COVID-19 Risk Estimation using a Time-varying SIR-model

Abstract

Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR epidemic model that ultimately yields a simplified color-coded risk level for each community. The risk score Γ that we propose is proportional to the probability of someone currently healthy getting infected in the next 24 hours based on their locality. We show how this risk score can be estimated using another useful metric of infection spread, , the time-varying average reproduction number which indicates the average number of individuals an infected person would infect in turn. The proposed approach also allows for quantification of uncertainty in the estimates of and Γ in the form of confidence intervals. Code and data from our effort have been open-sourced and are being applied to assess and communicate the risk of infection in the City and County of Los Angeles. CCS CONCEPTS • Computing methodologies → Modeling methodologies.

Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

“…In practice, measures the Covid-19 spread rate, and it changes as either the individuals gain immunity or die. The ODE system ( 2 ) with is also known as variable coefficient Susceptible-Infected-Removal (vSIR) [ 21 ], time-varying SIR epidemic [ 22 ], or simply as time-dependent SIR model [ 19 , 23 ]. Table 1 lists the mathematical symbols used in this work.…”

“…In order to circumvent the parameter issue of classic SIR-derived methods while still allowing the mathematical model to cope with time-varying coefficients, the use of Machine Learning strategies has been a popular choice and a trend. Indeed, recent developments involving variable-parameter SIR variants to assess the course of Covid-19 can be found in [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], which include the use of effective Artificial Intelligence (AI) strategies, for example in [ 18 , 19 , 29 , 30 , 31 , 32 , 33 ]. Following these recent efforts in modeling Covid-19 dynamics from epidemic models tuned with learning mechanisms, in this paper we propose an effective, data-driven SIR model whose parameters are fully calibrated by temporal functions, learned from individual regressors and trained on different data sources.…”

Assessment of Vaccination and Underreporting on COVID-19 Infections in Turkey Based On Effective Reproduction Number

“…In our model, we incorporated the daily R t values in order to account for the crowd behavior, such as using face coverings, social distancing or sheltering in place. Although this is not very common in mathematical models of epidemiology, Buckman et al [31], Kiamari et al [32] and Linka et al [33] utilize the effective reproduction number R t in their compartmental SIR models as where β is the contact rate and 1/ γ is the infectious period, [31, 33].…”

“…However, it would be more flexible and accurate to use a time dependent measure computed from the daily reported cases, namely effective reproduction number R t or R ( t ), to catch the dynamics of the disease [28, 29, 30]. Indeed, the transmission rate can be written in terms of the effective reproduction number as a time dependent function in some SIR models [31, 32, 33].…”

Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems

“…Understanding the spread pattern of COVID-19 in different areas can help people to choose their daily communications appropriately which helps in decreasing the spread of the virus. Particularly, we consider COVID-19 data from LA County Kiamari et al [2020] and use STREL formulas to understand the underlying patterns in the data. The data consists of the number of daily new cases of COVID-19 in different regions of LA County.…”