arXiv subjects

Kris V. Parag

Publications and source records attributed to Kris V. Parag.

5 recordsLinked to original sources

arXiv2026

Multi-scale measures of time-varying epidemic spread on human mobility networks

Human movement drives the spatial spread and persistence of many infectious diseases, yet existing theory and real-time operational tools for inferring the instantaneous reproduction number R(t) often assume static and/or homogeneously mixing populations and cannot describe how individuals generate and acquire infections heterogeneously based on their movement patterns within a day. Renewal equations underpin many such popular estimators of R(t), and here, we develop a network-based modelling framework from which we derive new mechanism-led renewal equations and control indicators for outbreaks of infectious diseases. These equations directly integrate within-day human movement to rigorously define a family of instantaneous reproduction numbers; inward, outward, and type R(t) for individual locations, R(t) between locations, R(t) at meeting locations, and R(t) for the entire mobility network. These quantities correct for the unsuitability of existing location-specific R(t) estimators that operate in closed, static populations. Applying our framework to epidemics on diverse types of networks alongside mobile phone data, we demonstrate how our new framework's outputs provide new, multi-scale control indicators at the network, location, and transmission corridor scales, and can be used to design targeted disease control interventions including the strength, type, and length of intervention required across space and time. We capture the biasing effects of different existing ways to measure location-specific and network-level transmission potential without capturing within-day human movements. This generalisable framework redefines reproduction numbers in real-world outbreaks that are shaped by individuals moving across connected locations, enabling more spatially and temporally precise interventions.

q-bio.QM↗

arXiv2025

Fundamental limits on taming infectious disease epidemics

Epidemic control frequently relies on adjusting interventions based on prevalence. But designing such policies is a highly non-trivial problem due to uncertain intervention effects, costs and the difficulty of quantifying key transmission mechanisms and parameters. Here, using exact mathematical and computational methods, we reveal a fundamental limit in epidemic control in that prevalence feedback policies are outperformed by a single optimally chosen constant control level. Specifically, we find no incentive to use prevalence based control under a wide class of cost functions that depend arbitrarily on interventions and scale with infections. We also identify regimes where prevalence feedback is beneficial. Our results challenge the current understanding that prevalence based interventions are required for epidemic control and suggest that, for many classes of epidemics, interventions should not be varied unless the epidemic is near the herd immunity threshold.

q-bio.PE↗

arXiv2025

A primer on inference and prediction with epidemic renewal models and sequential Monte Carlo

Renewal models are widely used in statistical epidemiology as semi-mechanistic models of disease transmission. While primarily used for estimating the instantaneous reproduction number, they can also be used for generating projections, estimating elimination probabilities, modelling the effect of interventions, and more. We demonstrate how simple sequential Monte Carlo methods (also known as particle filters) can be used to perform inference on these models. Our goal is to acquaint a reader who has a working knowledge of statistical inference with these methods and models and to provide a practical guide to their implementation. We focus on these methods' flexibility and their ability to handle multiple statistical and other biases simultaneously. We leverage this flexibility to unify existing methods for estimating the instantaneous reproduction number and generating projections. A companion website SMC and epidemic renewal models provides additional worked examples, self-contained code to reproduce the examples presented here, and additional materials.

stat.ME↗

arXiv2024

Renewal equations for vector-borne diseases

During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle

q-bio.PE↗

arXiv2022

Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States

The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The design of appropriate and timely mitigation strategies to curb the effects of this and future disease outbreaks requires close monitoring of their spatio-temporal trajectories. We present machine learning methods to anticipate sharp increases in COVID-19 activity in US counties in real-time. Our methods leverage Internet-based digital traces -- e.g., disease-related Internet search activity from the general population and clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories from neighboring locations -- to monitor potential changes in population-level health trends. Motivated by the need for finer spatial-resolution epidemiological insights to improve local decision-making, we build upon previous retrospective research efforts originally conceived at the state level and in the early months of the pandemic. Our methods -- tested in real-time and in an out-of-sample manner on a subset of 97 counties distributed across the US -- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before the onset of local outbreaks (defined as the time when the effective reproduction number $R_t$ becomes larger than 1 consistently). Given the continued emergence of COVID-19 variants of concern -- such as the most recent one, Omicron -- and the fact that multiple countries have not had full access to vaccines, the framework we present, while conceived for the county-level in the US, could be helpful in countries where similar data sources are available.

q-bio.QM↗