A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

Abstract

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 interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology. Author summaryWhy was this study done?• Statistical modeling is invaluable to public-health policy as it helps understand and anticipate the dynamics of the spread of infectious diseases. The available training data is often limited and reported with a low spatial and temporal resolution. This poses a challenge and makes it particularly important to incorporate domain knowledge and prior assumptions to guide the modeling process.• In order to evaluate the trustworthiness and reliability of a model's predictions, it is crucial to be able to interpret the model and quantify the model uncertainty.• To address this, we develop an interpretable model that uses Bayesian inference (rather than commonly used maximum likelihood estimation) and provides a probability distribution over inferred parameters.• We develop and test a single probabilistic model that learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviral enteritis and Lyme borreliosis) at the county level one week ahead of time.• We employ a Bayesian Monte Carlo regression approach that provides an estimate of the full probability distribution over inferred parameters as well as model predictions.• The model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.• The predictive performance of our model compares favorably with a contemporary reference model for all diseases tested.What do these findings mean?• Interpretable predictive models can be applied to surveillance data to gain insights into the dynamics of infectious diseases.• Probabilistic modeling approaches provide a suitable framework for many challenges of working with epidemiological data. Public-health agencies have the responsibility to detect, prevent and control infections in 2 the population. In Germany, the Robert Koch Institute collects a wide range of factors, 3 such as location, age, gender, pathogen, and further specifics, of laboratory confirmed 4 cases for approximately ...

Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches

“…Moreover, the classical methods only give a point estimate solution instead of a band of solutions using Bayesian inference; that is, in a Bayesian framework, one works with credible intervals. Some studies that have used Bayesian inference include [5,18,30,34,35,[54][55][56][57][58][59][60]. A Bayesian framework to model the spread of the first coronavirus (i.e., SARS-CoV) was presented in [24].…”

Current forecast of HIV/AIDS using Bayesian inference

“…Bocharov (2018), Stojanović et al (2019). Using Bayesian inference, the solutions of the inverse problem are obtained from the posterior distribution of the parameters of interest, and a solution of interest is obtained using the Maximum a Posterior, called MAP.…”

Current forecast of COVID-19: a Bayesian and Machine Learning approaches

“…Moreover, classical methods gives only a point estimate solution instead of a band of the solutions using Bayesian inference, i.e., in a Bayesian framework, one works with credible intervals. Some references of using Bayesian inference are in [3,8,9,10,11,14,17,21,22,31,48,62].…”