ArticlePLoS computational biology2020
Fast estimation of time-varying infectious disease transmission rates.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Robust Inverse Reconstruction of Time-Varying Transmission Rates Across Model Structures and Incidence Forms.Bulletin of mathematical biology · 2026Article
- Assessing spatial variability in observed infectious disease spread in a prospective time-space series.International journal of health geographics · 2025Article
- Bayesian changepoint detection for epidemic models.Scientific reports · 2025Article
- Accurate Estimation of Individual Transmission Rates Through Contact Analytics Using UWB Based Indoor Location Data.International Conference on Smart Applications, Communications and Networking : SmartNets. International Conference on Smart Applications, Communications and Networking · 2024Article
- An Approach to Identifying Spatial Variability in Observed Infectious Disease Spread in a Prospective Time-Space Series with Applications to COVID-19 and Dengue Incidence.Research square · 2024Article
- Physics-Informed Neural Networks Integrating Compartmental Model for Analyzing COVID-19 Transmission Dynamics.Viruses · 2023Article
- Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.Computers in biology and medicine · 2023Article
- A physics-informed neural network to model COVID-19 infection and hospitalization scenarios.Advances in continuous and discrete models · 2022Article
- Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate.Epidemiologia (Basel, Switzerland) · 2021Article
- Response: Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.Frontiers in public health · 2021Article
- Patterns of smallpox mortality in London, England, over three centuries.PLoS biology · 2020Article
- A century of transitions in New York City's measles dynamics.Journal of the Royal Society, Interface · 2015Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compartmental epidemic models have been used extensively to study the historical spread of infectious diseases and to inform strategies for future control. A critical parameter of any such model is the transmission rate. Temporal variation in the transmission rate has a profound influence on disease spread. For this reason, estimation of time-varying transmission rates is an important step in identifying mechanisms that underlie patterns in observed disease incidence and mortality. Here, we present and test fast methods for reconstructing transmission rates from time series of reported incidence. Using simulated data, we quantify the sensitivity of these methods to parameters of the data-generating process and to mis-specification of input parameters by the user. We show that sensitivity to the user's estimate of the initial number of susceptible individuals-considered to be a major limitation of similar methods-can be eliminated by an efficient, "peak-to-peak" iterative technique, which we propose. The method of transmission rate estimation that we advocate is extremely fast, for even the longest infectious disease time series that exist. It can be used independently or as a fast way to obtain better starting conditions for computationally expensive methods, such as iterated filtering and generalized profiling.
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