Exploring the impact of social stress on the adaptive dynamics of
COVID-19: Typing the behavior of na\"ive populations faced with epidemics
- URL: http://arxiv.org/abs/2311.13917v2
- Date: Mon, 12 Feb 2024 09:26:59 GMT
- Title: Exploring the impact of social stress on the adaptive dynamics of
COVID-19: Typing the behavior of na\"ive populations faced with epidemics
- Authors: Innokentiy Kastalskiy, Andrei Zinovyev, Evgeny Mirkes, Victor
Kazantsev and Alexander N. Gorban
- Abstract summary: The COVID-19 pandemic has brought to light profound variations among different countries in terms of their adaptive dynamics.
This emphasizes the crucial role of cultural characteristics in natural disaster analysis.
- Score: 43.50312332512221
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the context of natural disasters, human responses inevitably intertwine
with natural factors. The COVID-19 pandemic, as a significant stress factor,
has brought to light profound variations among different countries in terms of
their adaptive dynamics in addressing the spread of infection outbreaks across
different regions. This emphasizes the crucial role of cultural characteristics
in natural disaster analysis. The theoretical understanding of large-scale
epidemics primarily relies on mean-field kinetic models. However, conventional
SIR-like models failed to fully explain the observed phenomena at the onset of
the COVID-19 outbreak. These phenomena included the unexpected cessation of
exponential growth, the reaching of plateaus, and the occurrence of multi-wave
dynamics. In situations where an outbreak of a highly virulent and unfamiliar
infection arises, it becomes crucial to respond swiftly at a non-medical level
to mitigate the negative socio-economic impact. Here we present a theoretical
examination of the first wave of the epidemic based on a simple SIRSS model
(SIR with Social Stress). We conduct an analysis of the socio-cultural features
of na\"ive population behaviors across various countries worldwide. The unique
characteristics of each country/territory are encapsulated in only a few
constants within our model, derived from the fitted COVID-19 statistics. These
constants also reflect the societal response dynamics to the external stress
factor, underscoring the importance of studying the mutual behavior of humanity
and natural factors during global social disasters. Based on these distinctive
characteristics of specific regions, local authorities can optimize their
strategies to effectively combat epidemics until vaccines are developed.
Related papers
- Impact of Indoor Mobility Behavior on the Respiratory Infectious
Diseases Transmission Trends [26.806334364100074]
The importance of indoor human mobility in the transmission dynamics of respiratory infectious diseases has been acknowledged.
This study considers people's mobility behaviors in a general scenario, abstracting them into two categories: crowding behavior, related to the spatial aspect, and stopping, related to the temporal aspect.
This study investigates their impacts on disease spreading and the impact of individual-temporal distribution resulting from these mobility behaviors on epidemic transmission.
arXiv Detail & Related papers (2023-11-29T02:16:06Z) - Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance
of Containment Measures [65.62256987706128]
Compartmental epidemiological models categorize individuals based on their disease status.
We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens.
We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
arXiv Detail & Related papers (2023-07-28T08:01:05Z) - Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
This survey delves into various data-driven methodological and practical advancements.
We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting.
We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
arXiv Detail & Related papers (2022-07-19T16:15:11Z) - An Extended Epidemic Model on Interconnected Networks for COVID-19 to
Explore the Epidemic Dynamics [2.89591830279936]
The pandemic control necessitates epidemic models that capture the trends and impacts on infectious individuals.
Many exciting models can implement this but they lack practical interpretability.
This study combines the epidemiological and network theories and proposes a framework with causal interpretability.
arXiv Detail & Related papers (2021-04-10T06:46:01Z) - Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A
Cross-Country Comparative Study [0.0]
This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy.
Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
arXiv Detail & Related papers (2021-01-19T15:51:53Z) - STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological
Regularization [76.57716281104938]
We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously.
STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations.
We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
arXiv Detail & Related papers (2020-12-08T21:21:47Z) - Steering a Historical Disease Forecasting Model Under a Pandemic: Case
of Flu and COVID-19 [75.99038202534628]
We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist.
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
arXiv Detail & Related papers (2020-09-23T22:35:43Z) - A Data-driven Understanding of COVID-19 Dynamics Using Sequential
Genetic Algorithm Based Probabilistic Cellular Automata [4.36572039512405]
This study proposes that for an accurate data-driven modeling of this infection spread, cellular automata provides an excellent platform.
Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed.
The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.
arXiv Detail & Related papers (2020-08-27T09:53:21Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - Data-driven Simulation and Optimization for Covid-19 Exit Strategies [16.31545249131776]
The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy.
We have built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease.
arXiv Detail & Related papers (2020-06-12T11:18:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.