Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices
Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however it is often unrealistic. Efforts in modeling non-exponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (infectious period distribution) and the modeling needs (corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. We show that the same infectious period distribution at the population level can be reproduced by two modeling schemes -- host-based and population-based -- depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of an SIS transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on infection duration. In absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical …
Bayesian InferenceDisease PredictionEpidemiologyMultivariate Time Series Forecasting+1An analysis of the vectorial capacity using moment-generating functions
This paper describes a technique for analyzing the stochastic structure of the vectorial capacity using moment--generating functions. In such formulation, for an infectious disease transmitted by a vector, we obtain the …
SensitivityA Fourfold Pathogen Reference Ontology Suite
Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as…
Cross-Country Learning for National Infectious Disease Forecasting Using European Data
Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven forecasting approaches rely primarily on historical data from a single country, …
Data AugmentationIntervention Strategies for Epidemics: Does Ignoring Time Delay Lead to Incorrect Predictions?
Our paper investigates distributions of exposed and infectious time periods in an epidemic model and how applying a disease control strategy affects the model's accuracy. While ordinary differential equations are widely …