Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data,Sweden, 2020

We report on local nowcasting (short-term forecasting) of coronavirus disease (COVID-19) hospitalizations based on syndromic (symptom) data recorded in regular healthcare routines in Östergötland County (population ≈465,000), Sweden, early in the pandemic, when broad laboratory testing was unavailable. Daily nowcasts were supplied to the local healthcare management based on analyses of the time lag between telenursing calls with the chief complaints (cough by adult or fever by adult) and COVID-19 hospitalization. The complaint cough by adult showed satisfactory performance (Pearson correlation coefficient r>0.80; mean absolute percentage error <20%) in nowcasting the incidence of daily COVID-19 hospitalizations 14 days in advance until the incidence decreased to <1.5/100,000 population, whereas the corresponding performance for fever by adult was unsatisfactory. Our results support local nowcasting of hospitalizations on the basis of symptom data recorded in routine healthcare during the initial stage of a pandemic.     

Nowcasting influenza epidemics using non‐homogeneous hidden Markov models

Timeliness of a public health surveillance system is one of its most important characteristics. The process of predicting the present situation using available incomplete information from surveillance systems has received the term nowcasting and has high public health interest. Generally in Europe, general practitioners’ sentinel networks support the epidemiological surveillance of influenza activity, and each week's epidemiological bulletins are usually issued between Wednesday and Friday of the following week. In this work, we have developed a non‐homogeneous hidden Markov model (HMM) that, on a weekly basis, uses as covariates an early observation of influenza‐like illness (ILI) incidence rate and the number of ILI cases tested positive to nowcast the current week ILI rate and the probability that the influenza activity is in an epidemic state. We use Bayesian inference to find estimates of the model parameters and nowcasted quantities. The results obtained with data provided by the Portuguese influenza surveillance system show the additional value of using a non‐homogeneous HMM instead of a homogeneous one. The use of a non‐homogeneous HMM improves the surveillance system timeliness in 2 weeks. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t) , follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S′(t) . We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t) . The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach.     

A One Health real-time surveillance system for nowcasting Campylobacter gastrointestinal illness outbreaks,Norway, week 30 2010 to week 11 2022

Background Campylobacter is a leading cause of food and waterborne illness. Monitoring and modelling Campylobacter at chicken broiler farms, combined with weather pattern surveillance, can aid nowcasting of human gastrointestinal (GI) illness outbreaks. Near real-time sharing of data and model results with health authorities can help increase potential outbreak responsiveness.AimsTo leverage data on weather and Campylobacter on broiler farms to build a risk model for possible human Campylobacter outbreaks and to communicate risk assessments with health authorities.MethodsWe developed a spatio-temporal random effects model for weekly GI illness consultations in Norwegian municipalities with Campylobacter monitoring and weather data from week 30 2010 to 11 2022 to give 1-week nowcasts of GI illness outbreaks. The approach combined a municipality random effects baseline model for seasonally-adjusted GI illness with a second model for peak deviations from that baseline. Model results are communicated to national and local stakeholders through an interactive website: Sykdomspulsen One Health.ResultsLagged temperature and precipitation covariates, as well as 2-week-lagged positive Campylobacter sampling in broilers, were associated with higher levels of GI consultations. Significant inter-municipality variability in outbreak nowcasts were observed.Conclusions Campylobacter surveillance in broilers can be useful in GI illness outbreak nowcasting. Surveillance of Campylobacter along potential pathways from the environment to illness such as via water system monitoring may improve nowcasting. A One Health system that communicates near real-time surveillance data and nowcast changes in risk to health professionals facilitates the prevention of Campylobacter outbreaks and reduces impact on human health.