Using prediction polling to harness collective intelligence for disease forecasting

Background

The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking.

Methods

We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases.

Results

Consistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters.

Conclusions

Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12083-y.

Influence of ECG sampling rate in fetal heart rate variability analysis

Fetal hypoxia results in a fetal blood acidosis (pH<;7.10). In such a situation, the fetus develops several adaptation mechanisms regulated by the autonomic nervous system. Many studies demonstrated significant changes in heart rate variability in hypoxic fetuses. So, fetal heart rate variability analysis could be of precious help for fetal hypoxia prediction. Commonly used fetal heart rate variability analysis methods have been shown to be sensitive to the ECG signal sampling rate. Indeed, a low sampling rate could induce variability in the heart beat detection which will alter the heart rate variability estimation. In this paper, we introduce an original fetal heart rate variability analysis method. We hypothesize that this method will be less sensitive to ECG sampling frequency changes than common heart rate variability analysis methods. We then compared the results of this new heart rate variability analysis method with two different sampling frequencies (250-1000 Hz).

A new analysis of heart rate variability in the assessment of fetal parasympathetic activity: An experimental study in a fetal sheep model

Analysis of heart rate variability (HRV) is a recognized tool in the assessment of autonomic nervous system (ANS) activity. Indeed, both time and spectral analysis techniques enable us to obtain indexes that are related to the way the ANS regulates the heart rate. However, these techniques are limited in terms of the lack of thresholds of the numerical indexes, which is primarily due to high inter-subject variability. We proposed a new fetal HRV analysis method related to the parasympathetic activity of the ANS. The aim of this study was to evaluate the performance of our method compared to commonly used HRV analysis, with regard to i) the ability to detect changes in ANS activity and ii) inter-subject variability. This study was performed in seven sheep fetuses. In order to evaluate the sensitivity and specificity of our index in evaluating parasympathetic activity, we directly administered 2.5 mg intravenous atropine, to inhibit parasympathetic tone, and 5 mg propranolol to block sympathetic activity. Our index, as well as time analysis (root mean square of the successive differences; RMSSD) and spectral analysis (high frequency (HF) and low frequency (LF) spectral components obtained via fast Fourier transform), were measured before and after injection. Inter-subject variability was estimated by the coefficient of variance (%CV). In order to evaluate the ability of HRV parameters to detect fetal parasympathetic decrease, we also estimated the effect size for each HRV parameter before and after injections. As expected, our index, the HF spectral component, and the RMSSD were reduced after the atropine injection. Moreover, our index presented a higher effect size. The %CV was far lower for our index than for RMSSD, HF, and LF. Although LF decreased after propranolol administration, fetal stress index, RMSSD, and HF were not significantly different, confirming the fact that those indexes are specific to the parasympathetic nervous system. In conclusion, our method appeared to be effective in detecting parasympathetic inhibition. Moreover, inter-subject variability was much lower, and effect size higher, with our method compared to other HRV analysis methods.