Better strategies for containing COVID-19 pandemic: a study of 25 countries via a vSIADR model

Yan, Han; Zhu, Yuru; Gu, Jia; Huang, Yi‐Zhi; Sun, Hui; Zhang, Xinyu; Wang, Yuqing; Qiu, Yumou; Chen, Song Xi

doi:10.1098/rspa.2020.0440

Cited by 15 publications

(30 citation statements)

References 43 publications

(72 reference statements)

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“…Data was collected from media in six large countries in America and Europe. While these countries are not a random or representative selection of these continents, they provide contexts that have been affected to different extents and have, therefore, taken various strategies to handle the pandemic ( Yan et al, 2021 ). The countries selected in America were the United States (US), Brazil, and Colombia, while for Europe were the United Kingdom, Spain, and Germany.…”

Section: Methodsmentioning

confidence: 99%

The Role of Psychology in Media During the COVID-19 Pandemic: A Cross-National Study

et al. 2022

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Dealing with COVID-19 and with the preventative measures that have been taken to mitigate the transmission of the virus causing the pandemic has posed a great challenge to the population. While psychologists have expertise with regard to preventive behavior change and to dealing with the mental health impact of measures, their expertise needs to be effectively communicated to the public. Mass media play a critical role in times of crisis, in many cases being the only source of information. While most research focuses on the importance of information content as a factor affecting psychological responses to a collective traumatic event, the way information is framed in the media is likely to influence the way health professionals are perceived as trustworthy. This study aimed to analyze the media framing of information from psychology during the COVID-19 pandemic in six countries from America and Europe, identifying the most recurrent topics in the news (n news items = 541) related to psychology and mental health. In all six countries the media address the psychological needs of the population, which vary depending on the imposed restrictions. The news content is influenced by the scientific sources used by the media. While the most prevalent topics focus on psychological risk and the need to seek mental health care, the least prevalent topics relate to counseling and behavioral guidelines for managing the psychological consequences of the pandemic. The study findings provide insight into how psychological knowledge contributes to the understanding and mitigation of COVID-19 consequences in different countries and identified fields where psychologists were consulted to respond to a health emergency. They also show a preference to consult other experts when searching for contextual or more macro-social explanations of critical situation.

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Section: Methodsmentioning

confidence: 99%

The Role of Psychology in Media During the COVID-19 Pandemic: A Cross-National Study

et al. 2022

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“…First, we assume that the infection rate α and network effect parameter λ are time-invariant. In real life, they may change with the disease control measures such as travel restrictions and quarantine policies (Yan et al, 2021). It is interesting to extend our model to time-varying (α, λ).…”

Section: Discussionmentioning

confidence: 99%

Allocation of COVID-19 Testing Budget on a Commute Network of Counties

Huang,

Ke,

Jin

2021

Preprint

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The screening testing is an effective tool to control the early spread of an infectious disease such as COVID-19. When the total testing capacity is limited, we aim to optimally allocate testing resources among n counties. We build a (weighted) commute network on counties, with the weight between two counties a decreasing function of their traffic distance. We introduce a network-based disease model, in which the number of newly confirmed cases of each county depends on the numbers of hidden cases of all counties on the network. Our proposed testing allocation strategy first uses historical data to learn model parameters and then decides the testing rates for all counties by solving an optimization problem. We apply the method on the commute networks of Massachusetts, USA and Hubei, China and observe its advantages over testing allocation strategies that ignore the network structure. Our approach can also be extended to study the vaccine allocation problem.

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“…For example, Kraemer et al 21 used generalized linear models with daily new/cumulative cases as the outcome and mobility as one of the covariates. On the other hand, compartmental models with time‐varying coefficients have been employed to study the transmission dynamics of COVID‐19, 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 but a large portion of these works were not specifically aimed at investigating the effect of mobility on the spread of COVID‐19. For example, Wu et al 29 estimated the basic reproductive number of COVID‐19 under a compartmental modeling framework but did not attempt to associate it with mobility.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Giordano et al 34 fitted their compartmental model by minimizing the sum of the squares of the errors but did not provide confidence intervals for the parameters. Lastly, many existing works (such as Li et al, 30 Hao et al, 36 and Yan et al 38 ) assumed a Poisson model for the case counts, which can be vulnerable to overdispersion and large variability in the observed data. Aiming to overcome these limitations, we develop a time‐varying coefficient state‐space epidemiological model that allows us to explore the relationship between mobility and COVID‐19 spread.…”

Section: Introductionmentioning

confidence: 99%

Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model

Keller

Zhou

Kaplan

et al. 2022

Statistics in Medicine

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The spread of COVID‐19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population‐level dynamics of COVID‐19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID‐19, we have developed a novel Bayesian time‐varying coefficient state‐space model for infectious disease transmission. The foundation of this model is a time‐varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID‐19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID‐19.

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Better strategies for containing COVID-19 pandemic: a study of 25 countries via a vSIADR model

Cited by 15 publications

References 43 publications

The Role of Psychology in Media During the COVID-19 Pandemic: A Cross-National Study

The Role of Psychology in Media During the COVID-19 Pandemic: A Cross-National Study

Allocation of COVID-19 Testing Budget on a Commute Network of Counties

Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model

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