The Role of Age Distribution and Family Structure on COVID-19 Dynamics: A Preliminary Modeling Assessment for Hubei and Lombardy

Wilder, Bryan; Charpignon, Marie-Laure; Killian, Jackson A.; Ou, Han-Ching; Mate, Aditya; Jabbari, Shahin; Perrault, Andrew; Desai, Angel N.; Tambe, Milind; Majumder, Maimuna S.

doi:10.2139/ssrn.3564800

Cited by 44 publications

(30 citation statements)

References 46 publications

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“…2 Favero et al (2020) compare different age-specific policies for Italy and come to the same conclusion of the overwhelming dominance of confining elderly people longer. Fischer (2020) and Wilder et al (2020) also support a strong sheltering of the vulnerable persons. Brotherhood et al (2020) explore the impact of various confinement policies on the incentive of different age classes to behave efficiently.…”

Section: Gollier | 1747mentioning

confidence: 99%

Cost–benefit analysis of age‐specific deconfinement strategies

Gollier

2020

J Public Economic Theory

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Section: Gollier | 1747mentioning

confidence: 99%

Cost–benefit analysis of age‐specific deconfinement strategies

Gollier

2020

J Public Economic Theory

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“…As the classical SEIR models cannot capture all relevant aspects of COVID-19 such as differentiation between age groups and geographic location, various adaptations have been proposed [21,22,12,41,7]. An often used alternative is agent-based simulations [11,40,19], which allow for modeling at the individual level rather than aggregating over the entire population. This is important when modeling COVID-19 [5,24], as it allows for including social patterns that depend on age group and location.…”

Section: Methodsmentioning

confidence: 99%

“…We used a coarse-grained agent-based simulation model that differentiates between age groups and geographic regions. The model is akin to agent-based simulations [11,40,19] in that distinctive individuals are simulated who commute between their region of residence and region of employment. The main difference is that we did not include social interactions at an individual level but aggregated over groups of people with the same age, region of residence and work region.…”

Section: Methodsmentioning

confidence: 99%

Forecasting the Spread of Sars-Cov-2 is Inherently Ambiguous Given the Current State of Virus Research

et al. 2020

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Since the onset of the COVID-19 pandemic many researchers and health advisory institutions have focused on virus spread prediction through epidemiological models. Such models rely on virus-and disease characteristics of which most are uncertain or even unknown. This study addressed the validity of various assumptions using an epidemiological simulation model. We showed that multiple scenarios all lead to realistic numbers of deaths and ICU admissions, two observable and verifiable metrics, but gave different estimates for the number of infected and immune individuals. As these metrics are particularly important for policy makers, further research on virus and disease progression characteristics is essential. Until that time, epidemiological modeling studies cannot give conclusive results and should come with careful analysis of several scenarios on virus-and disease characteristics.

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“…This is an advantage of compartmental models over many 53 other approaches, which may require separate models for each quantity. 54 A number of agent-based COVID-19 models have been developed or adapted from 55 influenza pandemic models to simulate the individuals of a population and their 56 interactions [64][65][66][67][68]. This provides a mechanism for modelling interventions that target 57 contacts between individuals and does not assume the population exists in homogeneous 58 compartments as compartmental models generally do, but also requires a number of 59 assumptions to be made on the behavior and interactions within a population as well as 60 the infectivity of COVID-19.…”

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confidence: 99%

“…Deaths or other outcomes 66 may be modelled as a second step, for example using a negative binomial model that 67 predicts daily deaths conditional on the number of infections in recent days [70]. 68 Statistical models often eschew deterministic population dynamics and fit the 69 observed data as a function of time and possibly other covariates in a regression (or 70 equivalent) framework. Log-linear [74], generalized Richards [75], ARIMA [76,77], 71 exponential [78], Gaussian CDF [79], and logistic [80][81][82] models, which all 72 accommodate the generally sigmoidal shape of the cumulative infection count that is 73 often observed in epidemics, as well as various other models [83][84][85][86] including machine 74 learning algorithms [87][88][89] have been proposed for COVID-19.…”

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confidence: 99%

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Watson

Xiong

Zhang

et al. 2020

Preprint

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random : medRxiv preprint forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

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The Role of Age Distribution and Family Structure on COVID-19 Dynamics: A Preliminary Modeling Assessment for Hubei and Lombardy

Cited by 44 publications

References 46 publications

Cost–benefit analysis of age‐specific deconfinement strategies

Cost–benefit analysis of age‐specific deconfinement strategies

Forecasting the Spread of Sars-Cov-2 is Inherently Ambiguous Given the Current State of Virus Research

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

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