Corpus ID: 221654993

Tracking disease outbreaks from sparse data with Bayesian inference

@article{Wilder2021TrackingDO,
  title={Tracking disease outbreaks from sparse data with Bayesian inference},
  author={Bryan Wilder and Michael Joseph Mina and Milind Tambe},
  journal={ArXiv},
  year={2021},
  volume={abs/2009.05863}
}
The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an outbreak (formally, the time-varying reproduction number) from case counts. While standard methods exist, they work best at coarse-grained national or state scales with abundant data, and struggle to accommodate the partial observability and sparse data common at finer scales (e.g., individual schools or towns). For example, case counts may be sparse when… Expand

Figures and Tables from this paper

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic
TLDR
This work proposes to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate, modeled by a Brownian diffusion process embedded in a stochastic model. Expand
A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.
TLDR
This work proposes to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate modeled by a Brownian diffusion process embedded in a stochastic model. Expand
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms
TLDR
The findings reaffirm the predictive value of symptoms, such as anosmia and ageusia, and forecast that % of the population having COVID-19-like illness and those tested positive as 0.15% and 1.14% absolute error respectively could help aid faster development of the public health policy, particularly in areas with low levels of testing and having a greater reliance on self-reported symptoms. Expand
Can Self Reported Symptoms Predict Daily COVID-19 Cases?
TLDR
This work demonstrates that the models developed on crowd-sourced data, curated via online platforms, can complement the existing epidemiological surveillance infrastructure in a costeffective manner. Expand

References

SHOWING 1-10 OF 34 REFERENCES
Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data
TLDR
A probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework is developed and can be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains. Expand
Improved inference of time-varying reproduction numbers during infectious disease outbreaks
TLDR
It is shown that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: up-to-date observations of the serial interval to be included, and cases arising from local transmission to be distinguished from those imported from elsewhere. Expand
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
TLDR
This tool produces novel, statistically robust analytical estimates of R that incorporates uncertainty in the distribution of the serial interval and should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data. Expand
Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models
TLDR
It is demonstrated that time-varying parameters can improve the accuracy of model performances, and it is suggested that the methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain. Expand
An Interaction-Based Approach to Computational Epidemiology
TLDR
Simdemics details the demographic and geographic distributions of disease and provides decision makers with information about the consequences of a biological attack or natural outbreak, the resulting demand for health services, and the feasibility and effectiveness of response options. Expand
Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City
TLDR
An individual-level model of severe acute respiratory syndrome coronavirus 2 transmission is presented that accounts for population-specific factors such as age distributions, comorbidities, household structures, and contact patterns and finds that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population. Expand
Capturing the time-varying drivers of an epidemic using stochastic dynamical systems.
TLDR
This paper considers stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.) and introduces diffusion-driven susceptible exposed infected retired-type models with age structure. Expand
Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe
TLDR
The results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission and continued intervention should be considered to keep transmission of SARS-CoV-2 under control. Expand
SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources
TLDR
This paper presents SourceSeer, a novel algorithmic framework that combines spatio-temporal topic models with sourcebased anomaly detection techniques to effectively forecast the emergence and progression of infectious rare diseases. Expand
Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance
TLDR
It is concluded that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary. Expand
...
1
2
3
4
...