Section: Discussionmentioning

confidence: 99%

Reconstructing the Effectiveness of Policy Measures to Avoid Next-Wave COVID-19 Infections and Deaths Using a Dynamic Simulation Model: Implications for Health Technology Assessment

Hagens

Cordova-Pozo

Postma

et al. 2022

Front. Med. Technol.

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ObjectiveThe goal of this study was to dynamically model next-wave scenarios to observe the impact of different lockdown measures on the infection rates (IR) and mortality for two different prototype countries, mimicking the 1st year of the COVID-19 pandemic in Europe.MethodsA dynamic simulation SIRD model was designed to assess the effectiveness of policy measures on four next-wave scenarios, each preceded by two different lockdowns. The four scenarios were (1) no-measures, (2) uniform measures, (3) differential measures based on isolating > 60 years of age group, and (4) differential measures with additional contact reduction measures for the 20–60 years of age group. The dynamic simulation model was prepared for two prototype European countries, Northwestern (NW) and Southern (S) country. Both prototype countries were characterized based on age composition and contact matrix.ResultsThe results show that the outcomes of the next-wave scenarios depend on number of infections of previous lockdowns. All scenarios reduce the incremental deaths compared with a no-measures scenario. Differential measures show lower number of deaths despite an increase of infections. Additionally, prototype S shows overall more deaths compared with prototype NW due to a higher share of older citizens.ConclusionThis study shows that differential measures are a worthwhile option for controlling the COVID-19 epidemic. This may also be the case in situations where relevant parts of the population have taken up vaccination. Additionally, the effectiveness of interventions strongly depends on the number of previously infected individuals. The results of this study may be useful when planning and forecasting the impact of non-pharmacological interventions and vaccination campaigns.

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“…Some publications have applied TSSM like DTW (Müller, 2007) to compare the COVID‐19 curve among countries to predict cases in the future. Other authors (Rojas et al, 2020) used hierarchical clustering to determine the most similar countries to the eastern and western zone of the United States and to create models such as the Logistic, Gompertz and SIR models to estimate the future cases of the virus. The DTW is used very often because it has the great advantage of being able to find similarities even when there is a shift in time between the compared time series.…”

Section: State Of the Artmentioning

confidence: 99%

Forecasting COVID‐19 cases using dynamic time warping and incremental machine learning methods

2023

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The investment of time and resources for developing better strategies is key to dealing with future pandemics. In this work, we recreated the situation of COVID-19 across the year 2020, when the pandemic started spreading worldwide. We conducted experiments to predict the coronavirus cases for the 50 countries with the most cases during 2020. We compared the performance of state-of-the-art machine learning algorithms, such as long-short-term memory networks, against that of online incremental machine learning algorithms. To find the best strategy, we performed experiments to test three different approaches. In the first approach (single-country), we trained each model using data only from the country we were predicting. In the second one (multiple-country), we trained a model using the data from the 50 countries, and we used that model to predict each of the 50 countries. In the third experiment, we first applied clustering to calculate the nine most similar countries to the country that we were predicting. We consider two countries to be similar if the differences between the curve that represents the COVID-19 time series are small. To do so, we used time series similarity measures (TSSM) such as Euclidean Distance (ED) and Dynamic Time Warping (DTW). TSSM return a real value that represents the distance between the points in two time series which can be interpreted as how similar they are. Then, we trained the models with the data from the nine more similar countries to the one that was predicted and the predicted one. We used the model ARIMA as a baseline for our results. Results show that the idea of using TSSM is a very effective approach. By using it with the ED, the obtained RMSE in the singlecountry and multiple-country approaches was reduced by 74.21% and 74.70%, respectively. And by using the DTW, the RMSE was reduced by 74.89% and 75.36%.The main advantage of our methodology is that it is very simple and fast to apply since it is only based on time series data, as opposed to more complex methodologies that require a deep and thorough study to consider the number of parameters All authors have equally contributed to the development of this work.

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Estimation of COVID-19 Dynamics in the Different States of the United States during the First Months of the Pandemic

Cited by 4 publications

References 20 publications

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Reconstructing the Effectiveness of Policy Measures to Avoid Next-Wave COVID-19 Infections and Deaths Using a Dynamic Simulation Model: Implications for Health Technology Assessment

Forecasting COVID‐19 cases using dynamic time warping and incremental machine learning methods

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