A guide to state–space modeling of ecological time series
State–space models (SSMs) are an important modeling framework for analyzing ecological
time series. These hierarchical models are commonly used to model population dynamics …
time series. These hierarchical models are commonly used to model population dynamics …
Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity
R Subramanian, Q He… - Proceedings of the …, 2021 - National Acad Sciences
The contributions of asymptomatic infections to herd immunity and community transmission
are key to the resurgence and control of COVID-19, but are difficult to estimate using current …
are key to the resurgence and control of COVID-19, but are difficult to estimate using current …
Estimation of the time-varying reproduction number of COVID-19 outbreak in China
Background The 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China has
attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in …
attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in …
State‐space models for ecological time‐series data: Practical model‐fitting
State‐space models are an increasingly common and important tool in the quantitative
ecologists' armoury, particularly for the analysis of time‐series data. This is due to both their …
ecologists' armoury, particularly for the analysis of time‐series data. This is due to both their …
Statistical inference for partially observed Markov processes via the R package pomp
Partially observed Markov process (POMP) models, also known as hidden Markov models
or state space models, are ubiquitous tools for time series analysis. The R package pomp …
or state space models, are ubiquitous tools for time series analysis. The R package pomp …
Real-time pandemic surveillance using hospital admissions and mobility data
Forecasting the burden of COVID-19 has been impeded by limitations in data, with case
reporting biased by testing practices, death counts lagging far behind infections, and …
reporting biased by testing practices, death counts lagging far behind infections, and …
Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola
As an emergent infectious disease outbreak unfolds, public health response is reliant on
information on key epidemiological quantities, such as transmission potential and serial …
information on key epidemiological quantities, such as transmission potential and serial …
Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental surveillance data
Israel experienced an outbreak of wild poliovirus type 1 (WPV1) in 2013–2014, detected
through environmental surveillance of the sewage system. No cases of acute flaccid …
through environmental surveillance of the sewage system. No cases of acute flaccid …
Real-time forecasting of epidemic trajectories using computational dynamic ensembles
Forecasting the trajectory of social dynamic processes, such as the spread of infectious
diseases, poses significant challenges that call for methods that account for data and model …
diseases, poses significant challenges that call for methods that account for data and model …
Elements of sequential monte carlo
A core problem in statistics and probabilistic machine learning is to compute probability
distributions and expectations. This is the fundamental problem of Bayesian statistics and …
distributions and expectations. This is the fundamental problem of Bayesian statistics and …